{"as_of":"2026-08-07T07:10:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:07498b53f51febd029cd96b662c10a87cfb2515db2bf4db7ffd6814b0a325ae3","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":"2026-08-07T06:34:17.273281+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":"2026-08-07T06:03:45.165815Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-07-04T15:59:56.438855Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2406.07394","last_updated":"2024-06-13T07:19:06Z","snapshot_observed_at":"2026-07-06T18:29:00.001236Z","submitted_at":"2024-06-11T16:01:07Z","title":"Accessing GPT-4 level Mathematical Olympiad Solutions via Monte Carlo Tree Self-refine with LLaMa-3 8B","version":2},"cited_work":{"arxiv_id":"2406.07394","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2406.07394","snapshot_observed_at":"2026-07-04T15:59:56.438855Z","title":"Large language model-brained gui agents: A survey, 2024a","venue":null,"work_id":"a22fd02a-b313-40f1-9919-b320ccc8d4f4","year":2024},"citing_paper":{"arxiv_id":"2502.00955","last_updated":"2026-04-24T08:45:39Z","snapshot_observed_at":"2026-07-06T20:29:55.352526Z","submitted_at":"2025-02-02T23:20:16Z","title":"Efficient Multi-Agent System Training with Data Influence-Oriented Tree Search","version":2},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-05-23T04:06:23.521344Z"},"links":{"cited_paper":"/paper/2406.07394","citing_paper":"/paper/2502.00955"},"observation_digest":"sha256:4707ffbf548fe394f157ef5583cd55ed01fa9c56aa487402386ca3d6d52959db","observation_id":"9f48eb8c-5b7b-4570-af46-5120351b06a9","resolution":{"observed_at":"2026-05-23T04:07:30.446605Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2406.07394","last_updated":"2024-06-13T07:19:06Z","snapshot_observed_at":"2026-07-06T18:29:00.001236Z","submitted_at":"2024-06-11T16:01:07Z","title":"Accessing GPT-4 level Mathematical Olympiad Solutions via Monte Carlo Tree Self-refine with LLaMa-3 8B","version":2},"cited_work":{"arxiv_id":"2406.07394","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2406.07394","snapshot_observed_at":"2026-07-04T15:59:56.438855Z","title":"Large language model-brained gui agents: A survey, 2024a","venue":null,"work_id":"a22fd02a-b313-40f1-9919-b320ccc8d4f4","year":2024},"citing_paper":{"arxiv_id":"2502.17419","last_updated":"2025-06-25T02:24:46Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-02-24T18:50:52Z","title":"From System 1 to System 2: A Survey of Reasoning Large Language Models","version":6},"reference_index":133,"source":"pdf_text","source_observed_at":"2026-05-13T01:36:23.845366Z"},"links":{"cited_paper":"/paper/2406.07394","citing_paper":"/paper/2502.17419"},"observation_digest":"sha256:ccfb886478c4f2be51c03d302945c255d330f88c5ef717ae731bcbdc1b690ee3","observation_id":"fbdd7186-1d5c-4594-9b3c-07948ead76ed","resolution":{"observed_at":"2026-05-13T01:36:24.068130Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2406.07394","last_updated":"2024-06-13T07:19:06Z","snapshot_observed_at":"2026-07-06T18:29:00.001236Z","submitted_at":"2024-06-11T16:01:07Z","title":"Accessing GPT-4 level Mathematical Olympiad Solutions via Monte Carlo Tree Self-refine with LLaMa-3 8B","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.07394","snapshot_observed_at":"2026-08-07T06:03:45.165815Z","title":"How would you send the money?","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.06180","last_updated":"2025-06-06T15:44:12Z","snapshot_observed_at":"2026-08-07T05:56:36.170779Z","submitted_at":"2025-06-06T15:44:12Z","title":"Detecting Voice Phishing with Precision: Fine-Tuning Small Language Models","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-07T06:03:45.165815Z"},"links":{"cited_paper":"/paper/2406.07394","citing_paper":"/paper/2506.06180"},"observation_digest":"sha256:a9527bdab670318e78b2ec368805b58befe21cc03b76e545b2af6eafba6f27ed","observation_id":"4fad4418-7641-4ef4-ad2f-7d40574699c8","resolution":{"observed_at":"2026-08-07T06:03:45.165815Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.07394","last_updated":"2024-06-13T07:19:06Z","snapshot_observed_at":"2026-07-06T18:29:00.001236Z","submitted_at":"2024-06-11T16:01:07Z","title":"Accessing GPT-4 level Mathematical Olympiad Solutions via Monte Carlo Tree Self-refine with LLaMa-3 8B","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.07394","snapshot_observed_at":"2026-08-07T05:38:33.952668Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.07557","last_updated":"2025-06-09T08:52:27Z","snapshot_observed_at":"2026-08-07T05:28:24.761670Z","submitted_at":"2025-06-09T08:52:27Z","title":"SELT: Self-Evaluation Tree Search for LLMs with Task Decomposition","version":1},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-08-07T05:38:33.952668Z"},"links":{"cited_paper":"/paper/2406.07394","citing_paper":"/paper/2506.07557"},"observation_digest":"sha256:2f0774999a8467eb0e39922efcdff5dc37265a8b223abf43556c20e0691b2a57","observation_id":"e2c6b26e-99e2-404a-8ff1-d6eedf388ce3","resolution":{"observed_at":"2026-08-07T05:38:33.952668Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.07394","last_updated":"2024-06-13T07:19:06Z","snapshot_observed_at":"2026-07-06T18:29:00.001236Z","submitted_at":"2024-06-11T16:01:07Z","title":"Accessing