{"as_of":"2026-08-23T06:19:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:e8cb9cfd5f3ebe495511c33a519563d78b7f3378dfe000ddb176e03af2eb6db6","coverage":[{"denominator":43,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":43,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-15T20:05:49.018439Z","state":"measured"},{"denominator":43,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":43,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-22T06:32:14.747728+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/2506.13514/citation-record","integrity":"/paper/2506.13514/integrity","json":"/paper/2506.13514/citation-record.json","paper":"/paper/2506.13514"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T20:05:48.840506Z","title":"write newline","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.13514","last_updated":"2025-06-16T14:09:43Z","snapshot_observed_at":"2026-08-17T11:46:56.501732Z","submitted_at":"2025-06-16T14:09:43Z","title":"TensorSLM: Energy-efficient Embedding Compression of Sub-billion Parameter Language Models on Low-end Devices","version":1},"reference_index":1,"source":"arxiv_source","source_observed_at":"2026-08-15T20:05:48.840506Z"},"links":{"citing_paper":"/paper/2506.13514"},"observation_digest":"sha256:da0c4e9d36c2e2f9ad8254ff8c76e868e9a54f0fe355135922c98c69c0b53162","observation_id":"8e2ae83c-0a0f-4302-a385-1582db9a0108","resolution":{"observed_at":"2026-08-15T20:05:48.840506Z","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":"2026-08-15T20:05:49.684471Z","title":"S mol LM","venue":null,"work_id":"4a520def-2719-45d5-ae3f-eebaf83487b5","year":2024},"citing_paper":{"arxiv_id":"2506.13514","last_updated":"2025-06-16T14:09:43Z","snapshot_observed_at":"2026-08-17T11:46:56.501732Z","submitted_at":"2025-06-16T14:09:43Z","title":"TensorSLM: Energy-efficient Embedding Compression of Sub-billion Parameter Language Models on Low-end Devices","version":1},"reference_index":2,"source":"arxiv_source","source_observed_at":"2026-08-15T20:05:48.846060Z"},"links":{"citing_paper":"/paper/2506.13514"},"observation_digest":"sha256:9e4ffdf10ed4e9a0817df6516361a44a12bca1011c21746c812d5a2df4af4023","observation_id":"4b2dbd79-6b84-4bdb-aedb-e2954bb115d5","resolution":{"observed_at":"2026-08-15T20:05:49.688434Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2401.16367","last_updated":"2024-01-29T18:07:56Z","snapshot_observed_at":"2026-08-18T19:22:39.570896Z","submitted_at":"2024-01-29T18:07:56Z","title":"TQCompressor: improving tensor decomposition methods in neural networks via permutations","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.16367","snapshot_observed_at":"2026-08-15T20:05:48.850342Z","title":"Tqcompressor: improving tensor decomposition methods in neural networks via permutations","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.13514","last_updated":"2025-06-16T14:09:43Z","snapshot_observed_at":"2026-08-17T11:46:56.501732Z","submitted_at":"2025-06-16T14:09:43Z","title":"TensorSLM: Energy-efficient Embedding Compression of Sub-billion Parameter Language Models on Low-end Devices","version":1},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-08-15T20:05:48.850342Z"},"links":{"cited_paper":"/paper/2401.16367","citing_paper":"/paper/2506.13514"},"observation_digest":"sha256:9e5c914d1c14418255fba94ff6049a84811d8a7cebc33e2361deae60a0a01973","observation_id":"096d5103-b231-4ff5-9ca9-c6f4788ff257","resolution":{"observed_at":"2026-08-15T20:05:48.850342Z","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":"2026-08-15T20:05:49.671535Z","title":"Online embedding compression for text classification using low rank matrix factorization","venue":null,"work_id":"ffd33328-cff3-4773-a03c-7ccce2b5a0a9","year":2019},"citing_paper":{"arxiv_id":"2506.13514","last_updated":"2025-06-16T14:09:43Z","snapshot_observed_at":"2026-08-17T11:46:56.501732Z","submitted_at":"2025-06-16T14:09:43Z","title":"TensorSLM: Energy-efficient Embedding Compression of Sub-billion Parameter Language Models on Low-end Devices","version":1},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-08-15T20:05:48.855433Z"},"links":{"citing_paper":"/paper/2506.13514"},"observation_digest":"sha256:b195c06987531abc83cddc1cd59339f8bd664ea42d50412b06a066239cf6b213","observation_id":"ce945863-6c11-4289-8c58-06fed2db1ed5","resolution":{"observed_at":"2026-08-15T20:05:49.676012Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2106.08181","last_updated":"2021-08-03T07:16:57Z","snapshot_observed_at":"2026-08-20T08:07:42.133620Z","submitted_at":"2021-06-15T14:28:00Z","title":"Direction is what you need: Improving Word Embedding Compression in Large Language Models","version":2},"cited_work":{"arxiv_id":"2106.08181","doi":null,"metadata_source":"pith","pith_arxiv_id":"2106.08181","snapshot_observed_at":"2026-08-15T20:05:49.449838Z","title":"Direction is what you need: Improving Word Embedding Compression in Large Language Models","venue":"cs.CL","work_id":"dc5ba84e-700e-4fe7-833d-a799a41231ff","year":2021},"citing_paper":{"arxiv_id":"2506.13514","last_updated":"2025-06-16T14:09:43Z","snapshot_observed_at":"2026-08-17T11:46:56.501732Z","submitted_at":"2025-06-16T14:09:43Z","title":"TensorSLM: Energy-efficient Embedding Compression of Sub-billion Parameter Language Models on Low-end