{"as_of":"2026-08-17T21:44:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:df2b8bd1d53cf6c78f1e892dc83735ac8d1005e34ae408b4838353d7ccb39a76","coverage":[{"denominator":75,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":75,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-15T22:12:58.929907Z","state":"measured"},{"denominator":75,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":75,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-17T06:30:58.91139+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/2505.07793/citation-record","integrity":"/paper/2505.07793/integrity","json":"/paper/2505.07793/citation-record.json","paper":"/paper/2505.07793"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T22:12:58.516371Z","title":"Mechanistic evaluation of transformers and state space models","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2505.07793","last_updated":"2025-09-08T20:57:22Z","snapshot_observed_at":"2026-08-15T22:05:45.876584Z","submitted_at":"2025-05-12T17:45:05Z","title":"Overflow Prevention Enhances Long-Context Recurrent LLMs","version":2},"reference_index":1,"source":"arxiv_source","source_observed_at":"2026-08-15T22:12:58.516371Z"},"links":{"citing_paper":"/paper/2505.07793"},"observation_digest":"sha256:8620ec8217f642664b63324baf204b4c03ef70941e407233b6729f24a41f10ec","observation_id":"0839cd60-b2d5-40ea-ba45-77d27716ab1e","resolution":{"observed_at":"2026-08-15T22:12:58.516371Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2312.04927","last_updated":"2023-12-08T09:44:25Z","snapshot_observed_at":"2026-08-16T14:36:34.764352Z","submitted_at":"2023-12-08T09:44:25Z","title":"Zoology: Measuring and Improving Recall in Efficient Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2312.04927","snapshot_observed_at":"2026-08-15T22:12:58.523737Z","title":"Zoology: Measuring and improving recall in efficient language models, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.07793","last_updated":"2025-09-08T20:57:22Z","snapshot_observed_at":"2026-08-15T22:05:45.876584Z","submitted_at":"2025-05-12T17:45:05Z","title":"Overflow Prevention Enhances Long-Context Recurrent LLMs","version":2},"reference_index":2,"source":"arxiv_source","source_observed_at":"2026-08-15T22:12:58.523737Z"},"links":{"cited_paper":"/paper/2312.04927","citing_paper":"/paper/2505.07793"},"observation_digest":"sha256:51431a16089e0389be80a58b1ce0d86969db1206cbd45d3beb0913e4ceffaad0","observation_id":"299bcbfb-f401-40b0-af92-e1abe210d17b","resolution":{"observed_at":"2026-08-15T22:12:58.523737Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.18668","last_updated":"2025-03-07T18:57:52Z","snapshot_observed_at":"2026-08-16T14:14:23.399336Z","submitted_at":"2024-02-28T19:28:27Z","title":"Simple linear attention language models balance the recall-throughput tradeoff","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.18668","snapshot_observed_at":"2026-08-15T22:12:58.530414Z","title":"Simple linear attention language models balance the recall-throughput tradeoff, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.07793","last_updated":"2025-09-08T20:57:22Z","snapshot_observed_at":"2026-08-15T22:05:45.876584Z","submitted_at":"2025-05-12T17:45:05Z","title":"Overflow Prevention Enhances Long-Context Recurrent LLMs","version":2},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-08-15T22:12:58.530414Z"},"links":{"cited_paper":"/paper/2402.18668","citing_paper":"/paper/2505.07793"},"observation_digest":"sha256:dfcaf118fe72bcf03a822093acb6cf16f41420cdd52cfe58c1079156fcd567eb","observation_id":"4bb70cee-dead-41e2-bf03-c8d361983fe3","resolution":{"observed_at":"2026-08-15T22:12:58.530414Z","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":"2026-08-15T22:13:00.395302Z","title":"Mambaextend: A training-free approach to improve long context extension of mamba","venue":null,"work_id":"66eb6be7-c092-461b-a9db-617c50617189","year":2025},"citing_paper":{"arxiv_id":"2505.07793","last_updated":"2025-09-08T20:57:22Z","snapshot_observed_at":"2026-08-15T22:05:45.876584Z","submitted_at":"2025-05-12T17:45:05Z","title":"Overflow Prevention Enhances Long-Context Recurrent LLMs","version":2},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-08-15T22:12:58.536720Z"},"links":{"citing_paper":"/paper/2505.07793"},"observation_digest":"sha256:a8552c843473972107c062fe8475e7aa08a278fcd9d9f28bf4d875c8d5511c15","observation_id":"0430f030-7865-46a3-9a03-85e85277f11b","resolution":{"observed_at":"2026-08-15T22:13:00.400766Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1409.0473","last_updated":"2016-05-19T21:53:22Z","snapshot_observed_at":"2026-08-12T12:07:33.202888Z","submitted_at":"2014-09-01T16:33:02Z","title":"Neural Machine Translation by Jointly Learning to Align and Translate","version":7},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1409.0473","snapshot_observed_at":"2026-08-15T22:12:58.542005Z","title":"Neural machine translation by jointly learning to align and translate, 2016","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2505.07793","last_updated":"2025-09-08T20:57:22Z","snapshot_observed_at":"2026-08-15T22:05:45.876584Z","submitted_at":"2025-05-12T17:45:05Z","title":"Overflow Prevention Enhances Long-Context Recurrent LLMs","version":2},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-08-15T22:12:58.542005Z"},"links":{"cited_paper":"/paper/1409.0473","citing_paper":"/paper/2505.07793"},"observation_digest":"sha256:aa864808868dac8e16bf196f73206158d1fb9729f1af3f71c40282bb050793bb","observation_id":"c19fbd28-b18b-4691-8406-8fa8d29ab020","resolution":{"observed_at":"2026-08-15T22:12:58.542005Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2308.14508","last_updated":"2024-06-19T04:00:32Z","snapshot_observed_at":"2026-08-08T03:49:18.086396Z","submitted_at":"2023-08-28T11:53:40Z","title":"LongBench: A Bilingual, Multitask Benchmark for Long Context Understanding","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2308.14508","snapshot_observed_at":"2026-08-15T22:12:58.547553Z","title":"Longbench: A bilingual, multitask benchmark for long context understanding, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.07793","last_updated":"2025-09-08T20:57:22Z","snapshot_observed_at":"2026-08-15T22:05:45.876584Z","submitted_at":"2025-05-12T17:45:05Z","title":"Overflow Prevention Enhances Long-Context Recurrent LLMs","version":2},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-08-15T22:12:58.547553Z"},"links":{"cited_paper":"/paper/2308.14508","citing_paper":"/paper/2505.07793"},"observation_digest":"sha256:d6c9a6f746e3a144a03d1b85b11ce9ab1b9191f4e50b22159e9cc681478bedfd","observation_id":"4377b058-48a2-4a4a-afcf-5d6961666fa7","resolution":{"observed_at":"2026-08-15T22:12:58.547553Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2412.15204","last_updated":"2025-01-03T11:44:51Z","snapshot_observed_at":"2026-08-12T12:34:50.226758Z","submitted_at":"2024-12-19T18:59:17Z","title":"LongBench v2: Towards Deeper Understanding and Reasoning on Realistic Long-context Multitasks","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.15204","snapshot_observed_at":"2026-08-15T22:12:58.554285Z","title":"Longbench v2: Towards deeper understanding and reasoning on realistic long-context multitasks, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2505.07793","last_updated":"2025-09-08T20:57:22Z","snapshot_observed_at":"2026-08-15T22:05:45.876584Z","submitted_at":"2025-05-12T17:45:05Z","title":"Overflow Prevention Enhances Long-Context Recurrent LLMs","version":2},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-08-15T22:12:58.554285Z"},"links":{"cited_paper":"/paper/2412.15204","citing_paper":"/paper/2505.07793"},"observation_digest":"sha256:cefc9ec986f5366845a3a1d469e5773025b7b96594c504464fd8467ce6443abc","observation_id":"a5baaf6f-ae86-4b14-b915-03e9f0196fca","resolution":{"observed_at":"2026-08-15T22:12:58.554285Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.04517","last_updated":"2024-12-06T15:42:07Z","snapshot_observed_at":"2026-08-16T13:54:34.627474Z","submitted_at":"2024-05-07T17:50:21Z","title":"xLSTM: Extended Long Short-Term Memory","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.04517","snapshot_observed_at":"2026-08-15T22:12:58.559870Z","title":"xlstm: Extended long short-term memory, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.07793","last_updated":"2025-09-08T20:57:22Z","snapshot_observed_at":"2026-08-15T22:05:45.876584Z","submitted_at":"2025-05-12T17:45:05Z","title":"Overflow Prevention Enhances Long-Context Recurrent LLMs","version":2},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-08-15T22:12:58.559870Z"},"links":{"cited_paper":"/paper/2405.04517","citing_paper":"/paper/2505.07793"},"observation_digest":"sha256:eac6954b990dbc006c2e96b867b45d5a4bc0ea6bf84d78fe1a5056ec9c7faa0c","observation_id":"b49a4d34-c010-4aa6-8010-7e9cd41932e2","resolution":{"observed_at":"2026-08-15T22:12:58.559870Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2503.13427","last_updated":"2025-03-17T17:54:55Z","snapshot_observed_at":"2026-08-16T12:49:15.500002Z","submitted_at":"2025-03-17T17:54:55Z","title":"xLSTM 7B: A Recurrent LLM for Fast and Efficient Inference","version":1},"cited_work":{"arxiv_id":"2503.13427","doi":null,"metadata_source":"pith","pith_arxiv_id":"2503.13427","snapshot_observed_at":"2026-08-15T22:12:59.664965Z","title":"xLSTM 7B: A Recurrent LLM for Fast and Efficient