{"as_of":"2026-08-14T06:28:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:bcf9a2a2a321beaea1d03f7763e15f97a4400007cd25de3be934a7b4d8cd70b6","coverage":[{"denominator":96,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":96,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-12T19:35:30.417149Z","state":"measured"},{"denominator":96,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":96,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-13T06:32:02.005865+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2411.10606/citation-record","integrity":"/paper/2411.10606/integrity","json":"/paper/2411.10606/citation-record.json","paper":"/paper/2411.10606"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"2307.09288","last_updated":"2023-07-19T17:08:59Z","snapshot_observed_at":"2026-08-07T12:56:43.323460Z","submitted_at":"2023-07-18T14:31:57Z","title":"Llama 2: Open Foundation and Fine-Tuned Chat Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2307.09288","snapshot_observed_at":"2026-08-12T19:35:29.873235Z","title":"Llama 2: Open foundation and fine-tuned chat models","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2411.10606","last_updated":"2024-11-15T22:02:28Z","snapshot_observed_at":"2026-08-14T02:59:29.476989Z","submitted_at":"2024-11-15T22:02:28Z","title":"AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-12T19:35:29.873235Z"},"links":{"cited_paper":"/paper/2307.09288","citing_paper":"/paper/2411.10606"},"observation_digest":"sha256:96f87fcee3992698e941e85c7915118fafb0f945957ff4e7e1fc6c3b4815336b","observation_id":"7ac2c22a-3db1-4391-b9e4-b2faa01fe961","resolution":{"observed_at":"2026-08-12T19:35:29.873235Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:35:29.878998Z","title":"Introducing Meta Llama 3: The most capable openly available LLM to date, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.10606","last_updated":"2024-11-15T22:02:28Z","snapshot_observed_at":"2026-08-14T02:59:29.476989Z","submitted_at":"2024-11-15T22:02:28Z","title":"AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-12T19:35:29.878998Z"},"links":{"citing_paper":"/paper/2411.10606"},"observation_digest":"sha256:55688132c87997fb55a4d41eff5f378bdc3c7a699b96870e81b3d1740f850939","observation_id":"3a050736-faf3-42dd-af31-04a86d07851f","resolution":{"observed_at":"2026-08-12T19:35:29.878998Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2408.00118","last_updated":"2024-10-02T15:22:49Z","snapshot_observed_at":"2026-08-02T16:20:09.773989Z","submitted_at":"2024-07-31T19:13:07Z","title":"Gemma 2: Improving Open Language Models at a Practical Size","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.00118","snapshot_observed_at":"2026-08-12T19:35:29.883496Z","title":"Gemma 2: Improving open language models at a practical size","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.10606","last_updated":"2024-11-15T22:02:28Z","snapshot_observed_at":"2026-08-14T02:59:29.476989Z","submitted_at":"2024-11-15T22:02:28Z","title":"AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-12T19:35:29.883496Z"},"links":{"cited_paper":"/paper/2408.00118","citing_paper":"/paper/2411.10606"},"observation_digest":"sha256:b4164f8d5c33cf20c1ae0af873478f452eed64d6d3dd79088edc34a60771b9a8","observation_id":"4af90698-19e9-4c2d-b08c-d9bcc438bc60","resolution":{"observed_at":"2026-08-12T19:35:29.883496Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2303.08774","last_updated":"2024-03-04T06:01:33Z","snapshot_observed_at":"2026-08-07T07:30:12.213965Z","submitted_at":"2023-03-15T17:15:04Z","title":"GPT-4 Technical Report","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2303.08774","snapshot_observed_at":"2026-08-12T19:35:29.888698Z","title":"Gpt-4 technical report","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2411.10606","last_updated":"2024-11-15T22:02:28Z","snapshot_observed_at":"2026-08-14T02:59:29.476989Z","submitted_at":"2024-11-15T22:02:28Z","title":"AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-12T19:35:29.888698Z"},"links":{"cited_paper":"/paper/2303.08774","citing_paper":"/paper/2411.10606"},"observation_digest":"sha256:b48ffb2ebd1223e0f03ab0bef2ab564f7a9a283919b96564572f0e8a15b931f8","observation_id":"33cffe53-7a20-4394-b8c2-81a4d4a040de","resolution":{"observed_at":"2026-08-12T19:35:29.888698Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:35:29.893809Z","title":"SparseGPT: Massive language models can be accurately pruned in one-shot, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2411.10606","last_updated":"2024-11-15T22:02:28Z","snapshot_observed_at":"2026-08-14T02:59:29.476989Z","submitted_at":"2024-11-15T22:02:28Z","title":"AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-12T19:35:29.893809Z"},"links":{"citing_paper":"/paper/2411.10606"},"observation_digest":"sha256:4708134281299012e14fabd6cfb1eb7fe286863402b21f8f69dd335ef9276b6e","observation_id":"e39b3ff4-9a02-45c0-aeb8-91d111890281","resolution":{"observed_at":"2026-08-12T19:35:29.893809Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:35:29.898632Z","title":"A simple and effective pruning approach for large language models, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2411.10606","last_updated":"2024-11-15T22:02:28Z","snapshot_observed_at":"2026-08-14T02:59:29.476989Z","submitted_at":"2024-11-15T22:02:28Z","title":"AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-12T19:35:29.898632Z"},"links":{"citing_paper":"/paper/2411.10606"},"observation_digest":"sha256:3a26d77794477eef8a8df4f2e12fbfb83a89f72e06f2810880bee45bade081be","observation_id":"074d9850-6d45-47ac-afa7-87d5fa80a25c","resolution":{"observed_at":"2026-08-12T19:35:29.898632Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:35:29.904206Z","title":"Llm-pruner: On the structural pruning of large language models","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2411.10606","last_updated":"2024-11-15T22:02:28Z","snapshot_observed_at":"2026-08-14T02:59:29.476989Z","submitted_at":"2024-11-15T22:02:28Z","title":"AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-12T19:35:29.904206Z"},"links":{"citing_paper":"/paper/2411.10606"},"observation_digest":"sha256:08093c40628a88219358bdcc91d07b72999bdf00a315debf4fd858678822110f","observation_id":"ad6d53fb-7e62-4ac8-b6a7-51c0daa62415","resolution":{"observed_at":"2026-08-12T19:35:29.904206Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:35:29.908874Z","title":"Fluctuation-based adaptive structured pruning for large language models","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.10606","last_updated":"2024-11-15T22:02:28Z","snapshot_observed_at":"2026-08-14T02:59:29.476989Z","submitted_at":"2024-11-15T22:02:28Z","title":"AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-12T19:35:29.908874Z"},"links":{"citing_paper":"/paper/2411.10606"},"observation_digest":"sha256:7a89e5725bddbb6b7c982485814e200afa53e95aefdde54427929fea3d8cef85","observation_id":"6e89a609-dcc2-491c-8720-5ca2b7947cae","resolution":{"observed_at":"2026-08-12T19:35:29.908874Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.02834","last_updated":"2024-06-23T08:45:33Z","snapshot_observed_at":"2026-08-13T04:26:58.030844Z","submitted_at":"2024-02-05T09:44:49Z","title":"Shortened LLaMA: Depth Pruning for Large Language Models with Comparison of Retraining Methods","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.02834","snapshot_observed_at":"2026-08-12T19:35:29.913456Z","title":"Shortened llama: A simple depth pruning for large language models","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.10606","last_updated":"2024-11-15T22:02:28Z","snapshot_observed_at":"2026-08-14T02:59:29.476989Z","submitted_at":"2024-11-15T22:02:28Z","title":"AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-12T19:35:29.913456Z"},"links":{"cited_paper":"/paper/2402.02834","citing_paper":"/paper/2411.10606"},"observation_digest":"sha256:5f712c70cfad152d81d2fb46962c8a31b7e66029bf6b0a838652404af3dd9a69","observation_id":"49e3ff13-fe7c-44d7-8c7f-f67d75b71927","resolution":{"observed_at":"2026-08-12T19:35:29.913456Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.06694","last_updated":"2024-04-11T01:18:06Z","snapshot_observed_at":"2026-08-13T05:52:56.563579Z","submitted_at":"2023-10-10T15:13:30Z","title":"Sheared LLaMA: Accelerating Language Model Pre-training via Structured Pruning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.06694","snapshot_observed_at":"2026-08-12T19:35:30.016955Z","title":"Sheared llama: Accelerating language model pre-training via structured pruning","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2411.10606","last_updated":"2024-11-15T22:02:28Z","snapshot_observed_at":"2026-08-14T02:59:29.476989Z","submitted_at":"2024-11-15T22:02:28Z","title":"AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-12T19:35:30.016955Z"},"links":{"cited_paper":"/paper/2310.06694","citing_paper":"/paper/2411.10606"},"observation_digest":"sha256:da0d44ece01d8ac028ff0b4b54812eddff16f2db61e8692883f384eff150af81","observation_id":"912d186c-7927-4131-97bc-9acd9026720a","resolution":{"observed_at":"2026-08-12T19:35:30.016955Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:35:30.022979Z","title":"Bignas: Scaling up neural architecture search with big single-stage models","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2411.10606","last_updated":"2024-11-15T22:02:28Z","snapshot_observed_at":"2026-08-14T02:59:29.476989Z","submitted_at":"2024-11-15T22:02:28Z","title":"AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-12T19:35:30.022979Z"},"links":{"citing_paper":"/paper/2411.10606"},"observation_digest":"sha256:89bf7757128d2f9942cc7e0d48bd9348835f486dd2c6da1566ca206d79c341d8","observation_id":"21254411-3deb-4f1a-897e-846df2ca4c09","resolution":{"observed_at":"2026-08-12T19:35:30.022979Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:35:30.028587Z","title":"Attentivenas: Improving neural architecture search via attentive sampling","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2411.10606","last_updated":"2024-11-15T22:02:28Z","snapshot_observed_at":"2026-08-14T02:59:29.476989Z","submitted_at":"2024-11-15T22:02:28Z","title":"AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-12T19:35:30.028587Z"},"links":{"citing_paper":"/paper/2411.10606"},"observation_digest":"sha256:c2ddf3897b8d9dfb8210e3c60df03f2ee0af040d48375280134a32cbaa4bd99f","observation_id":"918895aa-2039-461c-b132-04d9e7021f1a","resolution":{"observed_at":"2026-08-12T19:35:30.028587Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:35:30.033535Z","title":"Alphanet: Improved training of supernets with alpha-divergence","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2411.10606","last_updated":"2024-11-15T22:02:28Z","snapshot_observed_at":"2026-08-14T02:59:29.476989Z","submitted_at":"2024-11-15T22:02:28Z","title":"AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-12T19:35:30.033535Z"},"links":{"citing_paper":"/paper/2411.10606"},"observation_digest":"sha256:a2644c2e686641c0e160ef23a7cc8205a7ad2908c58bf341057f55d9f528b634","observation_id":"8ffcdd17-8bb3-4407-a790-b3f4b800c2c6","resolution":{"observed_at":"2026-08-12T19:35:30.033535Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:35:30.039214Z","title":"Nasvit: Neural architecture search for efficient vision transformers with gradient conflict-aware supernet training","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2411.10606","last_updated":"2024-11-15T22:02:28Z","snapshot_observed_at":"2026-08-14T02:59:29.476989Z","submitted_at":"2024-11-15T22:02:28Z","title":"AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-12T19:35:30.039214Z"},"links":{"citing_paper":"/paper/2411.10606"},"observation_digest":"sha256:72294db718d8e3ab592071cae40e9cefb1470f36721642f08726823ef36dd3e0","observation_id":"09e77131-0bb1-4ea7-a568-fee8226a24c7","resolution":{"observed_at":"2026-08-12T19:35:30.039214Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1908.09791","last_updated":"2020-04-29T20:49:05Z","snapshot_observed_at":"2026-08-11T01:59:17.823546Z","submitted_at":"2019-08-26T16:46:23Z","title":"Once-for-All: Train One Network and Specialize it for Efficient Deployment","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1908.09791","snapshot_observed_at":"2026-08-12T19:35:30.044626Z","title":"Once-for-all: Train one network and specialize it for efficient deployment","venue":null,"work_id":null,"year":1908},"citing_paper":{"arxiv_id":"2411.10606","last_updated":"2024-11-15T22:02:28Z","snapshot_observed_at":"2026-08-14T02:59:29.476989Z","submitted_at":"2024-11-15T22:02:28Z","title":"AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-12T19:35:30.044626Z"},"links":{"cited_paper":"/paper/1908.09791","citing_paper":"/paper/2411.10606"},"observation_digest":"sha256:e0dde8f2ccbd08a9033102903ba7d6e15321c555dfa69728f6d35b3ab5b4b0a5","observation_id":"e7171d6d-3dc5-4d10-b95b-6203468d0ef0","resolution":{"observed_at":"2026-08-12T19:35:30.044626Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:35:30.050418Z","title":"Gradient surgery for multi-task learning","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2411.10606","last_updated":"2024-11-15T22:02:28Z","snapshot_observed_at":"2026-08-14T02:59:29.476989Z","submitted_at":"2024-11-15T22:02:28Z","title":"AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-12T19:35:30.050418Z"},"links":{"citing_paper":"/paper/2411.10606"},"observation_digest":"sha256:93fafd3294202eb73834bf7887943d5cd328c7c719c09d7af3fab48f70b16320","observation_id":"99092a33-0125-4b28-b0c0-989611a220fd","resolution":{"observed_at":"2026-08-12T19:35:30.050418Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:35:30.055502Z","title":"Conflict-averse gradient descent for multi-task learning","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2411.10606","last_updated":"2024-11-15T22:02:28Z","snapshot_observed_at":"2026-08-14T02:59:29.476989Z","submitted_at":"2024-11-15T22:02:28Z","title":"AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-12T19:35:30.055502Z"},"links":{"citing_paper":"/paper/2411.10606"},"observation_digest":"sha256:113baf81b99b1fb38e48706487dcfedefe103a68d5eda5ee8a7fbc8152fa9350","observation_id":"23df3483-031f-4980-83b1-fa4f5f06e275","resolution":{"observed_at":"2026-08-12T19:35:30.055502Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2106.09685","last_updated":"2021-10-16T18:40:34Z","snapshot_observed_at":"2026-08-11T08:20:29.798517Z","submitted_at":"2021-06-17T17:37:18Z","title":"LoRA: Low-Rank Adaptation of Large Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2106.09685","snapshot_observed_at":"2026-08-12T19:35:30.061058Z","title":"Lora: Low-rank adaptation of large language models","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2411.10606","last_updated":"2024-11-15T22:02:28Z","snapshot_observed_at":"2026-08-14T02:59:29.476989Z","submitted_at":"2024-11-15T22:02:28Z","title":"AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-12T19:35:30.061058Z"},"links":{"cited_paper":"/paper/2106.09685","citing_paper":"/paper/2411.10606"},"observation_digest":"sha256:cb27ddf95d835f633bf96bd22f20513e987002fb3c7bdd8db1694737eddc9916","observation_id":"2a24467d-a2fa-4b9c-a123-16d08bb7ce07","resolution":{"observed_at":"2026-08-12T19:35:30.061058Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:35:30.066109Z","title":"Tensorrt-llm, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.10606","last_updated":"2024-11-15T22:02:28Z","snapshot_observed_at":"2026-08-14T02:59:29.476989Z","submitted_at":"2024-11-15T22:02:28Z","title":"AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-12T19:35:30.066109Z"},"links":{"citing_paper":"/paper/2411.10606"},"observation_digest":"sha256:3dcf2744e63124d95aacc775d8250faa06a7e6057c917c85ff84f679ed019893","observation_id":"6e2a80fe-defc-43da-81d3-5dc364a2e3a8","resolution":{"observed_at":"2026-08-12T19:35:30.066109Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:35:30.070491Z","title":"MLC-LLM, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2411.10606","last_updated":"2024-11-15T22:02:28Z","snapshot_observed_at":"2026-08-14T02:59:29.476989Z","submitted_at":"2024-11-15T22:02:28Z","title":"AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-12T19:35:30.070491Z"},"links":{"citing_paper":"/paper/2411.10606"},"observation_digest":"sha256:5afbab18f937fc8f5a93eca6b597774e4ffd9fcc2fcc14d49c88ff947fb3e7b3","observation_id":"72be604c-366d-497e-919f-3598ab8f8f73","resolution":{"observed_at":"2026-08-12T19:35:30.070491Z","resolver_source":null,"status":"parse_uncertain"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:35:30.074742Z","title":"Pytorch: An imperative style, high-performance deep learning library","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2411.10606","last_updated":"2024-11-15T22:02:28Z","snapshot_observed_at":"2026-08-14T02:59:29.476989Z","submitted_at":"2024-11-15T22:02:28Z","title":"AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-12T19:35:30.074742Z"},"links":{"citing_paper":"/paper/2411.10606"},"observation_digest":"sha256:835b69b2cb28caf20042feff029a00520a5445737249e1114e0f7b77d1f90caf","observation_id":"f3395e26-8cd9-4141-9683-3d9f875dfe64","resolution":{"observed_at":"2026-08-12T19:35:30.074742Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:35:30.079145Z","title":"The theory of dynamic programming","venue":null,"work_id":null,"year":1954},"citing_paper":{"arxiv_id":"2411.10606","last_updated":"2024-11-15T22:02:28Z","snapshot_observed_at":"2026-08-14T02:59:29.476989Z","submitted_at":"2024-11-15T22:02:28Z","title":"AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-12T19:35:30.079145Z"},"links":{"citing_paper":"/paper/2411.10606"},"observation_digest":"sha256:b70649a8467dff3da74b9e6847a26c99a1e247baba6979f563500323e9deec65","observation_id":"d3468837-168b-4cd9-b4c3-f073a01e5a3e","resolution":{"observed_at":"2026-08-12T19:35:30.079145Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.05904","last_updated":"2024-10-01T12:08:23Z","snapshot_observed_at":"2026-08-13T04:28:03.664746Z","submitted_at":"2024-05-09T17:00:22Z","title":"Does Fine-Tuning LLMs on New Knowledge Encourage Hallucinations?","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.05904","snapshot_observed_at":"2026-08-12T19:35:30.083461Z","title":"Does fine-tuning llms on new knowledge encourage hallucinations? arXiv preprint arXiv:2405.05904, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.10606","last_updated":"2024-11-15T22:02:28Z","snapshot_observed_at":"2026-08-14T02:59:29.476989Z","submitted_at":"2024-11-15T22:02:28Z","title":"AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-12T19:35:30.083461Z"},"links":{"cited_paper":"/paper/2405.05904","citing_paper":"/paper/2411.10606"},"observation_digest":"sha256:235923a8bc8706aa3971f3d4bab80af78163f3587bc3ccf33c5ce6a4dabf36e8","observation_id":"31f05847-ef84-494d-971d-d9e1782e620e","resolution":{"observed_at":"2026-08-12T19:35:30.083461Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.01286","last_updated":"2024-11-17T06:50:44Z","snapshot_observed_at":"2026-08-13T04:50:01.828919Z","submitted_at":"2024-01-02T16:54:58Z","title":"A Comprehensive Study of Knowledge Editing for Large Language Models","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.01286","snapshot_observed_at":"2026-08-12T19:35:30.087787Z","title":"A comprehensive study of knowledge editing for large language models","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.10606","last_updated":"2024-11-15T22:02:28Z","snapshot_observed_at":"2026-08-14T02:59:29.476989Z","submitted_at":"2024-11-15T22:02:28Z","title":"AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-12T19:35:30.087787Z"},"links":{"cited_paper":"/paper/2401.01286","citing_paper":"/paper/2411.10606"},"observation_digest":"sha256:b86a022ee9472980447e77169e58cec59e01e4c38c76e24cc5749093590f3308","observation_id":"2d961584-828c-441e-afe4-097cb45c8b44","resolution":{"observed_at":"2026-08-12T19:35:30.087787Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2104.08696","last_updated":"2022-03-10T02:28:59Z","snapshot_observed_at":"2026-08-13T19:38:05.626182Z","submitted_at":"2021-04-18T03:38:26Z","title":"Knowledge Neurons in Pretrained Transformers","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2104.08696","snapshot_observed_at":"2026-08-12T19:35:30.092157Z","title":"Knowledge neurons in pretrained transformers","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2411.10606","last_updated":"2024-11-15T22:02:28Z","snapshot_observed_at":"2026-08-14T02:59:29.476989Z","submitted_at":"2024-11-15T22:02:28Z","title":"AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-12T19:35:30.092157Z"},"links":{"cited_paper":"/paper/2104.08696","citing_paper":"/paper/2411.10606"},"observation_digest":"sha256:e9ac8e73a9af76ca612765108f1b76e60c2e462c42ac5f69a61dbe9ad1ce6c92","observation_id":"15244494-8c89-43df-a223-6b0d52aace79","resolution":{"observed_at":"2026-08-12T19:35:30.092157Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2012.14913","last_updated":"2021-09-05T17:32:27Z","snapshot_observed_at":"2026-08-13T01:11:33.500647Z","submitted_at":"2020-12-29T19:12:05Z","title":"Transformer