{"as_of":"2026-08-17T16:24:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:758aa586b41c00991c560858b0f126390edb0b6b72a64a73502c1a37f6715661","coverage":[{"denominator":31,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":31,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-15T20:39:33.801954Z","state":"measured"},{"denominator":31,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":31,"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/2505.12328/citation-record","integrity":"/paper/2505.12328/integrity","json":"/paper/2505.12328/citation-record.json","paper":"/paper/2505.12328"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T20:39:33.679116Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.12328","last_updated":"2025-05-18T09:46:30Z","snapshot_observed_at":"2026-08-15T20:33:52.210927Z","submitted_at":"2025-05-18T09:46:30Z","title":"LLMSR@XLLM25: An Empirical Study of LLM for Structural Reasoning","version":1},"reference_index":1,"source":"arxiv_source","source_observed_at":"2026-08-15T20:39:33.679116Z"},"links":{"citing_paper":"/paper/2505.12328"},"observation_digest":"sha256:019ec09840ea811f7580a25f4a9c2d3649449491ef1d2965d951dd1a5c3e5a9f","observation_id":"6ecd7814-ab94-4e6b-a58d-2e0ec4dc299f","resolution":{"observed_at":"2026-08-15T20:39:33.679116Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2502.20238","last_updated":"2025-06-01T15:57:10Z","snapshot_observed_at":"2026-08-16T12:54:35.066776Z","submitted_at":"2025-02-27T16:23:25Z","title":"FINEREASON: Evaluating and Improving LLMs' Deliberate Reasoning through Reflective Puzzle Solving","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.20238","snapshot_observed_at":"2026-08-15T20:39:33.685041Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2505.12328","last_updated":"2025-05-18T09:46:30Z","snapshot_observed_at":"2026-08-15T20:33:52.210927Z","submitted_at":"2025-05-18T09:46:30Z","title":"LLMSR@XLLM25: An Empirical Study of LLM for Structural Reasoning","version":1},"reference_index":2,"source":"arxiv_source","source_observed_at":"2026-08-15T20:39:33.685041Z"},"links":{"cited_paper":"/paper/2502.20238","citing_paper":"/paper/2505.12328"},"observation_digest":"sha256:2d3c5fe834038a7e4d4497d4ddf388e525c85c514116ce92c51560a7d9ea1b7b","observation_id":"a2977020-4149-4ca1-9e41-639af0f5c818","resolution":{"observed_at":"2026-08-15T20:39:33.685041Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T20:39:33.689191Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.12328","last_updated":"2025-05-18T09:46:30Z","snapshot_observed_at":"2026-08-15T20:33:52.210927Z","submitted_at":"2025-05-18T09:46:30Z","title":"LLMSR@XLLM25: An Empirical Study of LLM for Structural Reasoning","version":1},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-08-15T20:39:33.689191Z"},"links":{"citing_paper":"/paper/2505.12328"},"observation_digest":"sha256:9363727d0ff3e1ae026b05e705c64fd379a169f19a6850f9c174676745d1d9a7","observation_id":"62608423-6418-4218-a361-8edde3eee31d","resolution":{"observed_at":"2026-08-15T20:39:33.689191Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2212.07919","last_updated":"2023-09-12T15:08:46Z","snapshot_observed_at":"2026-08-16T16:08:23.354691Z","submitted_at":"2022-12-15T15:52:39Z","title":"ROSCOE: A Suite of Metrics for Scoring Step-by-Step Reasoning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2212.07919","snapshot_observed_at":"2026-08-15T20:39:33.693834Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.12328","last_updated":"2025-05-18T09:46:30Z","snapshot_observed_at":"2026-08-15T20:33:52.210927Z","submitted_at":"2025-05-18T09:46:30Z","title":"LLMSR@XLLM25: An Empirical Study of LLM for Structural