Devices","version":1},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-08-15T20:05:48.859739Z"},"links":{"cited_paper":"/paper/2106.08181","citing_paper":"/paper/2506.13514"},"observation_digest":"sha256:9b9bb487e7a154610c051d241b3fffc54cb848ca187f9072abff6b3de9987994","observation_id":"687e1e87-bcd0-481e-8bb4-9e116184c792","resolution":{"observed_at":"2026-08-15T20:05:49.454425Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-15T20:05:49.658113Z","title":"Piqa: Reasoning about physical commonsense in natural language","venue":null,"work_id":"1dca820a-2842-40da-9392-ec5844a1fd3f","year":2020},"citing_paper":{"arxiv_id":"2506.13514","last_updated":"2025-06-16T14:09:43Z","snapshot_observed_at":"2026-08-17T11:46:56.501732Z","submitted_at":"2025-06-16T14:09:43Z","title":"TensorSLM: Energy-efficient Embedding Compression of Sub-billion Parameter Language Models on Low-end Devices","version":1},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-08-15T20:05:48.864325Z"},"links":{"citing_paper":"/paper/2506.13514"},"observation_digest":"sha256:c3b67d8f8d4e905189e4e184f5104858c5f8fecdcf956eb089eb8ea032a5a702","observation_id":"061bd846-ea40-4588-91d3-bc893f58cada","resolution":{"observed_at":"2026-08-15T20:05:49.662646Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2005.14165","last_updated":"2020-07-22T19:47:17Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2020-05-28T17:29:03Z","title":"Language Models are Few-Shot Learners","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2005.14165","snapshot_observed_at":"2026-08-15T20:05:48.868425Z","title":null,"venue":null,"work_id":null,"year":2005},"citing_paper":{"arxiv_id":"2506.13514","last_updated":"2025-06-16T14:09:43Z","snapshot_observed_at":"2026-08-17T11:46:56.501732Z","submitted_at":"2025-06-16T14:09:43Z","title":"TensorSLM: Energy-efficient Embedding Compression of Sub-billion Parameter Language Models on Low-end Devices","version":1},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-08-15T20:05:48.868425Z"},"links":{"cited_paper":"/paper/2005.14165","citing_paper":"/paper/2506.13514"},"observation_digest":"sha256:396781470a58d4e971e62471d1ff079725aa09ac74e7498e0aa9a2e920d8535a","observation_id":"21da2f3f-709e-4746-8034-092bb7587eef","resolution":{"observed_at":"2026-08-15T20:05:48.868425Z","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":"2026-08-15T20:05:49.644574Z","title":"Efficient GPT model pre-training using tensor train matrix representation","venue":null,"work_id":"a4b01b6e-be3d-45a0-94cf-124d61df3bff","year":2023},"citing_paper":{"arxiv_id":"2506.13514","last_updated":"2025-06-16T14:09:43Z","snapshot_observed_at":"2026-08-17T11:46:56.501732Z","submitted_at":"2025-06-16T14:09:43Z","title":"TensorSLM: Energy-efficient Embedding Compression of Sub-billion Parameter Language Models on Low-end Devices","version":1},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-08-15T20:05:48.873291Z"},"links":{"citing_paper":"/paper/2506.13514"},"observation_digest":"sha256:c47da632cb58babf57c297108804d12c985e415b8df2ca1f5986807700862f6a","observation_id":"3a4ab5cb-6f3a-4991-9327-182f9481632e","resolution":{"observed_at":"2026-08-15T20:05:49.648838Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2306.02697","last_updated":"2023-06-05T08:38:25Z","snapshot_observed_at":"2026-08-16T15:26:37.525689Z","submitted_at":"2023-06-05T08:38:25Z","title":"Efficient GPT Model Pre-training using Tensor Train Matrix Representation","version":1},"cited_work":{"arxiv_id":"2306.02697","doi":null,"metadata_source":"pith","pith_arxiv_id":"2306.02697","snapshot_observed_at":"2026-08-15T20:05:49.415073Z","title":"Efficient GPT Model Pre-training using Tensor Train Matrix Representation","venue":"cs.AI","work_id":"552f7017-80d8-42bd-84e5-8bb25a15f25f","year":2023},"citing_paper":{"arxiv_id":"2506.13514","last_updated":"2025-06-16T14:09:43Z","snapshot_observed_at":"2026-08-17T11:46:56.501732Z","submitted_at":"2025-06-16T14:09:43Z","title":"TensorSLM: Energy-efficient Embedding Compression of Sub-billion Parameter Language Models on Low-end Devices","version":1},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-08-15T20:05:48.877154Z"},"links":{"cited_paper":"/paper/2306.02697","citing_paper":"/paper/2506.13514"},"observation_digest":"sha256:de8bf4980ba69a6b0545c7474c692e88cc78a570e9c6a1eb49da136aeff0d1ea","observation_id":"fe1dc146-262c-4fed-9aba-89f156fbb013","resolution":{"observed_at":"2026-08-15T20:05:49.421397Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1312.3005","last_updated":"2014-03-04T18:30:26Z","snapshot_observed_at":"2026-08-14T23:52:07.066126Z","submitted_at":"2013-12-11T00:25:57Z","title":"One Billion Word Benchmark for Measuring Progress in Statistical Language Modeling","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1312.3005","snapshot_observed_at":"2026-08-15T20:05:48.881529Z","title":"One billion word benchmark for measuring progress in statistical language modeling","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2506.13514","last_updated":"2025-06-16T14:09:43Z","snapshot_observed_at":"2026-08-17T11:46:56.501732Z","submitted_at":"2025-06-16T14:09:43Z","title":"TensorSLM: Energy-efficient Embedding Compression of Sub-billion Parameter Language Models on Low-end Devices","version":1},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-08-15T20:05:48.881529Z"},"links":{"cited_paper":"/paper/1312.3005","citing_paper":"/paper/2506.13514"},"observation_digest":"sha256:2a99ba22b1fde61c9021056a929511cf12b0078ecf4f49c5ebe7d86f95a1ba4b","observation_id":"04118ae3-d401-4778-ae65-e2d23e24cdd9","resolution":{"observed_at":"2026-08-15T20:05:48.881529Z","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":"2026-08-15T20:05:49.630223Z","title":"Groupreduce: Block-wise low-rank approximation for neural language model shrinking","venue":null,"work_id":"31dfb1a8-a285-44f5-8553-d47b96c625ea","year":2018},"citing_paper":{"arxiv_id":"2506.13514","last_updated":"2025-06-16T14:09:43Z","snapshot_observed_at":"2026-08-17T11:46:56.501732Z","submitted_at":"2025-06-16T14:09:43Z","title":"TensorSLM: Energy-efficient Embedding Compression of Sub-billion Parameter Language Models on Low-end Devices","version":1},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-08-15T20:05:48.886121Z"},"links":{"citing_paper":"/paper/2506.13514"},"observation_digest":"sha256:225b24a3598dcead270f29f37ae4a47832dd79d6890b8d6b1cb9f775a7148b73","observation_id":"b1cd3950-c020-4ba3-99d6-2128fb2f754b","resolution":{"observed_at":"2026-08-15T20:05:49.635089Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-15T20:05:49.617012Z","title":"Drone: Data-aware low-rank compression for large nlp models","venue":null,"work_id":"1ec0f96b-f857-4888-8036-9264d64fba11","year":2021},"citing_paper":{"arxiv_id":"2506.13514","last_updated":"2025-06-16T14:09:43Z","snapshot_observed_at":"2026-08-17T11:46:56.501732Z","submitted_at":"2025-06-16T14:09:43Z","title":"TensorSLM: Energy-efficient Embedding Compression of Sub-billion Parameter Language Models on Low-end Devices","version":1},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-08-15T20:05:48.890168Z"},"links":{"citing_paper":"/paper/2506.13514"},"observation_digest":"sha256:c8cc57b02a08ba49d7da5b7145baad3064d8421fc0fa4b3c672b081302ee568c","observation_id":"1c46903c-6817-42a4-9d69-3fd0d53cc23d","resolution":{"observed_at":"2026-08-15T20:05:49.621289Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-15T20:05:49.603848Z","title":"R., and Sun, Y","venue":null,"work_id":"9f6b67b0-430b-4cd7-b464-87420eb54801","year":2018},"citing_paper":{"arxiv_id":"2506.13514","last_updated":"2025-06-16T14:09:43Z","snapshot_observed_at":"2026-08-17T11:46:56.501732Z","submitted_at":"2025-06-16T14:09:43Z","title":"TensorSLM: Energy-efficient Embedding Compression of Sub-billion Parameter Language Models on Low-end Devices","version":1},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-08-15T20:05:48.894106Z"},"links":{"citing_paper":"/paper/2506.13514"},"observation_digest":"sha256:8f22f3fbda1473a09bd7907f9961ddf19e791880658acd3517d13c8526ba287a","observation_id":"89d7b371-73ce-4e59-a2e5-199efec1872f","resolution":{"observed_at":"2026-08-15T20:05:49.607993Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1905.10044","last_updated":"2019-05-24T05:48:49Z","snapshot_observed_at":"2026-08-17T14:44:42.060038Z","submitted_at":"2019-05-24T05:48:49Z","title":"BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1905.10044","snapshot_observed_at":"2026-08-15T20:05:48.897991Z","title":"Boolq: Exploring the surprising difficulty of natural yes/no questions","venue":null,"work_id":null,"year":1905},"citing_paper":{"arxiv_id":"2506.13514","last_updated":"2025-06-16T14:09:43Z","snapshot_observed_at":"2026-08-17T11:46:56.501732Z","submitted_at":"2025-06-16T14:09:43Z","title":"TensorSLM: Energy-efficient Embedding Compression of Sub-billion Parameter Language Models on Low-end Devices","version":1},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-08-15T20:05:48.897991Z"},"links":{"cited_paper":"/paper/1905.10044","citing_paper":"/paper/2506.13514"},"observation_digest":"sha256:754a676a21d3594046f2f6a7a75a01335e489eb1efc255a61e137d02ffaa2309","observation_id":"bca0c8f3-0792-4bff-95ea-203fadd4e365","resolution":{"observed_at":"2026-08-15T20:05:48.897991Z","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":"2026-08-14T19:36:07.505691Z","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":"2026-08-15T20:05:48.902060Z","title":"Think you have solved question answering? try arc, the ai2 reasoning challenge","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2506.13514","last_updated":"2025-06-16T14:09:43Z","snapshot_observed_at":"2026-08-17T11:46:56.501732Z","submitted_at":"2025-06-16T14:09:43Z","title":"TensorSLM: Energy-efficient Embedding Compression of Sub-billion Parameter Language Models on Low-end Devices","version":1},"reference_index":15,"source":"arxiv_source","source_observed_at":"2026-08-15T20:05:48.902060Z"},"links":{"cited_paper":"/paper/1803.05457","citing_paper":"/paper/2506.13514"},"observation_digest":"sha256:08d7a68f2bc70906dbfa21530dd175ea28441c61ba50f39ce8292f39e0c201d6","observation_id":"b63d794f-de14-4a1a-8d75-f69484d54330","resolution":{"observed_at":"2026-08-15T20:05:48.902060Z","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":"2026-08-15T20:05:49.590205Z","title":"S., Desai, A., Poli, M., Grogan, J., Liu, A., Rao, A., Rudra, A., and Re, C","venue":null,"work_id":"11eea20f-bcbd-44a2-b3e7-a9edb5dd5a37","year":2022},"citing_paper":{"arxiv_id":"2506.13514","last_updated":"2025-06-16T14:09:43Z","snapshot_observed_at":"2026-08-17T11:46:56.501732Z","submitted_at":"2025-06-16T14:09:43Z","title":"TensorSLM: Energy-efficient Embedding Compression of Sub-billion Parameter Language Models on Low-end