Inference","venue":"cs.LG","work_id":"398dbcb2-acf6-4ff1-8f00-9d3d5def07c2","year":2025},"citing_paper":{"arxiv_id":"2505.07793","last_updated":"2025-09-08T20:57:22Z","snapshot_observed_at":"2026-08-15T22:05:45.876584Z","submitted_at":"2025-05-12T17:45:05Z","title":"Overflow Prevention Enhances Long-Context Recurrent LLMs","version":2},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-08-15T22:12:58.565294Z"},"links":{"cited_paper":"/paper/2503.13427","citing_paper":"/paper/2505.07793"},"observation_digest":"sha256:f000e9307a62dcfe1ab632951b7baf7799669fc64ed1f0bbd9e8bfb842d4190b","observation_id":"d6e4eacb-09b0-496a-8e11-f097ab19932f","resolution":{"observed_at":"2026-08-15T22:12:59.672917Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T22:13:00.378431Z","title":"Graph mamba: Towards learning on graphs with state space models","venue":null,"work_id":"5c49f9c0-9e2f-4c77-9eac-df6b551ce993","year":2024},"citing_paper":{"arxiv_id":"2505.07793","last_updated":"2025-09-08T20:57:22Z","snapshot_observed_at":"2026-08-15T22:05:45.876584Z","submitted_at":"2025-05-12T17:45:05Z","title":"Overflow Prevention Enhances Long-Context Recurrent LLMs","version":2},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-08-15T22:12:58.570817Z"},"links":{"citing_paper":"/paper/2505.07793"},"observation_digest":"sha256:90302bb3c97a3c31bce7827ec27a4618d830292e7e1935349bc1ea9652bc37fa","observation_id":"510f76d6-c812-4789-9079-5f839bb82343","resolution":{"observed_at":"2026-08-15T22:13:00.383707Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2406.14528","last_updated":"2025-04-09T22:43:46Z","snapshot_observed_at":"2026-08-16T13:41:00.636573Z","submitted_at":"2024-06-20T17:40:18Z","title":"DeciMamba: Exploring the Length Extrapolation Potential of Mamba","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.14528","snapshot_observed_at":"2026-08-15T22:12:58.576118Z","title":"Decimamba: Exploring the length extrapolation potential of mamba, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2505.07793","last_updated":"2025-09-08T20:57:22Z","snapshot_observed_at":"2026-08-15T22:05:45.876584Z","submitted_at":"2025-05-12T17:45:05Z","title":"Overflow Prevention Enhances Long-Context Recurrent LLMs","version":2},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-08-15T22:12:58.576118Z"},"links":{"cited_paper":"/paper/2406.14528","citing_paper":"/paper/2505.07793"},"observation_digest":"sha256:9b08cdf8583bad5792f37a601de31826dd2b2e59f2fa06fed385405bd87ce2a5","observation_id":"f13658b8-5ef2-4715-a90d-44e7ce5040ff","resolution":{"observed_at":"2026-08-15T22:12:58.576118Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2404.07839","last_updated":"2024-08-28T15:05:42Z","snapshot_observed_at":"2026-08-16T14:01:42.253014Z","submitted_at":"2024-04-11T15:27:22Z","title":"RecurrentGemma: Moving Past Transformers for Efficient Open Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.07839","snapshot_observed_at":"2026-08-15T22:12:58.581384Z","title":"Recurrentgemma: Moving past transformers for efficient open language models","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.07793","last_updated":"2025-09-08T20:57:22Z","snapshot_observed_at":"2026-08-15T22:05:45.876584Z","submitted_at":"2025-05-12T17:45:05Z","title":"Overflow Prevention Enhances Long-Context Recurrent LLMs","version":2},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-08-15T22:12:58.581384Z"},"links":{"cited_paper":"/paper/2404.07839","citing_paper":"/paper/2505.07793"},"observation_digest":"sha256:f149b723a23957407163b2c62205122b14eb6363db9c965f3a14f7342faf724f","observation_id":"a925740a-e3a8-4249-9275-39f8b164caa4","resolution":{"observed_at":"2026-08-15T22:12:58.581384Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2309.12307","last_updated":"2024-03-08T15:26:38Z","snapshot_observed_at":"2026-08-16T14:58:40.364858Z","submitted_at":"2023-09-21T17:59:11Z","title":"LongLoRA: Efficient Fine-tuning of Long-Context Large Language Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2309.12307","snapshot_observed_at":"2026-08-15T22:12:58.587147Z","title":"Longlora: Efficient fine-tuning of long-context large language models","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.07793","last_updated":"2025-09-08T20:57:22Z","snapshot_observed_at":"2026-08-15T22:05:45.876584Z","submitted_at":"2025-05-12T17:45:05Z","title":"Overflow Prevention Enhances Long-Context Recurrent LLMs","version":2},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-08-15T22:12:58.587147Z"},"links":{"cited_paper":"/paper/2309.12307","citing_paper":"/paper/2505.07793"},"observation_digest":"sha256:6601cbc38ee93be00c78bf4db542d33248f6561b7e27d0913feaf7c3121cdf3d","observation_id":"42045944-50c0-49b9-8143-123d8a807cf5","resolution":{"observed_at":"2026-08-15T22:12:58.587147Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.21060","last_updated":"2024-05-31T17:50:01Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2024-05-31T17:50:01Z","title":"Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.21060","snapshot_observed_at":"2026-08-15T22:12:58.593710Z","title":"Transformers are ssms: Generalized models and efficient algorithms through structured state space duality, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.07793","last_updated":"2025-09-08T20:57:22Z","snapshot_observed_at":"2026-08-15T22:05:45.876584Z","submitted_at":"2025-05-12T17:45:05Z","title":"Overflow Prevention Enhances Long-Context Recurrent LLMs","version":2},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-08-15T22:12:58.593710Z"},"links":{"cited_paper":"/paper/2405.21060","citing_paper":"/paper/2505.07793"},"observation_digest":"sha256:289e67bb8f71e12f8af0a1009a01fed5b920234cf61ff0f2b81fc38c96ae1e6e","observation_id":"881262d1-bd88-4dff-ade7-61bc6f8968c3","resolution":{"observed_at":"2026-08-15T22:12:58.593710Z","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":"2026-08-15T22:13:00.360792Z","title":"Griffin: Mixing gated linear recurrences with local attention for efficient language models","venue":null,"work_id":"4f2be4e3-80ce-446f-ad02-a225c7f69d66","year":2024},"citing_paper":{"arxiv_id":"2505.07793","last_updated":"2025-09-08T20:57:22Z","snapshot_observed_at":"2026-08-15T22:05:45.876584Z","submitted_at":"2025-05-12T17:45:05Z","title":"Overflow Prevention Enhances Long-Context Recurrent LLMs","version":2},"reference_index":15,"source":"arxiv_source","source_observed_at":"2026-08-15T22:12:58.598791Z"},"links":{"citing_paper":"/paper/2505.07793"},"observation_digest":"sha256:31b4151536b8ef579b35f6b47d6406330cd9ea287c2afbb6bac79ef0e11ce387","observation_id":"0ea116d7-d3d1-4387-9a0a-78e9ff8ada20","resolution":{"observed_at":"2026-08-15T22:13:00.366558Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2411.13676","last_updated":"2024-11-20T19:51:25Z","snapshot_observed_at":"2026-08-14T20:26:44.533985Z","submitted_at":"2024-11-20T19:51:25Z","title":"Hymba: A Hybrid-head Architecture for Small Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2411.13676","snapshot_observed_at":"2026-08-15T22:12:58.603905Z","title":"Hymba: A hybrid-head architecture for small language models, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.07793","last_updated":"2025-09-08T20:57:22Z","snapshot_observed_at":"2026-08-15T22:05:45.876584Z","submitted_at":"2025-05-12T17:45:05Z","title":"Overflow Prevention Enhances Long-Context Recurrent LLMs","version":2},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-08-15T22:12:58.603905Z"},"links":{"cited_paper":"/paper/2411.13676","citing_paper":"/paper/2505.07793"},"observation_digest":"sha256:609a9746f2dc1e233ffc7552c5a2ed28b8ca15003550da7a35f3b76c9fb06d9c","observation_id":"6e606860-5a8e-47d4-8058-ddd8ec7e988d","resolution":{"observed_at":"2026-08-15T22:12:58.603905Z","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":"2026-08-15T22:13:00.343426Z","title":"Vision- RWKV : Efficient and scalable visual perception with RWKV -like architectures","venue":null,"work_id":"898cb9e3-02e1-4879-b112-1d26e4b6b0f7","year":2025},"citing_paper":{"arxiv_id":"2505.07793","last_updated":"2025-09-08T20:57:22Z","snapshot_observed_at":"2026-08-15T22:05:45.876584Z","submitted_at":"2025-05-12T17:45:05Z","title":"Overflow Prevention Enhances Long-Context Recurrent LLMs","version":2},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-08-15T22:12:58.609334Z"},"links":{"citing_paper":"/paper/2505.07793"},"observation_digest":"sha256:1e4f7457709105f65dbd4006d05b17ce644fcfa1fd23f206b31030e1f6fa802f","observation_id":"ce9883a9-7a7d-4620-8427-3f8284b692ae","resolution":{"observed_at":"2026-08-15T22:13:00.348686Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2404.04478","last_updated":"2024-04-06T02:54:35Z","snapshot_observed_at":"2026-08-16T14:03:12.578100Z","submitted_at":"2024-04-06T02:54:35Z","title":"Diffusion-RWKV: Scaling RWKV-Like Architectures for Diffusion Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.04478","snapshot_observed_at":"2026-08-15T22:12:58.615329Z","title":"Diffusion-rwkv: Scaling rwkv-like architectures for diffusion models","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.07793","last_updated":"2025-09-08T20:57:22Z","snapshot_observed_at":"2026-08-15T22:05:45.876584Z","submitted_at":"2025-05-12T17:45:05Z","title":"Overflow Prevention Enhances Long-Context Recurrent