{"as_of":"2026-08-09T12:27:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:a5033ec5741e3254fcba17d95402c9b8461e410f6fcf285bf2817fa0506be1d8","coverage":[{"denominator":66,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":66,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T12:35:32.332255Z","state":"measured"},{"denominator":66,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":66,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+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/2505.24584/citation-record","integrity":"/paper/2505.24584/integrity","json":"/paper/2505.24584/citation-record.json","paper":"/paper/2505.24584"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"2412.03317","last_updated":"2025-01-19T08:31:39Z","snapshot_observed_at":"2026-08-07T23:11:56.982715Z","submitted_at":"2024-12-04T13:52:04Z","title":"FlashAttention on a Napkin: A Diagrammatic Approach to Deep Learning IO-Awareness","version":2},"cited_work":{"arxiv_id":"2412.03317","doi":null,"metadata_source":"pith","pith_arxiv_id":"2412.03317","snapshot_observed_at":"2026-08-07T12:35:34.471686Z","title":"FlashAttention on a Napkin: A Diagrammatic Approach to Deep Learning IO-Awareness","venue":"cs.LG","work_id":"c2f527be-b646-452c-b962-148105c5b85b","year":2024},"citing_paper":{"arxiv_id":"2505.24584","last_updated":"2025-08-18T16:52:22Z","snapshot_observed_at":"2026-08-09T10:10:52.166144Z","submitted_at":"2025-05-30T13:32:00Z","title":"AutoChemSchematic AI: Agentic Physics-Aware Automation for Chemical Manufacturing Scale-Up","version":3},"reference_index":1,"source":"arxiv_source","source_observed_at":"2026-08-07T12:35:25.786753Z"},"links":{"cited_paper":"/paper/2412.03317","citing_paper":"/paper/2505.24584"},"observation_digest":"sha256:53eec2b5e6bb5a1fdf403d8b9d191391b9dbfe5b91b3dc77c9af1e8f0e297ba5","observation_id":"a4bacbb4-930f-450d-9384-b543478c0bb9","resolution":{"observed_at":"2026-08-07T12:35:34.535496Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2502.18928","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:35:34.314194Z","title":"A., Goldstein, D","venue":null,"work_id":"b28487cb-c456-468d-b31d-d4c56c12ac58","year":2025},"citing_paper":{"arxiv_id":"2505.24584","last_updated":"2025-08-18T16:52:22Z","snapshot_observed_at":"2026-08-09T10:10:52.166144Z","submitted_at":"2025-05-30T13:32:00Z","title":"AutoChemSchematic AI: Agentic Physics-Aware Automation for Chemical Manufacturing Scale-Up","version":3},"reference_index":2,"source":"arxiv_source","source_observed_at":"2026-08-07T12:35:25.878975Z"},"links":{"citing_paper":"/paper/2505.24584"},"observation_digest":"sha256:50d9eb9c685054b1fd1bef8ff116ea2f878f0e22321cba2ccd37b40214975296","observation_id":"e717ffb5-b9d8-477e-9f32-5da05501b9b2","resolution":{"observed_at":"2026-08-07T12:35:34.375483Z","resolver_source":"raw_fallback","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2504.00294","last_updated":"2025-03-31T23:40:28Z","snapshot_observed_at":"2026-08-07T16:19:01.006011Z","submitted_at":"2025-03-31T23:40:28Z","title":"Inference-Time Scaling for Complex Tasks: Where We Stand and What Lies Ahead","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2504.00294","snapshot_observed_at":"2026-08-07T12:35:25.983954Z","title":"Inference-time scaling for complex tasks: Where we stand and what lies ahead","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2505.24584","last_updated":"2025-08-18T16:52:22Z","snapshot_observed_at":"2026-08-09T10:10:52.166144Z","submitted_at":"2025-05-30T13:32:00Z","title":"AutoChemSchematic AI: Agentic Physics-Aware Automation for Chemical Manufacturing Scale-Up","version":3},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-08-07T12:35:25.983954Z"},"links":{"cited_paper":"/paper/2504.00294","citing_paper":"/paper/2505.24584"},"observation_digest":"sha256:96768b25d478e4165d4ab1b307777b2d9ed59d196f7dffe3628e436b39d033e6","observation_id":"705c6ae9-44b7-4e00-b155-e205bfc9b69e","resolution":{"observed_at":"2026-08-07T12:35:25.983954Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2412.09078","last_updated":"2025-04-01T12:48:43Z","snapshot_observed_at":"2026-08-08T09:53:22.230954Z","submitted_at":"2024-12-12T09:01:18Z","title":"Forest-of-Thought: Scaling Test-Time Compute for Enhancing LLM Reasoning","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.09078","snapshot_observed_at":"2026-08-07T12:35:26.079482Z","title":"Forest-of-thought: Scaling test-time compute for enhancing llm