Reasoning","version":1},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-08-15T20:39:33.693834Z"},"links":{"cited_paper":"/paper/2212.07919","citing_paper":"/paper/2505.12328"},"observation_digest":"sha256:813666d74b52b6b30c4c9d3e60dabefa151bf4cd082fd3806eb2eb3c74f39679","observation_id":"d5fefe2b-f05d-4c68-9144-026ecdcedc12","resolution":{"observed_at":"2026-08-15T20:39:33.693834Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.08475","last_updated":"2025-05-29T04:09:28Z","snapshot_observed_at":"2026-08-16T13:10:29.258493Z","submitted_at":"2024-10-11T03:05:06Z","title":"GIVE: Structured Reasoning of Large Language Models with Knowledge Graph Inspired Veracity Extrapolation","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.08475","snapshot_observed_at":"2026-08-15T20:39:33.697878Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2505.12328","last_updated":"2025-05-18T09:46:30Z","snapshot_observed_at":"2026-08-15T20:33:52.210927Z","submitted_at":"2025-05-18T09:46:30Z","title":"LLMSR@XLLM25: An Empirical Study of LLM for Structural Reasoning","version":1},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-08-15T20:39:33.697878Z"},"links":{"cited_paper":"/paper/2410.08475","citing_paper":"/paper/2505.12328"},"observation_digest":"sha256:f0f4c6cb141d1d9411704a28a30fab9cee752b4f9ce016ea404e418f73757e59","observation_id":"511a204a-89df-4818-a0df-43536cce194e","resolution":{"observed_at":"2026-08-15T20:39:33.697878Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.00390","last_updated":"2024-06-29T10:09:49Z","snapshot_observed_at":"2026-08-17T08:27:56.611411Z","submitted_at":"2024-06-29T10:09:49Z","title":"Advancing Process Verification for Large Language Models via Tree-Based Preference Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.00390","snapshot_observed_at":"2026-08-15T20:39:33.701889Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.12328","last_updated":"2025-05-18T09:46:30Z","snapshot_observed_at":"2026-08-15T20:33:52.210927Z","submitted_at":"2025-05-18T09:46:30Z","title":"LLMSR@XLLM25: An Empirical Study of LLM for Structural Reasoning","version":1},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-08-15T20:39:33.701889Z"},"links":{"cited_paper":"/paper/2407.00390","citing_paper":"/paper/2505.12328"},"observation_digest":"sha256:7e1282ed269cbd075c5f82f8d77c4dd376b4f23f654733109b54efd5659f92c3","observation_id":"b849478b-d92e-4afa-8332-850f4acff804","resolution":{"observed_at":"2026-08-15T20:39:33.701889Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2501.13622","last_updated":"2025-08-22T14:48:22Z","snapshot_observed_at":"2026-08-15T04:53:18.102368Z","submitted_at":"2025-01-23T12:44:45Z","title":"Coarse-to-Fine Process Reward Modeling for Mathematical Reasoning","version":4},"cited_work":{"arxiv_id":"2501.13622","doi":null,"metadata_source":"pith","pith_arxiv_id":"2501.13622","snapshot_observed_at":"2026-08-15T20:39:33.995676Z","title":"Coarse-to-Fine Process Reward Modeling for Mathematical Reasoning","venue":"cs.AI","work_id":"9dd6f8f7-dd46-4773-baba-7f0622edf4b8","year":2025},"citing_paper":{"arxiv_id":"2505.12328","last_updated":"2025-05-18T09:46:30Z","snapshot_observed_at":"2026-08-15T20:33:52.210927Z","submitted_at":"2025-05-18T09:46:30Z","title":"LLMSR@XLLM25: An Empirical Study of LLM for Structural Reasoning","version":1},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-08-15T20:39:33.706526Z"},"links":{"cited_paper":"/paper/2501.13622","citing_paper":"/paper/2505.12328"},"observation_digest":"sha256:daa3303ddeffe0d030f842d2a393020169971c30d6e3d463f120e7cf465d05e8","observation_id":"d822cfe9-f980-458a-8f43-2dc0eaa89d52","resolution":{"observed_at":"2026-08-15T20:39:34.000646Z","resolver_source":"local_arxiv","status":"verified_exact"},"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":"2026-08-15T20:39:34.124474Z","title":null,"venue":null,"work_id":"f3300efb-a244-4110-9e2e-309d4f05e5be","year":2023},"citing_paper":{"arxiv_id":"2505.12328","last_updated":"2025-05-18T09:46:30Z","snapshot_observed_at":"2026-08-15T20:33:52.210927Z","submitted_at":"2025-05-18T09:46:30Z","title":"LLMSR@XLLM25: An Empirical Study of LLM for Structural Reasoning","version":1},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-08-15T20:39:33.711172Z"},"links":{"citing_paper":"/paper/2505.12328"},"observation_digest":"sha256:c5c0f6806dd64c86f5cbd4f53370847c1f158bf6594f13b220dbb1d4cb774a53","observation_id":"830b9d02-d2d0-4b1b-8b33-1ff51b8a0b66","resolution":{"observed_at":"2026-08-15T20:39:34.128426Z","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":"2026-08-15T20:39:33.715097Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.12328","last_updated":"2025-05-18T09:46:30Z","snapshot_observed_at":"2026-08-15T20:33:52.210927Z","submitted_at":"2025-05-18T09:46:30Z","title":"LLMSR@XLLM25: An Empirical Study of LLM for Structural Reasoning","version":1},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-08-15T20:39:33.715097Z"},"links":{"citing_paper":"/paper/2505.12328"},"observation_digest":"sha256:627aea5983b8e4359e8f89be873c2f6a022e4a2b47c2920639cd092a958d0432","observation_id":"277c71d1-8d48-4b7e-9225-7759ed6e4159","resolution":{"observed_at":"2026-08-15T20:39:33.715097Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2502.07803","last_updated":"2025-02-05T08:23:18Z","snapshot_observed_at":"2026-08-17T15:20:04.372945Z","submitted_at":"2025-02-05T08:23:18Z","title":"Reasoning-as-Logic-Units: Scaling Test-Time Reasoning in Large Language Models Through Logic Unit Alignment","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.07803","snapshot_observed_at":"2026-08-15T20:39:33.719665Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2505.12328","last_updated":"2025-05-18T09:46:30Z","snapshot_observed_at":"2026-08-15T20:33:52.210927Z","submitted_at":"2025-05-18T09:46:30Z","title":"LLMSR@XLLM25: An Empirical Study of LLM for Structural Reasoning","version":1},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-08-15T20:39:33.719665Z"},"links":{"cited_paper":"/paper/2502.07803","citing_paper":"/paper/2505.12328"},"observation_digest":"sha256:340f4efbc340217ffd4249a0970bcc0480450c9e6ee18bc1705315cc330aa644","observation_id":"f68f0223-2c54-4781-ae38-523d5130da68","resolution":{"observed_at":"2026-08-15T20:39:33.719665Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2305.20050","last_updated":"2023-05-31T17:24:00Z","snapshot_observed_at":"2026-08-17T09:42:34.746112Z","submitted_at":"2023-05-31T17:24:00Z","title":"Let's Verify Step by Step","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.20050","snapshot_observed_at":"2026-08-15T20:39:33.723776Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.12328","last_updated":"2025-05-18T09:46:30Z","snapshot_observed_at":"2026-08-15T20:33:52.210927Z","submitted_at":"2025-05-18T09:46:30Z","title":"LLMSR@XLLM25: An Empirical Study of LLM for Structural Reasoning","version":1},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-08-15T20:39:33.723776Z"},"links":{"cited_paper":"/paper/2305.20050","citing_paper":"/paper/2505.12328"},"observation_digest":"sha256:c1867b99215285e4390f7127e4a7a3b1e67f774dfc8586fa7d2eeeacc70f86f0","observation_id":"42533f6a-2117-4522-b0ff-2bba329de205","resolution":{"observed_at":"2026-08-15T20:39:33.723776Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T20:39:33.727780Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.12328","last_updated":"2025-05-18T09:46:30Z","snapshot_observed_at":"2026-08-15T20:33:52.210927Z","submitted_at":"2025-05-18T09:46:30Z","title":"LLMSR@XLLM25: An