LLMs","version":2},"reference_index":18,"source":"arxiv_source","source_observed_at":"2026-08-15T22:12:58.615329Z"},"links":{"cited_paper":"/paper/2404.04478","citing_paper":"/paper/2505.07793"},"observation_digest":"sha256:edd4063c6ac46b9c5ca877c651cb6fbe8655cb7f2d7fe3dc8e36a5345b855feb","observation_id":"80bbf0d6-e2da-4dd6-bd91-b16bab1ff352","resolution":{"observed_at":"2026-08-15T22:12:58.615329Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2109.10086","last_updated":"2021-09-21T10:43:42Z","snapshot_observed_at":"2026-08-16T17:55:10.471708Z","submitted_at":"2021-09-21T10:43:42Z","title":"SPLADE v2: Sparse Lexical and Expansion Model for Information Retrieval","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2109.10086","snapshot_observed_at":"2026-08-15T22:12:58.621761Z","title":"Splade v2: Sparse lexical and expansion model for information retrieval, 2021","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.07793","last_updated":"2025-09-08T20:57:22Z","snapshot_observed_at":"2026-08-15T22:05:45.876584Z","submitted_at":"2025-05-12T17:45:05Z","title":"Overflow Prevention Enhances Long-Context Recurrent LLMs","version":2},"reference_index":19,"source":"arxiv_source","source_observed_at":"2026-08-15T22:12:58.621761Z"},"links":{"cited_paper":"/paper/2109.10086","citing_paper":"/paper/2505.07793"},"observation_digest":"sha256:e7ee9cef428c39d257a8fc3cce50d9ca314e518dc140fed9ab65d3e11ae4a4f4","observation_id":"b351cdcb-7b58-47ed-9fe8-494dc50a4d06","resolution":{"observed_at":"2026-08-15T22:12:58.621761Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2312.00752","last_updated":"2024-05-31T17:55:27Z","snapshot_observed_at":"2026-08-17T20:47:46.242385Z","submitted_at":"2023-12-01T18:01:34Z","title":"Mamba: Linear-Time Sequence Modeling with Selective State Spaces","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2312.00752","snapshot_observed_at":"2026-08-15T22:12:58.627911Z","title":"Mamba: Linear-time sequence modeling with selective state spaces, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.07793","last_updated":"2025-09-08T20:57:22Z","snapshot_observed_at":"2026-08-15T22:05:45.876584Z","submitted_at":"2025-05-12T17:45:05Z","title":"Overflow Prevention Enhances Long-Context Recurrent LLMs","version":2},"reference_index":20,"source":"arxiv_source","source_observed_at":"2026-08-15T22:12:58.627911Z"},"links":{"cited_paper":"/paper/2312.00752","citing_paper":"/paper/2505.07793"},"observation_digest":"sha256:8a35c3e4393f5eb515446b8f376b42e75118b0255c80cc9dd440917934d82085","observation_id":"449eee57-050c-4e4b-b90d-96059b3a6543","resolution":{"observed_at":"2026-08-15T22:12:58.627911Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2111.00396","last_updated":"2022-08-05T17:54:38Z","snapshot_observed_at":"2026-08-14T01:02:41.198730Z","submitted_at":"2021-10-31T03:32:18Z","title":"Efficiently Modeling Long Sequences with Structured State Spaces","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2111.00396","snapshot_observed_at":"2026-08-15T22:12:58.633293Z","title":"Efficiently modeling long sequences with structured state spaces","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.07793","last_updated":"2025-09-08T20:57:22Z","snapshot_observed_at":"2026-08-15T22:05:45.876584Z","submitted_at":"2025-05-12T17:45:05Z","title":"Overflow Prevention Enhances Long-Context Recurrent LLMs","version":2},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-08-15T22:12:58.633293Z"},"links":{"cited_paper":"/paper/2111.00396","citing_paper":"/paper/2505.07793"},"observation_digest":"sha256:8c328ff1c1935a607b5cbf253e8cde3bd8aacb388a3bdb1530f8dda807f57042","observation_id":"182f5b51-3219-45e4-bfce-2c7c847df317","resolution":{"observed_at":"2026-08-15T22:12:58.633293Z","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":"2026-08-15T22:12:58.638417Z","title":"Combining recurrent, convolutional, and continuous-time models with linear state space layers","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.07793","last_updated":"2025-09-08T20:57:22Z","snapshot_observed_at":"2026-08-15T22:05:45.876584Z","submitted_at":"2025-05-12T17:45:05Z","title":"Overflow Prevention Enhances Long-Context Recurrent LLMs","version":2},"reference_index":22,"source":"arxiv_source","source_observed_at":"2026-08-15T22:12:58.638417Z"},"links":{"citing_paper":"/paper/2505.07793"},"observation_digest":"sha256:04ace58927a51ed792266142747a0ec9f4f23b0611c18a07ad2ec8ebbcaa3cac","observation_id":"49132fa4-12c5-430f-a3ef-16bec7bb9494","resolution":{"observed_at":"2026-08-15T22:12:58.638417Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2002.08909","last_updated":"2020-02-10T18:40:59Z","snapshot_observed_at":"2026-08-02T17:52:27.326803Z","submitted_at":"2020-02-10T18:40:59Z","title":"REALM: Retrieval-Augmented Language Model Pre-Training","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2002.08909","snapshot_observed_at":"2026-08-15T22:12:58.643502Z","title":"Realm: Retrieval-augmented language model pre-training, 2020","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2505.07793","last_updated":"2025-09-08T20:57:22Z","snapshot_observed_at":"2026-08-15T22:05:45.876584Z","submitted_at":"2025-05-12T17:45:05Z","title":"Overflow Prevention Enhances Long-Context Recurrent LLMs","version":2},"reference_index":23,"source":"arxiv_source","source_observed_at":"2026-08-15T22:12:58.643502Z"},"links":{"cited_paper":"/paper/2002.08909","citing_paper":"/paper/2505.07793"},"observation_digest":"sha256:53da3f56f2b4c25ed1d41cbe120b97b4fb9b20bcb7e83c20472970df4517d628","observation_id":"3763739d-26ce-4c6e-ad50-a6257f39e03b","resolution":{"observed_at":"2026-08-15T22:12:58.643502Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.08083","last_updated":"2025-03-25T17:54:37Z","snapshot_observed_at":"2026-08-17T00:22:29.770355Z","submitted_at":"2024-07-10T23:02:45Z","title":"MambaVision: A Hybrid Mamba-Transformer Vision Backbone","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.08083","snapshot_observed_at":"2026-08-15T22:12:58.648734Z","title":"Mambavision: A hybrid mamba-transformer vision backbone","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.07793","last_updated":"2025-09-08T20:57:22Z","snapshot_observed_at":"2026-08-15T22:05:45.876584Z","submitted_at":"2025-05-12T17:45:05Z","title":"Overflow Prevention Enhances Long-Context Recurrent LLMs","version":2},"reference_index":24,"source":"arxiv_source","source_observed_at":"2026-08-15T22:12:58.648734Z"},"links":{"cited_paper":"/paper/2407.08083","citing_paper":"/paper/2505.07793"},"observation_digest":"sha256:f8eef7b7d52e1f59923bc64482929a01ab643a6a6001aff2c7fadb9a0fd133f7","observation_id":"8c1e4821-50b5-49b4-9548-d19919123c3f","resolution":{"observed_at":"2026-08-15T22:12:58.648734Z","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":"2026-08-15T22:13:00.315634Z","title":"Decision mamba: Reinforcement learning via hybrid selective sequence modeling","venue":null,"work_id":"4a97316f-5248-463c-aff5-3e95a5aeab90","year":2024},"citing_paper":{"arxiv_id":"2505.07793","last_updated":"2025-09-08T20:57:22Z","snapshot_observed_at":"2026-08-15T22:05:45.876584Z","submitted_at":"2025-05-12T17:45:05Z","title":"Overflow Prevention Enhances Long-Context Recurrent LLMs","version":2},"reference_index":25,"source":"arxiv_source","source_observed_at":"2026-08-15T22:12:58.654245Z"},"links":{"citing_paper":"/paper/2505.07793"},"observation_digest":"sha256:915c1b2c717a161da67b9224d379481b403dcd90c7fcc1ef09ed6a2814ff8253","observation_id":"ffd4e0da-cec9-4412-b236-f115483feebf","resolution":{"observed_at":"2026-08-15T22:13:00.321441Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2112.09118","last_updated":"2022-08-29T12:17:32Z","snapshot_observed_at":"2026-08-14T13:19:21.742321Z","submitted_at":"2021-12-16T18:57:37Z","title":"Unsupervised Dense Information Retrieval with Contrastive Learning","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2112.09118","snapshot_observed_at":"2026-08-15T22:12:58.659471Z","title":"Unsupervised dense information retrieval with contrastive learning, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.07793","last_updated":"2025-09-08T20:57:22Z","snapshot_observed_at":"2026-08-15T22:05:45.876584Z","submitted_at":"2025-05-12T17:45:05Z","title":"Overflow Prevention Enhances Long-Context Recurrent LLMs","version":2},"reference_index":26,"source":"arxiv_source","source_observed_at":"2026-08-15T22:12:58.659471Z"},"links":{"cited_paper":"/paper/2112.09118","citing_paper":"/paper/2505.07793"},"observation_digest":"sha256:da718f128f4dd8e56e64053026cce952b8e14c27c41cf582e09de70281c6a84e","observation_id":"50a936a4-3ad9-4a8e-882f-196feba9d329","resolution":{"observed_at":"2026-08-15T22:12:58.659471Z","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":"2026-08-15T22:12:58.664675Z","title":"How can we know when language models know? on the calibration of language models for question answering","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.07793","last_updated":"2025-09-08T20:57:22Z","snapshot_observed_at":"2026-08-15T22:05:45.876584Z","submitted_at":"2025-05-12T17:45:05Z","title":"Overflow Prevention Enhances Long-Context Recurrent LLMs","version":2},"reference_index":27,"source":"arxiv_source","source_observed_at":"2026-08-15T22:12:58.664675Z"},"links":{"citing_paper":"/paper/2505.07793"},"observation_digest":"sha256:54fb54d8fce1f6bd348c2c5bde0e95e9fbcd81213f060fe5d5fb89a05f827b51","observation_id":"32d62886-2f3b-4408-978b-04702d88eb31","resolution":{"observed_at":"2026-08-15T22:12:58.664675Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2004.04906","last_updated":"2020-09-30T21:27:13Z","snapshot_observed_at":"2026-07-06T09:11:26.109763Z","submitted_at":"2020-04-10T04:53:17Z","title":"Dense