Feed-Forward Layers Are Key-Value Memories","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2012.14913","snapshot_observed_at":"2026-08-12T19:35:30.098960Z","title":"Transformer feed-forward layers are key-value memories","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2411.10606","last_updated":"2024-11-15T22:02:28Z","snapshot_observed_at":"2026-08-14T02:59:29.476989Z","submitted_at":"2024-11-15T22:02:28Z","title":"AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-12T19:35:30.098960Z"},"links":{"cited_paper":"/paper/2012.14913","citing_paper":"/paper/2411.10606"},"observation_digest":"sha256:674cffc33dd1edd1f436b1164f4c00a72681da434a7d28d44aa77f9f58f3f1e5","observation_id":"b0f2183a-70a2-4b89-90a6-1b841ee4eb84","resolution":{"observed_at":"2026-08-12T19:35:30.098960Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:35:30.103475Z","title":"What does bert learn about the structure of language? In ACL 2019-57th Annual Meeting of the Association for Computational Linguistics, 2019","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2411.10606","last_updated":"2024-11-15T22:02:28Z","snapshot_observed_at":"2026-08-14T02:59:29.476989Z","submitted_at":"2024-11-15T22:02:28Z","title":"AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-12T19:35:30.103475Z"},"links":{"citing_paper":"/paper/2411.10606"},"observation_digest":"sha256:9d6de7bcb6caaec210b3de70fe072a2c67fc38d560e08084f2171b0e7aa33ebb","observation_id":"b84bdce9-a71c-469c-a51e-6176cdee0a64","resolution":{"observed_at":"2026-08-12T19:35:30.103475Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:35:30.107989Z","title":"Locating and editing factual associations in gpt","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2411.10606","last_updated":"2024-11-15T22:02:28Z","snapshot_observed_at":"2026-08-14T02:59:29.476989Z","submitted_at":"2024-11-15T22:02:28Z","title":"AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-12T19:35:30.107989Z"},"links":{"citing_paper":"/paper/2411.10606"},"observation_digest":"sha256:83266db8dfca8069ce5e8b09fddcd40316f9a43a40e8a747c18e6d00af348a12","observation_id":"bb0176d4-7c36-4e88-a59a-e045890fa948","resolution":{"observed_at":"2026-08-12T19:35:30.107989Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.16061","last_updated":"2024-03-04T13:37:48Z","snapshot_observed_at":"2026-08-13T04:10:39.844256Z","submitted_at":"2024-02-25T11:15:42Z","title":"How Large Language Models Encode Context Knowledge? A Layer-Wise Probing Study","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.16061","snapshot_observed_at":"2026-08-12T19:35:30.112667Z","title":"How large language models encode context knowledge? a layer-wise probing study","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.10606","last_updated":"2024-11-15T22:02:28Z","snapshot_observed_at":"2026-08-14T02:59:29.476989Z","submitted_at":"2024-11-15T22:02:28Z","title":"AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-12T19:35:30.112667Z"},"links":{"cited_paper":"/paper/2402.16061","citing_paper":"/paper/2411.10606"},"observation_digest":"sha256:36c0c39b0f2c5fdedf8b3f999657548ae725099a70608df190245fc47df91fa1","observation_id":"ef8ef89b-5b45-4bb5-a05b-30d93c0b01aa","resolution":{"observed_at":"2026-08-12T19:35:30.112667Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:35:30.117360Z","title":"Deep residual learning for image recognition","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2411.10606","last_updated":"2024-11-15T22:02:28Z","snapshot_observed_at":"2026-08-14T02:59:29.476989Z","submitted_at":"2024-11-15T22:02:28Z","title":"AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-12T19:35:30.117360Z"},"links":{"citing_paper":"/paper/2411.10606"},"observation_digest":"sha256:e458a679edd44fd8e861a6a2f28e69135f49a6e975cd332c13030415644ffac7","observation_id":"6672af4e-b6f4-4403-9c7c-661b58a03566","resolution":{"observed_at":"2026-08-12T19:35:30.117360Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2305.13172","last_updated":"2023-11-30T08:55:24Z","snapshot_observed_at":"2026-08-13T11:37:07.822986Z","submitted_at":"2023-05-22T16:00:00Z","title":"Editing Large Language Models: Problems, Methods, and Opportunities","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.13172","snapshot_observed_at":"2026-08-12T19:35:30.122172Z","title":"Editing large language models: Problems, methods, and opportunities","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2411.10606","last_updated":"2024-11-15T22:02:28Z","snapshot_observed_at":"2026-08-14T02:59:29.476989Z","submitted_at":"2024-11-15T22:02:28Z","title":"AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-12T19:35:30.122172Z"},"links":{"cited_paper":"/paper/2305.13172","citing_paper":"/paper/2411.10606"},"observation_digest":"sha256:a88ba9b3dee177364280d6ea8850b55a130bbc04e88b470274b188f3708ed3c7","observation_id":"c879663c-11fb-4568-bba5-ac0ec6260f3a","resolution":{"observed_at":"2026-08-12T19:35:30.122172Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2308.07269","last_updated":"2024-06-24T02:17:57Z","snapshot_observed_at":"2026-08-13T10:34:46.891118Z","submitted_at":"2023-08-14T16:52:42Z","title":"EasyEdit: An Easy-to-use Knowledge Editing Framework for Large Language Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2308.07269","snapshot_observed_at":"2026-08-12T19:35:30.126702Z","title":"Easyedit: An easy-to-use knowledge editing framework for large language models","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2411.10606","last_updated":"2024-11-15T22:02:28Z","snapshot_observed_at":"2026-08-14T02:59:29.476989Z","submitted_at":"2024-11-15T22:02:28Z","title":"AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-12T19:35:30.126702Z"},"links":{"cited_paper":"/paper/2308.07269","citing_paper":"/paper/2411.10606"},"observation_digest":"sha256:e6094f9ad7056dff5e9e345785407b7cae75bd98359c785b8226d42130551c3f","observation_id":"a1a04534-4b26-4923-9e5b-cabe5f33667b","resolution":{"observed_at":"2026-08-12T19:35:30.126702Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2403.17887","last_updated":"2025-03-03T17:02:05Z","snapshot_observed_at":"2026-08-13T00:44:30.470984Z","submitted_at":"2024-03-26T17:20:04Z","title":"The Unreasonable Ineffectiveness of the Deeper Layers","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.17887","snapshot_observed_at":"2026-08-12T19:35:30.131623Z","title":"The unreasonable ineffectiveness of the deeper layers","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.10606","last_updated":"2024-11-15T22:02:28Z","snapshot_observed_at":"2026-08-14T02:59:29.476989Z","submitted_at":"2024-11-15T22:02:28Z","title":"AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-12T19:35:30.131623Z"},"links":{"cited_paper":"/paper/2403.17887","citing_paper":"/paper/2411.10606"},"observation_digest":"sha256:1300a8fbaf7c5bd2d283b5e2ac420bfdbb98a56a348d36eb573ce3343f35e698","observation_id":"392a327d-5116-4ffc-8d15-109b458dfbc8","resolution":{"observed_at":"2026-08-12T19:35:30.131623Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:35:31.704262Z","title":"Flexible group-level pruning of deep neural networks for on-device machine learning","venue":null,"work_id":"700a266d-5612-4278-9b16-ba951d71b8b6","year":2020},"citing_paper":{"arxiv_id":"2411.10606","last_updated":"2024-11-15T22:02:28Z","snapshot_observed_at":"2026-08-14T02:59:29.476989Z","submitted_at":"2024-11-15T22:02:28Z","title":"AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-12T19:35:30.136613Z"},"links":{"citing_paper":"/paper/2411.10606"},"observation_digest":"sha256:a495cb3d271c2a3ac6a90565b525e0f31718986df084992881e354505f0abbc4","observation_id":"e61bbb7e-0b56-47b8-82c1-f037072c12f1","resolution":{"observed_at":"2026-08-12T19:35:31.709874Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:35:30.141013Z","title":"Efficient joint optimization of layer-adaptive weight pruning in deep neural networks","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2411.10606","last_updated":"2024-11-15T22:02:28Z","snapshot_observed_at":"2026-08-14T02:59:29.476989Z","submitted_at":"2024-11-15T22:02:28Z","title":"AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-12T19:35:30.141013Z"},"links":{"citing_paper":"/paper/2411.10606"},"observation_digest":"sha256:d2bdb9219c565316751c0d6b56b59c74edcccaf39fcba44d724ad6520867c552","observation_id":"1d22120c-601d-4f95-9fb2-d78a6faa38a5","resolution":{"observed_at":"2026-08-12T19:35:30.141013Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1701.06538","last_updated":"2017-01-23T18:10:00Z","snapshot_observed_at":"2026-08-13T11:35:07.866136Z","submitted_at":"2017-01-23T18:10:00Z","title":"Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1701.06538","snapshot_observed_at":"2026-08-12T19:35:30.145804Z","title":"Outrageously large neural networks: The sparsely-gated mixture-of-experts layer","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2411.10606","last_updated":"2024-11-15T22:02:28Z","snapshot_observed_at":"2026-08-14T02:59:29.476989Z","submitted_at":"2024-11-15T22:02:28Z","title":"AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-12T19:35:30.145804Z"},"links":{"cited_paper":"/paper/1701.06538","citing_paper":"/paper/2411.10606"},"observation_digest":"sha256:1b359ebb5be8a006c5c6d99c34fc12eac88ae22dd16c82be2e95a02b41187793","observation_id":"0a017fc1-d8cb-4b73-a198-0bbf65be26f8","resolution":{"observed_at":"2026-08-12T19:35:30.145804Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:35:31.674167Z","title":"Mole: Mixture of lora experts","venue":null,"work_id":"66f18d68-031c-4d9f-bf90-e160c422bf02","year":2023},"citing_paper":{"arxiv_id":"2411.10606","last_updated":"2024-11-15T22:02:28Z","snapshot_observed_at":"2026-08-14T02:59:29.476989Z","submitted_at":"2024-11-15T22:02:28Z","title":"AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-12T19:35:30.150557Z"},"links":{"citing_paper":"/paper/2411.10606"},"observation_digest":"sha256:f0ffce94a70d938f99f2a3ec992da3acc7cddfd97e2252f34a3e4a32723aa22e","observation_id":"75dc7c37-c307-4666-87fb-a6bb8813fc63","resolution":{"observed_at":"2026-08-12T19:35:31.679009Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2401.16160","last_updated":"2024-01-30T15:44:58Z","snapshot_observed_at":"2026-08-13T04:32:42.021920Z","submitted_at":"2024-01-29T13:48:36Z","title":"LLaVA-MoLE: Sparse Mixture of LoRA Experts for Mitigating Data Conflicts in Instruction Finetuning MLLMs","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.16160","snapshot_observed_at":"2026-08-12T19:35:30.155374Z","title":"Llava-mole: Sparse mixture of lora experts for mitigating data conflicts in instruction finetuning mllms","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.10606","last_updated":"2024-11-15T22:02:28Z","snapshot_observed_at":"2026-08-14T02:59:29.476989Z","submitted_at":"2024-11-15T22:02:28Z","title":"AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-12T19:35:30.155374Z"},"links":{"cited_paper":"/paper/2401.16160","citing_paper":"/paper/2411.10606"},"observation_digest":"sha256:e6a51c0e1fc308a9f0de6c0c2aed2846c5d53183bd3aa83d33b62f6f6f8a921e","observation_id":"4d0a2219-df45-403c-a336-546f01181b40","resolution":{"observed_at":"2026-08-12T19:35:30.155374Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2404.15159","last_updated":"2024-07-20T02:26:49Z","snapshot_observed_at":"2026-08-13T00:25:04.004754Z","submitted_at":"2024-04-22T02:15:52Z","title":"MixLoRA: Enhancing Large Language Models Fine-Tuning with LoRA-based Mixture of Experts","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.15159","snapshot_observed_at":"2026-08-12T19:35:30.160491Z","title":"Mixlora: Enhancing large language models fine-tuning with lora based mixture of experts","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.10606","last_updated":"2024-11-15T22:02:28Z","snapshot_observed_at":"2026-08-14T02:59:29.476989Z","submitted_at":"2024-11-15T22:02:28Z","title":"AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-12T19:35:30.160491Z"},"links":{"cited_paper":"/paper/2404.15159","citing_paper":"/paper/2411.10606"},"observation_digest":"sha256:4c1bbe5e7f8b87bb42c36aa9a05ee20f133d04b0926980dfca06424b7881c735","observation_id":"50e26a20-33dc-4bd7-b0ca-8159b97d1588","resolution":{"observed_at":"2026-08-12T19:35:30.160491Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:35:31.657758Z","title":"Stanford alpaca: An instruction-following llama model","venue":null,"work_id":"16114f9b-3a29-4b0e-9694-8eab1e3f249a","year":2023},"citing_paper":{"arxiv_id":"2411.10606","last_updated":"2024-11-15T22:02:28Z","snapshot_observed_at":"2026-08-14T02:59:29.476989Z","submitted_at":"2024-11-15T22:02:28Z","title":"AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-12T19:35:30.165671Z"},"links":{"citing_paper":"/paper/2411.10606"},"observation_digest":"sha256:224f67ae8bb45ba6ecef2648a3c8a2cb0782750ef5cd2cb9274d78bc40935f29","observation_id":"4389acc7-60c2-4e0f-97e4-d38bd9f159f2","resolution":{"observed_at":"2026-08-12T19:35:31.662834Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:35:31.640192Z","title":"Vicuna: An open-source chatbot impressing gpt-4 with 90%* chatgpt quality, 2023","venue":null,"work_id":"fc0daee4-20c6-40a5-acf4-c6f23a6e3299","year":2023},"citing_paper":{"arxiv_id":"2411.10606","last_updated":"2024-11-15T22:02:28Z","snapshot_observed_at":"2026-08-14T02:59:29.476989Z","submitted_at":"2024-11-15T22:02:28Z","title":"AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-12T19:35:30.170468Z"},"links":{"citing_paper":"/paper/2411.10606"},"observation_digest":"sha256:d313ba111e20260692868fa221f8aacd3a4c3e524b1e4d5bec1dce2dcc55ef5c","observation_id":"1ebad3c0-60a4-402e-bffb-1c94a342d1b7","resolution":{"observed_at":"2026-08-12T19:35:31.645889Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:35:31.622557Z","title":"Language model evaluation harness (package version caaf9ab)","venue":null,"work_id":"6e8c4821-00cf-4f1a-a015-f9d2df0152c8","year":2023},"citing_paper":{"arxiv_id":"2411.10606","last_updated":"2024-11-15T22:02:28Z","snapshot_observed_at":"2026-08-14T02:59:29.476989Z","submitted_at":"2024-11-15T22:02:28Z","title":"AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-12T19:35:30.175250Z"},"links":{"citing_paper":"/paper/2411.10606"},"observation_digest":"sha256:8f269dfb0b5738b1d6de0207932d98d8745fbebdc7cefbaaaf431e8aa9e0a14e","observation_id":"9c9b451a-0730-4739-8088-e61a5f3d8690","resolution":{"observed_at":"2026-08-12T19:35:31.627647Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:35:31.605155Z","title":"BoolQ: Exploring the surprising difficulty of natural yes/no questions","venue":null,"work_id":"29053399-f7fb-4004-bcdc-9849c465bce0","year":2019},"citing_paper":{"arxiv_id":"2411.10606","last_updated":"2024-11-15T22:02:28Z","snapshot_observed_at":"2026-08-14T02:59:29.476989Z","submitted_at":"2024-11-15T22:02:28Z","title":"AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-12T19:35:30.180109Z"},"links":{"citing_paper":"/paper/2411.10606"},"observation_digest":"sha256:0a7115f70a567e803b36487fe542f107df18e768d76c3327b8340e9cef809ce7","observation_id":"f4bca867-0d03-444a-9e1b-96ef646fadb3","resolution":{"observed_at":"2026-08-12T19:35:31.610413Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:35:30.184945Z","title":"Piqa: Reasoning about physical commonsense in natural language","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2411.10606","last_updated":"2024-11-15T22:02:28Z","snapshot_observed_at":"2026-08-14T02:59:29.476989Z","submitted_at":"2024-11-15T22:02:28Z","title":"AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-12T19:35:30.184945Z"},"links":{"citing_paper":"/paper/2411.10606"},"observation_digest":"sha256:d00200d1ada19c9f8c54cd3edb488bd9707090653ef297a8b18525ebd95fec9a","observation_id":"2fd0be4b-855b-4448-b225-18cc1d0fa9f4","resolution":{"observed_at":"2026-08-12T19:35:30.184945Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:35:31.572609Z","title":"Hellaswag: Can a machine really finish your sentence? In ACL, 2019","venue":null,"work_id":"89818540-ab7c-4fae-a4d5-931241b17eb8","year":2019},"citing_paper":{"arxiv_id":"2411.10606","last_updated":"2024-11-15T22:02:28Z","snapshot_observed_at":"2026-08-14T02:59:29.476989Z","submitted_at":"2024-11-15T22:02:28Z","title":"AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-12T19:35:30.190059Z"},"links":{"citing_paper":"/paper/2411.10606"},"observation_digest":"sha256:e486f35d21d30f047f0d0907b012090672ecdb57ccd8b3eb833b654cc0268716","observation_id":"5c7ae752-0f65-4a87-bb5a-7d46a4e11e92","resolution":{"observed_at":"2026-08-12T19:35:31.577408Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1907.10641","last_updated":"2019-11-21T19:01:32Z","snapshot_observed_at":"2026-08-14T04:48:01.875703Z","submitted_at":"2019-07-24T18:11:59Z","title":"WinoGrande: An Adversarial Winograd Schema Challenge at Scale","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1907.10641","snapshot_observed_at":"2026-08-12T19:35:30.194734Z","title":"Winogrande: An adversarial winograd schema challenge at scale","venue":null,"work_id":null,"year":1907},"citing_paper":{"arxiv_id":"2411.10606","last_updated":"2024-11-15T22:02:28Z","snapshot_observed_at":"2026-08-14T02:59:29.476989Z","submitted_at":"2024-11-15T22:02:28Z","title":"AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-12T19:35:30.194734Z"},"links":{"cited_paper":"/paper/1907.10641","citing_paper":"/paper/2411.10606"},"observation_digest":"sha256:94f5c4e025568bdb7bd25f9de7214a46b27587fb80f56b220d26cbfb18519e85","observation_id":"4c388efe-3aac-439a-8f26-eed23ba10016","resolution":{"observed_at":"2026-08-12T19:35:30.194734Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1803.05457","last_updated":"2018-03-14T18:04:21Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2018-03-14T18:04:21Z","title":"Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1803.05457","snapshot_observed_at":"2026-08-12T19:35:30.199244Z","title":"Think you have solved question answering? try arc, the ai2 reasoning challenge","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2411.10606","last_updated":"2024-11-15T22:02:28Z","snapshot_observed_at":"2026-08-14T02:59:29.476989Z","submitted_at":"2024-11-15T22:02:28Z","title":"AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-12T19:35:30.199244Z"},"links":{"cited_paper":"/paper/1803.05457","citing_paper":"/paper/2411.10606"},"observation_digest":"sha256:9d4f78a18c518795f60a0c4d15061542bbde9dad804b77dc1121878cab50dbc5","observation_id":"b5373cc0-deb0-40d6-9b78-3119eacec35c","resolution":{"observed_at":"2026-08-12T19:35:30.199244Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:35:30.203624Z","title":"Can a suit of armor conduct electricity? a new dataset for open book question answering","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2411.10606","last_updated":"2024-11-15T22:02:28Z","snapshot_observed_at":"2026-08-14T02:59:29.476989Z","submitted_at":"2024-11-15T22:02:28Z","title":"AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-12T19:35:30.203624Z"},"links":{"citing_paper":"/paper/2411.10606"},"observation_digest":"sha256:0bda0d18230ff090dc493e3df39bbdf7a13330c07128ee14b2776073980497a4","observation_id":"08a44a45-7036-4352-bbdf-04874b73c224","resolution":{"observed_at":"2026-08-12T19:35:30.203624Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2009.03300","last_updated":"2021-01-12T18:57:11Z","snapshot_observed_at":"2026-08-13T20:44:28.824685Z","submitted_at":"2020-09-07T17:59:25Z","title":"Measuring Massive Multitask Language Understanding","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2009.03300","snapshot_observed_at":"2026-08-12T19:35:30.207874Z","title":"Measuring massive multitask language understanding","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2411.10606","last_updated":"2024-11-15T22:02:28Z","snapshot_observed_at":"2026-08-14T02:59:29.476989Z","submitted_at":"2024-11-15T22:02:28Z","title":"AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-12T19:35:30.207874Z"},"links":{"cited_paper":"/paper/2009.03300","citing_paper":"/paper/2411.10606"},"observation_digest":"sha256:f0b216e4ea16f7c66bf5e668d117b6cd39aa76ebb0137b457e1577cd9be35937","observation_id":"d4b33512-7f09-4ab1-a7d9-4e1cf8ab0af3","resolution":{"observed_at":"2026-08-12T19:35:30.207874Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:35:30.212614Z","title":"Pointer sentinel mixture models","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2411.10606","last_updated":"2024-11-15T22:02:28Z","snapshot_observed_at":"2026-08-14T02:59:29.476989Z","submitted_at":"2024-11-15T22:02:28Z","title":"AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-12T19:35:30.212614Z"},"links":{"citing_paper":"/paper/2411.10606"},"observation_digest":"sha256:77b7ac4c8a73dcc5b1c1a1498bb07c453ecffed2058c7ad879e1a72a7b8359c8","observation_id":"be7616b8-9517-4e6d-b67a-9ed25d877c74","resolution":{"observed_at":"2026-08-12T19:35:30.212614Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:35:30.216816Z","title":"Aligning