Empirical Study of LLM for Structural Reasoning","version":1},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-08-15T20:39:33.727780Z"},"links":{"citing_paper":"/paper/2505.12328"},"observation_digest":"sha256:cab216f4b18f17382e23712ff8eab674778fac918a332d51edf18a49d7c40403","observation_id":"3afb351c-15d6-4aff-a89d-31ded749b3e9","resolution":{"observed_at":"2026-08-15T20:39:33.727780Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T20:39:33.731525Z","title":"MANN and SANDRA A","venue":null,"work_id":null,"year":1988},"citing_paper":{"arxiv_id":"2505.12328","last_updated":"2025-05-18T09:46:30Z","snapshot_observed_at":"2026-08-15T20:33:52.210927Z","submitted_at":"2025-05-18T09:46:30Z","title":"LLMSR@XLLM25: An Empirical Study of LLM for Structural Reasoning","version":1},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-08-15T20:39:33.731525Z"},"links":{"citing_paper":"/paper/2505.12328"},"observation_digest":"sha256:6b924251555bf831958435b3df4b657604f4216a3e32f1b471f930aac024a4d3","observation_id":"c3b16758-1be8-4dc5-ac59-a1ee0b31d18f","resolution":{"observed_at":"2026-08-15T20:39:33.731525Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T20:39:34.099381Z","title":null,"venue":null,"work_id":"008ea1b8-4366-4ae5-af7b-c22b5d8b545a","year":1998},"citing_paper":{"arxiv_id":"2505.12328","last_updated":"2025-05-18T09:46:30Z","snapshot_observed_at":"2026-08-15T20:33:52.210927Z","submitted_at":"2025-05-18T09:46:30Z","title":"LLMSR@XLLM25: An Empirical Study of LLM for Structural Reasoning","version":1},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-08-15T20:39:33.735658Z"},"links":{"citing_paper":"/paper/2505.12328"},"observation_digest":"sha256:ab5fe1c0b5f683c6625c5bfa9f4a6bbb763abf88f3299a26f8f4d8693a7b2e45","observation_id":"2a0101b4-ec12-4aa9-9e96-7e696cf8da64","resolution":{"observed_at":"2026-08-15T20:39:34.103150Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T20:39:34.087237Z","title":null,"venue":null,"work_id":"c4c8bef5-1253-4b70-a2ad-3372ecee224c","year":2024},"citing_paper":{"arxiv_id":"2505.12328","last_updated":"2025-05-18T09:46:30Z","snapshot_observed_at":"2026-08-15T20:33:52.210927Z","submitted_at":"2025-05-18T09:46:30Z","title":"LLMSR@XLLM25: An Empirical Study of LLM for Structural Reasoning","version":1},"reference_index":15,"source":"arxiv_source","source_observed_at":"2026-08-15T20:39:33.739084Z"},"links":{"citing_paper":"/paper/2505.12328"},"observation_digest":"sha256:385911933b4be2819a52b405f5b933991315426f2a1068a57aff85e287fccc01","observation_id":"f031688b-1a3a-43b5-8eeb-e47c35de7ee3","resolution":{"observed_at":"2026-08-15T20:39:34.090868Z","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":"2026-08-15T20:39:33.742447Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.12328","last_updated":"2025-05-18T09:46:30Z","snapshot_observed_at":"2026-08-15T20:33:52.210927Z","submitted_at":"2025-05-18T09:46:30Z","title":"LLMSR@XLLM25: An Empirical Study of LLM for Structural Reasoning","version":1},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-08-15T20:39:33.742447Z"},"links":{"citing_paper":"/paper/2505.12328"},"observation_digest":"sha256:fe908b3fa97279b5b2bfdc0ca8f2ce82f95b8b92ed8b3023f6acf10598ee0d6c","observation_id":"2b83aac2-8285-4370-9b32-14cbe4fc0785","resolution":{"observed_at":"2026-08-15T20:39:33.742447Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T20:39:34.075420Z","title":null,"venue":null,"work_id":"a45f90c2-814f-4fb9-9d39-f5fd196d35f8","year":2023},"citing_paper":{"arxiv_id":"2505.12328","last_updated":"2025-05-18T09:46:30Z","snapshot_observed_at":"2026-08-15T20:33:52.210927Z","submitted_at":"2025-05-18T09:46:30Z","title":"LLMSR@XLLM25: An Empirical Study of LLM for Structural