Passage Retrieval for Open-Domain Question Answering","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2004.04906","snapshot_observed_at":"2026-08-15T22:12:58.669718Z","title":"Dense passage retrieval for open-domain question answering, 2020","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2505.07793","last_updated":"2025-09-08T20:57:22Z","snapshot_observed_at":"2026-08-15T22:05:45.876584Z","submitted_at":"2025-05-12T17:45:05Z","title":"Overflow Prevention Enhances Long-Context Recurrent LLMs","version":2},"reference_index":28,"source":"arxiv_source","source_observed_at":"2026-08-15T22:12:58.669718Z"},"links":{"cited_paper":"/paper/2004.04906","citing_paper":"/paper/2505.07793"},"observation_digest":"sha256:46c79094d4110ae42b7cccf0c75f5db4138642e5fe61b4b069d0e986d1046808","observation_id":"6f87bfa6-3970-453a-a13b-cd526de9275b","resolution":{"observed_at":"2026-08-15T22:12:58.669718Z","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":"2026-08-15T22:12:58.675029Z","title":"The impact of positional encoding on length generalization in transformers","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.07793","last_updated":"2025-09-08T20:57:22Z","snapshot_observed_at":"2026-08-15T22:05:45.876584Z","submitted_at":"2025-05-12T17:45:05Z","title":"Overflow Prevention Enhances Long-Context Recurrent LLMs","version":2},"reference_index":29,"source":"arxiv_source","source_observed_at":"2026-08-15T22:12:58.675029Z"},"links":{"citing_paper":"/paper/2505.07793"},"observation_digest":"sha256:f6ac1d3e19aae55b0057145ff158f8879d571eda0c3b647ba122e3749eacfdd5","observation_id":"a9e16972-cffe-4aa4-b133-070f4a25239f","resolution":{"observed_at":"2026-08-15T22:12:58.675029Z","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":"2026-08-15T22:13:00.276488Z","title":null,"venue":null,"work_id":"4d4e792d-2d64-4d78-b886-56988fcfa478","year":2025},"citing_paper":{"arxiv_id":"2505.07793","last_updated":"2025-09-08T20:57:22Z","snapshot_observed_at":"2026-08-15T22:05:45.876584Z","submitted_at":"2025-05-12T17:45:05Z","title":"Overflow Prevention Enhances Long-Context Recurrent LLMs","version":2},"reference_index":30,"source":"arxiv_source","source_observed_at":"2026-08-15T22:12:58.680109Z"},"links":{"citing_paper":"/paper/2505.07793"},"observation_digest":"sha256:6a787ed2b38d46f3dc5ea62c513177f5dc495275de7834a6a595dfc7bc7571c7","observation_id":"f3afb10a-87c2-4492-b0e5-dffe6f301edd","resolution":{"observed_at":"2026-08-15T22:13:00.281901Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T22:12:58.685461Z","title":"Fast inference from transformers via speculative decoding","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.07793","last_updated":"2025-09-08T20:57:22Z","snapshot_observed_at":"2026-08-15T22:05:45.876584Z","submitted_at":"2025-05-12T17:45:05Z","title":"Overflow Prevention Enhances Long-Context Recurrent LLMs","version":2},"reference_index":31,"source":"arxiv_source","source_observed_at":"2026-08-15T22:12:58.685461Z"},"links":{"citing_paper":"/paper/2505.07793"},"observation_digest":"sha256:1ebee8ec3fd68a504670ac3d2e34ecf2a60353f1b369a31650993d7cbd1ed814","observation_id":"9ec3d2bb-a164-47b0-84b6-42cc42036f05","resolution":{"observed_at":"2026-08-15T22:12:58.685461Z","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":"2026-08-15T22:12:58.691554Z","title":"u ttler, Mike Lewis, Wen-tau Yih, Tim Rockt \\","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2505.07793","last_updated":"2025-09-08T20:57:22Z","snapshot_observed_at":"2026-08-15T22:05:45.876584Z","submitted_at":"2025-05-12T17:45:05Z","title":"Overflow Prevention Enhances Long-Context Recurrent LLMs","version":2},"reference_index":32,"source":"arxiv_source","source_observed_at":"2026-08-15T22:12:58.691554Z"},"links":{"citing_paper":"/paper/2505.07793"},"observation_digest":"sha256:49720197b06fc107ff61f7671004df5f9e10f36ae8256c03e3ad1bc1bf91a318","observation_id":"84c50146-89a6-4254-a105-37b8c9250067","resolution":{"observed_at":"2026-08-15T22:12:58.691554Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.16833","last_updated":"2024-10-17T17:51:19Z","snapshot_observed_at":"2026-08-16T17:26:10.069691Z","submitted_at":"2024-07-23T20:51:52Z","title":"Retrieval Augmented Generation or Long-Context LLMs? A Comprehensive Study and Hybrid Approach","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.16833","snapshot_observed_at":"2026-08-15T22:12:58.696984Z","title":"Retrieval augmented generation or long-context llms? a comprehensive study and hybrid approach, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.07793","last_updated":"2025-09-08T20:57:22Z","snapshot_observed_at":"2026-08-15T22:05:45.876584Z","submitted_at":"2025-05-12T17:45:05Z","title":"Overflow Prevention Enhances Long-Context Recurrent LLMs","version":2},"reference_index":33,"source":"arxiv_source","source_observed_at":"2026-08-15T22:12:58.696984Z"},"links":{"cited_paper":"/paper/2407.16833","citing_paper":"/paper/2505.07793"},"observation_digest":"sha256:fbb0afd9d745abac633f322a8c2f2c1dbc6dc3819dae816c9d857d7ffb32cf07","observation_id":"fb37c494-bfb6-41de-baab-91b2ccb8d165","resolution":{"observed_at":"2026-08-15T22:12:58.696984Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2302.07452","last_updated":"2023-02-15T03:53:26Z","snapshot_observed_at":"2026-08-16T15:55:15.598559Z","submitted_at":"2023-02-15T03:53:26Z","title":"How to Train Your DRAGON: Diverse Augmentation Towards Generalizable Dense Retrieval","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2302.07452","snapshot_observed_at":"2026-08-15T22:12:58.703439Z","title":"How to train your dragon: Diverse augmentation towards generalizable dense retrieval, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.07793","last_updated":"2025-09-08T20:57:22Z","snapshot_observed_at":"2026-08-15T22:05:45.876584Z","submitted_at":"2025-05-12T17:45:05Z","title":"Overflow Prevention Enhances Long-Context Recurrent LLMs","version":2},"reference_index":34,"source":"arxiv_source","source_observed_at":"2026-08-15T22:12:58.703439Z"},"links":{"cited_paper":"/paper/2302.07452","citing_paper":"/paper/2505.07793"},"observation_digest":"sha256:68b190876e8945d9137337c948f3792ea15c3660363b4364d8cebaf942f8041b","observation_id":"2887e506-eb6a-4fb8-8a32-daf36d390d8e","resolution":{"observed_at":"2026-08-15T22:12:58.703439Z","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":"2026-08-15T22:13:00.235526Z","title":"Lost in the middle: How language models use long contexts","venue":null,"work_id":"03ab6b7f-9024-467b-8637-739f45411d29","year":2024},"citing_paper":{"arxiv_id":"2505.07793","last_updated":"2025-09-08T20:57:22Z","snapshot_observed_at":"2026-08-15T22:05:45.876584Z","submitted_at":"2025-05-12T17:45:05Z","title":"Overflow Prevention Enhances Long-Context Recurrent LLMs","version":2},"reference_index":35,"source":"arxiv_source","source_observed_at":"2026-08-15T22:12:58.708903Z"},"links":{"citing_paper":"/paper/2505.07793"},"observation_digest":"sha256:b9e3dec3bfbea75ef172f08637ac62870f6aea8e3fe4f1f89a633ac66a4b7d0f","observation_id":"79d1744f-60f6-4cd3-a592-e0ef00c04ed8","resolution":{"observed_at":"2026-08-15T22:13:00.241035Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2401.10166","last_updated":"2024-12-29T14:57:13Z","snapshot_observed_at":"2026-08-17T14:56:56.233298Z","submitted_at":"2024-01-18T17:55:39Z","title":"VMamba: Visual State Space Model","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.10166","snapshot_observed_at":"2026-08-15T22:12:58.714073Z","title":"Vmamba: Visual state space model","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.07793","last_updated":"2025-09-08T20:57:22Z","snapshot_observed_at":"2026-08-15T22:05:45.876584Z","submitted_at":"2025-05-12T17:45:05Z","title":"Overflow Prevention Enhances Long-Context Recurrent LLMs","version":2},"reference_index":36,"source":"arxiv_source","source_observed_at":"2026-08-15T22:12:58.714073Z"},"links":{"cited_paper":"/paper/2401.10166","citing_paper":"/paper/2505.07793"},"observation_digest":"sha256:cad4d3608d8416366efeec892906c3ad1343fa79ce025a50119fa1304c4f552b","observation_id":"c30582fe-b4a1-428f-b9f0-f85452f807e0","resolution":{"observed_at":"2026-08-15T22:12:58.714073Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2305.14952","last_updated":"2023-10-18T11:24:31Z","snapshot_observed_at":"2026-08-17T15:00:16.332468Z","submitted_at":"2023-05-24T09:42:30Z","title":"Focus Your Attention (with Adaptive IIR Filters)","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.14952","snapshot_observed_at":"2026-08-15T22:12:58.720528Z","title":"Focus your attention (with adaptive iir filters)","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.07793","last_updated":"2025-09-08T20:57:22Z","snapshot_observed_at":"2026-08-15T22:05:45.876584Z","submitted_at":"2025-05-12T17:45:05Z","title":"Overflow Prevention Enhances Long-Context Recurrent LLMs","version":2},"reference_index":37,"source":"arxiv_source","source_observed_at":"2026-08-15T22:12:58.720528Z"},"links":{"cited_paper":"/paper/2305.14952","citing_paper":"/paper/2505.07793"},"observation_digest":"sha256:c0beed56f207d38b0392bd5c3b06c57fb0fb76fd11b1ed54b6f936e1a262f18c","observation_id":"d7b8cd42-20bd-4c66-b037-9876bd7b165a","resolution":{"observed_at":"2026-08-15T22:12:58.720528Z","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":"2026-08-15T22:12:58.725834Z","title":"Decision