{"as_of":"2026-08-10T15:16:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:c338241a4fbc8412847e9d10f7ab2e78d2f2c1e0bf1197eb459ea291059c963d","coverage":[{"denominator":0,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":34,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":34,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-10T06:31:04.303077+00:00","state":"measured"},{"denominator":34,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":34,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-09T23:52:06.065071Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":1,"source":"arxiv_reference","source_observed_at":"2026-08-05T02:28:24.338817Z","state":"measured"}],"external_citation_measurements":[{"count":0,"observed_at":"2026-08-05T02:28:24.338817Z","source":"arxiv_reference"}],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2402.16837","last_updated":"2025-05-31T11:20:49Z","snapshot_observed_at":"2026-08-09T17:39:53.622177Z","submitted_at":"2024-02-26T18:57:54Z","title":"Do Large Language Models Latently Perform Multi-Hop Reasoning?","version":2},"cited_work":{"arxiv_id":"2402.16837","doi":"10.48550/arxiv.2402.16837","metadata_source":"arxiv_reference","pith_arxiv_id":"2402.16837","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Do large language models latently perform multi-hop reasoning? arXiv preprint arXiv:2402.16837","venue":"arXiv (Cornell University)","work_id":"4ab404e2-abd3-493f-8e58-856cfffbf35f","year":2025},"citing_paper":{"arxiv_id":"2409.12917","last_updated":"2024-10-04T17:28:45Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2024-09-19T17:16:21Z","title":"Training Language Models to Self-Correct via Reinforcement Learning","version":2},"reference_index":48,"source":"arxiv_source","source_observed_at":"2026-05-17T12:04:10.210508Z"},"links":{"cited_paper":"/paper/2402.16837","citing_paper":"/paper/2409.12917"},"observation_digest":"sha256:2fd03dc2ded7fd009ed06074df4d93ae958bad826727a2a6e015100ce1265a8d","observation_id":"b710ffb6-40d7-4418-b1e8-cf7c41b575d7","resolution":{"observed_at":"2026-05-17T12:04:10.460906Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.16837","last_updated":"2025-05-31T11:20:49Z","snapshot_observed_at":"2026-08-09T17:39:53.622177Z","submitted_at":"2024-02-26T18:57:54Z","title":"Do Large Language Models Latently Perform Multi-Hop Reasoning?","version":2},"cited_work":{"arxiv_id":"2402.16837","doi":"10.48550/arxiv.2402.16837","metadata_source":"arxiv_reference","pith_arxiv_id":"2402.16837","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Do large language models latently perform multi-hop reasoning? arXiv preprint arXiv:2402.16837","venue":"arXiv (Cornell University)","work_id":"4ab404e2-abd3-493f-8e58-856cfffbf35f","year":2025},"citing_paper":{"arxiv_id":"2412.06769","last_updated":"2025-11-03T00:53:34Z","snapshot_observed_at":"2026-08-07T06:05:27.895209Z","submitted_at":"2024-12-09T18:55:56Z","title":"Training Large Language Models to Reason in a Continuous Latent Space","version":3},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-05-11T10:29:05.384381Z"},"links":{"cited_paper":"/paper/2402.16837","citing_paper":"/paper/2412.06769"},"observation_digest":"sha256:b73f03d37590caf475e6615e8a13343a5ce346dc3594d7d33cf09b5273dba0de","observation_id":"9eaadc97-3dd9-43bc-bda8-f5b496fd6295","resolution":{"observed_at":"2026-05-11T10:29:05.857757Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.16837","last_updated":"2025-05-31T11:20:49Z","snapshot_observed_at":"2026-08-09T17:39:53.622177Z","submitted_at":"2024-02-26T18:57:54Z","title":"Do Large Language Models Latently Perform Multi-Hop Reasoning?","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.16837","snapshot_observed_at":"2026-08-09T23:52:06.065071Z","title":"Do Large Language Models Latently Perform Multi-hop Reasoning?","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2501.18356","last_updated":"2025-01-30T14:03:36Z","snapshot_observed_at":"2026-08-09T23:44:57.467275Z","submitted_at":"2025-01-30T14:03:36Z","title":"State Stream Transformer (SST) : Emergent Metacognitive Behaviours Through Latent State Persistence","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-09T23:52:06.065071Z"},"links":{"cited_paper":"/paper/2402.16837","citing_paper":"/paper/2501.18356"},"observation_digest":"sha256:0d55c141299084b6fb3f2cb0f0ffae6fbcb467f0df5110c6065fde3ff3724ede","observation_id":"cae666ff-4b37-4ac4-b1a2-8828e38e4120","resolution":{"observed_at":"2026-08-09T23:52:06.065071Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.16837","last_updated":"2025-05-31T11:20:49Z","snapshot_observed_at":"2026-08-09T17:39:53.622177Z","submitted_at":"2024-02-26T18:57:54Z","title":"Do Large Language Models Latently Perform Multi-Hop Reasoning?","version":2},"cited_work":{"arxiv_id":"2402.16837","doi":"10.48550/arxiv.2402.16837","metadata_source":"arxiv_reference","pith_arxiv_id":"2402.16837","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Do large language models latently perform multi-hop reasoning? arXiv preprint arXiv:2402.16837","venue":"arXiv (Cornell University)","work_id":"4ab404e2-abd3-493f-8e58-856cfffbf35f","year":2025},"citing_paper":{"arxiv_id":"2501.19201","last_updated":"2026-05-04T05:23:05Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-01-31T15:10:29Z","title":"Efficient