Reasoning","version":1},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-08-15T20:39:33.746226Z"},"links":{"citing_paper":"/paper/2505.12328"},"observation_digest":"sha256:d13c1006c854d2a48a3419a19c370678d19d2e07eadb2ad5c5932d8f10a73e0e","observation_id":"7175e17b-21eb-4185-90fe-85ff6b3ccf2d","resolution":{"observed_at":"2026-08-15T20:39:34.079258Z","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":{"arxiv_id":"2209.11302","last_updated":"2022-09-22T20:29:49Z","snapshot_observed_at":"2026-08-15T23:55:01.179556Z","submitted_at":"2022-09-22T20:29:49Z","title":"ProgPrompt: Generating Situated Robot Task Plans using Large Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2209.11302","snapshot_observed_at":"2026-08-15T20:39:33.749774Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.12328","last_updated":"2025-05-18T09:46:30Z","snapshot_observed_at":"2026-08-15T20:33:52.210927Z","submitted_at":"2025-05-18T09:46:30Z","title":"LLMSR@XLLM25: An Empirical Study of LLM for Structural Reasoning","version":1},"reference_index":18,"source":"arxiv_source","source_observed_at":"2026-08-15T20:39:33.749774Z"},"links":{"cited_paper":"/paper/2209.11302","citing_paper":"/paper/2505.12328"},"observation_digest":"sha256:28dedde5bc2de6283bc9042e307e0577dfaeb7c655b763b2b23ce53ad8a7841a","observation_id":"7529d104-fe13-4009-b263-849488e4f778","resolution":{"observed_at":"2026-08-15T20:39:33.749774Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2211.14275","last_updated":"2022-11-25T18:19:44Z","snapshot_observed_at":"2026-08-01T02:16:43.109337Z","submitted_at":"2022-11-25T18:19:44Z","title":"Solving math word problems with process- and outcome-based feedback","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2211.14275","snapshot_observed_at":"2026-08-15T20:39:33.753535Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.12328","last_updated":"2025-05-18T09:46:30Z","snapshot_observed_at":"2026-08-15T20:33:52.210927Z","submitted_at":"2025-05-18T09:46:30Z","title":"LLMSR@XLLM25: An Empirical Study of LLM for Structural Reasoning","version":1},"reference_index":19,"source":"arxiv_source","source_observed_at":"2026-08-15T20:39:33.753535Z"},"links":{"cited_paper":"/paper/2211.14275","citing_paper":"/paper/2505.12328"},"observation_digest":"sha256:d202569b9cef5fce50e056a9f63b28bc5f454586900c67fcdb37712a34480c90","observation_id":"5870f9ef-2ee3-439b-8e55-9fefd68ce687","resolution":{"observed_at":"2026-08-15T20:39:33.753535Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2312.08935","last_updated":"2024-02-19T14:07:53Z","snapshot_observed_at":"2026-08-14T10:08:59.886019Z","submitted_at":"2023-12-14T13:41:54Z","title":"Math-Shepherd: Verify and Reinforce LLMs Step-by-step without Human Annotations","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2312.08935","snapshot_observed_at":"2026-08-15T20:39:33.757407Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.12328","last_updated":"2025-05-18T09:46:30Z","snapshot_observed_at":"2026-08-15T20:33:52.210927Z","submitted_at":"2025-05-18T09:46:30Z","title":"LLMSR@XLLM25: An Empirical Study of LLM for Structural Reasoning","version":1},"reference_index":20,"source":"arxiv_source","source_observed_at":"2026-08-15T20:39:33.757407Z"},"links":{"cited_paper":"/paper/2312.08935","citing_paper":"/paper/2505.12328"},"observation_digest":"sha256:a4739304d60f081a97df0862c1e52965b98e6f92e0bc06a4435a1fafbb48fde7","observation_id":"34b258e3-4b99-4fbc-9968-a2556093ce25","resolution":{"observed_at":"2026-08-15T20:39:33.757407Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2203.11171","last_updated":"2023-03-07T17:57:37Z","snapshot_observed_at":"2026-08-16T01:49:22.176843Z","submitted_at":"2022-03-21T17:48:52Z","title":"Self-Consistency Improves Chain of Thought Reasoning in Language