GPT-4 level Mathematical Olympiad Solutions via Monte Carlo Tree Self-refine with LLaMa-3 8B","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.07394","snapshot_observed_at":"2026-08-07T04:45:49.655855Z","title":"Advances in Neural Information Processing Systems (NeurIPS)","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.11155","last_updated":"2025-06-11T15:11:37Z","snapshot_observed_at":"2026-08-07T04:36:58.955244Z","submitted_at":"2025-06-11T15:11:37Z","title":"Evaluating Multimodal Large Language Models on Video Captioning via Monte Carlo Tree Search","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-07T04:45:49.655855Z"},"links":{"cited_paper":"/paper/2406.07394","citing_paper":"/paper/2506.11155"},"observation_digest":"sha256:58120f1546f209852ac9289667d229f3fdaac5846342b1abfe4930320f93aad6","observation_id":"aca6c052-7370-41b5-9be1-b61b192335c7","resolution":{"observed_at":"2026-08-07T04:45:49.655855Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.07394","last_updated":"2024-06-13T07:19:06Z","snapshot_observed_at":"2026-07-06T18:29:00.001236Z","submitted_at":"2024-06-11T16:01:07Z","title":"Accessing GPT-4 level Mathematical Olympiad Solutions via Monte Carlo Tree Self-refine with LLaMa-3 8B","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.07394","snapshot_observed_at":"2026-08-07T04:52:57.680654Z","title":"Accessing gpt-4 level mathematical olympiad solutions via monte carlo tree self-refine with llama-3 8b,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.13983","last_updated":"2025-06-11T06:43:24Z","snapshot_observed_at":"2026-08-07T04:45:49.463046Z","submitted_at":"2025-06-11T06:43:24Z","title":"SANGAM: SystemVerilog Assertion Generation via Monte Carlo Tree Self-Refine","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-07T04:52:57.680654Z"},"links":{"cited_paper":"/paper/2406.07394","citing_paper":"/paper/2506.13983"},"observation_digest":"sha256:f1556f78a68de59c37e73d89b46e8079a2ad655c305f1f7ab5106b381b83d0e7","observation_id":"09eabde7-cfde-42d2-ae9c-2ad25a992f67","resolution":{"observed_at":"2026-08-07T04:52:57.680654Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.07394","last_updated":"2024-06-13T07:19:06Z","snapshot_observed_at":"2026-07-06T18:29:00.001236Z","submitted_at":"2024-06-11T16:01:07Z","title":"Accessing GPT-4 level Mathematical Olympiad Solutions via Monte Carlo Tree Self-refine with LLaMa-3 8B","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.07394","snapshot_observed_at":"2026-08-06T23:35:36.214815Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.17533","last_updated":"2025-06-21T01:11:01Z","snapshot_observed_at":"2026-08-06T23:28:16.216861Z","submitted_at":"2025-06-21T01:11:01Z","title":"DuaShepherd: Integrating Stepwise Correctness and Potential Rewards for Mathematical Reasoning","version":1},"reference_index":35,"source":"arxiv_source","source_observed_at":"2026-08-06T23:35:36.214815Z"},"links":{"cited_paper":"/paper/2406.07394","citing_paper":"/paper/2506.17533"},"observation_digest":"sha256:5e051d11c44ea24ca48dc006fda9fac6581824f13c7904719499a4a270708d0f","observation_id":"0ed12f59-2c2a-4be0-9baf-e34ad72ae4f7","resolution":{"observed_at":"2026-08-06T23:35:36.214815Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.07394","last_updated":"2024-06-13T07:19:06Z","snapshot_observed_at":"2026-07-06T18:29:00.001236Z","submitted_at":"2024-06-11T16:01:07Z","title":"Accessing GPT-4 level Mathematical Olympiad Solutions via Monte Carlo Tree Self-refine with LLaMa-3 8B","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.07394","snapshot_observed_at":"2026-08-06T21:59:13.605541Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.23056","last_updated":"2025-06-29T02:00:38Z","snapshot_observed_at":"2026-08-06T21:48:50.589205Z","submitted_at":"2025-06-29T02:00:38Z","title":"Boosting LLM's Molecular Structure Elucidation with Knowledge Enhanced Tree Search Reasoning","version":1},"reference_index":45,"source":"arxiv_source","source_observed_at":"2026-08-06T21:59:13.605541Z"},"links":{"cited_paper":"/paper/2406.07394","citing_paper":"/paper/2506.23056"},"observation_digest":"sha256:05916f0b803752749c1f662ec08e95f2e6bb80c99f8974ef0a34b6d0d22f3522","observation_id":"c19c2061-f94a-4c5f-b27d-eccf111c0e47","resolution":{"observed_at":"2026-08-06T21:59:13.605541Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.07394","last_updated":"2024-06-13T07:19:06Z","snapshot_observed_at":"2026-07-06T18:29:00.001236Z","submitted_at":"2024-06-11T16:01:07Z","title":"Accessing GPT-4 level Mathematical Olympiad Solutions via Monte Carlo Tree Self-refine with LLaMa-3 8B","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.07394","snapshot_observed_at":"2026-08-06T21:59:17.224605Z","title":"Current directions in psychological science, 16(2): 80–84","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.02946","last_updated":"2025-06-28T15:24:05Z","snapshot_observed_at":"2026-08-06T21:52:26.365788Z","submitted_at":"2025-06-28T15:24:05Z","title":"Iterative Zoom-In: Temporal Interval Exploration for Long Video