mamba: A multi-grained state space model with self-evolution regularization for offline rl","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.07793","last_updated":"2025-09-08T20:57:22Z","snapshot_observed_at":"2026-08-15T22:05:45.876584Z","submitted_at":"2025-05-12T17:45:05Z","title":"Overflow Prevention Enhances Long-Context Recurrent LLMs","version":2},"reference_index":38,"source":"arxiv_source","source_observed_at":"2026-08-15T22:12:58.725834Z"},"links":{"citing_paper":"/paper/2505.07793"},"observation_digest":"sha256:2c63aaa076e35fde05f5bc9d5d86cc830e4e3e434c53bb185e30c482bedf7012","observation_id":"6c4892c3-29cf-47c2-a24f-f1cee9a2a9f1","resolution":{"observed_at":"2026-08-15T22:12:58.725834Z","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":"2026-08-15T22:13:00.207004Z","title":"Uncertainty estimation in autoregressive structured prediction","venue":null,"work_id":"4f7315c5-e00e-4c1f-961c-41ced52bd19b","year":null},"citing_paper":{"arxiv_id":"2505.07793","last_updated":"2025-09-08T20:57:22Z","snapshot_observed_at":"2026-08-15T22:05:45.876584Z","submitted_at":"2025-05-12T17:45:05Z","title":"Overflow Prevention Enhances Long-Context Recurrent LLMs","version":2},"reference_index":39,"source":"arxiv_source","source_observed_at":"2026-08-15T22:12:58.731187Z"},"links":{"citing_paper":"/paper/2505.07793"},"observation_digest":"sha256:517389879c95227192b77e95c2f56a518cea5e2e1bc6293b4f7a6997defdc8f5","observation_id":"c69a663b-8271-4260-9d85-f259857943ed","resolution":{"observed_at":"2026-08-15T22:13:00.212609Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1609.07843","last_updated":"2016-09-26T04:06:13Z","snapshot_observed_at":"2026-08-17T01:46:43.513643Z","submitted_at":"2016-09-26T04:06:13Z","title":"Pointer Sentinel Mixture Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1609.07843","snapshot_observed_at":"2026-08-15T22:12:58.736403Z","title":"Pointer sentinel mixture models, 2016","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2505.07793","last_updated":"2025-09-08T20:57:22Z","snapshot_observed_at":"2026-08-15T22:05:45.876584Z","submitted_at":"2025-05-12T17:45:05Z","title":"Overflow Prevention Enhances Long-Context Recurrent LLMs","version":2},"reference_index":40,"source":"arxiv_source","source_observed_at":"2026-08-15T22:12:58.736403Z"},"links":{"cited_paper":"/paper/1609.07843","citing_paper":"/paper/2505.07793"},"observation_digest":"sha256:b292a87e7653bf705b1605e8e900ec01bd5184e4049a09acb68e8a4097d756fd","observation_id":"25db02cb-621f-41b4-ac4b-3868f7828e7b","resolution":{"observed_at":"2026-08-15T22:12:58.736403Z","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":"2026-08-15T22:13:00.189936Z","title":"Exploring the capability of mamba in speech applications","venue":null,"work_id":"3f4d3512-e51f-4438-8060-e456a551d00e","year":2024},"citing_paper":{"arxiv_id":"2505.07793","last_updated":"2025-09-08T20:57:22Z","snapshot_observed_at":"2026-08-15T22:05:45.876584Z","submitted_at":"2025-05-12T17:45:05Z","title":"Overflow Prevention Enhances Long-Context Recurrent LLMs","version":2},"reference_index":41,"source":"arxiv_source","source_observed_at":"2026-08-15T22:12:58.741949Z"},"links":{"citing_paper":"/paper/2505.07793"},"observation_digest":"sha256:dd61e6aecc5350ab58f42a0143582b8455789d83c9e657aec18311cfd6a6c688","observation_id":"bcf3eacd-f84a-48e2-a2da-4b2dd547ed0e","resolution":{"observed_at":"2026-08-15T22:13:00.195669Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2305.16300","last_updated":"2023-11-20T01:16:17Z","snapshot_observed_at":"2026-08-16T15:29:36.507599Z","submitted_at":"2023-05-25T17:53:42Z","title":"Landmark Attention: Random-Access Infinite Context Length for Transformers","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.16300","snapshot_observed_at":"2026-08-15T22:12:58.747002Z","title":"Landmark attention: Random-access infinite context length for transformers","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.07793","last_updated":"2025-09-08T20:57:22Z","snapshot_observed_at":"2026-08-15T22:05:45.876584Z","submitted_at":"2025-05-12T17:45:05Z","title":"Overflow Prevention Enhances Long-Context Recurrent LLMs","version":2},"reference_index":42,"source":"arxiv_source","source_observed_at":"2026-08-15T22:12:58.747002Z"},"links":{"cited_paper":"/paper/2305.16300","citing_paper":"/paper/2505.07793"},"observation_digest":"sha256:61117371d777e072028e2b6309315d16227e71de30a697429c45e4bdae4de171","observation_id":"2700c30d-4aed-491a-b41a-302523373e40","resolution":{"observed_at":"2026-08-15T22:12:58.747002Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2112.09332","last_updated":"2022-06-01T19:08:11Z","snapshot_observed_at":"2026-08-07T17:14:39.278754Z","submitted_at":"2021-12-17T05:43:43Z","title":"WebGPT: Browser-assisted question-answering with human feedback","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2112.09332","snapshot_observed_at":"2026-08-15T22:12:58.752185Z","title":"Webgpt: Browser-assisted question-answering with human feedback, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.07793","last_updated":"2025-09-08T20:57:22Z","snapshot_observed_at":"2026-08-15T22:05:45.876584Z","submitted_at":"2025-05-12T17:45:05Z","title":"Overflow Prevention Enhances Long-Context Recurrent LLMs","version":2},"reference_index":43,"source":"arxiv_source","source_observed_at":"2026-08-15T22:12:58.752185Z"},"links":{"cited_paper":"/paper/2112.09332","citing_paper":"/paper/2505.07793"},"observation_digest":"sha256:d3d07247dc7f4b15d05f89a5c7065e1db94447c75d936340ec0eb21d9ae20e1f","observation_id":"215a8d3b-99bc-4f36-a256-828bd76566e3","resolution":{"observed_at":"2026-08-15T22:12:58.752185Z","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":"2026-08-15T22:13:00.172575Z","title":"Revisiting associative recall in modern recurrent models","venue":null,"work_id":"af5c2d73-f2f6-426c-beb6-ee821b3a3c63","year":2025},"citing_paper":{"arxiv_id":"2505.07793","last_updated":"2025-09-08T20:57:22Z","snapshot_observed_at":"2026-08-15T22:05:45.876584Z","submitted_at":"2025-05-12T17:45:05Z","title":"Overflow Prevention Enhances Long-Context Recurrent LLMs","version":2},"reference_index":44,"source":"arxiv_source","source_observed_at":"2026-08-15T22:12:58.757411Z"},"links":{"citing_paper":"/paper/2505.07793"},"observation_digest":"sha256:2d08b279580701c51371ec0269d68eb04c6d1d17e9f8b8f71108b5116a10b7f0","observation_id":"bc012940-b9ea-4361-aeb8-67e81ff403a7","resolution":{"observed_at":"2026-08-15T22:13:00.178065Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2209.11895","last_updated":"2022-09-24T00:43:19Z","snapshot_observed_at":"2026-08-15T09:43:59.961298Z","submitted_at":"2022-09-24T00:43:19Z","title":"In-context Learning and Induction Heads","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2209.11895","snapshot_observed_at":"2026-08-15T22:12:58.762353Z","title":"In-context learning and induction heads, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.07793","last_updated":"2025-09-08T20:57:22Z","snapshot_observed_at":"2026-08-15T22:05:45.876584Z","submitted_at":"2025-05-12T17:45:05Z","title":"Overflow Prevention Enhances Long-Context Recurrent LLMs","version":2},"reference_index":45,"source":"arxiv_source","source_observed_at":"2026-08-15T22:12:58.762353Z"},"links":{"cited_paper":"/paper/2209.11895","citing_paper":"/paper/2505.07793"},"observation_digest":"sha256:a95a97b88e0037fe516cd12d481ac2e659c4b1156d13c16cac0f3adef7bf6675","observation_id":"b8181110-ec9c-45e9-93a1-d1b30dc4dfe5","resolution":{"observed_at":"2026-08-15T22:12:58.762353Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2303.06349","last_updated":"2023-03-11T08:53:11Z","snapshot_observed_at":"2026-08-16T15:49:03.576715Z","submitted_at":"2023-03-11T08:53:11Z","title":"Resurrecting Recurrent Neural Networks for Long Sequences","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2303.06349","snapshot_observed_at":"2026-08-15T22:12:58.767596Z","title":"Resurrecting recurrent neural networks for long sequences, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.07793","last_updated":"2025-09-08T20:57:22Z","snapshot_observed_at":"2026-08-15T22:05:45.876584Z","submitted_at":"2025-05-12T17:45:05Z","title":"Overflow Prevention Enhances Long-Context Recurrent LLMs","version":2},"reference_index":46,"source":"arxiv_source","source_observed_at":"2026-08-15T22:12:58.767596Z"},"links":{"cited_paper":"/paper/2303.06349","citing_paper":"/paper/2505.07793"},"observation_digest":"sha256:2b82aa891dc550ceb6678e6624afcbc0fb5ea899a71a9461ffe434d0095c8f6f","observation_id":"6006f1e1-4b2b-43e4-82f9-dcc8d0ad8aff","resolution":{"observed_at":"2026-08-15T22:12:58.767596Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2305.13048","last_updated":"2023-12-11T03:58:56Z","snapshot_observed_at":"2026-08-17T17:58:38.402665Z","submitted_at":"2023-05-22T13:57:41Z","title":"RWKV: Reinventing RNNs for the Transformer Era","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.13048","snapshot_observed_at":"2026-08-15T22:12:58.772906Z","title":"Rwkv: Reinventing rnns for the transformer era","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.07793","last_updated":"2025-09-08T20:57:22Z","snapshot_observed_at":"2026-08-15T22:05:45.876584Z","submitted_at":"2025-05-12T17:45:05Z","title":"Overflow