Reasoning with Hidden Thinking","version":2},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-05-23T04:45:38.608009Z"},"links":{"cited_paper":"/paper/2402.16837","citing_paper":"/paper/2501.19201"},"observation_digest":"sha256:c3c6921672b86fc5de2ad3e4ed5ba1850c83661ae8975531a9b15faa27a84ff4","observation_id":"b86bfb85-7f0d-47ef-bfc9-33b8afbc0cd8","resolution":{"observed_at":"2026-05-23T04:47:33.673125Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.16837","last_updated":"2025-05-31T11:20:49Z","snapshot_observed_at":"2026-08-09T17:39:53.622177Z","submitted_at":"2024-02-26T18:57:54Z","title":"Do Large Language Models Latently Perform Multi-Hop Reasoning?","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.16837","snapshot_observed_at":"2026-08-08T17:06:03.386382Z","title":"Do large language models latently perform multi-hop reasoning?arXiv preprint arXiv:2402.16837, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.06037","last_updated":"2025-09-10T16:22:20Z","snapshot_observed_at":"2026-08-09T05:31:11.961434Z","submitted_at":"2025-02-09T21:21:55Z","title":"Investigating Compositional Reasoning in Time Series Foundation Models","version":2},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-08T17:06:03.386382Z"},"links":{"cited_paper":"/paper/2402.16837","citing_paper":"/paper/2502.06037"},"observation_digest":"sha256:3993823e19046f57264d95ba57fbfaff2624dd4e1cf0c4a1237f2a09127da582","observation_id":"c36ab761-5ec1-4de4-b878-52d13683f14e","resolution":{"observed_at":"2026-08-08T17:06:03.386382Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.16837","last_updated":"2025-05-31T11:20:49Z","snapshot_observed_at":"2026-08-09T17:39:53.622177Z","submitted_at":"2024-02-26T18:57:54Z","title":"Do Large Language Models Latently Perform Multi-Hop Reasoning?","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.16837","snapshot_observed_at":"2026-08-08T14:13:38.418362Z","title":"Do large language models latently perform multi-hop reasoning? arXiv preprint arXiv:2402.16837, 2024 b","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.06975","last_updated":"2025-02-10T19:14:51Z","snapshot_observed_at":"2026-08-08T23:51:33.892065Z","submitted_at":"2025-02-10T19:14:51Z","title":"Position: Episodic Memory is the Missing Piece for Long-Term LLM Agents","version":1},"reference_index":103,"source":"arxiv_source","source_observed_at":"2026-08-08T14:13:38.418362Z"},"links":{"cited_paper":"/paper/2402.16837","citing_paper":"/paper/2502.06975"},"observation_digest":"sha256:541bda3699ec53ea76fb25a296d4c7f67d4539c829047b4becfb298d8e8395c2","observation_id":"b702e528-20a2-40a8-9370-468968a25892","resolution":{"observed_at":"2026-08-08T14:13:38.418362Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.16837","last_updated":"2025-05-31T11:20:49Z","snapshot_observed_at":"2026-08-09T17:39:53.622177Z","submitted_at":"2024-02-26T18:57:54Z","title":"Do Large Language Models Latently Perform Multi-Hop Reasoning?","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.16837","snapshot_observed_at":"2026-08-08T05:42:43.551459Z","title":"& Riedel, S","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.08301","last_updated":"2025-06-23T09:04:32Z","snapshot_observed_at":"2026-08-09T07:42:28.077349Z","submitted_at":"2025-02-12T11:02:59Z","title":"Compromising Honesty and Harmlessness in Language Models via Deception Attacks","version":2},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-08T05:42:43.551459Z"},"links":{"cited_paper":"/paper/2402.16837","citing_paper":"/paper/2502.08301"},"observation_digest":"sha256:8f0c078bb77c52ccff74ae71bfd24b2eea866c7d8177038bfc4d4818d83f1a53","observation_id":"c803a1f3-b23a-424c-9c33-cca2ba79e142","resolution":{"observed_at":"2026-08-08T05:42:43.551459Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.16837","last_updated":"2025-05-31T11:20:49Z","snapshot_observed_at":"2026-08-09T17:39:53.622177Z","submitted_at":"2024-02-26T18:57:54Z","title":"Do Large Language Models Latently Perform Multi-Hop Reasoning?","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.16837","snapshot_observed_at":"2026-08-07T15:15:43.540485Z","title":"Do large language models latently perform multi-hop reasoning? arXiv preprint arXiv:2402.16837 , 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.15778","last_updated":"2025-05-21T17:29:15Z","snapshot_observed_at":"2026-08-10T11:39:20.508638Z","submitted_at":"2025-05-21T17:29:15Z","title":"Soft Thinking: Unlocking the Reasoning Potential of LLMs in Continuous Concept Space","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-07T15:15:43.540485Z"},"links":{"cited_paper":"/paper/2402.16837","citing_paper":"/paper/2505.15778"},"observation_digest":"sha256:b31fe7ec9f44880ddd576a6149d15b99159872ebb042ca42c44844707e3ddd3b","observation_id":"aec01433-9c1d-49e3-876f-6f3a0dd0a2c5","resolution":{"observed_at":"2026-08-07T15:15:43.540485Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.16837","last_updated":"2025-05-31T11:20:49Z","snapshot_observed_at":"2026-08-09T17:39:53.622177Z","submitted_at":"2024-02-26T18:57:54Z","title":"Do Large Language Models Latently Perform Multi-Hop