Understanding","version":1},"reference_index":2007,"source":"pdf_text","source_observed_at":"2026-08-06T21:59:17.224605Z"},"links":{"cited_paper":"/paper/2406.07394","citing_paper":"/paper/2507.02946"},"observation_digest":"sha256:87219f0eb800ecb0eda51c8ea91032cd02afe56cf82358320f871837664639d0","observation_id":"a3101767-ba83-4287-90e5-94ed395b9614","resolution":{"observed_at":"2026-08-06T21:59:17.224605Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.07394","last_updated":"2024-06-13T07:19:06Z","snapshot_observed_at":"2026-07-06T18:29:00.001236Z","submitted_at":"2024-06-11T16:01:07Z","title":"Accessing GPT-4 level Mathematical Olympiad Solutions via Monte Carlo Tree Self-refine with LLaMa-3 8B","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.07394","snapshot_observed_at":"2026-08-06T18:04:55.362474Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.09374","last_updated":"2025-07-12T18:44:32Z","snapshot_observed_at":"2026-08-06T17:55:12.500864Z","submitted_at":"2025-07-12T18:44:32Z","title":"EduFlow: Advancing MLLMs' Problem-Solving Proficiency through Multi-Stage, Multi-Perspective Critique","version":1},"reference_index":34,"source":"arxiv_source","source_observed_at":"2026-08-06T18:04:55.362474Z"},"links":{"cited_paper":"/paper/2406.07394","citing_paper":"/paper/2507.09374"},"observation_digest":"sha256:27d57f460fc12af63f6b907c9b9ea300f62fbfbf145a4fe2f83bb5b829a188c7","observation_id":"52f14644-923a-4aac-9702-1f63c6eee755","resolution":{"observed_at":"2026-08-06T18:04:55.362474Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.07394","last_updated":"2024-06-13T07:19:06Z","snapshot_observed_at":"2026-07-06T18:29:00.001236Z","submitted_at":"2024-06-11T16:01:07Z","title":"Accessing GPT-4 level Mathematical Olympiad Solutions via Monte Carlo Tree Self-refine with LLaMa-3 8B","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.07394","snapshot_observed_at":"2026-08-06T16:14:06.999304Z","title":"Accessing gpt-4 level mathematical olympiad solutions via monte carlo tree search self-refinement, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.14295","last_updated":"2025-08-22T16:49:10Z","snapshot_observed_at":"2026-08-06T15:57:18.677408Z","submitted_at":"2025-07-18T18:07:38Z","title":"A Simple \"Try Again\" Can Elicit Multi-Turn LLM Reasoning","version":2},"reference_index":30,"source":"arxiv_source","source_observed_at":"2026-08-06T16:14:06.999304Z"},"links":{"cited_paper":"/paper/2406.07394","citing_paper":"/paper/2507.14295"},"observation_digest":"sha256:dd51c7b704863438a1ed4b9282c5e0814656ebd734ad0f3332e4d1a2b3ba3099","observation_id":"5aeb5db2-1b6c-4c40-84b4-26c61b1f9262","resolution":{"observed_at":"2026-08-06T16:14:06.999304Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.07394","last_updated":"2024-06-13T07:19:06Z","snapshot_observed_at":"2026-07-06T18:29:00.001236Z","submitted_at":"2024-06-11T16:01:07Z","title":"Accessing GPT-4 level Mathematical Olympiad Solutions via Monte Carlo Tree Self-refine with LLaMa-3 8B","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.07394","snapshot_observed_at":"2026-08-06T15:37:24.957990Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.15512","last_updated":"2025-09-09T08:04:09Z","snapshot_observed_at":"2026-08-06T15:27:54.651367Z","submitted_at":"2025-07-21T11:28:09Z","title":"Step-level Verifier-guided Hybrid Test-Time Scaling for Large Language Models","version":3},"reference_index":47,"source":"arxiv_source","source_observed_at":"2026-08-06T15:37:24.957990Z"},"links":{"cited_paper":"/paper/2406.07394","citing_paper":"/paper/2507.15512"},"observation_digest":"sha256:157d795504588451611389db8b45995374543ad95fc62e51f6d8c640a90b259c","observation_id":"a1ecf1ba-7fac-4019-a561-0bda22fb1dce","resolution":{"observed_at":"2026-08-06T15:37:24.957990Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.07394","last_updated":"2024-06-13T07:19:06Z","snapshot_observed_at":"2026-07-06T18:29:00.001236Z","submitted_at":"2024-06-11T16:01:07Z","title":"Accessing GPT-4 level Mathematical Olympiad Solutions via Monte Carlo Tree Self-refine with LLaMa-3 8B","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.07394","snapshot_observed_at":"2026-08-06T04:47:25.659057Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2508.03054","last_updated":"2025-08-05T03:58:15Z","snapshot_observed_at":"2026-08-06T15:30:46.727768Z","submitted_at":"2025-08-05T03:58:15Z","title":"Beyond Surface-Level Detection: Towards Cognitive-Driven Defense Against Jailbreak Attacks via Meta-Operations Reasoning","version":1},"reference_index":191,"source":"arxiv_source","source_observed_at":"2026-08-06T04:47:25.659057Z"},"links":{"cited_paper":"/paper/2406.07394","citing_paper":"/paper/2508.03054"},"observation_digest":"sha256:29a88754672731f0fc76ab8d05f1d98b16a81cb53d2a39496ae28e9783be7102","observation_id":"578c5894-73ba-4299-a386-f6c0bea4c18c","resolution":{"observed_at":"2026-08-06T04:47:25.659057Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.07394","last_updated":"2024-06-13T07:19:06Z","snapshot_observed_at":"2026-07-06T18:29:00.001236Z","submitted_at":"2024-06-11T16:01:07Z","title":"Accessing GPT-4 level Mathematical Olympiad Solutions via Monte Carlo Tree Self-refine with LLaMa-3 