Devices","version":1},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-08-15T20:05:48.906085Z"},"links":{"citing_paper":"/paper/2506.13514"},"observation_digest":"sha256:0e2e6c5a2dfe388ec2bb450830d194d491755328241386292cdb19563dc89264","observation_id":"e41ec1fa-21e6-44f3-a37a-4c1c49c26bfe","resolution":{"observed_at":"2026-08-15T20:05:49.594766Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2304.03208","last_updated":"2023-04-06T16:43:16Z","snapshot_observed_at":"2026-08-18T04:17:03.903884Z","submitted_at":"2023-04-06T16:43:16Z","title":"Cerebras-GPT: Open Compute-Optimal Language Models Trained on the Cerebras Wafer-Scale Cluster","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2304.03208","snapshot_observed_at":"2026-08-15T20:05:48.910097Z","title":"Cerebras-gpt: Open compute-optimal language models trained on the cerebras wafer-scale cluster","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.13514","last_updated":"2025-06-16T14:09:43Z","snapshot_observed_at":"2026-08-17T11:46:56.501732Z","submitted_at":"2025-06-16T14:09:43Z","title":"TensorSLM: Energy-efficient Embedding Compression of Sub-billion Parameter Language Models on Low-end Devices","version":1},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-08-15T20:05:48.910097Z"},"links":{"cited_paper":"/paper/2304.03208","citing_paper":"/paper/2506.13514"},"observation_digest":"sha256:3fd5f95df86ee32d0de930736394609dc3f0cec7d906f0ed7aedfe189bd9e986","observation_id":"c0412028-06e2-44ca-afdf-febb77c88730","resolution":{"observed_at":"2026-08-15T20:05:48.910097Z","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":"2026-08-15T20:05:48.914262Z","title":"Kronecker decomposition for GPT compression","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.13514","last_updated":"2025-06-16T14:09:43Z","snapshot_observed_at":"2026-08-17T11:46:56.501732Z","submitted_at":"2025-06-16T14:09:43Z","title":"TensorSLM: Energy-efficient Embedding Compression of Sub-billion Parameter Language Models on Low-end Devices","version":1},"reference_index":18,"source":"arxiv_source","source_observed_at":"2026-08-15T20:05:48.914262Z"},"links":{"citing_paper":"/paper/2506.13514"},"observation_digest":"sha256:8ca8b07800a3657caa0f76eb5e4c523ce1ef3145884555ad162eb01d41edeef7","observation_id":"84aa4f1e-f85d-4650-a997-4688d484e5a0","resolution":{"observed_at":"2026-08-15T20:05:48.914262Z","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":"2026-08-15T20:05:48.918714Z","title":"Tensorized embedding layers","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2506.13514","last_updated":"2025-06-16T14:09:43Z","snapshot_observed_at":"2026-08-17T11:46:56.501732Z","submitted_at":"2025-06-16T14:09:43Z","title":"TensorSLM: Energy-efficient Embedding Compression of Sub-billion Parameter Language Models on Low-end Devices","version":1},"reference_index":19,"source":"arxiv_source","source_observed_at":"2026-08-15T20:05:48.918714Z"},"links":{"citing_paper":"/paper/2506.13514"},"observation_digest":"sha256:7b07aa7490bf2c130b138bf797d2ccd8bbf1dab1cc37d2b9f5ed439f7d6acd75","observation_id":"4b6f979d-3dac-4d50-82ad-90109c2b4b66","resolution":{"observed_at":"2026-08-15T20:05:48.918714Z","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":"2026-08-15T20:05:48.922889Z","title":"Language model compression with weighted low-rank factorization","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.13514","last_updated":"2025-06-16T14:09:43Z","snapshot_observed_at":"2026-08-17T11:46:56.501732Z","submitted_at":"2025-06-16T14:09:43Z","title":"TensorSLM: Energy-efficient Embedding Compression of Sub-billion Parameter Language Models on Low-end Devices","version":1},"reference_index":20,"source":"arxiv_source","source_observed_at":"2026-08-15T20:05:48.922889Z"},"links":{"citing_paper":"/paper/2506.13514"},"observation_digest":"sha256:bfbc88cbd3e4fe2b41a6832e01588b92460f5f67ba852cf4ecdbe93dd6205f80","observation_id":"9e1d9c97-94e0-4094-829c-920ccdf796d5","resolution":{"observed_at":"2026-08-15T20:05:48.922889Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2403.12844","last_updated":"2024-07-25T22:27:38Z","snapshot_observed_at":"2026-08-21T08:53:26.814392Z","submitted_at":"2024-03-19T15:51:21Z","title":"MELTing point: Mobile Evaluation of Language Transformers","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.12844","snapshot_observed_at":"2026-08-15T20:05:48.926708Z","title":"Melting point: Mobile evaluation of language transformers","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.13514","last_updated":"2025-06-16T14:09:43Z","snapshot_observed_at":"2026-08-17T11:46:56.501732Z","submitted_at":"2025-06-16T14:09:43Z","title":"TensorSLM: Energy-efficient Embedding Compression of Sub-billion Parameter Language Models on Low-end Devices","version":1},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-08-15T20:05:48.926708Z"},"links":{"cited_paper":"/paper/2403.12844","citing_paper":"/paper/2506.13514"},"observation_digest":"sha256:77ee076940900d9d0d2a103f285c59d568091b3a12dd200b6833de596471d109","observation_id":"f0e3b06d-2744-44fa-8edf-29b05baf09ab","resolution":{"observed_at":"2026-08-15T20:05:48.926708Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2408.09632","last_updated":"2025-05-02T15:34:42Z","snapshot_observed_at":"2026-08-22T06:00:02.648693Z","submitted_at":"2024-08-19T01:30:14Z","title":"MoDeGPT: Modular Decomposition for Large Language Model Compression","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.09632","snapshot_observed_at":"2026-08-15T20:05:48.931004Z","title":"S., Patel, A., Tuli, S., Shen, Y., Jin, H., and Hsu, Y.