Reasoning?","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.16837","snapshot_observed_at":"2026-08-07T13:15:59.661139Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.22240","last_updated":"2025-06-09T00:31:09Z","snapshot_observed_at":"2026-08-07T13:09:46.299438Z","submitted_at":"2025-05-28T11:19:01Z","title":"BioHopR: A Benchmark for Multi-Hop, Multi-Answer Reasoning in Biomedical Domain","version":2},"reference_index":26,"source":"arxiv_source","source_observed_at":"2026-08-07T13:15:59.661139Z"},"links":{"cited_paper":"/paper/2402.16837","citing_paper":"/paper/2505.22240"},"observation_digest":"sha256:0c49b3541a964ccd1d2597ae6732e4ca9140f51b3c3e9ea9cd8519257e2ea23c","observation_id":"b05d59e1-3e0a-4dd5-b1a4-5f59b28d1b82","resolution":{"observed_at":"2026-08-07T13:15:59.661139Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.16837","last_updated":"2025-05-31T11:20:49Z","snapshot_observed_at":"2026-08-09T17:39:53.622177Z","submitted_at":"2024-02-26T18:57:54Z","title":"Do Large Language Models Latently Perform Multi-Hop Reasoning?","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.16837","snapshot_observed_at":"2026-08-07T13:01:14.988483Z","title":"Do large language models latently perform multi-hop reasoning? In arXiv preprint: abs/2402.16837, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.23013","last_updated":"2025-05-29T02:42:20Z","snapshot_observed_at":"2026-08-08T06:33:23.795716Z","submitted_at":"2025-05-29T02:42:20Z","title":"Scalable Complexity Control Facilitates Reasoning Ability of LLMs","version":1},"reference_index":72,"source":"pdf_text","source_observed_at":"2026-08-07T13:01:14.988483Z"},"links":{"cited_paper":"/paper/2402.16837","citing_paper":"/paper/2505.23013"},"observation_digest":"sha256:02cbeab057ab27fdfdbda1c62f8ee054c07db3ca61b0c2312c3f7163a20fed45","observation_id":"48406109-4071-4bce-a63b-9704781068d3","resolution":{"observed_at":"2026-08-07T13:01:14.988483Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.16837","last_updated":"2025-05-31T11:20:49Z","snapshot_observed_at":"2026-08-09T17:39:53.622177Z","submitted_at":"2024-02-26T18:57:54Z","title":"Do Large Language Models Latently Perform Multi-Hop Reasoning?","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.16837","snapshot_observed_at":"2026-08-07T12:46:40.093818Z","title":"Do large language models latently perform multi-hop reasoning?arXiv preprint arXiv:2402.16837, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.23683","last_updated":"2025-05-29T17:22:00Z","snapshot_observed_at":"2026-08-07T21:59:35.039840Z","submitted_at":"2025-05-29T17:22:00Z","title":"Learning Compositional Functions with Transformers from Easy-to-Hard Data","version":1},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-08-07T12:46:40.093818Z"},"links":{"cited_paper":"/paper/2402.16837","citing_paper":"/paper/2505.23683"},"observation_digest":"sha256:4eb103e333b776d0085ae00cc7c8e587add9e2799c88cfaa9714bc81427ec20c","observation_id":"2f6f0c7b-8d67-432a-b51b-fb187d7f4894","resolution":{"observed_at":"2026-08-07T12:46:40.093818Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.16837","last_updated":"2025-05-31T11:20:49Z","snapshot_observed_at":"2026-08-09T17:39:53.622177Z","submitted_at":"2024-02-26T18:57:54Z","title":"Do Large Language Models Latently Perform Multi-Hop Reasoning?","version":2},"cited_work":{"arxiv_id":"2402.16837","doi":"10.48550/arxiv.2402.16837","metadata_source":"arxiv_reference","pith_arxiv_id":"2402.16837","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Do large language models latently perform multi-hop reasoning? arXiv preprint arXiv:2402.16837","venue":"arXiv (Cornell University)","work_id":"4ab404e2-abd3-493f-8e58-856cfffbf35f","year":2025},"citing_paper":{"arxiv_id":"2506.04289","last_updated":"2026-05-11T09:35:19Z","snapshot_observed_at":"2026-08-02T06:08:01.726521Z","submitted_at":"2025-06-04T10:15:05Z","title":"Relational reasoning and inductive bias in transformers and large language models","version":3},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-05-19T11:31:37.942517Z"},"links":{"cited_paper":"/paper/2402.16837","citing_paper":"/paper/2506.04289"},"observation_digest":"sha256:aa31748a347ff7c88bedea7905b3f93c72971561a76a692d48893b2da5dae372","observation_id":"c528e740-4087-4c9d-a4a3-ba90d01358c5","resolution":{"observed_at":"2026-05-19T11:32:17.030178Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.16837","last_updated":"2025-05-31T11:20:49Z","snapshot_observed_at":"2026-08-09T17:39:53.622177Z","submitted_at":"2024-02-26T18:57:54Z","title":"Do Large Language Models Latently Perform Multi-Hop Reasoning?","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.16837","snapshot_observed_at":"2026-08-07T15:39:26.672211Z","title":"Do large language models latently perform multi-hop reasoning? arXiv preprint arXiv:2402.16837,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.11029","last_updated":"2025-05-20T14:31:06Z","snapshot_observed_at":"2026-08-07T23:55:50.036141Z","submitted_at":"2025-05-20T14:31:06Z","title":"Output Scaling: YingLong-Delayed Chain of Thought in a Large Pretrained Time Series Forecasting Model","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-07T15:39:26.672211Z"},"links":{"cited_paper":"/paper/2402.16837","citing_paper":"/paper/2506.11029"},"observation_digest":"sha256:20d094186f34f740be8a343ab24228c650cf1a5a4de5edb51904effdf0483ce0","observation_id":"58c6e6fe-54aa-418f-ae14-fe48498396b8","resolution":{"observed_at":"2026-08-07T15:39:26.672211Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.16837","last_updated":"2025-05-31T11:20:49Z","snapshot_observed_at":"2026-08-09T17:39:53.622177Z","submitted_at":"2024-02-26T18:57:54Z","title":"Do