{"as_of":"2026-08-14T10:06:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:dc3792bb6dcebed2f165fffbf741867c4dfa0d7c1bb0484b5ea76c8881c14647","coverage":[{"denominator":23,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":23,"source":"paper_references, paper_reference_links","source_observed_at":"2026-07-02T22:47:05.759610Z","state":"measured"},{"denominator":24,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":24,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-14T06:32:32.682623+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-07-10T04:03:37.649301Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"pith","source_observed_at":"2026-07-10T04:06:44.605939Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2606.10531","last_updated":"2026-07-01T03:38:14Z","snapshot_observed_at":"2026-08-13T14:32:02.259688Z","submitted_at":"2026-06-09T08:02:56Z","title":"LC-QAT: Data-Efficient 2-Bit QAT for LLMs via Linear-Constrained Vector Quantization","version":2},"cited_work":{"arxiv_id":"2606.10531","doi":null,"metadata_source":"pith","pith_arxiv_id":"2606.10531","snapshot_observed_at":"2026-07-10T04:06:44.605939Z","title":"LC-QAT: Data-Efficient 2-Bit QAT for LLMs via Linear-Constrained Vector Quantization","venue":"cs.CL","work_id":"6f7e06d7-4c05-46af-86df-fe706ea29da2","year":2026},"citing_paper":{"arxiv_id":"2607.08643","last_updated":"2026-07-09T16:17:10Z","snapshot_observed_at":"2026-08-06T21:01:29.401263Z","submitted_at":"2026-07-09T16:17:10Z","title":"BiSCo-LLM: Lookup-Free Binary Spherical Coding for Extreme Low-Bit Large Language Model Compression","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-07-10T04:03:37.649301Z"},"links":{"cited_paper":"/paper/2606.10531","citing_paper":"/paper/2607.08643"},"observation_digest":"sha256:328668c766912b85cd276fbb1d195b72001278670d565004d9e3fac88ee3ab04","observation_id":"8b7d278f-f55c-44e3-a5b4-49faa595d462","resolution":{"observed_at":"2026-07-10T04:06:44.608002Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2606.10531/citation-record","integrity":"/paper/2606.10531/integrity","json":"/paper/2606.10531/citation-record.json","paper":"/paper/2606.10531"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2505.17646","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-03T04:57:38.367551Z","title":"Understanding pre-training and fine-tuning from loss landscape perspectives","venue":null,"work_id":"eea59928-7026-41ae-92a7-e32519a2cd03","year":2025},"citing_paper":{"arxiv_id":"2606.10531","last_updated":"2026-07-01T03:38:14Z","snapshot_observed_at":"2026-08-13T14:32:02.259688Z","submitted_at":"2026-06-09T08:02:56Z","title":"LC-QAT: Data-Efficient 2-Bit QAT for LLMs via Linear-Constrained Vector Quantization","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-07-02T22:47:05.759610Z"},"links":{"citing_paper":"/paper/2606.10531"},"observation_digest":"sha256:fb825547de1c89667dab65dd505b5952dc75dda2dae7b819bf979a870407aa26","observation_id":"a51ae9af-5aba-4ac4-aded-bd05a42645d7","resolution":{"observed_at":"2026-07-02T22:47:25.253535Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2107.03374","last_updated":"2021-07-14T17:16:02Z","snapshot_observed_at":"2026-08-08T11:58:24.516369Z","submitted_at":"2021-07-07T17:41:24Z","title":"Evaluating Large Language Models Trained on Code","version":2},"cited_work":{"arxiv_id":"2107.03374","doi":"10.48550/arxiv.2107.03374","metadata_source":"pith","pith_arxiv_id":"2107.03374","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Evaluating Large Language Models Trained on Code","venue":"cs.LG","work_id":"042493e9-b26f-4b4e-bbde-382072ca9b08","year":2021},"citing_paper":{"arxiv_id":"2606.10531","last_updated":"2026-07-01T03:38:14Z","snapshot_observed_at":"2026-08-13T14:32:02.259688Z","submitted_at":"2026-06-09T08:02:56Z","title":"LC-QAT: Data-Efficient 2-Bit QAT for LLMs via Linear-Constrained Vector Quantization","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-07-02T22:47:05.759610Z"},"links":{"cited_paper":"/paper/2107.03374","citing_paper":"/paper/2606.10531"},"observation_digest":"sha256:b2794cf422140d13b7932a7f0c5beabd3a42acc1ecde948fbda1341b8127caf1","observation_id":"53375a1a-0c49-4d6d-988a-c7547cece42e","resolution":{"observed_at":"2026-07-02T22:47:25.271967Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-01T08:08:23.404839+00:00","source":"crossref_status_cache"},{"observed_at":"2026-08-01T08:08:23.404839+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2407.11062","last_updated":"2025-05-19T06:20:01Z","snapshot_observed_at":"2026-08-13T19:22:45.524352Z","submitted_at":"2024-07-10T17:53:30Z","title":"EfficientQAT: Efficient Quantization-Aware Training for Large Language Models","version":3},"cited_work":{"arxiv_id":"2407.11062","doi":"10.48550/arxiv.2407.11062","metadata_source":"pith","pith_arxiv_id":"2407.11062","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Efficientqat: Efficient quantization-aware training for large language models","venue":"cs.LG","work_id":"7d09dbf5-c36c-46e8-96bc-42b2cda1f7f7","year":2024},"citing_paper":{"arxiv_id":"2606.10531","last_updated":"2026-07-01T03:38:14Z","snapshot_observed_at":"2026-08-13T14:32:02.259688Z","submitted_at":"2026-06-09T08:02:56Z","title":"LC-QAT: Data-Efficient 2-Bit QAT for LLMs via Linear-Constrained Vector Quantization","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-07-02T22:47:05.759610Z"},"links":{"cited_paper":"/paper/2407.11062","citing_paper":"/paper/2606.10531"},"observation_digest":"sha256:89583a03be7f5156bdaabdefbebd324da46d4af52a9adec0826773bb928db74d","observation_id":"bafb2105-581c-4742-a818-6f6bdf055903","resolution":{"observed_at":"2026-07-02T22:47:25.239020Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-07-17T20:22:10.122064+00:00","source":"crossref_status_cache"},{"observed_at":"2026-07-17T20:22:10.122064+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"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":"1803.05457","doi":"10.1162/tacl_a_00448.https://aclanthology.org/2022.tacl-1.5","metadata_source":"pith","pith_arxiv_id":"1803.05457","snapshot_observed_at":"2026-07-11T11:50:26.030339Z","title":"Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge","venue":"cs.AI","work_id":"28ea1282-d657-4c61-a83c-f1249be6d6b1","year":2018},"citing_paper":{"arxiv_id":"2606.10531","last_updated":"2026-07-01T03:38:14Z","snapshot_observed_at":"2026-08-13T14:32:02.259688Z","submitted_at":"2026-06-09T08:02:56Z","title":"LC-QAT: Data-Efficient 2-Bit QAT for LLMs via Linear-Constrained Vector Quantization","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-07-02T22:47:05.759610Z"},"links":{"cited_paper":"/paper/1803.05457","citing_paper":"/paper/2606.10531"},"observation_digest":"sha256:b2eb2b1a0df08d3d06d91ddec29d696bef292cf52819f3110002d95e5d49b20e","observation_id":"d331fbbc-3ee9-4358-b86c-7acdaa81742e","resolution":{"observed_at":"2026-07-02T22:47:25.223718Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2110.14168","last_updated":"2021-11-18T00:23:45Z","snapshot_observed_at":"2026-08-14T02:43:01.480086Z","submitted_at":"2021-10-27T04:49:45Z","title":"Training Verifiers to Solve Math Word Problems","version":2},"cited_work":{"arxiv_id":"2110.14168","doi":"10.1002/j.1545-","metadata_source":"pith","pith_arxiv_id":"2110.14168","snapshot_observed_at":"2026-07-11T11:50:26.030339Z","title":"Training Verifiers to Solve Math Word Problems","venue":"cs.LG","work_id":"acab1aa8-b4d6-40e0-a3ee-25341701dca2","year":2021},"citing_paper":{"arxiv_id":"2606.10531","last_updated":"2026-07-01T03:38:14Z","snapshot_observed_at":"2026-08-13T14:32:02.259688Z","submitted_at":"2026-06-09T08:02:56Z","title":"LC-QAT: Data-Efficient 2-Bit QAT for LLMs via Linear-Constrained Vector Quantization","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-07-02T22:47:05.759610Z"},"links":{"cited_paper":"/paper/2110.14168","citing_paper":"/paper/2606.10531"},"observation_digest":"sha256:d1bdf1a79534ae0c630247808f7c2e532364b94221dcd90df8a5447efd20b1bd","observation_id":"f0fd8366-b7f0-4db7-8d88-dbe624b501ae","resolution":{"observed_at":"2026-07-02T22:47:25.272635Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2210.17323","last_updated":"2023-03-22T13:10:47Z","snapshot_observed_at":"2026-08-11T21:19:33.078277Z","submitted_at":"2022-10-31T13:42:40Z","title":"GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers","version":2},"cited_work":{"arxiv_id":"2210.17323","doi":"10.48550/arxiv.2210.17323","metadata_source":"pith","pith_arxiv_id":"2210.17323","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers","venue":"cs.LG","work_id":"19ed8c44-202a-48f6-8169-637d5a5f2408","year":2022},"citing_paper":{"arxiv_id":"2606.10531","last_updated":"2026-07-01T03:38:14Z","snapshot_observed_at":"2026-08-13T14:32:02.259688Z","submitted_at":"2026-06-09T08:02:56Z","title":"LC-QAT: Data-Efficient 2-Bit QAT for LLMs via Linear-Constrained Vector Quantization","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-07-02T22:47:05.759610Z"},"links":{"cited_paper":"/paper/2210.17323","citing_paper":"/paper/2606.10531"},"observation_digest":"sha256:8c9185fe418e3fff5b876e81654f824bd01060a070a67a5b38c33a2e009398de","observation_id":"c40e9d83-52ab-4a7c-aec8-ff44745c40a0","resolution":{"observed_at":"2026-07-02T22:47:25.238995Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-07-17T20:22:03.028003+00:00","source":"crossref_status_cache"},{"observed_at":"2026-07-17T20:22:03.028003+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2505.01043","last_updated":"2026-07-29T08:00:39Z","snapshot_observed_at":"2026-08-14T01:09:32.052334Z","submitted_at":"2025-05-02T06:33:25Z","title":"Low-Precision Training of Large Language Models: Methods, Challenges, and Opportunities","version":2},"cited_work":{"arxiv_id":"2505.01043","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2505.01043","snapshot_observed_at":"2026-07-30T01:18:51.459998Z","title":"Low-precision training of large language models: Methods, challenges, and opportunities","venue":null,"work_id":"bf4a3b3b-fb8b-47f4-bc34-9c5da16141a0","year":2025},"citing_paper":{"arxiv_id":"2606.10531","last_updated":"2026-07-01T03:38:14Z","snapshot_observed_at":"2026-08-13T14:32:02.259688Z","submitted_at":"2026-06-09T08:02:56Z","title":"LC-QAT: Data-Efficient 2-Bit QAT for LLMs via Linear-Constrained Vector Quantization","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-07-02T22:47:05.759610Z"},"links":{"cited_paper":"/paper/2505.01043","citing_paper":"/paper/2606.10531"},"observation_digest":"sha256:30278ae207e495b33a048ecea9f9daf19dbb0295927dd5f56ae2e9e76e79f4c3","observation_id":"e8f262cc-5885-442e-bd3d-e59dd40bb7d3","resolution":{"observed_at":"2026-07-30T01:18:51.459998Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"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":"2009.03300","doi":"10.48550/arxiv.2009.03300","metadata_source":"pith","pith_arxiv_id":"2009.03300","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Measuring Massive Multitask Language Understanding","venue":"cs.CY","work_id":"e87ec49a-544b-4ec8-8991-75298c64ff5e","year":2020},"citing_paper":{"arxiv_id":"2606.10531","last_updated":"2026-07-01T03:38:14Z","snapshot_observed_at":"2026-08-13T14:32:02.259688Z","submitted_at":"2026-06-09T08:02:56Z","title":"LC-QAT: Data-Efficient 2-Bit QAT for LLMs via Linear-Constrained Vector