Models","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2203.11171","snapshot_observed_at":"2026-08-15T20:39:33.761188Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.12328","last_updated":"2025-05-18T09:46:30Z","snapshot_observed_at":"2026-08-15T20:33:52.210927Z","submitted_at":"2025-05-18T09:46:30Z","title":"LLMSR@XLLM25: An Empirical Study of LLM for Structural Reasoning","version":1},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-08-15T20:39:33.761188Z"},"links":{"cited_paper":"/paper/2203.11171","citing_paper":"/paper/2505.12328"},"observation_digest":"sha256:344cd837b9fcf05bdb4297adf0350e349cb9fc3696d616ba3e9a34d43e58a15b","observation_id":"7637a4b1-b4eb-41ff-8359-fef08fa53a43","resolution":{"observed_at":"2026-08-15T20:39:33.761188Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2201.11903","last_updated":"2023-01-10T23:07:57Z","snapshot_observed_at":"2026-08-13T07:04:41.220509Z","submitted_at":"2022-01-28T02:33:07Z","title":"Chain-of-Thought Prompting Elicits Reasoning in Large Language Models","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2201.11903","snapshot_observed_at":"2026-08-15T20:39:33.765177Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.12328","last_updated":"2025-05-18T09:46:30Z","snapshot_observed_at":"2026-08-15T20:33:52.210927Z","submitted_at":"2025-05-18T09:46:30Z","title":"LLMSR@XLLM25: An Empirical Study of LLM for Structural Reasoning","version":1},"reference_index":22,"source":"arxiv_source","source_observed_at":"2026-08-15T20:39:33.765177Z"},"links":{"cited_paper":"/paper/2201.11903","citing_paper":"/paper/2505.12328"},"observation_digest":"sha256:645ea8d9846e7bf1f1808b480a05f0d0a110a16bfbd432eb123ab0df39f2c0a8","observation_id":"77057709-1e82-4103-8424-c4cf67cd4252","resolution":{"observed_at":"2026-08-15T20:39:33.765177Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2404.05692","last_updated":"2025-01-14T05:39:40Z","snapshot_observed_at":"2026-08-16T14:02:38.932317Z","submitted_at":"2024-04-08T17:18:04Z","title":"Evaluating Mathematical Reasoning Beyond Accuracy","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.05692","snapshot_observed_at":"2026-08-15T20:39:33.769211Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2505.12328","last_updated":"2025-05-18T09:46:30Z","snapshot_observed_at":"2026-08-15T20:33:52.210927Z","submitted_at":"2025-05-18T09:46:30Z","title":"LLMSR@XLLM25: An Empirical Study of LLM for Structural Reasoning","version":1},"reference_index":23,"source":"arxiv_source","source_observed_at":"2026-08-15T20:39:33.769211Z"},"links":{"cited_paper":"/paper/2404.05692","citing_paper":"/paper/2505.12328"},"observation_digest":"sha256:1b4a8195fce896d35d1ac934ec36d428852baa55a03f0c564074cc2209ffc8b0","observation_id":"85c697fd-1f87-48d1-94b2-f0e33b94f42c","resolution":{"observed_at":"2026-08-15T20:39:33.769211Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2307.07696","last_updated":"2023-07-15T03:29:59Z","snapshot_observed_at":"2026-08-16T15:16:05.175762Z","submitted_at":"2023-07-15T03:29:59Z","title":"Coupling Large Language Models with Logic Programming for Robust and General Reasoning from Text","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2307.07696","snapshot_observed_at":"2026-08-15T20:39:33.773480Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.12328","last_updated":"2025-05-18T09:46:30Z","snapshot_observed_at":"2026-08-15T20:33:52.210927Z","submitted_at":"2025-05-18T09:46:30Z","title":"LLMSR@XLLM25: An Empirical Study of LLM for Structural