Large Language Models Latently Perform Multi-Hop Reasoning?","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.16837","snapshot_observed_at":"2026-08-07T00:41:59.556203Z","title":"Do large language models latently perform multi-hop reasoning?, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.13253","last_updated":"2025-06-16T08:49:42Z","snapshot_observed_at":"2026-08-07T23:54:43.526349Z","submitted_at":"2025-06-16T08:49:42Z","title":"Distinct Computations Emerge From Compositional Curricula in In-Context Learning","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-07T00:41:59.556203Z"},"links":{"cited_paper":"/paper/2402.16837","citing_paper":"/paper/2506.13253"},"observation_digest":"sha256:2eceb8057489626241b1c2d030de6046931152ebafb69d228f643460156184a0","observation_id":"e4a91d68-ac92-4773-a86b-1f300fa3a6dc","resolution":{"observed_at":"2026-08-07T00:41:59.556203Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.16837","last_updated":"2025-05-31T11:20:49Z","snapshot_observed_at":"2026-08-09T17:39:53.622177Z","submitted_at":"2024-02-26T18:57:54Z","title":"Do Large Language Models Latently Perform Multi-Hop Reasoning?","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.16837","snapshot_observed_at":"2026-08-06T19:14:32.725694Z","title":"Do large language models latently perform multi-hop reasoning? arXiv preprint arXiv:2402.16837, 2024 a","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.06203","last_updated":"2025-07-10T16:43:36Z","snapshot_observed_at":"2026-08-07T04:57:37.201438Z","submitted_at":"2025-07-08T17:29:07Z","title":"A Survey on Latent Reasoning","version":2},"reference_index":120,"source":"arxiv_source","source_observed_at":"2026-08-06T19:14:32.725694Z"},"links":{"cited_paper":"/paper/2402.16837","citing_paper":"/paper/2507.06203"},"observation_digest":"sha256:f444c8233a5e178c49d4429182a4655e8147353fbc29c29f7c7d9552a3259a35","observation_id":"34988dc6-feec-4d4a-855b-26cf813545c2","resolution":{"observed_at":"2026-08-06T19:14:32.725694Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.16837","last_updated":"2025-05-31T11:20:49Z","snapshot_observed_at":"2026-08-09T17:39:53.622177Z","submitted_at":"2024-02-26T18:57:54Z","title":"Do Large Language Models Latently Perform Multi-Hop Reasoning?","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.16837","snapshot_observed_at":"2026-08-04T13:54:18.569016Z","title":"Do large language models latently perform multi-hop reasoning? arXiv preprint arXiv:2402.16837, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.24653","last_updated":"2026-07-13T03:51:39Z","snapshot_observed_at":"2026-08-09T06:52:58.188256Z","submitted_at":"2025-09-29T12:02:05Z","title":"Unveiling the Mechanisms of Multi-Hop Reasoning in Transformers via Identity Bridge","version":2},"reference_index":33,"source":"arxiv_source","source_observed_at":"2026-08-04T13:54:18.569016Z"},"links":{"cited_paper":"/paper/2402.16837","citing_paper":"/paper/2509.24653"},"observation_digest":"sha256:45b2f78c1280b54afdc74d943a83594289bfdff1506b48d0731f23a84d69ea10","observation_id":"e8c858ae-4451-4738-85bc-81a339a9e098","resolution":{"observed_at":"2026-08-04T13:54:18.569016Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.16837","last_updated":"2025-05-31T11:20:49Z","snapshot_observed_at":"2026-08-09T17:39:53.622177Z","submitted_at":"2024-02-26T18:57:54Z","title":"Do Large Language Models Latently Perform Multi-Hop Reasoning?","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.16837","snapshot_observed_at":"2026-08-04T09:48:10.675483Z","title":"Do large language models latently perform multi-hop reasoning?, 2025 b","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2510.13554","last_updated":"2026-06-08T06:02:10Z","snapshot_observed_at":"2026-08-04T09:47:55.188440Z","submitted_at":"2025-10-15T13:49:51Z","title":"Attention Illuminates LLM Reasoning: The Preplan-and-Anchor Rhythm Enables Fine-Grained Policy Optimization","version":2},"reference_index":72,"source":"arxiv_source","source_observed_at":"2026-08-04T09:48:10.675483Z"},"links":{"cited_paper":"/paper/2402.16837","citing_paper":"/paper/2510.13554"},"observation_digest":"sha256:70650d3fa9d171cd711c0205d083d8618ae80714c972c9f2fd40573ef3b65646","observation_id":"048f9597-689c-4420-97fb-1446b7b0552c","resolution":{"observed_at":"2026-08-04T09:48:10.675483Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.16837","last_updated":"2025-05-31T11:20:49Z","snapshot_observed_at":"2026-08-09T17:39:53.622177Z","submitted_at":"2024-02-26T18:57:54Z","title":"Do Large Language Models Latently Perform Multi-Hop Reasoning?","version":2},"cited_work":{"arxiv_id":"2402.16837","doi":"10.48550/arxiv.2402.16837","metadata_source":"arxiv_reference","pith_arxiv_id":"2402.16837","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Do large language models latently perform multi-hop reasoning? arXiv preprint arXiv:2402.16837","venue":"arXiv (Cornell University)","work_id":"4ab404e2-abd3-493f-8e58-856cfffbf35f","year":2025},"citing_paper":{"arxiv_id":"2510.24941","last_updated":"2026-05-26T22:58:09Z","snapshot_observed_at":"2026-08-08T14:16:18.113872Z","submitted_at":"2025-10-28T20:14:02Z","title":"Can