Quantization","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-07-02T22:47:05.759610Z"},"links":{"cited_paper":"/paper/2009.03300","citing_paper":"/paper/2606.10531"},"observation_digest":"sha256:a9b959370050ed74e5f1c5902495afc336195df66cfb7ce656fd51073c275ac9","observation_id":"85b81d36-0bcd-4c32-8c96-bb3d12747fb1","resolution":{"observed_at":"2026-07-02T22:47:25.274836Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-04T01:08:06.256034+00:00","source":"crossref_status_cache"},{"observed_at":"2026-08-04T01:08:06.256034+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2404.06395","last_updated":"2024-06-03T08:54:38Z","snapshot_observed_at":"2026-08-12T13:10:06.472493Z","submitted_at":"2024-04-09T15:36:50Z","title":"MiniCPM: Unveiling the Potential of Small Language Models with Scalable Training Strategies","version":3},"cited_work":{"arxiv_id":"2404.06395","doi":"10.48550/arxiv.2404.06395","metadata_source":"pith","pith_arxiv_id":"2404.06395","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"MiniCPM: Unveiling the Potential of Small Language Models with Scalable Training Strategies","venue":"cs.CL","work_id":"f20a4304-bd39-414a-923b-d18322e29258","year":2024},"citing_paper":{"arxiv_id":"2606.10531","last_updated":"2026-07-01T03:38:14Z","snapshot_observed_at":"2026-08-13T14:32:02.259688Z","submitted_at":"2026-06-09T08:02:56Z","title":"LC-QAT: Data-Efficient 2-Bit QAT for LLMs via Linear-Constrained Vector Quantization","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-07-02T22:47:05.759610Z"},"links":{"cited_paper":"/paper/2404.06395","citing_paper":"/paper/2606.10531"},"observation_digest":"sha256:1b5ab27dfce741dba2b4cea3747d49ec2793fb697528a55c739a1876bec7a00d","observation_id":"ffcc7459-17e8-46ff-84d9-fcad965d5780","resolution":{"observed_at":"2026-07-02T22:47:25.253434Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2305.20050","last_updated":"2023-05-31T17:24:00Z","snapshot_observed_at":"2026-08-11T17:22:43.545531Z","submitted_at":"2023-05-31T17:24:00Z","title":"Let's Verify Step by Step","version":1},"cited_work":{"arxiv_id":"2305.20050","doi":"10.1007/bf00262952","metadata_source":"pith","pith_arxiv_id":"2305.20050","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Let's Verify Step by Step","venue":"cs.LG","work_id":"6d05b790-04c5-4fd2-91b2-ba1dfdd5770f","year":2023},"citing_paper":{"arxiv_id":"2606.10531","last_updated":"2026-07-01T03:38:14Z","snapshot_observed_at":"2026-08-13T14:32:02.259688Z","submitted_at":"2026-06-09T08:02:56Z","title":"LC-QAT: Data-Efficient 2-Bit QAT for LLMs via Linear-Constrained Vector Quantization","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-07-02T22:47:05.759610Z"},"links":{"cited_paper":"/paper/2305.20050","citing_paper":"/paper/2606.10531"},"observation_digest":"sha256:0e87dcd46319c0f903f9edc65286e4a49daa3172ca3145cbadf3d79c2bb524cf","observation_id":"532c621b-663a-4d2b-831a-529cf7e5b993","resolution":{"observed_at":"2026-07-02T22:47:25.266683Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2305.17888","last_updated":"2023-05-29T05:22:11Z","snapshot_observed_at":"2026-08-13T11:30:33.812255Z","submitted_at":"2023-05-29T05:22:11Z","title":"LLM-QAT: Data-Free Quantization Aware Training for Large Language Models","version":1},"cited_work":{"arxiv_id":"2305.17888","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2305.17888","snapshot_observed_at":"2026-07-04T11:09:46.244627Z","title":"Llm-qat: Data-free quantization aware training for large language models","venue":null,"work_id":"aa772a15-dd3c-421b-a1a0-0fcfd75b9862","year":2023},"citing_paper":{"arxiv_id":"2606.10531","last_updated":"2026-07-01T03:38:14Z","snapshot_observed_at":"2026-08-13T14:32:02.259688Z","submitted_at":"2026-06-09T08:02:56Z","title":"LC-QAT: Data-Efficient 2-Bit QAT for LLMs via Linear-Constrained Vector