books and movies: Towards story-like visual explanations by watching movies and reading books","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2411.10606","last_updated":"2024-11-15T22:02:28Z","snapshot_observed_at":"2026-08-14T02:59:29.476989Z","submitted_at":"2024-11-15T22:02:28Z","title":"AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-12T19:35:30.216816Z"},"links":{"citing_paper":"/paper/2411.10606"},"observation_digest":"sha256:67564c5a9138a34b5754c05e485f8b1349e746858949caa20b638f4360d42366","observation_id":"83c901e8-269b-4f7e-8d6c-dd18bd80c43f","resolution":{"observed_at":"2026-08-12T19:35:30.216816Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:35:30.221053Z","title":"Attention is all you need","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2411.10606","last_updated":"2024-11-15T22:02:28Z","snapshot_observed_at":"2026-08-14T02:59:29.476989Z","submitted_at":"2024-11-15T22:02:28Z","title":"AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-12T19:35:30.221053Z"},"links":{"citing_paper":"/paper/2411.10606"},"observation_digest":"sha256:84b9f52307ccc0ecc7beedfb108e9a11e936188b3e4412c0ee2556a62619628a","observation_id":"89bdc06e-e9b4-48e4-89e8-fe858dfb5870","resolution":{"observed_at":"2026-08-12T19:35:30.221053Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1810.04805","last_updated":"2019-05-24T20:37:26Z","snapshot_observed_at":"2026-07-30T09:12:38.100527Z","submitted_at":"2018-10-11T00:50:01Z","title":"BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1810.04805","snapshot_observed_at":"2026-08-12T19:35:30.225307Z","title":"Bert: Pre-training of deep bidirectional transformers for language understanding","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2411.10606","last_updated":"2024-11-15T22:02:28Z","snapshot_observed_at":"2026-08-14T02:59:29.476989Z","submitted_at":"2024-11-15T22:02:28Z","title":"AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-12T19:35:30.225307Z"},"links":{"cited_paper":"/paper/1810.04805","citing_paper":"/paper/2411.10606"},"observation_digest":"sha256:ffebeac494d09629bd0f066ae3a8a0bed79a2136c11ef0d9c5be21e2d087d54c","observation_id":"6d369cb1-5597-4b4d-84c0-08e19845b49f","resolution":{"observed_at":"2026-08-12T19:35:30.225307Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:35:30.229536Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2411.10606","last_updated":"2024-11-15T22:02:28Z","snapshot_observed_at":"2026-08-14T02:59:29.476989Z","submitted_at":"2024-11-15T22:02:28Z","title":"AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-12T19:35:30.229536Z"},"links":{"citing_paper":"/paper/2411.10606"},"observation_digest":"sha256:dff0635a6851d387a365de695191c832abbe37d258caa48f4463bd1f3284f887","observation_id":"77165d89-cad7-4935-a838-451e523d9a5a","resolution":{"observed_at":"2026-08-12T19:35:30.229536Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2203.17189","last_updated":"2022-03-31T17:12:13Z","snapshot_observed_at":"2026-08-13T16:10:46.515920Z","submitted_at":"2022-03-31T17:12:13Z","title":"Scaling Up Models and Data with $\\texttt{t5x}$ and $\\texttt{seqio}$","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2203.17189","snapshot_observed_at":"2026-08-12T19:35:30.233705Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2411.10606","last_updated":"2024-11-15T22:02:28Z","snapshot_observed_at":"2026-08-14T02:59:29.476989Z","submitted_at":"2024-11-15T22:02:28Z","title":"AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-12T19:35:30.233705Z"},"links":{"cited_paper":"/paper/2203.17189","citing_paper":"/paper/2411.10606"},"observation_digest":"sha256:9a886ab3887d54a134a44a048d918599f4e243c473dcb06c299fd56e9db6cd7b","observation_id":"f88bf95c-bf63-4ef7-9be9-96c82bc7ec7c","resolution":{"observed_at":"2026-08-12T19:35:30.233705Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2307.14995","last_updated":"2024-01-19T07:47:01Z","snapshot_observed_at":"2026-08-13T10:46:43.498640Z","submitted_at":"2023-07-27T16:45:33Z","title":"TransNormerLLM: A Faster and Better Large Language Model with Improved TransNormer","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2307.14995","snapshot_observed_at":"2026-08-12T19:35:30.238787Z","title":"Scaling transnormer to 175 billion parameters","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2411.10606","last_updated":"2024-11-15T22:02:28Z","snapshot_observed_at":"2026-08-14T02:59:29.476989Z","submitted_at":"2024-11-15T22:02:28Z","title":"AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-12T19:35:30.238787Z"},"links":{"cited_paper":"/paper/2307.14995","citing_paper":"/paper/2411.10606"},"observation_digest":"sha256:84b7c082f22f581d47ea684fc57df5a50b249190527df74d8d2750dfa1fb447f","observation_id":"fec69dda-b3ea-4cd3-aa62-1ba83d646261","resolution":{"observed_at":"2026-08-12T19:35:30.238787Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2001.08361","last_updated":"2020-01-23T03:59:20Z","snapshot_observed_at":"2026-08-13T17:41:53.092611Z","submitted_at":"2020-01-23T03:59:20Z","title":"Scaling Laws for Neural Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2001.08361","snapshot_observed_at":"2026-08-12T19:35:30.243328Z","title":"Scaling laws for neural language models","venue":null,"work_id":null,"year":2001},"citing_paper":{"arxiv_id":"2411.10606","last_updated":"2024-11-15T22:02:28Z","snapshot_observed_at":"2026-08-14T02:59:29.476989Z","submitted_at":"2024-11-15T22:02:28Z","title":"AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-12T19:35:30.243328Z"},"links":{"cited_paper":"/paper/2001.08361","citing_paper":"/paper/2411.10606"},"observation_digest":"sha256:71d05017c23ab61b1a3d6233742b08acd544f37b833aa9dcf8630197b04e7ab1","observation_id":"89a4e57a-69a7-4f85-8f7b-eb14d374a32f","resolution":{"observed_at":"2026-08-12T19:35:30.243328Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:35:30.248083Z","title":"Pythia: A suite for analyzing large language models across training and scaling","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2411.10606","last_updated":"2024-11-15T22:02:28Z","snapshot_observed_at":"2026-08-14T02:59:29.476989Z","submitted_at":"2024-11-15T22:02:28Z","title":"AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-12T19:35:30.248083Z"},"links":{"citing_paper":"/paper/2411.10606"},"observation_digest":"sha256:599e83e5bc69ba7e7866e9272f9210bd8a63957cb82fbb191f3b556030fb3529","observation_id":"4108ab37-9d2f-4907-b9e5-871be0a33a11","resolution":{"observed_at":"2026-08-12T19:35:30.248083Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2103.10360","last_updated":"2022-03-17T11:49:55Z","snapshot_observed_at":"2026-08-14T02:58:45.675852Z","submitted_at":"2021-03-18T16:30:26Z","title":"GLM: General Language Model Pretraining with Autoregressive Blank Infilling","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2103.10360","snapshot_observed_at":"2026-08-12T19:35:30.252384Z","title":"Glm: General language model pretraining with autoregressive blank infilling","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2411.10606","last_updated":"2024-11-15T22:02:28Z","snapshot_observed_at":"2026-08-14T02:59:29.476989Z","submitted_at":"2024-11-15T22:02:28Z","title":"AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment","version":1},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-12T19:35:30.252384Z"},"links":{"cited_paper":"/paper/2103.10360","citing_paper":"/paper/2411.10606"},"observation_digest":"sha256:920a4e51ab4fc3d2f59ba156646c06ffcdb142b7d373085d0d6c37aa32314231","observation_id":"14985555-59ac-44e1-8547-bda8b4f4c931","resolution":{"observed_at":"2026-08-12T19:35:30.252384Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2205.01068","last_updated":"2022-06-21T17:04:40Z","snapshot_observed_at":"2026-08-06T03:13:37.403059Z","submitted_at":"2022-05-02T17:49:50Z","title":"OPT: Open Pre-trained Transformer Language Models","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2205.01068","snapshot_observed_at":"2026-08-12T19:35:30.257228Z","title":"Opt: Open pre-trained transformer language models","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2411.10606","last_updated":"2024-11-15T22:02:28Z","snapshot_observed_at":"2026-08-14T02:59:29.476989Z","submitted_at":"2024-11-15T22:02:28Z","title":"AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment","version":1},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-12T19:35:30.257228Z"},"links":{"cited_paper":"/paper/2205.01068","citing_paper":"/paper/2411.10606"},"observation_digest":"sha256:eb69cf5bd51a3e73089da81f1835610e8f53b5c6fbe63ae1647f52f9cb317f0a","observation_id":"6248c978-7fbe-4e07-9a93-4dca453955fb","resolution":{"observed_at":"2026-08-12T19:35:30.257228Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2211.05100","last_updated":"2023-06-27T09:57:58Z","snapshot_observed_at":"2026-08-04T18:56:03.233715Z","submitted_at":"2022-11-09T18:48:09Z","title":"BLOOM: A 176B-Parameter Open-Access Multilingual Language Model","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2211.05100","snapshot_observed_at":"2026-08-12T19:35:30.261920Z","title":"Bloom: A 176b-parameter open-access multilingual language model","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2411.10606","last_updated":"2024-11-15T22:02:28Z","snapshot_observed_at":"2026-08-14T02:59:29.476989Z","submitted_at":"2024-11-15T22:02:28Z","title":"AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment","version":1},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-12T19:35:30.261920Z"},"links":{"cited_paper":"/paper/2211.05100","citing_paper":"/paper/2411.10606"},"observation_digest":"sha256:3607ec4a382c39b0140835796468ef897222db06ada721bcc994cc34b0775eac","observation_id":"0bf18923-75a3-43ca-81b0-1690dc5eaaf0","resolution":{"observed_at":"2026-08-12T19:35:30.261920Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:35:30.266571Z","title":"Specializing smaller language models towards multi-step