Aha Moments Be Fake? Towards Quantifying Decorative and True Thinking in Chain-of-Thought","version":3},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-05-18T02:44:48.729794Z"},"links":{"cited_paper":"/paper/2402.16837","citing_paper":"/paper/2510.24941"},"observation_digest":"sha256:7340bd09f6c84c29fe818e49fdb521af0a6377fff9f05ecc50a2a98be0de0f2a","observation_id":"8db51c76-c766-452d-a7b0-d365be575c04","resolution":{"observed_at":"2026-05-18T02:45:46.214308Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.16837","last_updated":"2025-05-31T11:20:49Z","snapshot_observed_at":"2026-08-09T17:39:53.622177Z","submitted_at":"2024-02-26T18:57:54Z","title":"Do Large Language Models Latently Perform Multi-Hop Reasoning?","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.16837","snapshot_observed_at":"2026-08-04T07:43:11.632882Z","title":"Do large language models latently perform multi-hop reasoning?arXiv preprint arXiv:2402.16837,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2510.24941","last_updated":"2026-05-26T22:58:09Z","snapshot_observed_at":"2026-08-08T14:16:18.113872Z","submitted_at":"2025-10-28T20:14:02Z","title":"Can Aha Moments Be Fake? Towards Quantifying Decorative and True Thinking in Chain-of-Thought","version":4},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-04T07:43:11.632882Z"},"links":{"cited_paper":"/paper/2402.16837","citing_paper":"/paper/2510.24941"},"observation_digest":"sha256:ab60d783ec0f03a41c0c6af57dcae287bf6f0c44e8d8e0cb27d72595519a9f20","observation_id":"5ec7ad7a-df65-446d-bf40-6aa87876a466","resolution":{"observed_at":"2026-08-04T07:43:11.632882Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.16837","last_updated":"2025-05-31T11:20:49Z","snapshot_observed_at":"2026-08-09T17:39:53.622177Z","submitted_at":"2024-02-26T18:57:54Z","title":"Do Large Language Models Latently Perform Multi-Hop Reasoning?","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.16837","snapshot_observed_at":"2026-08-03T01:14:58.578330Z","title":"teaching","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2602.10352","last_updated":"2026-06-02T04:32:29Z","snapshot_observed_at":"2026-08-06T01:08:22.482910Z","submitted_at":"2026-02-10T22:50:02Z","title":"Learning Self-Interpretation from Interpretability Artifacts: Training Lightweight Adapters on Vector-Label Pairs","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-03T01:14:58.578330Z"},"links":{"cited_paper":"/paper/2402.16837","citing_paper":"/paper/2602.10352"},"observation_digest":"sha256:a19cd79de13fa75bc7d946464ea7b8d72dba990c0f4331ada95ab3e69f650d02","observation_id":"1d899097-a7a3-44c9-8a4f-950fb72b03c9","resolution":{"observed_at":"2026-08-03T01:14:58.578330Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.16837","last_updated":"2025-05-31T11:20:49Z","snapshot_observed_at":"2026-08-09T17:39:53.622177Z","submitted_at":"2024-02-26T18:57:54Z","title":"Do Large Language Models Latently Perform Multi-Hop Reasoning?","version":2},"cited_work":{"arxiv_id":"2402.16837","doi":"10.48550/arxiv.2402.16837","metadata_source":"arxiv_reference","pith_arxiv_id":"2402.16837","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Do large language models latently perform multi-hop reasoning? arXiv preprint arXiv:2402.16837","venue":"arXiv (Cornell University)","work_id":"4ab404e2-abd3-493f-8e58-856cfffbf35f","year":2025},"citing_paper":{"arxiv_id":"2604.08299","last_updated":"2026-04-19T12:06:32Z","snapshot_observed_at":"2026-07-06T22:57:26.678728Z","submitted_at":"2026-04-09T14:32:07Z","title":"SeLaR: Selective Latent Reasoning in Large Language Models","version":2},"reference_index":52,"source":"arxiv_source","source_observed_at":"2026-05-10T18:27:36.132030Z"},"links":{"cited_paper":"/paper/2402.16837","citing_paper":"/paper/2604.08299"},"observation_digest":"sha256:5d3fda0f6ce2de3a143d1c57f26687fb654d8439adde0f653a995e0a6a42a706","observation_id":"7b4f08d4-a41f-4180-bf61-b0d6667bec2b","resolution":{"observed_at":"2026-05-11T00:35:49.655868Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.16837","last_updated":"2025-05-31T11:20:49Z","snapshot_observed_at":"2026-08-09T17:39:53.622177Z","submitted_at":"2024-02-26T18:57:54Z","title":"Do Large Language Models Latently Perform Multi-Hop Reasoning?","version":2},"cited_work":{"arxiv_id":"2402.16837","doi":"10.48550/arxiv.2402.16837","metadata_source":"arxiv_reference","pith_arxiv_id":"2402.16837","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Do large language models latently perform multi-hop reasoning? arXiv preprint arXiv:2402.16837","venue":"arXiv (Cornell University)","work_id":"4ab404e2-abd3-493f-8e58-856cfffbf35f","year":2025},"citing_paper":{"arxiv_id":"2604.15529","last_updated":"2026-05-11T04:41:40Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2026-04-16T21:19:35Z","title":"LACE: Lattice Attention for Cross-thread Exploration","version":1},"reference_index":46,"source":"arxiv_source","source_observed_at":"2026-05-10T10:24:16.283375Z"},"links":{"cited_paper":"/paper/2402.16837","citing_paper":"/paper/2604.15529"},"observation_digest":"sha256:c92bc1eb320b6b5a4ae6912a754518572e86cded33e5fa1d3642dcdd4c6a20b1","observation_id":"be1186bb-c9fb-4445-812b-64786b3e6e5c","resolution":{"observed_at":"2026-05-10T10:24:21.278624Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.16837","last_updated":"2025-05-31T11:20:49Z","snapshot_observed_at":"2026-08-09T17:39:53.622177Z","submitted_at":"2024-02-26T18:57:54Z","title":"Do