8B","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.07394","snapshot_observed_at":"2026-08-05T19:12:40.852662Z","title":"Accessing gpt-4 level mathematical olympiad solutions via monte carlo tree self-refine with llama-3 8b","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2508.13037","last_updated":"2025-08-18T15:56:10Z","snapshot_observed_at":"2026-08-05T19:12:25.091367Z","submitted_at":"2025-08-18T15:56:10Z","title":"Can Large Models Teach Student Models to Solve Mathematical Problems Like Human Beings? A Reasoning Distillation Method via Multi-LoRA Interaction","version":1},"reference_index":50,"source":"arxiv_source","source_observed_at":"2026-08-05T19:12:40.852662Z"},"links":{"cited_paper":"/paper/2406.07394","citing_paper":"/paper/2508.13037"},"observation_digest":"sha256:a433e10f3e0e3da3550d4fe6845c5a95fadd74075e07c18722c04e58e5b2537d","observation_id":"a78a6695-8d00-409a-b27a-13042f14688d","resolution":{"observed_at":"2026-08-05T19:12:40.852662Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.07394","last_updated":"2024-06-13T07:19:06Z","snapshot_observed_at":"2026-07-06T18:29:00.001236Z","submitted_at":"2024-06-11T16:01:07Z","title":"Accessing GPT-4 level Mathematical Olympiad Solutions via Monte Carlo Tree Self-refine with LLaMa-3 8B","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.07394","snapshot_observed_at":"2026-08-05T13:19:49.719782Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.00740","last_updated":"2025-08-31T08:07:56Z","snapshot_observed_at":"2026-08-05T13:19:45.400239Z","submitted_at":"2025-08-31T08:07:56Z","title":"Efficient Graph Understanding with LLMs via Structured Context Injection","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-05T13:19:49.719782Z"},"links":{"cited_paper":"/paper/2406.07394","citing_paper":"/paper/2509.00740"},"observation_digest":"sha256:03c64afc28b73ca8cae02f8ed0de8db424a70925203c4665883dae454a684191","observation_id":"d9080972-003c-4cd5-a42a-33991ca544a4","resolution":{"observed_at":"2026-08-05T13:19:49.719782Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.07394","last_updated":"2024-06-13T07:19:06Z","snapshot_observed_at":"2026-07-06T18:29:00.001236Z","submitted_at":"2024-06-11T16:01:07Z","title":"Accessing GPT-4 level Mathematical Olympiad Solutions via Monte Carlo Tree Self-refine with LLaMa-3 8B","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.07394","snapshot_observed_at":"2026-08-04T21:28:51.031284Z","title":"Accessing gpt-4 level mathematical olympiad solutions via monte carlo tree self-refine with llama-3 8b.arXiv preprint arXiv:2406.07394,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2509.07980","last_updated":"2025-09-12T17:15:56Z","snapshot_observed_at":"2026-08-04T21:28:47.405179Z","submitted_at":"2025-09-09T17:59:35Z","title":"Parallel-R1: Towards Parallel Thinking via Reinforcement Learning","version":2},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-04T21:28:51.031284Z"},"links":{"cited_paper":"/paper/2406.07394","citing_paper":"/paper/2509.07980"},"observation_digest":"sha256:4eddcf85c2883a7fbe65cafdb24997be66a50dbe763eb7497aca46705d9c006c","observation_id":"7449f66d-9235-43f2-b462-499ca818af64","resolution":{"observed_at":"2026-08-04T21:28:51.031284Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.07394","last_updated":"2024-06-13T07:19:06Z","snapshot_observed_at":"2026-07-06T18:29:00.001236Z","submitted_at":"2024-06-11T16:01:07Z","title":"Accessing GPT-4 level Mathematical Olympiad Solutions via Monte Carlo Tree Self-refine with LLaMa-3 8B","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.07394","snapshot_observed_at":"2026-08-04T20:20:36.977106Z","title":"Accessing gpt-4 level mathematical olympiad solutions via monte carlo tree self-refine with llama-3 8b.arXiv preprint arXiv:2406.07394, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.08621","last_updated":"2025-09-10T14:17:53Z","snapshot_observed_at":"2026-08-06T13:15:27.312512Z","submitted_at":"2025-09-10T14:17:53Z","title":"AdsQA: Towards Advertisement Video Understanding","version":1},"reference_index":84,"source":"pdf_text","source_observed_at":"2026-08-04T20:20:36.977106Z"},"links":{"cited_paper":"/paper/2406.07394","citing_paper":"/paper/2509.08621"},"observation_digest":"sha256:32e149db175daf7c11dd166c4be58db49cc31c55e7de0d5253896aac351e4159","observation_id":"d77c79e4-847b-43b5-8876-1b723fa07992","resolution":{"observed_at":"2026-08-04T20:20:36.977106Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.07394","last_updated":"2024-06-13T07:19:06Z","snapshot_observed_at":"2026-07-06T18:29:00.001236Z","submitted_at":"2024-06-11T16:01:07Z","title":"Accessing GPT-4 level Mathematical Olympiad Solutions via Monte Carlo Tree Self-refine with LLaMa-3 8B","version":2},"cited_work":{"arxiv_id":"2406.07394","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2406.07394","snapshot_observed_at":"2026-07-04T15:59:56.438855Z","title":"Large language model-brained gui agents: A survey, 2024a","venue":null,"work_id":"a22fd02a-b313-40f1-9919-b320ccc8d4f4","year":2024},"citing_paper":{"arxiv_id":"2509.25454","last_updated":"2026-04-06T19:16:24Z","snapshot_observed_at":"2026-07-06T22:31:10.099674Z","submitted_at":"2025-09-29T20:00:29Z","title":"DeepSearch: Overcome the