Prevention Enhances Long-Context Recurrent LLMs","version":2},"reference_index":47,"source":"arxiv_source","source_observed_at":"2026-08-15T22:12:58.772906Z"},"links":{"cited_paper":"/paper/2305.13048","citing_paper":"/paper/2505.07793"},"observation_digest":"sha256:cbb064731cf8506cad5b19c1a0a657065d627d73885969d2de977d5a8380a077","observation_id":"6416ef27-f4c2-497e-aa3d-1bb2432d77dd","resolution":{"observed_at":"2026-08-15T22:12:58.772906Z","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":"2026-08-15T22:12:58.777736Z","title":"Eagle and finch: RWKV with matrix-valued states and dynamic recurrence","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.07793","last_updated":"2025-09-08T20:57:22Z","snapshot_observed_at":"2026-08-15T22:05:45.876584Z","submitted_at":"2025-05-12T17:45:05Z","title":"Overflow Prevention Enhances Long-Context Recurrent LLMs","version":2},"reference_index":48,"source":"arxiv_source","source_observed_at":"2026-08-15T22:12:58.777736Z"},"links":{"citing_paper":"/paper/2505.07793"},"observation_digest":"sha256:81a578ae8cc00e7492a1fe3c55f98a12fbd61ce9318adc44e53a81dfca47a2f8","observation_id":"6d309fdc-8af8-4738-b243-784335ffa6b6","resolution":{"observed_at":"2026-08-15T22:12:58.777736Z","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":"2026-08-15T22:13:00.143050Z","title":"Mechanistic design and scaling of hybrid architectures","venue":null,"work_id":"079fb285-7e3d-45d7-b768-77313558acdd","year":2024},"citing_paper":{"arxiv_id":"2505.07793","last_updated":"2025-09-08T20:57:22Z","snapshot_observed_at":"2026-08-15T22:05:45.876584Z","submitted_at":"2025-05-12T17:45:05Z","title":"Overflow Prevention Enhances Long-Context Recurrent LLMs","version":2},"reference_index":49,"source":"arxiv_source","source_observed_at":"2026-08-15T22:12:58.782473Z"},"links":{"citing_paper":"/paper/2505.07793"},"observation_digest":"sha256:fea14faad56bdd3442aaf21f2299e3e2352ade7d172b68fb42635f47fbdbd7cf","observation_id":"44be2798-a1f5-4b1b-8fd4-0cdbfa50a484","resolution":{"observed_at":"2026-08-15T22:13:00.148466Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T22:13:00.125353Z","title":"Train short, test long: Attention with linear biases enables input length extrapolation","venue":null,"work_id":"3e792913-4dae-44cc-9a52-2df52202d367","year":2021},"citing_paper":{"arxiv_id":"2505.07793","last_updated":"2025-09-08T20:57:22Z","snapshot_observed_at":"2026-08-15T22:05:45.876584Z","submitted_at":"2025-05-12T17:45:05Z","title":"Overflow Prevention Enhances Long-Context Recurrent LLMs","version":2},"reference_index":50,"source":"arxiv_source","source_observed_at":"2026-08-15T22:12:58.787919Z"},"links":{"citing_paper":"/paper/2505.07793"},"observation_digest":"sha256:e99f7d5052c424cb905f4419a8dcc549daca9ec0a641b4f47557aa5039b5973f","observation_id":"860c4099-159e-454e-b869-39cc05f56cfd","resolution":{"observed_at":"2026-08-15T22:13:00.130953Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2306.17563","last_updated":"2024-03-28T13:59:09Z","snapshot_observed_at":"2026-08-16T15:19:46.748918Z","submitted_at":"2023-06-30T11:32:25Z","title":"Large Language Models are Effective Text Rankers with Pairwise Ranking Prompting","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2306.17563","snapshot_observed_at":"2026-08-15T22:12:58.793431Z","title":"Large language models are effective text rankers with pairwise ranking prompting, 2024 a","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.07793","last_updated":"2025-09-08T20:57:22Z","snapshot_observed_at":"2026-08-15T22:05:45.876584Z","submitted_at":"2025-05-12T17:45:05Z","title":"Overflow Prevention Enhances Long-Context Recurrent LLMs","version":2},"reference_index":51,"source":"arxiv_source","source_observed_at":"2026-08-15T22:12:58.793431Z"},"links":{"cited_paper":"/paper/2306.17563","citing_paper":"/paper/2505.07793"},"observation_digest":"sha256:aa08236ca5bb3dff71c95fc3fa29724b98ebf0b9a676fdf0998de7ad9443f8dc","observation_id":"4e911ab0-c26a-4a45-b36a-4cc7f586817e","resolution":{"observed_at":"2026-08-15T22:12:58.793431Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2404.07904","last_updated":"2024-08-19T17:16:55Z","snapshot_observed_at":"2026-08-16T14:01:40.481918Z","submitted_at":"2024-04-11T16:43:03Z","title":"HGRN2: Gated Linear RNNs with State Expansion","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.07904","snapshot_observed_at":"2026-08-15T22:12:58.798549Z","title":"Hgrn2: Gated linear rnns with state expansion, 2024 b","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.07793","last_updated":"2025-09-08T20:57:22Z","snapshot_observed_at":"2026-08-15T22:05:45.876584Z","submitted_at":"2025-05-12T17:45:05Z","title":"Overflow Prevention Enhances Long-Context Recurrent LLMs","version":2},"reference_index":52,"source":"arxiv_source","source_observed_at":"2026-08-15T22:12:58.798549Z"},"links":{"cited_paper":"/paper/2404.07904","citing_paper":"/paper/2505.07793"},"observation_digest":"sha256:e0213e13d489af50337cf49611edc585a9a436eb80f0390223c95217e425ee9e","observation_id":"7842a8f4-3636-4956-99e9-e157b6e0d9e1","resolution":{"observed_at":"2026-08-15T22:12:58.798549Z","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":"2026-08-15T22:12:58.804302Z","title":"Exploring the limits of transfer learning with a unified text-to-text transformer","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2505.07793","last_updated":"2025-09-08T20:57:22Z","snapshot_observed_at":"2026-08-15T22:05:45.876584Z","submitted_at":"2025-05-12T17:45:05Z","title":"Overflow Prevention Enhances Long-Context Recurrent LLMs","version":2},"reference_index":53,"source":"arxiv_source","source_observed_at":"2026-08-15T22:12:58.804302Z"},"links":{"citing_paper":"/paper/2505.07793"},"observation_digest":"sha256:245d9ee4befbd48cc2809374171e8cc12e40ce0cba5211e66b592ed108222e9f","observation_id":"67422e54-4e1d-436c-9662-7108f593773f","resolution":{"observed_at":"2026-08-15T22:12:58.804302Z","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":"2026-08-15T22:13:00.095708Z","title":"Know what you don't know: Unanswerable questions for squad, 2018","venue":null,"work_id":"d92343c3-11b8-4b10-921e-6f50fcef1bde","year":2018},"citing_paper":{"arxiv_id":"2505.07793","last_updated":"2025-09-08T20:57:22Z","snapshot_observed_at":"2026-08-15T22:05:45.876584Z","submitted_at":"2025-05-12T17:45:05Z","title":"Overflow Prevention Enhances Long-Context Recurrent LLMs","version":2},"reference_index":54,"source":"arxiv_source","source_observed_at":"2026-08-15T22:12:58.809811Z"},"links":{"citing_paper":"/paper/2505.07793"},"observation_digest":"sha256:f7374d9f78cfc8a6d9444874685fae3a8d46c1a83586360f85cdee063d3cf494","observation_id":"37c8d3cb-b785-41b1-9418-1e664863964c","resolution":{"observed_at":"2026-08-15T22:13:00.101332Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2406.07522","last_updated":"2025-02-28T02:20:49Z","snapshot_observed_at":"2026-08-16T13:44:03.977685Z","submitted_at":"2024-06-11T17:50:51Z","title":"Samba: Simple Hybrid State Space Models for Efficient Unlimited Context Language Modeling","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.07522","snapshot_observed_at":"2026-08-15T22:12:58.815331Z","title":"Samba: Simple hybrid state space models for efficient unlimited context language modeling, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.07793","last_updated":"2025-09-08T20:57:22Z","snapshot_observed_at":"2026-08-15T22:05:45.876584Z","submitted_at":"2025-05-12T17:45:05Z","title":"Overflow Prevention Enhances Long-Context Recurrent LLMs","version":2},"reference_index":55,"source":"arxiv_source","source_observed_at":"2026-08-15T22:12:58.815331Z"},"links":{"cited_paper":"/paper/2406.07522","citing_paper":"/paper/2505.07793"},"observation_digest":"sha256:dff45ed22ac0c0da4ca16680eb7e36085f3f01294b53dd91a9831e2f7347c97e","observation_id":"38ed34b6-f46b-4a6b-afda-7c1dc8ab06af","resolution":{"observed_at":"2026-08-15T22:12:58.815331Z","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":"2026-08-15T22:13:00.078790Z","title":"A study of branch prediction strategies","venue":null,"work_id":"fb1fe555-4490-47bb-a98e-1518c166b4ad","year":1998},"citing_paper":{"arxiv_id":"2505.07793","last_updated":"2025-09-08T20:57:22Z","snapshot_observed_at":"2026-08-15T22:05:45.876584Z","submitted_at":"2025-05-12T17:45:05Z","title":"Overflow Prevention Enhances Long-Context Recurrent LLMs","version":2},"reference_index":56,"source":"arxiv_source","source_observed_at":"2026-08-15T22:12:58.821518Z"},"links":{"citing_paper":"/paper/2505.07793"},"observation_digest":"sha256:41a56172281ea6a8d502b6361a792c9be7bfc08c17cb2fffb934c122c9a53485","observation_id":"41015505-ee9b-4f17-8b72-c12f710dd5a6","resolution":{"observed_at":"2026-08-15T22:13:00.084217Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T22:12:58.827097Z","title":"Roformer: Enhanced transformer with rotary position embedding","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.07793","last_updated":"2025-09-08T20:57:22Z","snapshot_observed_at":"2026-08-15T22:05:45.876584Z","submitted_at":"2025-05-12T17:45:05Z","title":"Overflow Prevention Enhances Long-Context Recurrent LLMs","version":2},"reference_index":57,"source":"arxiv_source","source_observed_at":"2026-08-15T22:12:58.827097Z"},"links":{"citing_paper":"/paper/2505.07793"},"observation_digest":"sha256:ea99106ef00980adfd03ac53968c1f622efc8676474a69f7f26ba7b1cced63f9","observation_id":"da91294c-a82f-4f65-ba97-5f4eaf756049","resolution":{"observed_at":"2026-08-15T22:12:58.827097Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.04620","last_updated":"2025-08-31T18:32:59Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2024-07-05T16:23:20Z","title":"Learning