Large Language Models Latently Perform Multi-Hop Reasoning?","version":2},"cited_work":{"arxiv_id":"2402.16837","doi":"10.48550/arxiv.2402.16837","metadata_source":"arxiv_reference","pith_arxiv_id":"2402.16837","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Do large language models latently perform multi-hop reasoning? arXiv preprint arXiv:2402.16837","venue":"arXiv (Cornell University)","work_id":"4ab404e2-abd3-493f-8e58-856cfffbf35f","year":2025},"citing_paper":{"arxiv_id":"2604.15529","last_updated":"2026-05-11T04:41:40Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2026-04-16T21:19:35Z","title":"LACE: Lattice Attention for Cross-thread Exploration","version":2},"reference_index":46,"source":"arxiv_source","source_observed_at":"2026-05-11T00:47:51.440441Z"},"links":{"cited_paper":"/paper/2402.16837","citing_paper":"/paper/2604.15529"},"observation_digest":"sha256:624f8d5ad38a190215e713a27870b7983e4b849a6030abdcd05e8441f35f25f6","observation_id":"6317cc9c-2618-4616-8831-0806e224524c","resolution":{"observed_at":"2026-05-11T00:50:50.144622Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.16837","last_updated":"2025-05-31T11:20:49Z","snapshot_observed_at":"2026-08-09T17:39:53.622177Z","submitted_at":"2024-02-26T18:57:54Z","title":"Do Large Language Models Latently Perform Multi-Hop Reasoning?","version":2},"cited_work":{"arxiv_id":"2402.16837","doi":"10.48550/arxiv.2402.16837","metadata_source":"arxiv_reference","pith_arxiv_id":"2402.16837","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Do large language models latently perform multi-hop reasoning? arXiv preprint arXiv:2402.16837","venue":"arXiv (Cornell University)","work_id":"4ab404e2-abd3-493f-8e58-856cfffbf35f","year":2025},"citing_paper":{"arxiv_id":"2604.15529","last_updated":"2026-05-11T04:41:40Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2026-04-16T21:19:35Z","title":"LACE: Lattice Attention for Cross-thread Exploration","version":3},"reference_index":46,"source":"arxiv_source","source_observed_at":"2026-05-12T04:05:35.063305Z"},"links":{"cited_paper":"/paper/2402.16837","citing_paper":"/paper/2604.15529"},"observation_digest":"sha256:8e58ccf7c5842e2257b492c0c56f080b58038b4c2fb7cb67d8a32e04f1e1f1d6","observation_id":"eea2fe31-344d-47cc-9b60-193cb9b93019","resolution":{"observed_at":"2026-05-12T06:36:28.633744Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.16837","last_updated":"2025-05-31T11:20:49Z","snapshot_observed_at":"2026-08-09T17:39:53.622177Z","submitted_at":"2024-02-26T18:57:54Z","title":"Do Large Language Models Latently Perform Multi-Hop Reasoning?","version":2},"cited_work":{"arxiv_id":"2402.16837","doi":"10.48550/arxiv.2402.16837","metadata_source":"arxiv_reference","pith_arxiv_id":"2402.16837","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Do large language models latently perform multi-hop reasoning? arXiv preprint arXiv:2402.16837","venue":"arXiv (Cornell University)","work_id":"4ab404e2-abd3-493f-8e58-856cfffbf35f","year":2025},"citing_paper":{"arxiv_id":"2604.17458","last_updated":"2026-04-21T06:43:15Z","snapshot_observed_at":"2026-07-06T23:04:32.734175Z","submitted_at":"2026-04-19T14:18:49Z","title":"EHRAG: Bridging Semantic Gaps in Lightweight GraphRAG via Hybrid Hypergraph Construction and Retrieval","version":2},"reference_index":172,"source":"arxiv_source","source_observed_at":"2026-05-10T05:43:04.813867Z"},"links":{"cited_paper":"/paper/2402.16837","citing_paper":"/paper/2604.17458"},"observation_digest":"sha256:7889745cc739b472ab5db1090dea73fa687d4266af68f279d0e89a33ee045824","observation_id":"d52e8b0d-c62a-4355-9135-5e45d783a24c","resolution":{"observed_at":"2026-05-10T05:51:10.671407Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.16837","last_updated":"2025-05-31T11:20:49Z","snapshot_observed_at":"2026-08-09T17:39:53.622177Z","submitted_at":"2024-02-26T18:57:54Z","title":"Do Large Language Models Latently Perform Multi-Hop Reasoning?","version":2},"cited_work":{"arxiv_id":"2402.16837","doi":"10.48550/arxiv.2402.16837","metadata_source":"arxiv_reference","pith_arxiv_id":"2402.16837","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Do large language models latently perform multi-hop reasoning? arXiv preprint arXiv:2402.16837","venue":"arXiv (Cornell University)","work_id":"4ab404e2-abd3-493f-8e58-856cfffbf35f","year":2025},"citing_paper":{"arxiv_id":"2604.21027","last_updated":"2026-08-02T16:15:42Z","snapshot_observed_at":"2026-08-06T23:24:26.820832Z","submitted_at":"2026-04-22T19:18:36Z","title":"HypEHR: Hyperbolic Modeling of Electronic Health Records for Efficient Question Answering","version":1},"reference_index":102,"source":"arxiv_source","source_observed_at":"2026-05-09T23:51:47.724033Z"},"links":{"cited_paper":"/paper/2402.16837","citing_paper":"/paper/2604.21027"},"observation_digest":"sha256:cbdd307a216b209b410d4682dc032eb6495f98f1ff71cb9ca68efc8cd3de9a2b","observation_id":"5e20edb5-609e-4567-9cf7-2bb9003b826a","resolution":{"observed_at":"2026-05-09T23:54:45.064991Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.16837","last_updated":"2025-05-31T11:20:49Z","snapshot_observed_at":"2026-08-09T17:39:53.622177Z","submitted_at":"2024-02-26T18:57:54Z","title":"Do