Reasoning","version":1},"reference_index":24,"source":"arxiv_source","source_observed_at":"2026-08-15T20:39:33.773480Z"},"links":{"cited_paper":"/paper/2307.07696","citing_paper":"/paper/2505.12328"},"observation_digest":"sha256:a3a5b31931253ec55f1251288cd258aeeb8df71728b4e1d4f15397485803ccf0","observation_id":"43a2f1b6-21ca-4f2c-9ee6-939ed2f9a0b2","resolution":{"observed_at":"2026-08-15T20:39:33.773480Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2305.10601","last_updated":"2023-12-03T22:50:35Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-05-17T23:16:17Z","title":"Tree of Thoughts: Deliberate Problem Solving with Large Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.10601","snapshot_observed_at":"2026-08-15T20:39:33.777624Z","title":"Griffiths, Yuan Cao, and Karthik Narasimhan","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.12328","last_updated":"2025-05-18T09:46:30Z","snapshot_observed_at":"2026-08-15T20:33:52.210927Z","submitted_at":"2025-05-18T09:46:30Z","title":"LLMSR@XLLM25: An Empirical Study of LLM for Structural Reasoning","version":1},"reference_index":25,"source":"arxiv_source","source_observed_at":"2026-08-15T20:39:33.777624Z"},"links":{"cited_paper":"/paper/2305.10601","citing_paper":"/paper/2505.12328"},"observation_digest":"sha256:9303558f9c0b0c60b4befd439e5f1f7d4b243e5fb6196fce5f2a85dac291e71b","observation_id":"bc15464c-bd4a-41b4-914a-810ae99ab371","resolution":{"observed_at":"2026-08-15T20:39:33.777624Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2403.13560","last_updated":"2024-08-28T14:45:57Z","snapshot_observed_at":"2026-08-16T14:08:03.416193Z","submitted_at":"2024-03-20T12:52:38Z","title":"eRST: A Signaled Graph Theory of Discourse Relations and Organization","version":2},"cited_work":{"arxiv_id":"2403.13560","doi":null,"metadata_source":"pith","pith_arxiv_id":"2403.13560","snapshot_observed_at":"2026-08-15T20:39:33.879557Z","title":"eRST: A Signaled Graph Theory of Discourse Relations and Organization","venue":"cs.CL","work_id":"4d303344-b9c1-454b-8d23-85434530caf1","year":2024},"citing_paper":{"arxiv_id":"2505.12328","last_updated":"2025-05-18T09:46:30Z","snapshot_observed_at":"2026-08-15T20:33:52.210927Z","submitted_at":"2025-05-18T09:46:30Z","title":"LLMSR@XLLM25: An Empirical Study of LLM for Structural Reasoning","version":1},"reference_index":26,"source":"arxiv_source","source_observed_at":"2026-08-15T20:39:33.781781Z"},"links":{"cited_paper":"/paper/2403.13560","citing_paper":"/paper/2505.12328"},"observation_digest":"sha256:fa23d7bd655529f2b7ec5ec71437d7fdb9204b05a9820f31c76e306dec7a8e7e","observation_id":"5ce8a393-4c7b-40bf-8525-039bc60a2020","resolution":{"observed_at":"2026-08-15T20:39:33.885968Z","resolver_source":"local_arxiv","status":"verified_exact"},"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":"2026-08-15T20:39:34.064398Z","title":null,"venue":null,"work_id":"95b4e4de-e34d-437d-af9e-156ee18a1ac1","year":2022},"citing_paper":{"arxiv_id":"2505.12328","last_updated":"2025-05-18T09:46:30Z","snapshot_observed_at":"2026-08-15T20:33:52.210927Z","submitted_at":"2025-05-18T09:46:30Z","title":"LLMSR@XLLM25: An Empirical Study of LLM for Structural Reasoning","version":1},"reference_index":27,"source":"arxiv_source","source_observed_at":"2026-08-15T20:39:33.785794Z"},"links":{"citing_paper":"/paper/2505.12328"},"observation_digest":"sha256:98243f419708592d16439cb71affc1f3f66c53a0f1fe2f4dd4eca1b1a4444b1c","observation_id":"c2c14ca7-56c2-45bf-97ec-e5cbd465f963","resolution":{"observed_at":"2026-08-15T20:39:34.067945Z","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":{"arxiv_id":"2210.03493","last_updated":"2022-10-07T12:28:21Z","snapshot_observed_at":"2026-07-06T14:01:50.333970Z","submitted_at":"2022-10-07T12:28:21Z","title":"Automatic Chain of Thought Prompting in