Bottleneck of Reinforcement Learning with Verifiable Rewards via Monte Carlo Tree Search","version":4},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-05-18T12:12:25.437344Z"},"links":{"cited_paper":"/paper/2406.07394","citing_paper":"/paper/2509.25454"},"observation_digest":"sha256:545b6d33abc2859ee8938bdd1973cb913261aa10d803e6b8c22e867b140c94c1","observation_id":"2992b6ad-f340-4065-85f9-89513ca16dc4","resolution":{"observed_at":"2026-05-18T12:12:35.888849Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2406.07394","last_updated":"2024-06-13T07:19:06Z","snapshot_observed_at":"2026-07-06T18:29:00.001236Z","submitted_at":"2024-06-11T16:01:07Z","title":"Accessing GPT-4 level Mathematical Olympiad Solutions via Monte Carlo Tree Self-refine with LLaMa-3 8B","version":2},"cited_work":{"arxiv_id":"2406.07394","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2406.07394","snapshot_observed_at":"2026-07-04T15:59:56.438855Z","title":"Large language model-brained gui agents: A survey, 2024a","venue":null,"work_id":"a22fd02a-b313-40f1-9919-b320ccc8d4f4","year":2024},"citing_paper":{"arxiv_id":"2509.25835","last_updated":"2026-04-10T02:38:23Z","snapshot_observed_at":"2026-08-06T14:12:01.889582Z","submitted_at":"2025-09-30T06:18:44Z","title":"Chain-in-Tree: Back to Sequential Reasoning in LLM Tree Search","version":4},"reference_index":20,"source":"arxiv_source","source_observed_at":"2026-05-18T13:09:02.577020Z"},"links":{"cited_paper":"/paper/2406.07394","citing_paper":"/paper/2509.25835"},"observation_digest":"sha256:e47caf11e3785eef4c5c08af7851b5167cc470b6c0e0a2a0bdae1c94c1c00f53","observation_id":"93a4c9cb-934a-477b-99fa-9ff8a87a01e7","resolution":{"observed_at":"2026-05-18T13:11:24.118440Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2406.07394","last_updated":"2024-06-13T07:19:06Z","snapshot_observed_at":"2026-07-06T18:29:00.001236Z","submitted_at":"2024-06-11T16:01:07Z","title":"Accessing GPT-4 level Mathematical Olympiad Solutions via Monte Carlo Tree Self-refine with LLaMa-3 8B","version":2},"cited_work":{"arxiv_id":"2406.07394","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2406.07394","snapshot_observed_at":"2026-07-04T15:59:56.438855Z","title":"Large language model-brained gui agents: A survey, 2024a","venue":null,"work_id":"a22fd02a-b313-40f1-9919-b320ccc8d4f4","year":2024},"citing_paper":{"arxiv_id":"2601.21619","last_updated":"2026-05-09T01:57:30Z","snapshot_observed_at":"2026-07-06T22:43:30.761426Z","submitted_at":"2026-01-29T12:22:45Z","title":"On the Overscaling Curse of Parallel Thinking: System Efficacy Contradicts Sample Efficiency","version":2},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-05-16T10:38:09.875786Z"},"links":{"cited_paper":"/paper/2406.07394","citing_paper":"/paper/2601.21619"},"observation_digest":"sha256:4015e8448d908f539f388aa8dc91ca697f34d66e6910a029ee6e72120b8af250","observation_id":"6c0d1bf8-6d4b-40f5-8553-125b013568be","resolution":{"observed_at":"2026-05-16T10:40:51.323576Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2406.07394","last_updated":"2024-06-13T07:19:06Z","snapshot_observed_at":"2026-07-06T18:29:00.001236Z","submitted_at":"2024-06-11T16:01:07Z","title":"Accessing GPT-4 level Mathematical Olympiad Solutions via Monte Carlo Tree Self-refine with LLaMa-3 8B","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.07394","snapshot_observed_at":"2026-08-02T23:43:07.803719Z","title":"reversal curse,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2602.12966","last_updated":"2026-06-09T04:02:52Z","snapshot_observed_at":"2026-08-02T23:42:58.142462Z","submitted_at":"2026-02-13T14:33:13Z","title":"ProbeLLM: Automating Principled Diagnosis of LLM Failures","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-02T23:43:07.803719Z"},"links":{"cited_paper":"/paper/2406.07394","citing_paper":"/paper/2602.12966"},"observation_digest":"sha256:f4e7205cb172c0ab4bd46b3a525d0b9c558fa366479f1c2dbdceb62395a271dc","observation_id":"7e203e4a-8339-481e-8f50-b0f2ab11aa5e","resolution":{"observed_at":"2026-08-02T23:43:07.803719Z","resolver_source":null,"status":"malformed_identifier"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.07394","last_updated":"2024-06-13T07:19:06Z","snapshot_observed_at":"2026-07-06T18:29:00.001236Z","submitted_at":"2024-06-11T16:01:07Z","title":"Accessing GPT-4 level Mathematical Olympiad Solutions via Monte Carlo Tree Self-refine with LLaMa-3 8B","version":2},"cited_work":{"arxiv_id":"2406.07394","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2406.07394","snapshot_observed_at":"2026-07-04T15:59:56.438855Z","title":"Large language model-brained gui agents: A survey, 2024a","venue":null,"work_id":"a22fd02a-b313-40f1-9919-b320ccc8d4f4","year":2024},"citing_paper":{"arxiv_id":"2604.04386","last_updated":"2026-04-06T03:27:48Z","snapshot_observed_at":"2026-07-06T22:53:24.585766Z","submitted_at":"2026-04-06T03:27:48Z","title":"Automatically Generating Hard Math Problems from Hypothesis-Driven Error