to (Learn at Test Time): RNNs with Expressive Hidden States","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.04620","snapshot_observed_at":"2026-08-15T22:12:58.833037Z","title":"Learning to (learn at test time): Rnns with expressive hidden states, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2505.07793","last_updated":"2025-09-08T20:57:22Z","snapshot_observed_at":"2026-08-15T22:05:45.876584Z","submitted_at":"2025-05-12T17:45:05Z","title":"Overflow Prevention Enhances Long-Context Recurrent LLMs","version":2},"reference_index":58,"source":"arxiv_source","source_observed_at":"2026-08-15T22:12:58.833037Z"},"links":{"cited_paper":"/paper/2407.04620","citing_paper":"/paper/2505.07793"},"observation_digest":"sha256:a2271c21b6958627e15f1a516da499feb381d4e24920394b3aa5447d921803b0","observation_id":"57353519-5423-4925-b935-c0010f195994","resolution":{"observed_at":"2026-08-15T22:12:58.833037Z","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":"2026-08-15T22:13:00.046471Z","title":"The falcon 3 family of open models, December 2024","venue":null,"work_id":"105bab84-865d-4252-b08c-0bb29d207c9c","year":2024},"citing_paper":{"arxiv_id":"2505.07793","last_updated":"2025-09-08T20:57:22Z","snapshot_observed_at":"2026-08-15T22:05:45.876584Z","submitted_at":"2025-05-12T17:45:05Z","title":"Overflow Prevention Enhances Long-Context Recurrent LLMs","version":2},"reference_index":59,"source":"arxiv_source","source_observed_at":"2026-08-15T22:12:58.838005Z"},"links":{"citing_paper":"/paper/2505.07793"},"observation_digest":"sha256:48c02407ebf5bcb03c6bafb296ef102ffc1258f7dbb86410574a13c877429916","observation_id":"580fc529-7685-48bc-800f-524c5fa2c8b5","resolution":{"observed_at":"2026-08-15T22:13:00.052450Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2403.08295","last_updated":"2024-04-16T12:52:47Z","snapshot_observed_at":"2026-08-03T03:29:01.959523Z","submitted_at":"2024-03-13T06:59:16Z","title":"Gemma: Open Models Based on Gemini Research and Technology","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.08295","snapshot_observed_at":"2026-08-15T22:12:58.843073Z","title":"Gemma: Open models based on gemini research and technology","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.07793","last_updated":"2025-09-08T20:57:22Z","snapshot_observed_at":"2026-08-15T22:05:45.876584Z","submitted_at":"2025-05-12T17:45:05Z","title":"Overflow Prevention Enhances Long-Context Recurrent LLMs","version":2},"reference_index":60,"source":"arxiv_source","source_observed_at":"2026-08-15T22:12:58.843073Z"},"links":{"cited_paper":"/paper/2403.08295","citing_paper":"/paper/2505.07793"},"observation_digest":"sha256:998a5a7da9333686a45a62a7e6909f9d3f3dc940b7cfbee7fc458293892ed1ec","observation_id":"613c8566-c94f-4c2d-a684-b228bccd8610","resolution":{"observed_at":"2026-08-15T22:12:58.843073Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2408.12570","last_updated":"2024-08-22T17:38:59Z","snapshot_observed_at":"2026-08-16T13:24:31.428329Z","submitted_at":"2024-08-22T17:38:59Z","title":"Jamba-1.5: Hybrid Transformer-Mamba Models at Scale","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.12570","snapshot_observed_at":"2026-08-15T22:12:58.848466Z","title":"Jamba-1.5: Hybrid transformer-mamba models at scale, 2024 b","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.07793","last_updated":"2025-09-08T20:57:22Z","snapshot_observed_at":"2026-08-15T22:05:45.876584Z","submitted_at":"2025-05-12T17:45:05Z","title":"Overflow Prevention Enhances Long-Context Recurrent LLMs","version":2},"reference_index":61,"source":"arxiv_source","source_observed_at":"2026-08-15T22:12:58.848466Z"},"links":{"cited_paper":"/paper/2408.12570","citing_paper":"/paper/2505.07793"},"observation_digest":"sha256:42c213eae549d3e737e7d49cd3598017f90bbb3cbebb4e238c707bba8c7a2cc5","observation_id":"addbb256-ed53-4c87-8d7b-92dc86554052","resolution":{"observed_at":"2026-08-15T22:12:58.848466Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2104.08663","last_updated":"2021-10-21T01:18:28Z","snapshot_observed_at":"2026-08-09T23:22:21.200279Z","submitted_at":"2021-04-17T23:29:55Z","title":"BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Models","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2104.08663","snapshot_observed_at":"2026-08-15T22:12:58.853959Z","title":"Beir: A heterogenous benchmark for zero-shot evaluation of information retrieval models, 2021","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.07793","last_updated":"2025-09-08T20:57:22Z","snapshot_observed_at":"2026-08-15T22:05:45.876584Z","submitted_at":"2025-05-12T17:45:05Z","title":"Overflow Prevention Enhances Long-Context Recurrent LLMs","version":2},"reference_index":62,"source":"arxiv_source","source_observed_at":"2026-08-15T22:12:58.853959Z"},"links":{"cited_paper":"/paper/2104.08663","citing_paper":"/paper/2505.07793"},"observation_digest":"sha256:c6b103e82d5c8e8da80ccc2a67057598a734009faabd6bb2970efc7a0b7c7b40","observation_id":"b218ec04-a458-490f-87ab-b013833a6c48","resolution":{"observed_at":"2026-08-15T22:12:58.853959Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1706.03762","last_updated":"2023-08-02T00:41:18Z","snapshot_observed_at":"2026-08-17T01:19:18.409791Z","submitted_at":"2017-06-12T17:57:34Z","title":"Attention Is All You Need","version":7},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1706.03762","snapshot_observed_at":"2026-08-15T22:12:58.859395Z","title":"Gomez, Lukasz Kaiser, and Illia Polosukhin","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.07793","last_updated":"2025-09-08T20:57:22Z","snapshot_observed_at":"2026-08-15T22:05:45.876584Z","submitted_at":"2025-05-12T17:45:05Z","title":"Overflow Prevention Enhances Long-Context Recurrent LLMs","version":2},"reference_index":63,"source":"arxiv_source","source_observed_at":"2026-08-15T22:12:58.859395Z"},"links":{"cited_paper":"/paper/1706.03762","citing_paper":"/paper/2505.07793"},"observation_digest":"sha256:07b5210e93b61ed9e4a4e7070ff9a16c9c1a3c3d48376f99934f7cddc7b4bcab","observation_id":"84abae5f-b81a-4ec0-ae4c-336ecc00fcab","resolution":{"observed_at":"2026-08-15T22:12:58.859395Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.07887","last_updated":"2024-06-12T05:25:15Z","snapshot_observed_at":"2026-07-06T18:29:21.709395Z","submitted_at":"2024-06-12T05:25:15Z","title":"An Empirical Study of Mamba-based Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.07887","snapshot_observed_at":"2026-08-15T22:12:58.864401Z","title":"An empirical study of mamba-based language models","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.07793","last_updated":"2025-09-08T20:57:22Z","snapshot_observed_at":"2026-08-15T22:05:45.876584Z","submitted_at":"2025-05-12T17:45:05Z","title":"Overflow Prevention Enhances Long-Context Recurrent LLMs","version":2},"reference_index":64,"source":"arxiv_source","source_observed_at":"2026-08-15T22:12:58.864401Z"},"links":{"cited_paper":"/paper/2406.07887","citing_paper":"/paper/2505.07793"},"observation_digest":"sha256:ca3dcc8b266420953164f41ec435b1cb213da40c39a0817832dff7a806e8381a","observation_id":"d15eccbc-6ae3-46c7-9d78-89c5c9206bd8","resolution":{"observed_at":"2026-08-15T22:12:58.864401Z","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":"2026-08-15T22:13:00.027712Z","title":"Mambabyte: Token-free selective state space model","venue":null,"work_id":"374a0126-926a-4fd6-85aa-bb531e9f88b4","year":null},"citing_paper":{"arxiv_id":"2505.07793","last_updated":"2025-09-08T20:57:22Z","snapshot_observed_at":"2026-08-15T22:05:45.876584Z","submitted_at":"2025-05-12T17:45:05Z","title":"Overflow Prevention Enhances Long-Context Recurrent LLMs","version":2},"reference_index":65,"source":"arxiv_source","source_observed_at":"2026-08-15T22:12:58.869604Z"},"links":{"citing_paper":"/paper/2505.07793"},"observation_digest":"sha256:42fa5eca3d1b5a2008804f2f001f9c1b26809793b0426ade820f1e88b952519d","observation_id":"0c2e74e8-34f5-4793-aedd-e6fe54a970fe","resolution":{"observed_at":"2026-08-15T22:13:00.033825Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T22:13:00.009959Z","title":"Unlocking efficiency in large language model inference: A comprehensive survey of speculative decoding","venue":null,"work_id":"a88308dc-97bd-4fc3-b5ea-5acba0a5f37a","year":2024},"citing_paper":{"arxiv_id":"2505.07793","last_updated":"2025-09-08T20:57:22Z","snapshot_observed_at":"2026-08-15T22:05:45.876584Z","submitted_at":"2025-05-12T17:45:05Z","title":"Overflow Prevention Enhances Long-Context Recurrent LLMs","version":2},"reference_index":66,"source":"arxiv_source","source_observed_at":"2026-08-15T22:12:58.874922Z"},"links":{"citing_paper":"/paper/2505.07793"},"observation_digest":"sha256:c7b881c5e6a04edbe2d8fd00d0c50d50a24d4a5702c33076cec7c5e05a9ed966","observation_id":"121b702b-bdb1-49fd-b28e-92b8bbcdc7f2","resolution":{"observed_at":"2026-08-15T22:13:00.015448Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2310.03025","last_updated":"2024-01-23T07:49:13Z","snapshot_observed_at":"2026-08-16T14:55:01.925234Z","submitted_at":"2023-10-04T17:59:41Z","title":"Retrieval meets Long Context