Large Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2210.03493","snapshot_observed_at":"2026-08-15T20:39:33.790067Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.12328","last_updated":"2025-05-18T09:46:30Z","snapshot_observed_at":"2026-08-15T20:33:52.210927Z","submitted_at":"2025-05-18T09:46:30Z","title":"LLMSR@XLLM25: An Empirical Study of LLM for Structural Reasoning","version":1},"reference_index":28,"source":"arxiv_source","source_observed_at":"2026-08-15T20:39:33.790067Z"},"links":{"cited_paper":"/paper/2210.03493","citing_paper":"/paper/2505.12328"},"observation_digest":"sha256:a49651cdc6356634b8bb145e6d542bff3d631cef60048fcbdf4c30efc1c90e09","observation_id":"8066f0ab-a82f-4b12-b3d4-8c4fc3945dd9","resolution":{"observed_at":"2026-08-15T20:39:33.790067Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2205.10625","last_updated":"2023-04-16T22:08:08Z","snapshot_observed_at":"2026-08-16T05:27:41.446467Z","submitted_at":"2022-05-21T15:34:53Z","title":"Least-to-Most Prompting Enables Complex Reasoning in Large Language Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2205.10625","snapshot_observed_at":"2026-08-15T20:39:33.793730Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.12328","last_updated":"2025-05-18T09:46:30Z","snapshot_observed_at":"2026-08-15T20:33:52.210927Z","submitted_at":"2025-05-18T09:46:30Z","title":"LLMSR@XLLM25: An Empirical Study of LLM for Structural Reasoning","version":1},"reference_index":29,"source":"arxiv_source","source_observed_at":"2026-08-15T20:39:33.793730Z"},"links":{"cited_paper":"/paper/2205.10625","citing_paper":"/paper/2505.12328"},"observation_digest":"sha256:e12fd8b4285a4f8707a290b8d62931ae8283bd1f955e5f209b083f192f500bc8","observation_id":"6d3ccabb-e69a-45d4-ae55-031338ec9247","resolution":{"observed_at":"2026-08-15T20:39:33.793730Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T20:39:33.797887Z","title":"online\" 'onlinestring :=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.12328","last_updated":"2025-05-18T09:46:30Z","snapshot_observed_at":"2026-08-15T20:33:52.210927Z","submitted_at":"2025-05-18T09:46:30Z","title":"LLMSR@XLLM25: An Empirical Study of LLM for Structural Reasoning","version":1},"reference_index":30,"source":"arxiv_source","source_observed_at":"2026-08-15T20:39:33.797887Z"},"links":{"citing_paper":"/paper/2505.12328"},"observation_digest":"sha256:284979e9b6890a83d014b841a822ececc844b8a1adfd904dbc33109dd3415187","observation_id":"1f15a880-5199-478d-bc00-2ef6d20f7b9a","resolution":{"observed_at":"2026-08-15T20:39:33.797887Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T20:39:33.801954Z","title":"write newline","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.12328","last_updated":"2025-05-18T09:46:30Z","snapshot_observed_at":"2026-08-15T20:33:52.210927Z","submitted_at":"2025-05-18T09:46:30Z","title":"LLMSR@XLLM25: An Empirical Study of LLM for Structural Reasoning","version":1},"reference_index":31,"source":"arxiv_source","source_observed_at":"2026-08-15T20:39:33.801954Z"},"links":{"citing_paper":"/paper/2505.12328"},"observation_digest":"sha256:07b8e4bff512648ed7d40bde20732f5401b544c27c2d49d96f13e7d6a07a4747","observation_id":"54ee6aad-74af-4e68-9dfd-ff232b26035e","resolution":{"observed_at":"2026-08-15T20:39:33.801954Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2505.12328","last_updated":"2025-05-18T09:46:30Z","latest_version":1,"primary_category":"cs.CL","snapshot_observed_at":"2026-08-15T20:33:52.210927Z","submitted_at":"2025-05-18T09:46:30Z","title":"LLMSR@XLLM25: An Empirical Study of LLM for Structural Reasoning"},"reference_resolution":{"displayed":31,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":29,"verified_exact":2,"verified_fuzzy":0},"total_outbound_references":31},"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 31 of 31 outbound references and 0 inbound Pith citation observations for arXiv:2505.12328."}