Analysis","version":1},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-05-10T19:39:36.819142Z"},"links":{"cited_paper":"/paper/2406.07394","citing_paper":"/paper/2604.04386"},"observation_digest":"sha256:4931f8e5e9766b27b92ce603f8d0abd06cd15e3590511ec31dc0ae5ef6b90b1b","observation_id":"5055cda9-a6f6-47aa-9e3b-c971250a8484","resolution":{"observed_at":"2026-05-10T22:40:49.917598Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2406.07394","last_updated":"2024-06-13T07:19:06Z","snapshot_observed_at":"2026-07-06T18:29:00.001236Z","submitted_at":"2024-06-11T16:01:07Z","title":"Accessing GPT-4 level Mathematical Olympiad Solutions via Monte Carlo Tree Self-refine with LLaMa-3 8B","version":2},"cited_work":{"arxiv_id":"2406.07394","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2406.07394","snapshot_observed_at":"2026-07-04T15:59:56.438855Z","title":"Large language model-brained gui agents: A survey, 2024a","venue":null,"work_id":"a22fd02a-b313-40f1-9919-b320ccc8d4f4","year":2024},"citing_paper":{"arxiv_id":"2604.14853","last_updated":"2026-04-16T10:39:22Z","snapshot_observed_at":"2026-07-06T23:02:31.717911Z","submitted_at":"2026-04-16T10:39:22Z","title":"Adaptive Test-Time Compute Allocation for Reasoning LLMs via Constrained Policy Optimization","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-05-10T11:57:44.680423Z"},"links":{"cited_paper":"/paper/2406.07394","citing_paper":"/paper/2604.14853"},"observation_digest":"sha256:24cc72c3900c95d0322fa32a62c541df53686e7ba11ae410a2ee5a216dfcdc1c","observation_id":"8ec343c9-271f-48b0-8ece-0ded54885972","resolution":{"observed_at":"2026-05-10T12:00:22.141710Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2406.07394","last_updated":"2024-06-13T07:19:06Z","snapshot_observed_at":"2026-07-06T18:29:00.001236Z","submitted_at":"2024-06-11T16:01:07Z","title":"Accessing GPT-4 level Mathematical Olympiad Solutions via Monte Carlo Tree Self-refine with LLaMa-3 8B","version":2},"cited_work":{"arxiv_id":"2406.07394","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2406.07394","snapshot_observed_at":"2026-07-04T15:59:56.438855Z","title":"Large language model-brained gui agents: A survey, 2024a","venue":null,"work_id":"a22fd02a-b313-40f1-9919-b320ccc8d4f4","year":2024},"citing_paper":{"arxiv_id":"2604.15709","last_updated":"2026-04-17T05:31:40Z","snapshot_observed_at":"2026-07-06T23:03:12.000381Z","submitted_at":"2026-04-17T05:31:40Z","title":"Bilevel Optimization of Agent Skills via Monte Carlo Tree Search","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-05-10T09:16:58.661353Z"},"links":{"cited_paper":"/paper/2406.07394","citing_paper":"/paper/2604.15709"},"observation_digest":"sha256:983ef5b00ddc8bfe104ab09bd72606fc19e410cff6db76baa388a3399e05a1b7","observation_id":"cc27ff90-df89-4452-8449-76557cec77f3","resolution":{"observed_at":"2026-05-10T09:18:31.365598Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2406.07394","last_updated":"2024-06-13T07:19:06Z","snapshot_observed_at":"2026-07-06T18:29:00.001236Z","submitted_at":"2024-06-11T16:01:07Z","title":"Accessing GPT-4 level Mathematical Olympiad Solutions via Monte Carlo Tree Self-refine with LLaMa-3 8B","version":2},"cited_work":{"arxiv_id":"2406.07394","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2406.07394","snapshot_observed_at":"2026-07-04T15:59:56.438855Z","title":"Large language model-brained gui agents: A survey, 2024a","venue":null,"work_id":"a22fd02a-b313-40f1-9919-b320ccc8d4f4","year":2024},"citing_paper":{"arxiv_id":"2604.26644","last_updated":"2026-08-03T06:41:06Z","snapshot_observed_at":"2026-08-06T23:31:02.960392Z","submitted_at":"2026-04-29T13:11:39Z","title":"When to Vote, When to Rewrite: Disagreement-Guided Strategy Routing for Test-Time Scaling","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-05-07T11:00:21.413246Z"},"links":{"cited_paper":"/paper/2406.07394","citing_paper":"/paper/2604.26644"},"observation_digest":"sha256:2761cc315a35d2d71cba54a4a1394db55472eac03e4e2f38d3cc3714abddd503","observation_id":"5116dc0b-fb27-4961-a164-2ac2618dc397","resolution":{"observed_at":"2026-05-12T09:26:25.785229Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2406.07394","last_updated":"2024-06-13T07:19:06Z","snapshot_observed_at":"2026-07-06T18:29:00.001236Z","submitted_at":"2024-06-11T16:01:07Z","title":"Accessing GPT-4 level Mathematical Olympiad Solutions via Monte Carlo Tree Self-refine with LLaMa-3 8B","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.07394","snapshot_observed_at":"2026-08-04T05:23:31.355740Z","title":"Zhang, X","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2604.26644","last_updated":"2026-08-03T06:41:06Z","snapshot_observed_at":"2026-08-06T23:31:02.960392Z","submitted_at":"2026-04-29T13:11:39Z","title":"When to Vote, When to Rewrite: Disagreement-Guided Strategy Routing for Test-Time Scaling","version":2},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-04T05:23:31.355740Z"},"links":{"cited_paper":"/paper/2406.07394","citing_paper":"/paper/2604.26644"},"observation_digest":"sha256:3e42d9715ace20cd741709a8f40dce951a3ad8900ab7d1278196349a34f28701","observation_id":"0a5e149a-ff73-4cf9-84a2-813865ceed17","resolution":{"observed_at":"2026-08-04T05:23:31.355740Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.07394","last_updated":"2024-06-13T07:19:06Z","snapshot_observed_at":"2026-07-06T18:29:00.001236Z","submitted_at":"2024-06-11T16:01:07Z","title":"Accessing