Quantization","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-07-02T22:47:05.759610Z"},"links":{"cited_paper":"/paper/2305.17888","citing_paper":"/paper/2606.10531"},"observation_digest":"sha256:bd510c0cb7f8d28934878792dfc53f0e64122d705942e1b6bebf75c69f4fd41e","observation_id":"a5a612fb-9ba0-40d2-b5d0-995de1e16987","resolution":{"observed_at":"2026-07-02T22:47:25.227142Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2407.21783","last_updated":"2024-11-23T23:27:33Z","snapshot_observed_at":"2026-08-13T17:20:44.002518Z","submitted_at":"2024-07-31T17:54:27Z","title":"The Llama 3 Herd of Models","version":3},"cited_work":{"arxiv_id":"2407.21783","doi":"10.1016/s0749-0720(15","metadata_source":"pith","pith_arxiv_id":"2407.21783","snapshot_observed_at":"2026-07-11T11:50:26.030339Z","title":"The Llama 3 Herd of Models","venue":"cs.AI","work_id":"1549a635-88af-4ac1-acfe-51ae7bb53345","year":2024},"citing_paper":{"arxiv_id":"2606.10531","last_updated":"2026-07-01T03:38:14Z","snapshot_observed_at":"2026-08-13T14:32:02.259688Z","submitted_at":"2026-06-09T08:02:56Z","title":"LC-QAT: Data-Efficient 2-Bit QAT for LLMs via Linear-Constrained Vector Quantization","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-07-02T22:47:05.759610Z"},"links":{"cited_paper":"/paper/2407.21783","citing_paper":"/paper/2606.10531"},"observation_digest":"sha256:afa0fead955222c627f5f1558c4a02dc48c2ac68842cf05b93703e2385d04f04","observation_id":"cc5e1356-2771-4ec5-bdeb-6753fbb55409","resolution":{"observed_at":"2026-07-02T22:47:25.250996Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2504.12285","last_updated":"2025-04-25T03:07:55Z","snapshot_observed_at":"2026-08-12T13:07:34.152853Z","submitted_at":"2025-04-16T17:51:43Z","title":"BitNet b1.58 2B4T Technical Report","version":2},"cited_work":{"arxiv_id":"2504.12285","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2504.12285","snapshot_observed_at":"2026-07-04T20:00:08.922149Z","title":"Bitnet b1","venue":null,"work_id":"40bc01dc-f02b-4154-a692-77c8e15399b3","year":2025},"citing_paper":{"arxiv_id":"2606.10531","last_updated":"2026-07-01T03:38:14Z","snapshot_observed_at":"2026-08-13T14:32:02.259688Z","submitted_at":"2026-06-09T08:02:56Z","title":"LC-QAT: Data-Efficient 2-Bit QAT for LLMs via Linear-Constrained Vector Quantization","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-07-02T22:47:05.759610Z"},"links":{"cited_paper":"/paper/2504.12285","citing_paper":"/paper/2606.10531"},"observation_digest":"sha256:6cbcf37cb1d3b048666d678b4683e7517359699e4d4bdaa6b776a2b7a9d806d5","observation_id":"8a249931-a43c-494b-965c-b38b6b71fac3","resolution":{"observed_at":"2026-07-02T22:47:25.270163Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1609.07843","last_updated":"2016-09-26T04:06:13Z","snapshot_observed_at":"2026-07-06T05:12:10.387914Z","submitted_at":"2016-09-26T04:06:13Z","title":"Pointer Sentinel Mixture Models","version":1},"cited_work":{"arxiv_id":"1609.07843","doi":"10.1007/978-3-030-62077-6","metadata_source":"pith","pith_arxiv_id":"1609.07843","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Pointer Sentinel Mixture Models","venue":"cs.CL","work_id":"fef3833e-dc80-42a3-a1e0-ffbfafee6fff","year":2016},"citing_paper":{"arxiv_id":"2606.10531","last_updated":"2026-07-01T03:38:14Z","snapshot_observed_at":"2026-08-13T14:32:02.259688Z","submitted_at":"2026-06-09T08:02:56Z","title":"LC-QAT: Data-Efficient 2-Bit QAT for LLMs via Linear-Constrained Vector Quantization","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-07-02T22:47:05.759610Z"},"links":{"cited_paper":"/paper/1609.07843","citing_paper":"/paper/2606.10531"},"observation_digest":"sha256:e9d63631319925f4ea2bc0c931ffdaf19a45be0516cfd5483328cc56e05d6532","observation_id":"ea39d109-18e3-400e-83fd-486326f90ca4","resolution":{"observed_at":"2026-07-02T22:47:25.244629Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1809.02789","last_updated":"2018-09-08T11:47:16Z","snapshot_observed_at":"2026-08-14T07:00:02.529732Z","submitted_at":"2018-09-08T11:47:16Z","title":"Can