-C","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.13514","last_updated":"2025-06-16T14:09:43Z","snapshot_observed_at":"2026-08-17T11:46:56.501732Z","submitted_at":"2025-06-16T14:09:43Z","title":"TensorSLM: Energy-efficient Embedding Compression of Sub-billion Parameter Language Models on Low-end Devices","version":1},"reference_index":22,"source":"arxiv_source","source_observed_at":"2026-08-15T20:05:48.931004Z"},"links":{"cited_paper":"/paper/2408.09632","citing_paper":"/paper/2506.13514"},"observation_digest":"sha256:e2134a0b2bda8bfc13579af0b178206f6ab2b2426369c5c4ac8fbf80beae0613","observation_id":"1bf48ed5-8582-4e11-a5af-41f9318467b8","resolution":{"observed_at":"2026-08-15T20:05:48.931004Z","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":"2026-08-15T20:05:49.568427Z","title":"Mcunet: Tiny deep learning on iot devices","venue":null,"work_id":"accad69e-7b6c-408a-b45f-af572d648c67","year":2020},"citing_paper":{"arxiv_id":"2506.13514","last_updated":"2025-06-16T14:09:43Z","snapshot_observed_at":"2026-08-17T11:46:56.501732Z","submitted_at":"2025-06-16T14:09:43Z","title":"TensorSLM: Energy-efficient Embedding Compression of Sub-billion Parameter Language Models on Low-end Devices","version":1},"reference_index":23,"source":"arxiv_source","source_observed_at":"2026-08-15T20:05:48.935086Z"},"links":{"citing_paper":"/paper/2506.13514"},"observation_digest":"sha256:cea31b0a0da042405d52842794378dd43e01412d631c42d5060e640af038b0bb","observation_id":"7696def2-c67b-4ca3-be10-3ea81dd47638","resolution":{"observed_at":"2026-08-15T20:05:49.573161Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-15T20:05:49.555443Z","title":"A., and Rezagholizadeh, M","venue":null,"work_id":"7ea04126-fb82-4168-bec6-88290998b55b","year":2020},"citing_paper":{"arxiv_id":"2506.13514","last_updated":"2025-06-16T14:09:43Z","snapshot_observed_at":"2026-08-17T11:46:56.501732Z","submitted_at":"2025-06-16T14:09:43Z","title":"TensorSLM: Energy-efficient Embedding Compression of Sub-billion Parameter Language Models on Low-end Devices","version":1},"reference_index":24,"source":"arxiv_source","source_observed_at":"2026-08-15T20:05:48.939375Z"},"links":{"citing_paper":"/paper/2506.13514"},"observation_digest":"sha256:da2690e198a2b8fba88d1ade05ab8c51e7b132ca515f12cd5505a371309f8109","observation_id":"309f4730-044b-4900-9e2d-7b6c8ec224c6","resolution":{"observed_at":"2026-08-15T20:05:49.559575Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1509.05195","last_updated":"2015-09-17T10:19:37Z","snapshot_observed_at":"2026-08-14T22:32:00.949049Z","submitted_at":"2015-09-17T10:19:37Z","title":"Improved Residual Vector Quantization for High-dimensional Approximate Nearest Neighbor Search","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1509.05195","snapshot_observed_at":"2026-08-15T20:05:48.943366Z","title":"Improved residual vector quantization for high-dimensional approximate nearest neighbor search","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2506.13514","last_updated":"2025-06-16T14:09:43Z","snapshot_observed_at":"2026-08-17T11:46:56.501732Z","submitted_at":"2025-06-16T14:09:43Z","title":"TensorSLM: Energy-efficient Embedding Compression of Sub-billion Parameter Language Models on Low-end Devices","version":1},"reference_index":25,"source":"arxiv_source","source_observed_at":"2026-08-15T20:05:48.943366Z"},"links":{"cited_paper":"/paper/1509.05195","citing_paper":"/paper/2506.13514"},"observation_digest":"sha256:f9367e465d7e5018553a44cb95a6ead5a1013840f6b2cc15e29227e21b6b331f","observation_id":"813bddd6-0537-47ba-a895-963e4cbf22dc","resolution":{"observed_at":"2026-08-15T20:05:48.943366Z","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":"2026-08-15T20:05:49.541756Z","title":"M obile LLM : Optimizing sub-billion parameter language models for on-device use cases","venue":null,"work_id":"a5e01683-d497-414c-81f6-9ee7ade595ac","year":2024},"citing_paper":{"arxiv_id":"2506.13514","last_updated":"2025-06-16T14:09:43Z","snapshot_observed_at":"2026-08-17T11:46:56.501732Z","submitted_at":"2025-06-16T14:09:43Z","title":"TensorSLM: Energy-efficient Embedding Compression of Sub-billion Parameter Language Models on Low-end Devices","version":1},"reference_index":26,"source":"arxiv_source","source_observed_at":"2026-08-15T20:05:48.947676Z"},"links":{"citing_paper":"/paper/2506.13514"},"observation_digest":"sha256:82ce394f06353457bc807390b256b83a95cb688d13aaaf55be01ea7c210d6b3b","observation_id":"0027a201-55fb-49c0-9094-656f5eb565af","resolution":{"observed_at":"2026-08-15T20:05:49.546545Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2409.15790","last_updated":"2025-02-26T06:34:55Z","snapshot_observed_at":"2026-08-19T23:45:09.602398Z","submitted_at":"2024-09-24T06:36:56Z","title":"Small Language Models: Survey, Measurements, and Insights","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2409.15790","snapshot_observed_at":"2026-08-15T20:05:48.951738Z","title":"D., and Xu, M","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.13514","last_updated":"2025-06-16T14:09:43Z","snapshot_observed_at":"2026-08-17T11:46:56.501732Z","submitted_at":"2025-06-16T14:09:43Z","title":"TensorSLM: Energy-efficient Embedding Compression of Sub-billion Parameter Language Models on Low-end Devices","version":1},"reference_index":27,"source":"arxiv_source","source_observed_at":"2026-08-15T20:05:48.951738Z"},"links":{"cited_paper":"/paper/2409.15790","citing_paper":"/paper/2506.13514"},"observation_digest":"sha256:33d5e3ea254299a7db552966aa6c0b640c033853329ee2920b52fe92e5bafeed","observation_id":"b697e34a-3625-4ea3-8b48-5adb76c24978","resolution":{"observed_at":"2026-08-15T20:05:48.951738Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.00907","last_updated":"2024-10-02T15:34:12Z","snapshot_observed_at":"2026-08-16T13:13:38.358882Z","submitted_at":"2024-10-01T17:53:28Z","title":"Addition