Large Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.03025","snapshot_observed_at":"2026-08-15T22:12:58.880191Z","title":"Retrieval meets long context large language models, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.07793","last_updated":"2025-09-08T20:57:22Z","snapshot_observed_at":"2026-08-15T22:05:45.876584Z","submitted_at":"2025-05-12T17:45:05Z","title":"Overflow Prevention Enhances Long-Context Recurrent LLMs","version":2},"reference_index":67,"source":"arxiv_source","source_observed_at":"2026-08-15T22:12:58.880191Z"},"links":{"cited_paper":"/paper/2310.03025","citing_paper":"/paper/2505.07793"},"observation_digest":"sha256:bc9590d1b7c7caa12c82b334762739197898f103c7de04930e46ecebf0293423","observation_id":"a198b541-807f-4073-baa9-925cc26c7a05","resolution":{"observed_at":"2026-08-15T22:12:58.880191Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2412.06464","last_updated":"2025-03-06T06:57:34Z","snapshot_observed_at":"2026-08-14T21:08:22.323819Z","submitted_at":"2024-12-09T13:09:04Z","title":"Gated Delta Networks: Improving Mamba2 with Delta Rule","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.06464","snapshot_observed_at":"2026-08-15T22:12:58.885874Z","title":"Gated delta networks: Improving mamba2 with delta rule, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2505.07793","last_updated":"2025-09-08T20:57:22Z","snapshot_observed_at":"2026-08-15T22:05:45.876584Z","submitted_at":"2025-05-12T17:45:05Z","title":"Overflow Prevention Enhances Long-Context Recurrent LLMs","version":2},"reference_index":68,"source":"arxiv_source","source_observed_at":"2026-08-15T22:12:58.885874Z"},"links":{"cited_paper":"/paper/2412.06464","citing_paper":"/paper/2505.07793"},"observation_digest":"sha256:38680eb9b31b0f3fba7d596e42a4e2ffd27478d174fb73e564ce5d463445927b","observation_id":"405011f3-6c05-4d92-9fa2-30ea7f7a40a9","resolution":{"observed_at":"2026-08-15T22:12:58.885874Z","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":"2026-08-15T22:12:59.991594Z","title":"Longmamba: Enhancing mamba's long-context capabilities via training-free receptive field enlargement","venue":null,"work_id":"cee0f0f3-52f0-4790-bf8f-9adbf5a54008","year":2025},"citing_paper":{"arxiv_id":"2505.07793","last_updated":"2025-09-08T20:57:22Z","snapshot_observed_at":"2026-08-15T22:05:45.876584Z","submitted_at":"2025-05-12T17:45:05Z","title":"Overflow Prevention Enhances Long-Context Recurrent LLMs","version":2},"reference_index":69,"source":"arxiv_source","source_observed_at":"2026-08-15T22:12:58.891667Z"},"links":{"citing_paper":"/paper/2505.07793"},"observation_digest":"sha256:89daad7f280e9fa6066469c08f3bb10ff7854e284f299255ac3c38ced57004b3","observation_id":"86eb18fd-360c-42d9-9557-0754a52e247c","resolution":{"observed_at":"2026-08-15T22:12:59.997031Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T22:12:59.973150Z","title":"Useful confidence measures: Beyond the max score","venue":null,"work_id":"e69d5b77-8976-4a1d-bfe6-151eef7842d8","year":2022},"citing_paper":{"arxiv_id":"2505.07793","last_updated":"2025-09-08T20:57:22Z","snapshot_observed_at":"2026-08-15T22:05:45.876584Z","submitted_at":"2025-05-12T17:45:05Z","title":"Overflow Prevention Enhances Long-Context Recurrent LLMs","version":2},"reference_index":70,"source":"arxiv_source","source_observed_at":"2026-08-15T22:12:58.897907Z"},"links":{"citing_paper":"/paper/2505.07793"},"observation_digest":"sha256:9f189aea8bf80b0045fa78b2a965855a078524001176cba9aaa795b5989ded57","observation_id":"0bfe843f-0ae5-44a5-ba0c-144c7ce52a93","resolution":{"observed_at":"2026-08-15T22:12:59.978790Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.13718","last_updated":"2024-02-24T15:07:55Z","snapshot_observed_at":"2026-08-16T14:16:38.556803Z","submitted_at":"2024-02-21T11:30:29Z","title":"$\\infty$Bench: Extending Long Context Evaluation Beyond 100K Tokens","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.13718","snapshot_observed_at":"2026-08-15T22:12:58.904457Z","title":"bench: Extending long context evaluation beyond 100k tokens, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.07793","last_updated":"2025-09-08T20:57:22Z","snapshot_observed_at":"2026-08-15T22:05:45.876584Z","submitted_at":"2025-05-12T17:45:05Z","title":"Overflow Prevention Enhances Long-Context Recurrent LLMs","version":2},"reference_index":71,"source":"arxiv_source","source_observed_at":"2026-08-15T22:12:58.904457Z"},"links":{"cited_paper":"/paper/2402.13718","citing_paper":"/paper/2505.07793"},"observation_digest":"sha256:f388bbdfe3fa10eac7fa2ba6f759ae73ea5048ac9aca2e1f42435cfc003fc1e6","observation_id":"736774dc-445e-49dd-b7af-a0680b598b84","resolution":{"observed_at":"2026-08-15T22:12:58.904457Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.09342","last_updated":"2024-10-12T03:13:44Z","snapshot_observed_at":"2026-08-16T13:10:08.094720Z","submitted_at":"2024-10-12T03:13:44Z","title":"LLM$\\times$MapReduce: Simplified Long-Sequence Processing using Large Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.09342","snapshot_observed_at":"2026-08-15T22:12:58.909939Z","title":"Llm mapreduce: Simplified long-sequence processing using large language models, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.07793","last_updated":"2025-09-08T20:57:22Z","snapshot_observed_at":"2026-08-15T22:05:45.876584Z","submitted_at":"2025-05-12T17:45:05Z","title":"Overflow Prevention Enhances Long-Context Recurrent LLMs","version":2},"reference_index":72,"source":"arxiv_source","source_observed_at":"2026-08-15T22:12:58.909939Z"},"links":{"cited_paper":"/paper/2410.09342","citing_paper":"/paper/2505.07793"},"observation_digest":"sha256:ce1b94f1cee3b2c6b0efcc5e55220b81027ba69262c8ab6de2c49a811ba51015","observation_id":"48f3ac94-d8f0-4e05-b061-028589427d13","resolution":{"observed_at":"2026-08-15T22:12:58.909939Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.09417","last_updated":"2024-11-14T02:00:33Z","snapshot_observed_at":"2026-08-14T11:12:31.002605Z","submitted_at":"2024-01-17T18:56:18Z","title":"Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.09417","snapshot_observed_at":"2026-08-15T22:12:58.915776Z","title":"Vision mamba: Efficient visual representation learning with bidirectional state space model","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.07793","last_updated":"2025-09-08T20:57:22Z","snapshot_observed_at":"2026-08-15T22:05:45.876584Z","submitted_at":"2025-05-12T17:45:05Z","title":"Overflow Prevention Enhances Long-Context Recurrent LLMs","version":2},"reference_index":73,"source":"arxiv_source","source_observed_at":"2026-08-15T22:12:58.915776Z"},"links":{"cited_paper":"/paper/2401.09417","citing_paper":"/paper/2505.07793"},"observation_digest":"sha256:3fd1c9f1549269dd2c16b4b69c459d8801bd06a1f54acb25519156b512161c98","observation_id":"089b4a14-c8b6-49d2-9b12-125b17f78c72","resolution":{"observed_at":"2026-08-15T22:12:58.915776Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.05355","last_updated":"2024-10-07T15:40:45Z","snapshot_observed_at":"2026-08-16T13:11:51.749166Z","submitted_at":"2024-10-07T15:40:45Z","title":"Falcon Mamba: The First Competitive Attention-free 7B Language Model","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.05355","snapshot_observed_at":"2026-08-15T22:12:58.924215Z","title":"Falcon mamba: The first competitive attention-free 7b language model","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.07793","last_updated":"2025-09-08T20:57:22Z","snapshot_observed_at":"2026-08-15T22:05:45.876584Z","submitted_at":"2025-05-12T17:45:05Z","title":"Overflow Prevention Enhances Long-Context Recurrent LLMs","version":2},"reference_index":74,"source":"arxiv_source","source_observed_at":"2026-08-15T22:12:58.924215Z"},"links":{"cited_paper":"/paper/2410.05355","citing_paper":"/paper/2505.07793"},"observation_digest":"sha256:3326a0ba618d5eec5942df415dd8c6e48d087629d54ef237748334db4f3f30a1","observation_id":"dea9d758-9b54-491d-82d8-b94c74b1c1be","resolution":{"observed_at":"2026-08-15T22:12:58.924215Z","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":"2026-08-15T22:12:58.929907Z","title":"write newline","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.07793","last_updated":"2025-09-08T20:57:22Z","snapshot_observed_at":"2026-08-15T22:05:45.876584Z","submitted_at":"2025-05-12T17:45:05Z","title":"Overflow Prevention Enhances Long-Context Recurrent LLMs","version":2},"reference_index":75,"source":"arxiv_source","source_observed_at":"2026-08-15T22:12:58.929907Z"},"links":{"citing_paper":"/paper/2505.07793"},"observation_digest":"sha256:7b5dfe208f092fc9686b2af9dda548e046d18bda8888bcee27e92bc4210c4430","observation_id":"f40453bd-2316-45d6-a23a-4037b3920b6f","resolution":{"observed_at":"2026-08-15T22:12:58.929907Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2505.07793","last_updated":"2025-09-08T20:57:22Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-15T22:05:45.876584Z","submitted_at":"2025-05-12T17:45:05Z","title":"Overflow Prevention Enhances Long-Context Recurrent LLMs"},"reference_resolution":{"displayed":75,"state_counts":{"malformed_identifier":0,"metadata_mismatch":1,"parse_uncertain":0,"unresolved":56,"verified_exact":0,"verified_fuzzy":18},"total_outbound_references":75},"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":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"thesis":"As of 17 August 2026, this Paper Citation Record lists 75 of 75 outbound references and 0 inbound Pith citation observations for arXiv:2505.07793."}