Large Language Models Latently Perform Multi-Hop Reasoning?","version":2},"cited_work":{"arxiv_id":"2402.16837","doi":"10.48550/arxiv.2402.16837","metadata_source":"arxiv_reference","pith_arxiv_id":"2402.16837","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Do large language models latently perform multi-hop reasoning? arXiv preprint arXiv:2402.16837","venue":"arXiv (Cornell University)","work_id":"4ab404e2-abd3-493f-8e58-856cfffbf35f","year":2025},"citing_paper":{"arxiv_id":"2604.22951","last_updated":"2026-07-08T19:29:03Z","snapshot_observed_at":"2026-07-12T23:17:30.297545Z","submitted_at":"2026-04-24T18:49:08Z","title":"The Power of Power Law: Asymmetry Enables Compositional Reasoning","version":1},"reference_index":56,"source":"arxiv_source","source_observed_at":"2026-05-08T11:49:49.787123Z"},"links":{"cited_paper":"/paper/2402.16837","citing_paper":"/paper/2604.22951"},"observation_digest":"sha256:dd38ab4f6ba5b5627ea52dfc7fdc5fcfdf9d20cbe9ee32dd485e1595de327517","observation_id":"780d4da8-597a-4dcf-9034-687e8de595c6","resolution":{"observed_at":"2026-05-11T19:31:08.225792Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.16837","last_updated":"2025-05-31T11:20:49Z","snapshot_observed_at":"2026-08-09T17:39:53.622177Z","submitted_at":"2024-02-26T18:57:54Z","title":"Do Large Language Models Latently Perform Multi-Hop Reasoning?","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.16837","snapshot_observed_at":"2026-07-12T18:26:05.728364Z","title":"Do large language models latently perform multi-hop reasoning? arXiv preprint arXiv:2402.16837, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2604.22951","last_updated":"2026-07-08T19:29:03Z","snapshot_observed_at":"2026-07-12T23:17:30.297545Z","submitted_at":"2026-04-24T18:49:08Z","title":"The Power of Power Law: Asymmetry Enables Compositional Reasoning","version":2},"reference_index":56,"source":"arxiv_source","source_observed_at":"2026-07-12T18:26:05.728364Z"},"links":{"cited_paper":"/paper/2402.16837","citing_paper":"/paper/2604.22951"},"observation_digest":"sha256:c7f75416a7ec3d52c86d0c6696e34cf8691cc543e16213f8080794d0dd7f4710","observation_id":"0d4344c0-95fb-4103-bd41-d6625e10ceec","resolution":{"observed_at":"2026-07-12T18:26:05.728364Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.16837","last_updated":"2025-05-31T11:20:49Z","snapshot_observed_at":"2026-08-09T17:39:53.622177Z","submitted_at":"2024-02-26T18:57:54Z","title":"Do Large Language Models Latently Perform Multi-Hop Reasoning?","version":2},"cited_work":{"arxiv_id":"2402.16837","doi":"10.48550/arxiv.2402.16837","metadata_source":"arxiv_reference","pith_arxiv_id":"2402.16837","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Do large language models latently perform multi-hop reasoning? arXiv preprint arXiv:2402.16837","venue":"arXiv (Cornell University)","work_id":"4ab404e2-abd3-493f-8e58-856cfffbf35f","year":2025},"citing_paper":{"arxiv_id":"2605.08221","last_updated":"2026-05-06T13:58:55Z","snapshot_observed_at":"2026-07-06T23:20:28.875586Z","submitted_at":"2026-05-06T13:58:55Z","title":"NoisyCoconut: Counterfactual Consensus via Latent Space Reasoning","version":1},"reference_index":43,"source":"arxiv_source","source_observed_at":"2026-05-12T00:51:40.815981Z"},"links":{"cited_paper":"/paper/2402.16837","citing_paper":"/paper/2605.08221"},"observation_digest":"sha256:6764876da949b2adbff601412da25421408451044e25fb6f67d69efc2e513217","observation_id":"31a7b3af-4a34-479d-a0c7-1a4ff8a2b7f3","resolution":{"observed_at":"2026-05-12T08:41:24.466243Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.16837","last_updated":"2025-05-31T11:20:49Z","snapshot_observed_at":"2026-08-09T17:39:53.622177Z","submitted_at":"2024-02-26T18:57:54Z","title":"Do Large Language Models Latently Perform Multi-Hop Reasoning?","version":2},"cited_work":{"arxiv_id":"2402.16837","doi":"10.48550/arxiv.2402.16837","metadata_source":"arxiv_reference","pith_arxiv_id":"2402.16837","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Do large language models latently perform multi-hop reasoning? arXiv preprint arXiv:2402.16837","venue":"arXiv (Cornell University)","work_id":"4ab404e2-abd3-493f-8e58-856cfffbf35f","year":2025},"citing_paper":{"arxiv_id":"2606.07157","last_updated":"2026-08-04T17:04:36Z","snapshot_observed_at":"2026-08-07T23:11:38.083619Z","submitted_at":"2026-06-05T11:17:08Z","title":"Think Fast: Estimating No-CoT Task-Completion Time Horizons of Frontier AI Models","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-06-27T22:10:01.701850Z"},"links":{"cited_paper":"/paper/2402.16837","citing_paper":"/paper/2606.07157"},"observation_digest":"sha256:a4732b6645d86de8b3c1b814ec098f4732c35fda507a5e7dd4f1c1bc112f0292","observation_id":"e7722079-e52b-4b9e-b654-b298b321fd60","resolution":{"observed_at":"2026-07-02T17:07:12.978958Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.16837","last_updated":"2025-05-31T11:20:49Z","snapshot_observed_at":"2026-08-09T17:39:53.622177Z","submitted_at":"2024-02-26T18:57:54Z","title":"Do Large Language Models Latently Perform Multi-Hop