reasoning","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.24584","last_updated":"2025-08-18T16:52:22Z","snapshot_observed_at":"2026-08-09T10:10:52.166144Z","submitted_at":"2025-05-30T13:32:00Z","title":"AutoChemSchematic AI: Agentic Physics-Aware Automation for Chemical Manufacturing Scale-Up","version":3},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-08-07T12:35:26.079482Z"},"links":{"cited_paper":"/paper/2412.09078","citing_paper":"/paper/2505.24584"},"observation_digest":"sha256:da566e2364b5bd19be56e5d4627fc7f2b86501136185950fc223f4099a807146","observation_id":"d888a5bc-173e-4cac-98e9-514556a83858","resolution":{"observed_at":"2026-08-07T12:35:26.079482Z","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":"2026-08-07T12:35:26.166697Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2505.24584","last_updated":"2025-08-18T16:52:22Z","snapshot_observed_at":"2026-08-09T10:10:52.166144Z","submitted_at":"2025-05-30T13:32:00Z","title":"AutoChemSchematic AI: Agentic Physics-Aware Automation for Chemical Manufacturing Scale-Up","version":3},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-08-07T12:35:26.166697Z"},"links":{"citing_paper":"/paper/2505.24584"},"observation_digest":"sha256:6c9a2bc814ae45cd36f3ac5d2d11684c4b23c8fbafcf16fbb4233de1851c659f","observation_id":"db082e09-d584-468a-8218-d8259385cb45","resolution":{"observed_at":"2026-08-07T12:35:26.166697Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2409.16997","last_updated":"2024-09-26T06:13:04Z","snapshot_observed_at":"2026-07-06T19:21:50.692950Z","submitted_at":"2024-09-25T15:02:25Z","title":"INT-FlashAttention: Enabling Flash Attention for INT8 Quantization","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2409.16997","snapshot_observed_at":"2026-08-07T12:35:26.271599Z","title":"Int-flashattention: Enabling flash attention for int8 quantization","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.24584","last_updated":"2025-08-18T16:52:22Z","snapshot_observed_at":"2026-08-09T10:10:52.166144Z","submitted_at":"2025-05-30T13:32:00Z","title":"AutoChemSchematic AI: Agentic Physics-Aware Automation for Chemical Manufacturing Scale-Up","version":3},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-08-07T12:35:26.271599Z"},"links":{"cited_paper":"/paper/2409.16997","citing_paper":"/paper/2505.24584"},"observation_digest":"sha256:754fe69a2666f4a0ce377a29e6eb94bc80ec4111c2b6e37f124fa603169e5884","observation_id":"1f7acff9-cd17-4e12-b317-b384025f2562","resolution":{"observed_at":"2026-08-07T12:35:26.271599Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.17244","last_updated":"2024-10-09T20:13:51Z","snapshot_observed_at":"2026-08-09T04:14:53.467250Z","submitted_at":"2024-01-30T18:37:45Z","title":"LLaMP: Large Language Model Made Powerful for High-fidelity Materials Knowledge Retrieval and Distillation","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.17244","snapshot_observed_at":"2026-08-07T12:35:26.372367Z","title":"LLAMP: Large language model made powerful for high-fidelity materials knowledge retrieval and distillation","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.24584","last_updated":"2025-08-18T16:52:22Z","snapshot_observed_at":"2026-08-09T10:10:52.166144Z","submitted_at":"2025-05-30T13:32:00Z","title":"AutoChemSchematic AI: Agentic Physics-Aware Automation for Chemical Manufacturing Scale-Up","version":3},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-08-07T12:35:26.372367Z"},"links":{"cited_paper":"/paper/2401.17244","citing_paper":"/paper/2505.24584"},"observation_digest":"sha256:4eab96a32814d5600be524ce9746fc42f5aedc6f44948940a7505329471df815","observation_id":"e9b0dfb2-a01e-4b78-a5d5-c18e58551f5a","resolution":{"observed_at":"2026-08-07T12:35:26.372367Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2307.08691","last_updated":"2023-07-17T17:50:36Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-07-17T17:50:36Z","title":"FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2307.08691","snapshot_observed_at":"2026-08-07T12:35:26.477976Z","title":"Flashattention-2: Faster attention with better parallelism and work partitioning","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.24584","last_updated":"2025-08-18T16:52:22Z","snapshot_observed_at":"2026-08-09T10:10:52.166144Z","submitted_at":"2025-05-30T13:32:00Z","title":"AutoChemSchematic AI: Agentic Physics-Aware Automation for Chemical Manufacturing