reasoning","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2411.10606","last_updated":"2024-11-15T22:02:28Z","snapshot_observed_at":"2026-08-14T02:59:29.476989Z","submitted_at":"2024-11-15T22:02:28Z","title":"AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment","version":1},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-12T19:35:30.266571Z"},"links":{"citing_paper":"/paper/2411.10606"},"observation_digest":"sha256:91dd1fb0a081b43d47a4d3cfe9cd4285e4297d2d554f562660fdfaf30e9aa102","observation_id":"b33d280d-b0b7-4812-bf90-27c9eba21c60","resolution":{"observed_at":"2026-08-12T19:35:30.266571Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2305.02301","last_updated":"2023-07-05T16:59:31Z","snapshot_observed_at":"2026-07-06T15:22:55.122322Z","submitted_at":"2023-05-03T17:50:56Z","title":"Distilling Step-by-Step! Outperforming Larger Language Models with Less Training Data and Smaller Model Sizes","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.02301","snapshot_observed_at":"2026-08-12T19:35:30.271219Z","title":"Distilling step-by-step! outperform- ing larger language models with less training data and smaller model sizes","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2411.10606","last_updated":"2024-11-15T22:02:28Z","snapshot_observed_at":"2026-08-14T02:59:29.476989Z","submitted_at":"2024-11-15T22:02:28Z","title":"AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment","version":1},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-08-12T19:35:30.271219Z"},"links":{"cited_paper":"/paper/2305.02301","citing_paper":"/paper/2411.10606"},"observation_digest":"sha256:f49d0ec5af364676c02c9bb3c64a012109b7a908a437c91737900fa9fd56d0fc","observation_id":"2b37154b-71db-43cc-a101-e69071724284","resolution":{"observed_at":"2026-08-12T19:35:30.271219Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:35:30.276293Z","title":"Optq: Accurate quantization for generative pre-trained transformers","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2411.10606","last_updated":"2024-11-15T22:02:28Z","snapshot_observed_at":"2026-08-14T02:59:29.476989Z","submitted_at":"2024-11-15T22:02:28Z","title":"AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment","version":1},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-08-12T19:35:30.276293Z"},"links":{"citing_paper":"/paper/2411.10606"},"observation_digest":"sha256:a78e752ee30a79171b4aa55c9f381819e1a4e420c0366a10b0b0bc1f3a3bcba7","observation_id":"00f0aae3-c47b-4802-98ec-a2ff828c0117","resolution":{"observed_at":"2026-08-12T19:35:30.276293Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:35:31.465309Z","title":"LLM.int8(): 8-bit matrix multiplication for transformers at scale","venue":null,"work_id":"8e9b1439-51cc-4726-8aaf-cc86fbfcff7d","year":2022},"citing_paper":{"arxiv_id":"2411.10606","last_updated":"2024-11-15T22:02:28Z","snapshot_observed_at":"2026-08-14T02:59:29.476989Z","submitted_at":"2024-11-15T22:02:28Z","title":"AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment","version":1},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-08-12T19:35:30.280919Z"},"links":{"citing_paper":"/paper/2411.10606"},"observation_digest":"sha256:ccddcb5494816f1b9fe82d6059adbcc13a34a9858672afb3097b177985363ca0","observation_id":"02385843-c7da-4565-a2ed-347b0e363b5a","resolution":{"observed_at":"2026-08-12T19:35:31.470009Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:35:31.449948Z","title":"Smoothquant: Accurate and efficient post-training quantization for large language models","venue":null,"work_id":"13c38fb7-ffee-40ff-b7b7-db9aef81823e","year":2023},"citing_paper":{"arxiv_id":"2411.10606","last_updated":"2024-11-15T22:02:28Z","snapshot_observed_at":"2026-08-14T02:59:29.476989Z","submitted_at":"2024-11-15T22:02:28Z","title":"AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment","version":1},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-08-12T19:35:30.285078Z"},"links":{"citing_paper":"/paper/2411.10606"},"observation_digest":"sha256:454435f165eb880c567f5c64bbb7334c750d8dd729705b4d75e358b2fd5b20f2","observation_id":"7767e87b-1cd3-4e64-bcd6-8c7d2ebda0bd","resolution":{"observed_at":"2026-08-12T19:35:31.454494Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:35:31.434638Z","title":"GPTQ: Accurate post-training compression for generative pretrained transformers","venue":null,"work_id":"e5e19ae6-06a4-4b3a-a4a1-949874639ae0","year":2023},"citing_paper":{"arxiv_id":"2411.10606","last_updated":"2024-11-15T22:02:28Z","snapshot_observed_at":"2026-08-14T02:59:29.476989Z","submitted_at":"2024-11-15T22:02:28Z","title":"AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment","version":1},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-08-12T19:35:30.289140Z"},"links":{"citing_paper":"/paper/2411.10606"},"observation_digest":"sha256:3aa240da9ba5d5d2dea30ff383961e9ca70264db59164231a274fb06cd08e6fc","observation_id":"9b7f6f70-38e4-4c71-87fb-f8902d9004d2","resolution":{"observed_at":"2026-08-12T19:35:31.439474Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:35:30.292984Z","title":"Spqr: A sparse-quantized representation for near-lossless llm weight compression, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2411.10606","last_updated":"2024-11-15T22:02:28Z","snapshot_observed_at":"2026-08-14T02:59:29.476989Z","submitted_at":"2024-11-15T22:02:28Z","title":"AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment","version":1},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-08-12T19:35:30.292984Z"},"links":{"citing_paper":"/paper/2411.10606"},"observation_digest":"sha256:57ccb41c1543da5a59c61bc88f526a546074ece4ee5646ecfdfdb1a445dfdfde","observation_id":"497a3f44-4ab2-4102-8b9b-231eb065c366","resolution":{"observed_at":"2026-08-12T19:35:30.292984Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2306.00978","last_updated":"2026-04-25T06:58:16Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-06-01T17:59:10Z","title":"AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2306.00978","snapshot_observed_at":"2026-08-12T19:35:30.296985Z","title":"Awq: Activation-aware weight quantization for llm compression and acceleration","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2411.10606","last_updated":"2024-11-15T22:02:28Z","snapshot_observed_at":"2026-08-14T02:59:29.476989Z","submitted_at":"2024-11-15T22:02:28Z","title":"AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment","version":1},"reference_index":69,"source":"pdf_text","source_observed_at":"2026-08-12T19:35:30.296985Z"},"links":{"cited_paper":"/paper/2306.00978","citing_paper":"/paper/2411.10606"},"observation_digest":"sha256:5b7fda93a3bc7683535d990750678813d58d03bf9d0f5337fd80b34f33710ae4","observation_id":"c448eb37-af1f-47f7-874c-095e0719d7e3","resolution":{"observed_at":"2026-08-12T19:35:30.296985Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2307.08691","last_updated":"2023-07-17T17:50:36Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-07-17T17:50:36Z","title":"FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2307.08691","snapshot_observed_at":"2026-08-12T19:35:30.301267Z","title":"Flashattention-2: Faster attention with better parallelism and work partitioning","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2411.10606","last_updated":"2024-11-15T22:02:28Z","snapshot_observed_at":"2026-08-14T02:59:29.476989Z","submitted_at":"2024-11-15T22:02:28Z","title":"AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment","version":1},"reference_index":70,"source":"pdf_text","source_observed_at":"2026-08-12T19:35:30.301267Z"},"links":{"cited_paper":"/paper/2307.08691","citing_paper":"/paper/2411.10606"},"observation_digest":"sha256:9ee4b3fe1d5f790d1f42518226acce4a5739f1199c365a371ea7f12cdbff9ad6","observation_id":"f8db1388-8254-44c6-9180-6c42739eeef2","resolution":{"observed_at":"2026-08-12T19:35:30.301267Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:35:30.305471Z","title":"Efficient memory management for large language model serving with pagedattention","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2411.10606","last_updated":"2024-11-15T22:02:28Z","snapshot_observed_at":"2026-08-14T02:59:29.476989Z","submitted_at":"2024-11-15T22:02:28Z","title":"AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment","version":1},"reference_index":71,"source":"pdf_text","source_observed_at":"2026-08-12T19:35:30.305471Z"},"links":{"citing_paper":"/paper/2411.10606"},"observation_digest":"sha256:e5fadd353eb2d95e49090f21334b02238600fcbe33a86e4267e53a79857dc9e3","observation_id":"dbfbca65-b0e2-4c9b-849e-7eef92a2f4d1","resolution":{"observed_at":"2026-08-12T19:35:30.305471Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:35:31.396084Z","title":"Llm-pruner: On the structural pruning of large language models, 2023","venue":null,"work_id":"0155a16a-9e26-4391-8ba8-48d6d266382b","year":2023},"citing_paper":{"arxiv_id":"2411.10606","last_updated":"2024-11-15T22:02:28Z","snapshot_observed_at":"2026-08-14T02:59:29.476989Z","submitted_at":"2024-11-15T22:02:28Z","title":"AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment","version":1},"reference_index":72,"source":"pdf_text","source_observed_at":"2026-08-12T19:35:30.309524Z"},"links":{"citing_paper":"/paper/2411.10606"},"observation_digest":"sha256:0c2200b3c1a1ede4bdb4a3261a57974504546719dd3b0cdc24ebce900e17bb1d","observation_id":"57c18ccb-a4a9-463f-aead-949073402a78","resolution":{"observed_at":"2026-08-12T19:35:31.400730Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2102.04010","last_updated":"2021-04-18T10:18:00Z","snapshot_observed_at":"2026-08-11T00:45:43.321851Z","submitted_at":"2021-02-08T05:55:47Z","title":"Learning N:M Fine-grained Structured Sparse Neural Networks From Scratch","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2102.04010","snapshot_observed_at":"2026-08-12T19:35:30.313605Z","title":"Learning n: m fine-grained structured sparse neural networks from scratch","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2411.10606","last_updated":"2024-11-15T22:02:28Z","snapshot_observed_at":"2026-08-14T02:59:29.476989Z","submitted_at":"2024-11-15T22:02:28Z","title":"AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment","version":1},"reference_index":73,"source":"pdf_text","source_observed_at":"2026-08-12T19:35:30.313605Z"},"links":{"cited_paper":"/paper/2102.04010","citing_paper":"/paper/2411.10606"},"observation_digest":"sha256:3a2cd874cf155529e58567a36cf51ebe9b0d70fc917cae5f641fc64ebccfb76d","observation_id":"e6c46938-4012-4947-b6b3-73e52a18c3dd","resolution":{"observed_at":"2026-08-12T19:35:30.313605Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1812.08928","last_updated":"2018-12-21T03:36:48Z","snapshot_observed_at":"2026-08-02T06:18:15.521345Z","submitted_at":"2018-12-21T03:36:48Z","title":"Slimmable