a Suit of Armor Conduct Electricity? A New Dataset for Open Book Question Answering","version":1},"cited_work":{"arxiv_id":"1809.02789","doi":"10.48550/arxiv.1809.02789","metadata_source":"pith","pith_arxiv_id":"1809.02789","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Can a Suit of Armor Conduct Electricity? A New Dataset for Open Book Question Answering","venue":"cs.CL","work_id":"b9d68fc0-5b23-4def-bc5e-6ad71d64eec6","year":2018},"citing_paper":{"arxiv_id":"2606.10531","last_updated":"2026-07-01T03:38:14Z","snapshot_observed_at":"2026-08-13T14:32:02.259688Z","submitted_at":"2026-06-09T08:02:56Z","title":"LC-QAT: Data-Efficient 2-Bit QAT for LLMs via Linear-Constrained Vector Quantization","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-07-02T22:47:05.759610Z"},"links":{"cited_paper":"/paper/1809.02789","citing_paper":"/paper/2606.10531"},"observation_digest":"sha256:537605e6ad2aee108d9153fd7dae04def327198a502567bd37110b8cb34e9e5e","observation_id":"6006294d-9c60-4bf4-b99f-7bed29ef6141","resolution":{"observed_at":"2026-07-02T22:47:25.263995Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2406.17557","last_updated":"2024-10-31T11:37:49Z","snapshot_observed_at":"2026-08-02T15:25:02.551919Z","submitted_at":"2024-06-25T13:50:56Z","title":"The FineWeb Datasets: Decanting the Web for the Finest Text Data at Scale","version":2},"cited_work":{"arxiv_id":"2406.17557","doi":"10.48550/arxiv.2406.17557","metadata_source":"pith","pith_arxiv_id":"2406.17557","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"The FineWeb Datasets: Decanting the Web for the Finest Text Data at Scale","venue":"cs.CL","work_id":"1ac90585-1330-4f90-8836-6382fa63c4eb","year":2024},"citing_paper":{"arxiv_id":"2606.10531","last_updated":"2026-07-01T03:38:14Z","snapshot_observed_at":"2026-08-13T14:32:02.259688Z","submitted_at":"2026-06-09T08:02:56Z","title":"LC-QAT: Data-Efficient 2-Bit QAT for LLMs via Linear-Constrained Vector Quantization","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-07-02T22:47:05.759610Z"},"links":{"cited_paper":"/paper/2406.17557","citing_paper":"/paper/2606.10531"},"observation_digest":"sha256:bbcf09d22e30681dc73ab1247a75d38318881f3548c058d0b2da7a2edec9d8bd","observation_id":"e7d9c0a0-b8bf-46f7-9ce3-42aaad1fe245","resolution":{"observed_at":"2026-07-02T22:47:25.255983Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2505.22988","last_updated":"2026-06-03T05:56:05Z","snapshot_observed_at":"2026-08-07T12:53:31.543703Z","submitted_at":"2025-05-29T01:53:00Z","title":"Model-Preserving Adaptive Rounding","version":3},"cited_work":{"arxiv_id":"2505.22988","doi":null,"metadata_source":"pith","pith_arxiv_id":"2505.22988","snapshot_observed_at":"2026-07-10T14:47:14.555786Z","title":"Model-preserving adaptive rounding","venue":"cs.LG","work_id":"b0acf978-4f39-48f2-bca1-f29efb72d070","year":2025},"citing_paper":{"arxiv_id":"2606.10531","last_updated":"2026-07-01T03:38:14Z","snapshot_observed_at":"2026-08-13T14:32:02.259688Z","submitted_at":"2026-06-09T08:02:56Z","title":"LC-QAT: Data-Efficient 2-Bit QAT for LLMs via Linear-Constrained Vector Quantization","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-07-02T22:47:05.759610Z"},"links":{"cited_paper":"/paper/2505.22988","citing_paper":"/paper/2606.10531"},"observation_digest":"sha256:fedea38058f2f14abcb993476eab9a15cdd4535efc4f519d2f4ae2c12bf26cda","observation_id":"5d6c43d1-7e80-4924-935f-77413081bcb4","resolution":{"observed_at":"2026-07-02T22:47:25.215524Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2501.17116","last_updated":"2025-05-23T09:44:25Z","snapshot_observed_at":"2026-08-11T17:00:01.375103Z","submitted_at":"2025-01-28T18:04:50Z","title":"Optimizing