is All You Need for Energy-efficient Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.00907","snapshot_observed_at":"2026-08-15T20:05:48.955948Z","title":"and Sun, W","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.13514","last_updated":"2025-06-16T14:09:43Z","snapshot_observed_at":"2026-08-17T11:46:56.501732Z","submitted_at":"2025-06-16T14:09:43Z","title":"TensorSLM: Energy-efficient Embedding Compression of Sub-billion Parameter Language Models on Low-end Devices","version":1},"reference_index":28,"source":"arxiv_source","source_observed_at":"2026-08-15T20:05:48.955948Z"},"links":{"cited_paper":"/paper/2410.00907","citing_paper":"/paper/2506.13514"},"observation_digest":"sha256:28c483aad9d62a468cbdff0486ed4cba76619a6979a5a165f9c1f84ef56b67dc","observation_id":"1c639db2-ec9a-43f3-8495-e7f0704df5e3","resolution":{"observed_at":"2026-08-15T20:05:48.955948Z","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":"2026-08-15T20:05:48.959990Z","title":"L., Daly, R","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"2506.13514","last_updated":"2025-06-16T14:09:43Z","snapshot_observed_at":"2026-08-17T11:46:56.501732Z","submitted_at":"2025-06-16T14:09:43Z","title":"TensorSLM: Energy-efficient Embedding Compression of Sub-billion Parameter Language Models on Low-end Devices","version":1},"reference_index":29,"source":"arxiv_source","source_observed_at":"2026-08-15T20:05:48.959990Z"},"links":{"citing_paper":"/paper/2506.13514"},"observation_digest":"sha256:e96b3cf05a836c167de1d32ba912c7a6579ad484c9df210f164ae0c6044742ed","observation_id":"2034f3cf-ac27-4629-ade2-df947b248929","resolution":{"observed_at":"2026-08-15T20:05:48.959990Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2004.04124","last_updated":"2020-10-21T15:15:11Z","snapshot_observed_at":"2026-08-12T08:57:23.610459Z","submitted_at":"2020-04-08T17:18:56Z","title":"LadaBERT: Lightweight Adaptation of BERT through Hybrid Model Compression","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2004.04124","snapshot_observed_at":"2026-08-15T20:05:48.964077Z","title":"Ladabert: Lightweight adaptation of bert through hybrid model compression","venue":null,"work_id":null,"year":2004},"citing_paper":{"arxiv_id":"2506.13514","last_updated":"2025-06-16T14:09:43Z","snapshot_observed_at":"2026-08-17T11:46:56.501732Z","submitted_at":"2025-06-16T14:09:43Z","title":"TensorSLM: Energy-efficient Embedding Compression of Sub-billion Parameter Language Models on Low-end Devices","version":1},"reference_index":30,"source":"arxiv_source","source_observed_at":"2026-08-15T20:05:48.964077Z"},"links":{"cited_paper":"/paper/2004.04124","citing_paper":"/paper/2506.13514"},"observation_digest":"sha256:1bd62a17b6d5db9c3d19d9126c16832f876d06a323ae36454041ee2a2011c464","observation_id":"27b4b824-7203-4504-8547-4de25b94baca","resolution":{"observed_at":"2026-08-15T20:05:48.964077Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2404.14619","last_updated":"2024-05-02T00:30:57Z","snapshot_observed_at":"2026-08-16T19:20:26.111702Z","submitted_at":"2024-04-22T23:12:03Z","title":"OpenELM: An Efficient Language Model Family with Open Training and Inference Framework","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.14619","snapshot_observed_at":"2026-08-15T20:05:48.968387Z","title":"H., Cao, Q., Horton, M., Jin, Y., Sun, C., Mirzadeh, I., Najibi, M., Belenko, D., Zatloukal, P., et al","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.13514","last_updated":"2025-06-16T14:09:43Z","snapshot_observed_at":"2026-08-17T11:46:56.501732Z","submitted_at":"2025-06-16T14:09:43Z","title":"TensorSLM: Energy-efficient Embedding Compression of Sub-billion Parameter Language Models on Low-end Devices","version":1},"reference_index":31,"source":"arxiv_source","source_observed_at":"2026-08-15T20:05:48.968387Z"},"links":{"cited_paper":"/paper/2404.14619","citing_paper":"/paper/2506.13514"},"observation_digest":"sha256:276c090688f770dff27f2a41049e670d0a87bacec220901b417c7bbd0ba2612b","observation_id":"e38ad9cf-0ac9-4eee-b301-58cd127e06d1","resolution":{"observed_at":"2026-08-15T20:05:48.968387Z","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":"2026-08-15T20:05:48.972498Z","title":"Pointer sentinel mixture models","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2506.13514","last_updated":"2025-06-16T14:09:43Z","snapshot_observed_at":"2026-08-17T11:46:56.501732Z","submitted_at":"2025-06-16T14:09:43Z","title":"TensorSLM: Energy-efficient Embedding Compression of Sub-billion Parameter Language Models on Low-end Devices","version":1},"reference_index":32,"source":"arxiv_source","source_observed_at":"2026-08-15T20:05:48.972498Z"},"links":{"citing_paper":"/paper/2506.13514"},"observation_digest":"sha256:ca05e1e7f55eaac0ac5273f20f8852ed263ca599161007d12459179b6145c18d","observation_id":"1d97bba3-4aab-4de9-8f7a-1f1cfe8bbe9b","resolution":{"observed_at":"2026-08-15T20:05:48.972498Z","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":"2026-08-15T20:05:48.976705Z","title":null,"venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"2506.13514","last_updated":"2025-06-16T14:09:43Z","snapshot_observed_at":"2026-08-17T11:46:56.501732Z","submitted_at":"2025-06-16T14:09:43Z","title":"TensorSLM: Energy-efficient Embedding Compression of Sub-billion Parameter Language Models on Low-end Devices","version":1},"reference_index":33,"source":"arxiv_source","source_observed_at":"2026-08-15T20:05:48.976705Z"},"links":{"citing_paper":"/paper/2506.13514"},"observation_digest":"sha256:781c433bdeeff6f3a7a10adbfda38219c1dd9b4402abf191f633701d5506df03","observation_id":"d333239d-2837-4906-b870-1968cbdb9865","resolution":{"observed_at":"2026-08-15T20:05:48.976705Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.06248","last_updated":"2024-06-10T13:25:43Z","snapshot_observed_at":"2026-08-16T13:44:34.800206Z","submitted_at":"2024-06-10T13:25:43Z","title":"Compute Better Spent: Replacing Dense Layers with Structured Matrices","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.06248","snapshot_observed_at":"2026-08-15T20:05:48.980854Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.13514","last_updated":"2025-06-16T14:09:43Z","snapshot_observed_at":"2026-08-17T11:46:56.501732Z","submitted_at":"2025-06-16T14:09:43Z","title":"TensorSLM: Energy-efficient Embedding Compression of Sub-billion Parameter Language Models on Low-end Devices","version":1},"reference_index":34,"source":"arxiv_source","source_observed_at":"2026-08-15T20:05:48.980854Z"},"links":{"cited_paper":"/paper/2406.06248","citing_paper":"/paper/2506.13514"},"observation_digest":"sha256:a3825fcdfe9ecd7612c23b1dae5f906d5d0be24756b2eb4ea52923a41c37db44","observation_id":"43cd2287-c832-4cc2-9a50-f72b304087a7","resolution":{"observed_at":"2026-08-15T20:05:48.980854Z","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":"2026-08-15T20:05:48.985581Z","title":"Language models are unsupervised multitask learners","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2506.13514","last_updated":"2025-06-16T14:09:43Z","snapshot_observed_at":"2026-08-17T11:46:56.501732Z","submitted_at":"2025-06-16T14:09:43Z","title":"TensorSLM: Energy-efficient Embedding Compression of Sub-billion Parameter Language Models on Low-end Devices","version":1},"reference_index":35,"source":"arxiv_source","source_observed_at":"2026-08-15T20:05:48.985581Z"},"links":{"citing_paper":"/paper/2506.13514"},"observation_digest":"sha256:4845d99a1890c36ba28e9551708011a771daefb410b94ab0e83fc59c39925812","observation_id":"0b44286a-1109-4365-88a3-53e0f95816a0","resolution":{"observed_at":"2026-08-15T20:05:48.985581Z","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":"2026-08-15T20:05:48.989727Z","title":"L., Bhagavatula, C., and Choi, Y","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.13514","last_updated":"2025-06-16T14:09:43Z","snapshot_observed_at":"2026-08-17T11:46:56.501732Z","submitted_at":"2025-06-16T14:09:43Z","title":"TensorSLM: Energy-efficient Embedding Compression of Sub-billion Parameter Language Models on Low-end Devices","version":1},"reference_index":36,"source":"arxiv_source","source_observed_at":"2026-08-15T20:05:48.989727Z"},"links":{"citing_paper":"/paper/2506.13514"},"observation_digest":"sha256:a8af17691bf5596eb2f7fdc23877349c95743986021bb6fdfbbc793d940e3952","observation_id":"09ed62fe-900e-4b14-bd76-409656540a30","resolution":{"observed_at":"2026-08-15T20:05:48.989727Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1910.01108","last_updated":"2020-03-01T02:57:50Z","snapshot_observed_at":"2026-08-07T19:07:36.327251Z","submitted_at":"2019-10-02T17:56:28Z","title":"DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1910.01108","snapshot_observed_at":"2026-08-15T20:05:48.993633Z","title":"Distilbert, a distilled version of bert: Smaller, faster, cheaper and lighter","venue":null,"work_id":null,"year":1910},"citing_paper":{"arxiv_id":"2506.13514","last_updated":"2025-06-16T14:09:43Z","snapshot_observed_at":"2026-08-17T11:46:56.501732Z","submitted_at":"2025-06-16T14:09:43Z","title":"TensorSLM: Energy-efficient Embedding Compression of Sub-billion Parameter Language Models on Low-end Devices","version":1},"reference_index":37,"source":"arxiv_source","source_observed_at":"2026-08-15T20:05:48.993633Z"},"links":{"cited_paper":"/paper/1910.01108","citing_paper":"/paper/2506.13514"},"observation_digest":"sha256:511f579a15900a0d0f9869a899e5fdc9f4ad778b401a62d1f29b8a86975e5faf","observation_id":"026a0697-0890-4fe3-ab35-25803b8dc523","resolution":{"observed_at":"2026-08-15T20:05:48.993633Z","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":"2026-08-15T20:05:48.998143Z","title":"Social iqa: Commonsense reasoning about social interactions","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2506.13514","last_updated":"2025-06-16T14:09:43Z","snapshot_observed_at":"2026-08-17T11:46:56.501732Z","submitted_at":"2025-06-16T14:09:43Z","title":"TensorSLM: Energy-efficient Embedding Compression of Sub-billion Parameter Language Models on Low-end Devices","version":1},"reference_index":38,"source":"arxiv_source","source_observed_at":"2026-08-15T20:05:48.998143Z"},"links":{"citing_paper":"/paper/2506.13514"},"observation_digest":"sha256:090f72bd2759e482b04334931d9fca98b8a534876a161525926ddafae7aa4973","observation_id":"01e88523-a923-4c5b-8f4c-f704559b60e0","resolution":{"observed_at":"2026-08-15T20:05:48.998143Z","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":"10.18653/v1/2022.naacl-main.154","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T20:05:49.485593Z","title":"K ronecker BERT : Significant compression of pre-trained