Neural Networks","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1812.08928","snapshot_observed_at":"2026-08-12T19:35:30.318165Z","title":"Slimmable neural networks","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2411.10606","last_updated":"2024-11-15T22:02:28Z","snapshot_observed_at":"2026-08-14T02:59:29.476989Z","submitted_at":"2024-11-15T22:02:28Z","title":"AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment","version":1},"reference_index":74,"source":"pdf_text","source_observed_at":"2026-08-12T19:35:30.318165Z"},"links":{"cited_paper":"/paper/1812.08928","citing_paper":"/paper/2411.10606"},"observation_digest":"sha256:4a60949bc7938a87ef4ec96a66e9d322246c57f19fc4fff13fe5d1a42e1f598d","observation_id":"56f3729f-7d3a-4baa-b28a-a2f3d84f0c51","resolution":{"observed_at":"2026-08-12T19:35:30.318165Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:35:31.380980Z","title":"Universally slimmable networks and improved training techniques","venue":null,"work_id":"7154a955-5902-4de1-94b9-d4ce13528c1e","year":2019},"citing_paper":{"arxiv_id":"2411.10606","last_updated":"2024-11-15T22:02:28Z","snapshot_observed_at":"2026-08-14T02:59:29.476989Z","submitted_at":"2024-11-15T22:02:28Z","title":"AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment","version":1},"reference_index":75,"source":"pdf_text","source_observed_at":"2026-08-12T19:35:30.322462Z"},"links":{"citing_paper":"/paper/2411.10606"},"observation_digest":"sha256:baed41c0b36f00cb56c0bb7f74ed24d48fcf263d9f854026124da2760d7710d3","observation_id":"774472ea-f6cd-4826-b023-fc7a80e3487f","resolution":{"observed_at":"2026-08-12T19:35:31.385443Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1903.11728","last_updated":"2019-06-01T03:19:54Z","snapshot_observed_at":"2026-08-01T14:19:06.163558Z","submitted_at":"2019-03-27T23:17:28Z","title":"AutoSlim: Towards One-Shot Architecture Search for Channel Numbers","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1903.11728","snapshot_observed_at":"2026-08-12T19:35:30.326810Z","title":"Autoslim: Towards one-shot architecture search for channel numbers","venue":null,"work_id":null,"year":1903},"citing_paper":{"arxiv_id":"2411.10606","last_updated":"2024-11-15T22:02:28Z","snapshot_observed_at":"2026-08-14T02:59:29.476989Z","submitted_at":"2024-11-15T22:02:28Z","title":"AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment","version":1},"reference_index":76,"source":"pdf_text","source_observed_at":"2026-08-12T19:35:30.326810Z"},"links":{"cited_paper":"/paper/1903.11728","citing_paper":"/paper/2411.10606"},"observation_digest":"sha256:ae99b0fdc72819653ea9711e14b8f296c203f8bade54534be5b8a6e01b65274c","observation_id":"306ff83c-6aae-470e-bf53-c0e8dc670e27","resolution":{"observed_at":"2026-08-12T19:35:30.326810Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:35:31.365161Z","title":"Adabits: Neural network quantization with adaptive bit-widths","venue":null,"work_id":"80891bf4-b4fd-47ff-850e-ef6412d338ff","year":2020},"citing_paper":{"arxiv_id":"2411.10606","last_updated":"2024-11-15T22:02:28Z","snapshot_observed_at":"2026-08-14T02:59:29.476989Z","submitted_at":"2024-11-15T22:02:28Z","title":"AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment","version":1},"reference_index":77,"source":"pdf_text","source_observed_at":"2026-08-12T19:35:30.331319Z"},"links":{"citing_paper":"/paper/2411.10606"},"observation_digest":"sha256:23ae1ea5ae961d6af75e30f7169fa4c60eef1821ab6bcef22738e99baf53cb00","observation_id":"ec61eaac-c47a-4463-99d2-fae350495337","resolution":{"observed_at":"2026-08-12T19:35:31.370401Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2002.02815","last_updated":"2020-02-07T14:43:44Z","snapshot_observed_at":"2026-07-06T08:55:34.632694Z","submitted_at":"2020-02-07T14:43:44Z","title":"Switchable Precision Neural Networks","version":1},"cited_work":{"arxiv_id":"2002.02815","doi":null,"metadata_source":"pith","pith_arxiv_id":"2002.02815","snapshot_observed_at":"2026-08-12T19:35:30.490956Z","title":"Switchable Precision Neural Networks","venue":"cs.CV","work_id":"a9e19ccd-d092-4dab-8103-4ac3da5c5a97","year":2020},"citing_paper":{"arxiv_id":"2411.10606","last_updated":"2024-11-15T22:02:28Z","snapshot_observed_at":"2026-08-14T02:59:29.476989Z","submitted_at":"2024-11-15T22:02:28Z","title":"AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment","version":1},"reference_index":78,"source":"pdf_text","source_observed_at":"2026-08-12T19:35:30.335398Z"},"links":{"cited_paper":"/paper/2002.02815","citing_paper":"/paper/2411.10606"},"observation_digest":"sha256:0405a67b72862b7544ec6168cea0727a5a049a4f34382182f5b9a2dbf4f1705c","observation_id":"1de285e0-fa7b-4d95-ad15-87b8462be3c1","resolution":{"observed_at":"2026-08-12T19:35:30.497574Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:35:31.348177Z","title":"Any-precision deep neural networks","venue":null,"work_id":"8916607e-0631-4675-807a-8f6aa14161b8","year":2021},"citing_paper":{"arxiv_id":"2411.10606","last_updated":"2024-11-15T22:02:28Z","snapshot_observed_at":"2026-08-14T02:59:29.476989Z","submitted_at":"2024-11-15T22:02:28Z","title":"AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment","version":1},"reference_index":79,"source":"pdf_text","source_observed_at":"2026-08-12T19:35:30.340215Z"},"links":{"citing_paper":"/paper/2411.10606"},"observation_digest":"sha256:e7e308228d24fcc71d8604e6ca569ff4055c0428c00235c9e02a9d4105034a01","observation_id":"fc99e223-59ff-4770-8c17-212bfc2e0caa","resolution":{"observed_at":"2026-08-12T19:35:31.353646Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.10517","last_updated":"2024-06-21T05:20:56Z","snapshot_observed_at":"2026-08-13T04:17:46.355441Z","submitted_at":"2024-02-16T09:06:06Z","title":"Any-Precision LLM: Low-Cost Deployment of Multiple, Different-Sized LLMs","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.10517","snapshot_observed_at":"2026-08-12T19:35:30.344764Z","title":"Any-precision llm: Low-cost deployment of multiple, different-sized llms","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.10606","last_updated":"2024-11-15T22:02:28Z","snapshot_observed_at":"2026-08-14T02:59:29.476989Z","submitted_at":"2024-11-15T22:02:28Z","title":"AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment","version":1},"reference_index":80,"source":"pdf_text","source_observed_at":"2026-08-12T19:35:30.344764Z"},"links":{"cited_paper":"/paper/2402.10517","citing_paper":"/paper/2411.10606"},"observation_digest":"sha256:07ab4c4dac281f7dd1a834e689595712b2da84a07414c8a33c90559ee0dcbaa2","observation_id":"07fa6e38-2884-47e1-9a07-4cc8ab5e4d49","resolution":{"observed_at":"2026-08-12T19:35:30.344764Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.10260","last_updated":"2024-08-28T17:26:03Z","snapshot_observed_at":"2026-08-12T23:45:58.945662Z","submitted_at":"2024-06-11T01:16:10Z","title":"Flextron: Many-in-One Flexible Large Language Model","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.10260","snapshot_observed_at":"2026-08-12T19:35:30.349664Z","title":"Flextron: Many-in-one flexible large language model","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.10606","last_updated":"2024-11-15T22:02:28Z","snapshot_observed_at":"2026-08-14T02:59:29.476989Z","submitted_at":"2024-11-15T22:02:28Z","title":"AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment","version":1},"reference_index":81,"source":"pdf_text","source_observed_at":"2026-08-12T19:35:30.349664Z"},"links":{"cited_paper":"/paper/2406.10260","citing_paper":"/paper/2411.10606"},"observation_digest":"sha256:5b6ba8b2ac09acd1ff658ec94dbdea5d6f37f9b42f86ef732f5159a8f9c257f6","observation_id":"10100ce0-99e4-4c7e-8823-e7e87bf0f52e","resolution":{"observed_at":"2026-08-12T19:35:30.349664Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:35:31.331296Z","title":"Guidelines: • The answer NA means that the abstract and introduction do not include the claims made in the paper","venue":null,"work_id":"1b8ce035-5a56-40b3-a89a-08c0a2c7fd28","year":null},"citing_paper":{"arxiv_id":"2411.10606","last_updated":"2024-11-15T22:02:28Z","snapshot_observed_at":"2026-08-14T02:59:29.476989Z","submitted_at":"2024-11-15T22:02:28Z","title":"AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment","version":1},"reference_index":82,"source":"pdf_text","source_observed_at":"2026-08-12T19:35:30.354735Z"},"links":{"citing_paper":"/paper/2411.10606"},"observation_digest":"sha256:120ac7bc5ee8202706e251bd62375d658336e324abd83a4e5f634bad28e31941","observation_id":"e446d2b3-ef50-410f-bd39-cf73787386e1","resolution":{"observed_at":"2026-08-12T19:35:31.336842Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:35:31.314676Z","title":"Limitations","venue":null,"work_id":"24e2b8c2-03fd-4a06-9cf8-416a8d11c26e","year":null},"citing_paper":{"arxiv_id":"2411.10606","last_updated":"2024-11-15T22:02:28Z","snapshot_observed_at":"2026-08-14T02:59:29.476989Z","submitted_at":"2024-11-15T22:02:28Z","title":"AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment","version":1},"reference_index":83,"source":"pdf_text","source_observed_at":"2026-08-12T19:35:30.359545Z"},"links":{"citing_paper":"/paper/2411.10606"},"observation_digest":"sha256:5c4060cb8226b770d2e0995265231aac2f8889c508ebfd72d56cbb98aad1ee0d","observation_id":"3c338bb7-ca03-4d79-b2b2-4685d57f63c6","resolution":{"observed_at":"2026-08-12T19:35:31.319618Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:35:31.299126Z","title":"Guidelines: • The answer NA means that the paper does not include theoretical results","venue":null,"work_id":"d3d7ec8f-8071-46ec-bb6d-52f9225ffdc5","year":null},"citing_paper":{"arxiv_id":"2411.10606","last_updated":"2024-11-15T22:02:28Z","snapshot_observed_at":"2026-08-14T02:59:29.476989Z","submitted_at":"2024-11-15T22:02:28Z","title":"AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment","version":1},"reference_index":84,"source":"pdf_text","source_observed_at":"2026-08-12T19:35:30.364256Z"},"links":{"citing_paper":"/paper/2411.10606"},"observation_digest":"sha256:3d09a8649f00821e2ba1147baf62740e0a530c71c509634455ec203687b0e97f","observation_id":"d12b038f-019b-408d-bb0f-53af9e068fe5","resolution":{"observed_at":"2026-08-12T19:35:31.303621Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:35:31.283286Z","title":"Guidelines: • The answer NA means that the paper does not include