GPT-4 level Mathematical Olympiad Solutions via Monte Carlo Tree Self-refine with LLaMa-3 8B","version":2},"cited_work":{"arxiv_id":"2406.07394","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2406.07394","snapshot_observed_at":"2026-07-04T15:59:56.438855Z","title":"Large language model-brained gui agents: A survey, 2024a","venue":null,"work_id":"a22fd02a-b313-40f1-9919-b320ccc8d4f4","year":2024},"citing_paper":{"arxiv_id":"2605.00323","last_updated":"2026-05-01T01:03:05Z","snapshot_observed_at":"2026-08-02T17:57:46.804255Z","submitted_at":"2026-05-01T01:03:05Z","title":"Online Self-Calibration Against Hallucination in Vision-Language Models","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-05-09T20:20:27.931679Z"},"links":{"cited_paper":"/paper/2406.07394","citing_paper":"/paper/2605.00323"},"observation_digest":"sha256:2124c61fe3bf853a78903394fa9d1b4eaa21290a60f5811d7f7839a7ce426476","observation_id":"1aafabd6-89a3-45ae-bd52-dc6db2eff0d3","resolution":{"observed_at":"2026-05-11T15:16:10.747659Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2406.07394","last_updated":"2024-06-13T07:19:06Z","snapshot_observed_at":"2026-07-06T18:29:00.001236Z","submitted_at":"2024-06-11T16:01:07Z","title":"Accessing GPT-4 level Mathematical Olympiad Solutions via Monte Carlo Tree Self-refine with LLaMa-3 8B","version":2},"cited_work":{"arxiv_id":"2406.07394","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2406.07394","snapshot_observed_at":"2026-07-04T15:59:56.438855Z","title":"Large language model-brained gui agents: A survey, 2024a","venue":null,"work_id":"a22fd02a-b313-40f1-9919-b320ccc8d4f4","year":2024},"citing_paper":{"arxiv_id":"2605.02452","last_updated":"2026-05-04T10:56:05Z","snapshot_observed_at":"2026-08-02T17:05:44.125833Z","submitted_at":"2026-05-04T10:56:05Z","title":"Position: How can Graphs Help Large Language Models?","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-05-08T18:48:03.257015Z"},"links":{"cited_paper":"/paper/2406.07394","citing_paper":"/paper/2605.02452"},"observation_digest":"sha256:00863c81284b015b6a8a7702f02c8393f207410280e8bcc5594bdde11e093cc3","observation_id":"1bc86d6a-f596-44a6-b2ff-b919062898c5","resolution":{"observed_at":"2026-05-09T06:10:42.924197Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2406.07394","last_updated":"2024-06-13T07:19:06Z","snapshot_observed_at":"2026-07-06T18:29:00.001236Z","submitted_at":"2024-06-11T16:01:07Z","title":"Accessing GPT-4 level Mathematical Olympiad Solutions via Monte Carlo Tree Self-refine with LLaMa-3 8B","version":2},"cited_work":{"arxiv_id":"2406.07394","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2406.07394","snapshot_observed_at":"2026-07-04T15:59:56.438855Z","title":"Large language model-brained gui agents: A survey, 2024a","venue":null,"work_id":"a22fd02a-b313-40f1-9919-b320ccc8d4f4","year":2024},"citing_paper":{"arxiv_id":"2605.06914","last_updated":"2026-05-07T20:23:32Z","snapshot_observed_at":"2026-08-02T05:38:27.537862Z","submitted_at":"2026-05-07T20:23:32Z","title":"Regulating Branch Parallelism in LLM Serving","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-05-11T01:00:53.308946Z"},"links":{"cited_paper":"/paper/2406.07394","citing_paper":"/paper/2605.06914"},"observation_digest":"sha256:947f673267c18044542d94c2b82efd9d102a27d51f94c9cf7915c52ff1bdc101","observation_id":"b4e7df69-40bd-4c04-96a5-7fbf660f9760","resolution":{"observed_at":"2026-05-11T04:50:58.401589Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2406.07394","last_updated":"2024-06-13T07:19:06Z","snapshot_observed_at":"2026-07-06T18:29:00.001236Z","submitted_at":"2024-06-11T16:01:07Z","title":"Accessing GPT-4 level Mathematical Olympiad Solutions via Monte Carlo Tree Self-refine with LLaMa-3 8B","version":2},"cited_work":{"arxiv_id":"2406.07394","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2406.07394","snapshot_observed_at":"2026-07-04T15:59:56.438855Z","title":"Large language model-brained gui agents: A survey, 2024a","venue":null,"work_id":"a22fd02a-b313-40f1-9919-b320ccc8d4f4","year":2024},"citing_paper":{"arxiv_id":"2606.05253","last_updated":"2026-06-03T14:51:33Z","snapshot_observed_at":"2026-08-01T00:36:11.076649Z","submitted_at":"2026-06-03T14:51:33Z","title":"Alpha-RTL: Test-Time Training for RTL Hardware Optimization","version":1},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-06-28T07:11:52.187869Z"},"links":{"cited_paper":"/paper/2406.07394","citing_paper":"/paper/2606.05253"},"observation_digest":"sha256:e1f77d8b8b4ead8c2c39a1d452eb0cdc8e3c0d9c077cd76129aae323ca3958a7","observation_id":"34b0cb2a-c2e7-48e9-9a84-4477636c9292","resolution":{"observed_at":"2026-07-02T07:06:44.169477Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2406.07394","last_updated":"2024-06-13T07:19:06Z","snapshot_observed_at":"2026-07-06T18:29:00.001236Z","submitted_at":"2024-06-11T16:01:07Z","title":"Accessing GPT-4 level Mathematical Olympiad Solutions via Monte Carlo Tree Self-refine with LLaMa-3 8B","version":2},"cited_work":{"arxiv_id":"2406.07394","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2406.07394","snapshot_observed_at":"2026-07-04T15:59:56.438855Z","title":"Large