Large Language Model Training Using FP4 Quantization","version":2},"cited_work":{"arxiv_id":"2501.17116","doi":null,"metadata_source":"pith","pith_arxiv_id":"2501.17116","snapshot_observed_at":"2026-07-03T07:27:44.449472Z","title":"Optimizing Large Language Model Training Using FP4 Quantization","venue":"cs.LG","work_id":"dd7006b4-487d-4b92-9319-2fc36e31d954","year":2025},"citing_paper":{"arxiv_id":"2606.10531","last_updated":"2026-07-01T03:38:14Z","snapshot_observed_at":"2026-08-13T14:32:02.259688Z","submitted_at":"2026-06-09T08:02:56Z","title":"LC-QAT: Data-Efficient 2-Bit QAT for LLMs via Linear-Constrained Vector Quantization","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-07-02T22:47:05.759610Z"},"links":{"cited_paper":"/paper/2501.17116","citing_paper":"/paper/2606.10531"},"observation_digest":"sha256:9043658e6c34620ac8086e7cb33bcdc9e9e8cabd0536128f6339e38c91ba5f63","observation_id":"a6e1e0fc-dee2-4ecf-ac80-73c2c42e9c06","resolution":{"observed_at":"2026-07-02T22:47:25.269295Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2505.09388","last_updated":"2025-05-14T13:41:34Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-05-14T13:41:34Z","title":"Qwen3 Technical Report","version":1},"cited_work":{"arxiv_id":"2505.09388","doi":"10.1016/j.aiopen.2022.12","metadata_source":"pith","pith_arxiv_id":"2505.09388","snapshot_observed_at":"2026-07-11T11:50:26.030339Z","title":"Qwen3 Technical Report","venue":"cs.CL","work_id":"25a4e30c-1232-48e7-9925-02fa12ba7c9e","year":2025},"citing_paper":{"arxiv_id":"2606.10531","last_updated":"2026-07-01T03:38:14Z","snapshot_observed_at":"2026-08-13T14:32:02.259688Z","submitted_at":"2026-06-09T08:02:56Z","title":"LC-QAT: Data-Efficient 2-Bit QAT for LLMs via Linear-Constrained Vector Quantization","version":2},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-07-02T22:47:05.759610Z"},"links":{"cited_paper":"/paper/2505.09388","citing_paper":"/paper/2606.10531"},"observation_digest":"sha256:f20f1acb0bbc3bc6206c9b4373f3775635d0201a59200461dc7367c88bdb416d","observation_id":"192cbd20-acdb-41f4-88ba-745b19a23c7e","resolution":{"observed_at":"2026-07-02T22:47:25.258730Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1903.05662","last_updated":"2019-09-25T14:33:44Z","snapshot_observed_at":"2026-07-06T07:39:06.044012Z","submitted_at":"2019-03-13T18:23:43Z","title":"Understanding Straight-Through Estimator in Training Activation Quantized Neural Nets","version":4},"cited_work":{"arxiv_id":"1903.05662","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1903.05662","snapshot_observed_at":"2026-07-04T16:59:58.427669Z","title":"Understanding straight-through estimator in training activation quantized neural nets","venue":null,"work_id":"88958bb5-572d-40be-8cc7-9a5173285339","year":1903},"citing_paper":{"arxiv_id":"2606.10531","last_updated":"2026-07-01T03:38:14Z","snapshot_observed_at":"2026-08-13T14:32:02.259688Z","submitted_at":"2026-06-09T08:02:56Z","title":"LC-QAT: Data-Efficient 2-Bit QAT for LLMs via Linear-Constrained Vector Quantization","version":2},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-07-02T22:47:05.759610Z"},"links":{"cited_paper":"/paper/1903.05662","citing_paper":"/paper/2606.10531"},"observation_digest":"sha256:4b70c5d830b0e38eff32e91e40ad2a33b8c45b8ca352a55a6b0da57601fa9f41","observation_id":"91240b77-ba6c-4466-8c78-2814b97af4dc","resolution":{"observed_at":"2026-07-02T22:47:25.256083Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2311.07911","last_updated":"2023-11-14T05:13:55Z","snapshot_observed_at":"2026-07-06T16:47:08.877195Z","submitted_at":"2023-11-14T05:13:55Z","title":"Instruction-Following Evaluation for Large Language