experiments","venue":null,"work_id":"51c6c1ca-9988-4649-ab99-d621dcd184c7","year":null},"citing_paper":{"arxiv_id":"2411.10606","last_updated":"2024-11-15T22:02:28Z","snapshot_observed_at":"2026-08-14T02:59:29.476989Z","submitted_at":"2024-11-15T22:02:28Z","title":"AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment","version":1},"reference_index":85,"source":"pdf_text","source_observed_at":"2026-08-12T19:35:30.368477Z"},"links":{"citing_paper":"/paper/2411.10606"},"observation_digest":"sha256:6859ce11ccd3ca94956d5afe04d2c1d142c2a625ab9a61d544e218e5e6f8d824","observation_id":"67efbfa6-dfea-420d-bd68-9bca4378fce2","resolution":{"observed_at":"2026-08-12T19:35:31.288060Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:35:31.267931Z","title":"Guidelines: • The answer NA means that paper does not include experiments requiring code","venue":null,"work_id":"2f19fcc7-098f-4ee4-bd95-cc2b8e677028","year":null},"citing_paper":{"arxiv_id":"2411.10606","last_updated":"2024-11-15T22:02:28Z","snapshot_observed_at":"2026-08-14T02:59:29.476989Z","submitted_at":"2024-11-15T22:02:28Z","title":"AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment","version":1},"reference_index":86,"source":"pdf_text","source_observed_at":"2026-08-12T19:35:30.373662Z"},"links":{"citing_paper":"/paper/2411.10606"},"observation_digest":"sha256:749626b429d998470731d138129b394f6239d2dbca6c69abe594220d53bfc2c6","observation_id":"bb66ef43-b795-4d21-b61c-b70ee5fb40e7","resolution":{"observed_at":"2026-08-12T19:35:31.272817Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:35:31.252526Z","title":"5.1 of our paper and also provided sufficient references","venue":null,"work_id":"34227827-98be-4c05-aaed-831e1c4fd9ab","year":null},"citing_paper":{"arxiv_id":"2411.10606","last_updated":"2024-11-15T22:02:28Z","snapshot_observed_at":"2026-08-14T02:59:29.476989Z","submitted_at":"2024-11-15T22:02:28Z","title":"AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment","version":1},"reference_index":87,"source":"pdf_text","source_observed_at":"2026-08-12T19:35:30.378057Z"},"links":{"citing_paper":"/paper/2411.10606"},"observation_digest":"sha256:63b5089d24cff6992b4e2c90433d25c1a19b008f0526547077cf4038fba1390e","observation_id":"80b9e18c-2625-447a-a961-25721f6d8a66","resolution":{"observed_at":"2026-08-12T19:35:31.257119Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:35:31.237642Z","title":"Guidelines: • The answer NA means that the paper does not include experiments","venue":null,"work_id":"179ed2b8-36ed-42f4-bd8e-8d35efc94a09","year":null},"citing_paper":{"arxiv_id":"2411.10606","last_updated":"2024-11-15T22:02:28Z","snapshot_observed_at":"2026-08-14T02:59:29.476989Z","submitted_at":"2024-11-15T22:02:28Z","title":"AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment","version":1},"reference_index":88,"source":"pdf_text","source_observed_at":"2026-08-12T19:35:30.382864Z"},"links":{"citing_paper":"/paper/2411.10606"},"observation_digest":"sha256:83cea6be4e4b29b70247e379f02c531f791a1731dbb846345da2fa0e071dd5a8","observation_id":"ee4d9df4-a653-48fe-87d2-a4f5ff39be85","resolution":{"observed_at":"2026-08-12T19:35:31.242240Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:35:31.221845Z","title":"5.1 of our paper","venue":null,"work_id":"a74d73cc-976a-47ce-b436-d54ba779a5a2","year":null},"citing_paper":{"arxiv_id":"2411.10606","last_updated":"2024-11-15T22:02:28Z","snapshot_observed_at":"2026-08-14T02:59:29.476989Z","submitted_at":"2024-11-15T22:02:28Z","title":"AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment","version":1},"reference_index":89,"source":"pdf_text","source_observed_at":"2026-08-12T19:35:30.387221Z"},"links":{"citing_paper":"/paper/2411.10606"},"observation_digest":"sha256:696095c8c964063dd5d5491a12c293553d3afdbc8d399cc5fbd7c4d504830bd5","observation_id":"7f4c3ed7-2993-43e5-8c27-2281d4b193b6","resolution":{"observed_at":"2026-08-12T19:35:31.226830Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:35:31.205026Z","title":"Guidelines: • The answer NA means that the authors have not reviewed the NeurIPS Code of Ethics","venue":null,"work_id":"cfebfdee-ea51-4aeb-a87c-d73b6ac29c12","year":null},"citing_paper":{"arxiv_id":"2411.10606","last_updated":"2024-11-15T22:02:28Z","snapshot_observed_at":"2026-08-14T02:59:29.476989Z","submitted_at":"2024-11-15T22:02:28Z","title":"AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment","version":1},"reference_index":90,"source":"pdf_text","source_observed_at":"2026-08-12T19:35:30.391665Z"},"links":{"citing_paper":"/paper/2411.10606"},"observation_digest":"sha256:6b65daae9e8ee7e6abc53648a9ae4afb92e3eb8c470b020f8697cf8d96d00829","observation_id":"d6d1ab37-1ce8-46b3-9fb7-811e0d605ea4","resolution":{"observed_at":"2026-08-12T19:35:31.210370Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:35:31.187513Z","title":"Guidelines: • The answer NA means that there is no societal impact of the work performed","venue":null,"work_id":"7efb0cbe-336e-492d-9f9d-160430c4a976","year":null},"citing_paper":{"arxiv_id":"2411.10606","last_updated":"2024-11-15T22:02:28Z","snapshot_observed_at":"2026-08-14T02:59:29.476989Z","submitted_at":"2024-11-15T22:02:28Z","title":"AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment","version":1},"reference_index":91,"source":"pdf_text","source_observed_at":"2026-08-12T19:35:30.395935Z"},"links":{"citing_paper":"/paper/2411.10606"},"observation_digest":"sha256:d2a8ce4e65587d32ff61ab594417a1edaa5821a24a24fcb0523008ab801e83b3","observation_id":"db2c3b4d-a307-4365-a513-5752fa96d014","resolution":{"observed_at":"2026-08-12T19:35:31.192850Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:35:31.170182Z","title":"Guidelines: • The answer NA means that the paper poses no such risks","venue":null,"work_id":"6af8b099-55bd-4e2d-bfaa-59569d13ca24","year":null},"citing_paper":{"arxiv_id":"2411.10606","last_updated":"2024-11-15T22:02:28Z","snapshot_observed_at":"2026-08-14T02:59:29.476989Z","submitted_at":"2024-11-15T22:02:28Z","title":"AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment","version":1},"reference_index":92,"source":"pdf_text","source_observed_at":"2026-08-12T19:35:30.400170Z"},"links":{"citing_paper":"/paper/2411.10606"},"observation_digest":"sha256:4dca424b7589c9cad9372a980bfddfb72c67c306bc3b6f0e78dfb613387367a4","observation_id":"1053ed95-0f29-4744-bb99-43298f9855a5","resolution":{"observed_at":"2026-08-12T19:35:31.175198Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:35:31.154421Z","title":"Guidelines: • The answer NA means that the paper does not use existing assets","venue":null,"work_id":"3bcae2c8-c07b-4dc2-b608-e7c710d44bab","year":null},"citing_paper":{"arxiv_id":"2411.10606","last_updated":"2024-11-15T22:02:28Z","snapshot_observed_at":"2026-08-14T02:59:29.476989Z","submitted_at":"2024-11-15T22:02:28Z","title":"AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment","version":1},"reference_index":93,"source":"pdf_text","source_observed_at":"2026-08-12T19:35:30.404513Z"},"links":{"citing_paper":"/paper/2411.10606"},"observation_digest":"sha256:736b1d979fb2bb16255ed71e602f822283d5fca9ec649dd929089e24efc0fcf7","observation_id":"3c702b83-c509-48f1-a92a-b8fe33c1a627","resolution":{"observed_at":"2026-08-12T19:35:31.159213Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:35:31.138667Z","title":"Guidelines: • The answer NA means that the paper does not release new assets","venue":null,"work_id":"ccf4f7e6-041c-410b-bac5-db7a442f04e2","year":null},"citing_paper":{"arxiv_id":"2411.10606","last_updated":"2024-11-15T22:02:28Z","snapshot_observed_at":"2026-08-14T02:59:29.476989Z","submitted_at":"2024-11-15T22:02:28Z","title":"AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment","version":1},"reference_index":94,"source":"pdf_text","source_observed_at":"2026-08-12T19:35:30.408527Z"},"links":{"citing_paper":"/paper/2411.10606"},"observation_digest":"sha256:770034d121478a8cf4a3435542752acf78e6e14742376c73b51575f9a356d328","observation_id":"c82ef08d-fa6a-4304-886c-de4e33e462af","resolution":{"observed_at":"2026-08-12T19:35:31.143558Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:35:31.122510Z","title":"Guidelines: • The answer NA means that the paper does not involve crowdsourcing nor research with human subjects","venue":null,"work_id":"44584124-2c39-466d-9948-933fee0081ac","year":null},"citing_paper":{"arxiv_id":"2411.10606","last_updated":"2024-11-15T22:02:28Z","snapshot_observed_at":"2026-08-14T02:59:29.476989Z","submitted_at":"2024-11-15T22:02:28Z","title":"AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment","version":1},"reference_index":95,"source":"pdf_text","source_observed_at":"2026-08-12T19:35:30.412847Z"},"links":{"citing_paper":"/paper/2411.10606"},"observation_digest":"sha256:9cd9b33fec3dc2a95dd7fbf5d5c65eb5f3e4f315ff561de2cf247c1bd33f8c01","observation_id":"3729f352-9fba-40bb-89d5-9291098ffdaa","resolution":{"observed_at":"2026-08-12T19:35:31.127838Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:35:31.105251Z","title":"Guidelines: • The answer NA means that the paper does not involve crowdsourcing nor research with human subjects","venue":null,"work_id":"fa75893a-fb1f-47d4-9a23-7323f26bad63","year":null},"citing_paper":{"arxiv_id":"2411.10606","last_updated":"2024-11-15T22:02:28Z","snapshot_observed_at":"2026-08-14T02:59:29.476989Z","submitted_at":"2024-11-15T22:02:28Z","title":"AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment","version":1},"reference_index":96,"source":"pdf_text","source_observed_at":"2026-08-12T19:35:30.417149Z"},"links":{"citing_paper":"/paper/2411.10606"},"observation_digest":"sha256:d369ddaf0fececb49f80b6278223669f7e1ea7ce0f3d9ab745064573ef96058b","observation_id":"9c54f489-23f4-41e9-b7e4-311f76b1a11e","resolution":{"observed_at":"2026-08-12T19:35:31.110658Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2411.10606","last_updated":"2024-11-15T22:02:28Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-14T02:59:29.476989Z","submitted_at":"2024-11-15T22:02:28Z","title":"AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment"},"reference_resolution":{"displayed":96,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":1,"unresolved":65,"verified_exact":1,"verified_fuzzy":29},"total_outbound_references":96},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"thesis":"As of 14 August 2026, this Paper Citation Record lists 96 of 96 outbound references and 0 inbound Pith citation observations for arXiv:2411.10606."}