language models through kronecker decomposition and knowledge distillation","venue":null,"work_id":"80c89dba-f09a-420f-a361-d4ad1441d4e1","year":2022},"citing_paper":{"arxiv_id":"2506.13514","last_updated":"2025-06-16T14:09:43Z","snapshot_observed_at":"2026-08-17T11:46:56.501732Z","submitted_at":"2025-06-16T14:09:43Z","title":"TensorSLM: Energy-efficient Embedding Compression of Sub-billion Parameter Language Models on Low-end Devices","version":1},"reference_index":39,"source":"arxiv_source","source_observed_at":"2026-08-15T20:05:49.002079Z"},"links":{"citing_paper":"/paper/2506.13514"},"observation_digest":"sha256:13aea7c3460963aee6e17413d6b5e2276706087439efd55e3e8e0c5b306b38a4","observation_id":"e26b55c5-332b-44bf-bcec-e90dc31aa0a9","resolution":{"observed_at":"2026-08-15T20:05:49.490072Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-15T20:05:49.006053Z","title":"Lighttoken: A task and model-agnostic lightweight token embedding framework for pre-trained language models","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.13514","last_updated":"2025-06-16T14:09:43Z","snapshot_observed_at":"2026-08-17T11:46:56.501732Z","submitted_at":"2025-06-16T14:09:43Z","title":"TensorSLM: Energy-efficient Embedding Compression of Sub-billion Parameter Language Models on Low-end Devices","version":1},"reference_index":40,"source":"arxiv_source","source_observed_at":"2026-08-15T20:05:49.006053Z"},"links":{"citing_paper":"/paper/2506.13514"},"observation_digest":"sha256:7d9bafaebb65b20df0092f79b42d9f552f1832d9bb78d11f2a25301992202863","observation_id":"e04d29f9-9fed-4bb4-8754-28b2a0e9085f","resolution":{"observed_at":"2026-08-15T20:05:49.006053Z","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":"2026-08-15T20:05:49.010129Z","title":"A method to estimate the energy consumption of deep neural networks","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2506.13514","last_updated":"2025-06-16T14:09:43Z","snapshot_observed_at":"2026-08-17T11:46:56.501732Z","submitted_at":"2025-06-16T14:09:43Z","title":"TensorSLM: Energy-efficient Embedding Compression of Sub-billion Parameter Language Models on Low-end Devices","version":1},"reference_index":41,"source":"arxiv_source","source_observed_at":"2026-08-15T20:05:49.010129Z"},"links":{"citing_paper":"/paper/2506.13514"},"observation_digest":"sha256:3d9469e386a3574469f4365dad74129af6abc9680ac1f5fcaf67ea41a40d616c","observation_id":"a3a2cf4b-ceca-4708-89f9-cad469bb521f","resolution":{"observed_at":"2026-08-15T20:05:49.010129Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2312.05821","last_updated":"2025-08-28T03:57:52Z","snapshot_observed_at":"2026-08-16T15:03:40.750477Z","submitted_at":"2023-12-10T08:41:24Z","title":"ASVD: Activation-aware Singular Value Decomposition for Compressing Large Language Models","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2312.05821","snapshot_observed_at":"2026-08-15T20:05:49.014038Z","title":"Asvd: Activation-aware singular value decomposition for compressing large language models","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.13514","last_updated":"2025-06-16T14:09:43Z","snapshot_observed_at":"2026-08-17T11:46:56.501732Z","submitted_at":"2025-06-16T14:09:43Z","title":"TensorSLM: Energy-efficient Embedding Compression of Sub-billion Parameter Language Models on Low-end Devices","version":1},"reference_index":42,"source":"arxiv_source","source_observed_at":"2026-08-15T20:05:49.014038Z"},"links":{"cited_paper":"/paper/2312.05821","citing_paper":"/paper/2506.13514"},"observation_digest":"sha256:71ba8440e9aa89e3a04fffda0134c882ac8f9078fb28fdf31fd713e12dd96de2","observation_id":"34d21b8d-1122-4fd5-ab62-3ab51ef93aec","resolution":{"observed_at":"2026-08-15T20:05:49.014038Z","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":"2026-08-15T20:05:49.018439Z","title":"Hellaswag: Can a machine really finish your sentence? In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, 2019","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2506.13514","last_updated":"2025-06-16T14:09:43Z","snapshot_observed_at":"2026-08-17T11:46:56.501732Z","submitted_at":"2025-06-16T14:09:43Z","title":"TensorSLM: Energy-efficient Embedding Compression of Sub-billion Parameter Language Models on Low-end Devices","version":1},"reference_index":43,"source":"arxiv_source","source_observed_at":"2026-08-15T20:05:49.018439Z"},"links":{"citing_paper":"/paper/2506.13514"},"observation_digest":"sha256:1d2803aa4fd7b4a9e4082bf5d657f7e945d92d23ad665deefd741c0ebd37b792","observation_id":"874f9d9c-65b6-497b-a5a5-1b0215135601","resolution":{"observed_at":"2026-08-15T20:05:49.018439Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2506.13514","last_updated":"2025-06-16T14:09:43Z","latest_version":1,"primary_category":"cs.CL","snapshot_observed_at":"2026-08-17T11:46:56.501732Z","submitted_at":"2025-06-16T14:09:43Z","title":"TensorSLM: Energy-efficient Embedding Compression of Sub-billion Parameter Language Models on Low-end Devices"},"reference_resolution":{"displayed":43,"state_counts":{"malformed_identifier":0,"metadata_mismatch":1,"parse_uncertain":0,"unresolved":29,"verified_exact":2,"verified_fuzzy":11},"total_outbound_references":43},"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":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"thesis":"As of 23 August 2026, this Paper Citation Record lists 43 of 43 outbound references and 0 inbound Pith citation observations for arXiv:2506.13514."}