Models","version":1},"cited_work":{"arxiv_id":"2311.07911","doi":"10.48550/arxiv.2311.07911","metadata_source":"pith","pith_arxiv_id":"2311.07911","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Instruction-Following Evaluation for Large Language Models","venue":"cs.CL","work_id":"3aa06177-125a-4f5a-8f4a-8070c5986c26","year":2023},"citing_paper":{"arxiv_id":"2606.10531","last_updated":"2026-07-01T03:38:14Z","snapshot_observed_at":"2026-08-13T14:32:02.259688Z","submitted_at":"2026-06-09T08:02:56Z","title":"LC-QAT: Data-Efficient 2-Bit QAT for LLMs via Linear-Constrained Vector Quantization","version":2},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-07-02T22:47:05.759610Z"},"links":{"cited_paper":"/paper/2311.07911","citing_paper":"/paper/2606.10531"},"observation_digest":"sha256:d594dca15adc25aab7ede14cbecbccaf5e215ee700443b68f0656ad5d68d9add","observation_id":"ddd0a765-c47f-4ee2-8964-e8c23d26b99b","resolution":{"observed_at":"2026-07-02T22:47:25.264138Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2507.07145","last_updated":"2025-07-09T06:04:14Z","snapshot_observed_at":"2026-08-12T21:38:35.588868Z","submitted_at":"2025-07-09T06:04:14Z","title":"CCQ: Convolutional Code for Extreme Low-bit Quantization in LLMs","version":1},"cited_work":{"arxiv_id":"2507.07145","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2507.07145","snapshot_observed_at":"2026-07-03T04:57:38.330281Z","title":"CCQ: Convolutional code for extreme low-bit quantization in llms.CoRR, abs/2507.07145,","venue":null,"work_id":"b11ff0f5-e40a-45d3-84c4-56e85cf34398","year":null},"citing_paper":{"arxiv_id":"2606.10531","last_updated":"2026-07-01T03:38:14Z","snapshot_observed_at":"2026-08-13T14:32:02.259688Z","submitted_at":"2026-06-09T08:02:56Z","title":"LC-QAT: Data-Efficient 2-Bit QAT for LLMs via Linear-Constrained Vector Quantization","version":2},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-07-02T22:47:05.759610Z"},"links":{"cited_paper":"/paper/2507.07145","citing_paper":"/paper/2606.10531"},"observation_digest":"sha256:2889739b4510c4dffd9b42c5b171f5e642223e7200f9e198ce719577de947b6f","observation_id":"291b7156-4a4c-43fc-ab4e-2557744742a2","resolution":{"observed_at":"2026-07-02T22:47:25.261681Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-07-05T18:31:23.162228Z","title":"METHOD PTQ TIME (H) QAT TIME (H) T OTAL TIME (H) LC-QAT 6 55 61 PARETO Q N/A 417 417 A.3","venue":null,"work_id":"0dded8db-d884-4a5d-b248-b94b22eb0558","year":2025},"citing_paper":{"arxiv_id":"2606.10531","last_updated":"2026-07-01T03:38:14Z","snapshot_observed_at":"2026-08-13T14:32:02.259688Z","submitted_at":"2026-06-09T08:02:56Z","title":"LC-QAT: Data-Efficient 2-Bit QAT for LLMs via Linear-Constrained Vector Quantization","version":2},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-07-02T22:47:05.759610Z"},"links":{"citing_paper":"/paper/2606.10531"},"observation_digest":"sha256:0bc95e52f8ff9768fcedb435f9ef2bf1728e822bf4b0632fdecaa58c2e57b2c2","observation_id":"942c4db7-960f-4274-8c25-6661349b7d24","resolution":{"observed_at":"2026-07-05T18:31:23.163976Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2606.10531","last_updated":"2026-07-01T03:38:14Z","latest_version":2,"primary_category":"cs.CL","snapshot_observed_at":"2026-08-13T14:32:02.259688Z","submitted_at":"2026-06-09T08:02:56Z","title":"LC-QAT: Data-Efficient 2-Bit QAT for LLMs via Linear-Constrained Vector Quantization"},"reference_resolution":{"displayed":23,"state_counts":{"malformed_identifier":0,"metadata_mismatch":2,"parse_uncertain":0,"unresolved":0,"verified_exact":20,"verified_fuzzy":1},"total_outbound_references":23},"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":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"thesis":"As of 14 August 2026, this Paper Citation Record lists 23 of 23 outbound references and 1 inbound Pith citation observation for arXiv:2606.10531."}