Reasoning?","version":2},"cited_work":{"arxiv_id":"2402.16837","doi":"10.48550/arxiv.2402.16837","metadata_source":"arxiv_reference","pith_arxiv_id":"2402.16837","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Do large language models latently perform multi-hop reasoning? arXiv preprint arXiv:2402.16837","venue":"arXiv (Cornell University)","work_id":"4ab404e2-abd3-493f-8e58-856cfffbf35f","year":2025},"citing_paper":{"arxiv_id":"2606.07157","last_updated":"2026-08-04T17:04:36Z","snapshot_observed_at":"2026-08-07T23:11:38.083619Z","submitted_at":"2026-06-05T11:17:08Z","title":"Think Fast: Estimating No-CoT Task-Completion Time Horizons of Frontier AI Models","version":3},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-06-29T05:50:40.480874Z"},"links":{"cited_paper":"/paper/2402.16837","citing_paper":"/paper/2606.07157"},"observation_digest":"sha256:da223ef76a5ed364e068a6dda5a09120178c6022ee83738570312cf035e74783","observation_id":"033ae7bb-7b03-4482-add9-7381bfbaccc9","resolution":{"observed_at":"2026-06-29T05:53:08.723518Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.16837","last_updated":"2025-05-31T11:20:49Z","snapshot_observed_at":"2026-08-09T17:39:53.622177Z","submitted_at":"2024-02-26T18:57:54Z","title":"Do Large Language Models Latently Perform Multi-Hop Reasoning?","version":2},"cited_work":{"arxiv_id":"2402.16837","doi":"10.48550/arxiv.2402.16837","metadata_source":"arxiv_reference","pith_arxiv_id":"2402.16837","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Do large language models latently perform multi-hop reasoning? arXiv preprint arXiv:2402.16837","venue":"arXiv (Cornell University)","work_id":"4ab404e2-abd3-493f-8e58-856cfffbf35f","year":2025},"citing_paper":{"arxiv_id":"2607.00341","last_updated":"2026-07-27T05:15:18Z","snapshot_observed_at":"2026-08-02T09:18:41.625301Z","submitted_at":"2026-07-01T02:32:02Z","title":"DiscoLoop: Looping Discrete Embeddings and Continuous Hidden States for Multi-hop Reasoning","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-07-02T13:46:59.407102Z"},"links":{"cited_paper":"/paper/2402.16837","citing_paper":"/paper/2607.00341"},"observation_digest":"sha256:167dde735cf6aaaefa774eb285814eea8086b65712b149c747bd98c0ad18bba7","observation_id":"5556419a-d99d-4be2-ae1c-a973271fb81e","resolution":{"observed_at":"2026-07-02T13:56:59.360506Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.16837","last_updated":"2025-05-31T11:20:49Z","snapshot_observed_at":"2026-08-09T17:39:53.622177Z","submitted_at":"2024-02-26T18:57:54Z","title":"Do Large Language Models Latently Perform Multi-Hop Reasoning?","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.16837","snapshot_observed_at":"2026-08-02T09:18:44.931948Z","title":"Do large language models latently perform multi-hop reasoning?arXiv preprint arXiv:2402.16837,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.00341","last_updated":"2026-07-27T05:15:18Z","snapshot_observed_at":"2026-08-02T09:18:41.625301Z","submitted_at":"2026-07-01T02:32:02Z","title":"DiscoLoop: Looping Discrete Embeddings and Continuous Hidden States for Multi-hop Reasoning","version":2},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-02T09:18:44.931948Z"},"links":{"cited_paper":"/paper/2402.16837","citing_paper":"/paper/2607.00341"},"observation_digest":"sha256:d48a779070f0d03477dd69a281aa6ace2bcbef59cf5b01fdc6e57820af29da3c","observation_id":"4dda5438-1583-401d-b354-44247404042b","resolution":{"observed_at":"2026-08-02T09:18:44.931948Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.16837","last_updated":"2025-05-31T11:20:49Z","snapshot_observed_at":"2026-08-09T17:39:53.622177Z","submitted_at":"2024-02-26T18:57:54Z","title":"Do Large Language Models Latently Perform Multi-Hop Reasoning?","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.16837","snapshot_observed_at":"2026-08-01T23:15:30.900344Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.15495","last_updated":"2026-07-16T22:54:30Z","snapshot_observed_at":"2026-08-07T08:22:43.917790Z","submitted_at":"2026-07-16T22:54:30Z","title":"Verbalizable Representations Form a Global Workspace in Language Models","version":1},"reference_index":181,"source":"pdf_text","source_observed_at":"2026-08-01T23:15:30.900344Z"},"links":{"cited_paper":"/paper/2402.16837","citing_paper":"/paper/2607.15495"},"observation_digest":"sha256:58cb5cb8d6b6471e8e7ef2d5b28ecf5da3119772fe03818757df79f63b04443a","observation_id":"95edb54a-6153-42ca-8d77-edd2957068c2","resolution":{"observed_at":"2026-08-01T23:15:30.900344Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2402.16837/citation-record","integrity":"/paper/2402.16837/integrity","json":"/paper/2402.16837/citation-record.json","paper":"/paper/2402.16837"},"outbound":[],"paper":{"arxiv_id":"2402.16837","last_updated":"2025-05-31T11:20:49Z","latest_version":2,"primary_category":"cs.CL","snapshot_observed_at":"2026-08-09T17:39:53.622177Z","submitted_at":"2024-02-26T18:57:54Z","title":"Do Large Language Models Latently Perform Multi-Hop Reasoning?"},"reference_resolution":{"displayed":0,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":0},"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":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"thesis":"As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 34 inbound Pith citation observations for arXiv:2402.16837."}