language model-brained gui agents: A survey, 2024a","venue":null,"work_id":"a22fd02a-b313-40f1-9919-b320ccc8d4f4","year":2024},"citing_paper":{"arxiv_id":"2606.05464","last_updated":"2026-06-03T21:43:38Z","snapshot_observed_at":"2026-08-03T10:19:08.677126Z","submitted_at":"2026-06-03T21:43:38Z","title":"Step-by-Step Optimization-like Reasoning in LLMs over Expanding Search Spaces","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-06-28T05:46:26.938277Z"},"links":{"cited_paper":"/paper/2406.07394","citing_paper":"/paper/2606.05464"},"observation_digest":"sha256:1d0884c40b042fbfdc529d24b1a4a291d8b437d80408156af4ad34c5c3c13c86","observation_id":"d91a1a4e-a73f-43fb-9ca5-7224ad972b65","resolution":{"observed_at":"2026-07-02T08:46:48.939754Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2406.07394","last_updated":"2024-06-13T07:19:06Z","snapshot_observed_at":"2026-07-06T18:29:00.001236Z","submitted_at":"2024-06-11T16:01:07Z","title":"Accessing GPT-4 level Mathematical Olympiad Solutions via Monte Carlo Tree Self-refine with LLaMa-3 8B","version":2},"cited_work":{"arxiv_id":"2406.07394","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2406.07394","snapshot_observed_at":"2026-07-04T15:59:56.438855Z","title":"Large language model-brained gui agents: A survey, 2024a","venue":null,"work_id":"a22fd02a-b313-40f1-9919-b320ccc8d4f4","year":2024},"citing_paper":{"arxiv_id":"2606.26453","last_updated":"2026-06-24T23:28:09Z","snapshot_observed_at":"2026-07-07T00:00:46.505337Z","submitted_at":"2026-06-24T23:28:09Z","title":"Optimizing CUDA like a Human: Micro-Profiling Tools as Expert Surrogates for LLM-Based GPU Kernel Optimization","version":1},"reference_index":36,"source":"arxiv_source","source_observed_at":"2026-06-26T01:12:04.486570Z"},"links":{"cited_paper":"/paper/2406.07394","citing_paper":"/paper/2606.26453"},"observation_digest":"sha256:c59d0722b53f06297e7b0d30d4532a6d9cfc7c8280a8785a6d9c9fccba1e545b","observation_id":"ddeaee63-054b-49ad-bc4e-03eb1403b852","resolution":{"observed_at":"2026-07-04T15:59:56.440376Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2406.07394","last_updated":"2024-06-13T07:19:06Z","snapshot_observed_at":"2026-07-06T18:29:00.001236Z","submitted_at":"2024-06-11T16:01:07Z","title":"Accessing GPT-4 level Mathematical Olympiad Solutions via Monte Carlo Tree Self-refine with LLaMa-3 8B","version":2},"cited_work":{"arxiv_id":"2406.07394","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2406.07394","snapshot_observed_at":"2026-07-04T15:59:56.438855Z","title":"Large language model-brained gui agents: A survey, 2024a","venue":null,"work_id":"a22fd02a-b313-40f1-9919-b320ccc8d4f4","year":2024},"citing_paper":{"arxiv_id":"2606.26728","last_updated":"2026-06-25T08:11:48Z","snapshot_observed_at":"2026-07-30T15:56:35.880037Z","submitted_at":"2026-06-25T08:11:48Z","title":"Scientific discovery as meta-optimization: a combinatorial optimization case study","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-06-26T05:12:43.536126Z"},"links":{"cited_paper":"/paper/2406.07394","citing_paper":"/paper/2606.26728"},"observation_digest":"sha256:d5f93b3d78632bf9970849fffa213ce7f393e656869befa86011a32755a0d38c","observation_id":"18396e6c-741a-40bc-8a04-f694d48958be","resolution":{"observed_at":"2026-07-04T13:29:51.435978Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2406.07394","last_updated":"2024-06-13T07:19:06Z","snapshot_observed_at":"2026-07-06T18:29:00.001236Z","submitted_at":"2024-06-11T16:01:07Z","title":"Accessing GPT-4 level Mathematical Olympiad Solutions via Monte Carlo Tree Self-refine with LLaMa-3 8B","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.07394","snapshot_observed_at":"2026-08-01T13:54:18.136663Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.18979","last_updated":"2026-07-21T11:14:33Z","snapshot_observed_at":"2026-08-06T13:13:54.204332Z","submitted_at":"2026-07-21T11:14:33Z","title":"Fishing Out Free Riders: Shapley-Based Reward Attribution for Parallel Reasoning via Reinforcement Learning","version":1},"reference_index":35,"source":"arxiv_source","source_observed_at":"2026-08-01T13:54:18.136663Z"},"links":{"cited_paper":"/paper/2406.07394","citing_paper":"/paper/2607.18979"},"observation_digest":"sha256:367471406792ebd80cdd6ae2a6f632f25dc736560c2a4d27403333483ce6aebd","observation_id":"9743939e-5216-4144-93a6-0009a22b48f9","resolution":{"observed_at":"2026-08-01T13:54:18.136663Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2406.07394/citation-record","integrity":"/paper/2406.07394/integrity","json":"/paper/2406.07394/citation-record.json","paper":"/paper/2406.07394"},"outbound":[],"paper":{"arxiv_id":"2406.07394","last_updated":"2024-06-13T07:19:06Z","latest_version":2,"primary_category":"cs.AI","snapshot_observed_at":"2026-07-06T18:29:00.001236Z","submitted_at":"2024-06-11T16:01:07Z","title":"Accessing GPT-4 level Mathematical Olympiad Solutions via Monte Carlo Tree Self-refine with LLaMa-3 8B"},"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":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"thesis":"As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 34 inbound Pith citation observations for arXiv:2406.07394."}