Scale-Up","version":3},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-08-07T12:35:26.477976Z"},"links":{"cited_paper":"/paper/2307.08691","citing_paper":"/paper/2505.24584"},"observation_digest":"sha256:1be86f68970647673f3c2c142a57df07f63d993dd6bde64a85eb73ce6fe3f484","observation_id":"255016c4-5d8c-4921-934f-659350c73366","resolution":{"observed_at":"2026-08-07T12:35:26.477976Z","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":"2026-08-07T12:35:26.553938Z","title":"Flashattention: Fast and memory-efficient exact attention with io-awareness","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.24584","last_updated":"2025-08-18T16:52:22Z","snapshot_observed_at":"2026-08-09T10:10:52.166144Z","submitted_at":"2025-05-30T13:32:00Z","title":"AutoChemSchematic AI: Agentic Physics-Aware Automation for Chemical Manufacturing Scale-Up","version":3},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-08-07T12:35:26.553938Z"},"links":{"citing_paper":"/paper/2505.24584"},"observation_digest":"sha256:813ff480831e67ecdd42e7137febe8224d58f996ed7d7c57cc2862bf630efcea","observation_id":"60e3fa44-b85e-4df7-93bc-c7e90bff523d","resolution":{"observed_at":"2026-08-07T12:35:26.553938Z","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":"2026-08-07T12:35:26.629325Z","title":"Qlora: Efficient finetuning of quantized llms","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.24584","last_updated":"2025-08-18T16:52:22Z","snapshot_observed_at":"2026-08-09T10:10:52.166144Z","submitted_at":"2025-05-30T13:32:00Z","title":"AutoChemSchematic AI: Agentic Physics-Aware Automation for Chemical Manufacturing Scale-Up","version":3},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-08-07T12:35:26.629325Z"},"links":{"citing_paper":"/paper/2505.24584"},"observation_digest":"sha256:ff822ba42c22404677d95bf3ab71af9d854480d998b572860f734475aa0a8130","observation_id":"976ef2c4-e5e3-43ec-a6bd-af7401e5c566","resolution":{"observed_at":"2026-08-07T12:35:26.629325Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2404.16130","last_updated":"2025-02-19T10:49:41Z","snapshot_observed_at":"2026-07-06T18:05:11.700127Z","submitted_at":"2024-04-24T18:38:11Z","title":"From Local to Global: A Graph RAG Approach to Query-Focused Summarization","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.16130","snapshot_observed_at":"2026-08-07T12:35:26.773120Z","title":"O., and Larson, J","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.24584","last_updated":"2025-08-18T16:52:22Z","snapshot_observed_at":"2026-08-09T10:10:52.166144Z","submitted_at":"2025-05-30T13:32:00Z","title":"AutoChemSchematic AI: Agentic Physics-Aware Automation for Chemical Manufacturing Scale-Up","version":3},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-08-07T12:35:26.773120Z"},"links":{"cited_paper":"/paper/2404.16130","citing_paper":"/paper/2505.24584"},"observation_digest":"sha256:4ee2cfc164f974e86660e93f56aed9dd303ad09df8825761eebd2e6620aa1825","observation_id":"ffa5f494-7192-4c1b-be11-d809b2e676b0","resolution":{"observed_at":"2026-08-07T12:35:26.773120Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.02057","last_updated":"2024-02-03T06:37:50Z","snapshot_observed_at":"2026-08-07T21:53:50.848655Z","submitted_at":"2024-02-03T06:37:50Z","title":"Break the Sequential Dependency of LLM Inference Using Lookahead Decoding","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.02057","snapshot_observed_at":"2026-08-07T12:35:26.878891Z","title":"Break the sequential dependency of llm inference using lookahead decoding","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.24584","last_updated":"2025-08-18T16:52:22Z","snapshot_observed_at":"2026-08-09T10:10:52.166144Z","submitted_at":"2025-05-30T13:32:00Z","title":"AutoChemSchematic AI: Agentic Physics-Aware Automation for Chemical Manufacturing Scale-Up","version":3},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-08-07T12:35:26.878891Z"},"links":{"cited_paper":"/paper/2402.02057","citing_paper":"/paper/2505.24584"},"observation_digest":"sha256:cf48eb203095cd1eae94a17eb8d236b012e66fde9f94f5f685fea94a2044456f","observation_id":"95399fe2-9958-48b3-81f7-48399dedba3f","resolution":{"observed_at":"2026-08-07T12:35:26.878891Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.10576","last_updated":"2025-07-03T04:28:09Z","snapshot_observed_at":"2026-07-06T18:31:27.512384Z","submitted_at":"2024-06-15T09:31:03Z","title":"Bypass Back-propagation: Optimization-based Structural Pruning for Large Language Models via Policy Gradient","version":3},"cited_work":{"arxiv_id":"2406.10576","doi":null,"metadata_source":"pith","pith_arxiv_id":"2406.10576","snapshot_observed_at":"2026-08-07T12:35:33.922278Z","title":"Bypass Back-propagation: Optimization-based Structural Pruning for Large Language Models via Policy Gradient","venue":"cs.LG","work_id":"a49dd9c1-f423-4896-ad06-20394773c3e8","year":2024},"citing_paper":{"arxiv_id":"2505.24584","last_updated":"2025-08-18T16:52:22Z","snapshot_observed_at":"2026-08-09T10:10:52.166144Z","submitted_at":"2025-05-30T13:32:00Z","title":"AutoChemSchematic AI: Agentic Physics-Aware Automation for Chemical Manufacturing Scale-Up","version":3},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-08-07T12:35:26.935733Z"},"links":{"cited_paper":"/paper/2406.10576","citing_paper":"/paper/2505.24584"},"observation_digest":"sha256:fd8b7004e91f5b5e8b5a25a4f34db3d847e6147be92a46bb332d69660b71b097","observation_id":"b426954e-e551-4e8c-bb54-66d341b6a0d0","resolution":{"observed_at":"2026-08-07T12:35:33.972984Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2412.12898","last_updated":"2024-12-17T13:21:26Z","snapshot_observed_at":"2026-08-09T10:35:30.103781Z","submitted_at":"2024-12-17T13:21:26Z","title":"An Agentic Approach to Automatic Creation of P&ID Diagrams from Natural Language Descriptions","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.12898","snapshot_observed_at":"2026-08-07T12:35:27.036284Z","title":"An agentic approach to automatic creation of p&id diagrams from natural language descriptions","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.24584","last_updated":"2025-08-18T16:52:22Z","snapshot_observed_at":"2026-08-09T10:10:52.166144Z","submitted_at":"2025-05-30T13:32:00Z","title":"AutoChemSchematic AI: Agentic Physics-Aware Automation for Chemical Manufacturing Scale-Up","version":3},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-08-07T12:35:27.036284Z"},"links":{"cited_paper":"/paper/2412.12898","citing_paper":"/paper/2505.24584"},"observation_digest":"sha256:8eb33d4b7925a8bdb3ec5543385b02f14867e9607d2e51c1d164afb2bb2a9d6f","observation_id":"bbd20854-87d2-44af-9bf8-318f299ace95","resolution":{"observed_at":"2026-08-07T12:35:27.036284Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2501.12948","last_updated":"2026-01-04T03:57:36Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-01-22T15:19:35Z","title":"DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.12948","snapshot_observed_at":"2026-08-07T12:35:27.138716Z","title":"Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2505.24584","last_updated":"2025-08-18T16:52:22Z","snapshot_observed_at":"2026-08-09T10:10:52.166144Z","submitted_at":"2025-05-30T13:32:00Z","title":"AutoChemSchematic AI: Agentic Physics-Aware Automation for Chemical Manufacturing Scale-Up","version":3},"reference_index":15,"source":"arxiv_source","source_observed_at":"2026-08-07T12:35:27.138716Z"},"links":{"cited_paper":"/paper/2501.12948","citing_paper":"/paper/2505.24584"},"observation_digest":"sha256:9a72d01dfeff9dc48a44740bb6ddc5a134e141df023773cb0721583367e6cb2c","observation_id":"24d8fb79-383a-4d2a-88aa-ee296b003dce","resolution":{"observed_at":"2026-08-07T12:35:27.138716Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.17066","last_updated":"2024-05-27T11:37:36Z","snapshot_observed_at":"2026-08-06T15:32:09.308687Z","submitted_at":"2024-05-27T11:37:36Z","title":"Saturn: Sample-efficient Generative Molecular Design using Memory Manipulation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.17066","snapshot_observed_at":"2026-08-07T12:35:27.244503Z","title":"and Schwaller, P","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.24584","last_updated":"2025-08-18T16:52:22Z","snapshot_observed_at":"2026-08-09T10:10:52.166144Z","submitted_at":"2025-05-30T13:32:00Z","title":"AutoChemSchematic AI: Agentic Physics-Aware Automation for Chemical Manufacturing Scale-Up","version":3},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-08-07T12:35:27.244503Z"},"links":{"cited_paper":"/paper/2405.17066","citing_paper":"/paper/2505.24584"},"observation_digest":"sha256:40bfe7b742865543a1a3db5ef48fad0a672742e3fcdd06d8b0db8333cdb6cff0","observation_id":"5bc667be-7af6-43f2-8d3f-f2b5093c8ec4","resolution":{"observed_at":"2026-08-07T12:35:27.244503Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2501.00309","last_updated":"2025-01-08T05:16:25Z","snapshot_observed_at":"2026-08-07T12:37:34.294206Z","submitted_at":"2024-12-31T06:59:35Z","title":"Retrieval-Augmented Generation with Graphs (GraphRAG)","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.00309","snapshot_observed_at":"2026-08-07T12:35:27.359712Z","title":"A., Mukherjee, S., Tang, X., et al","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.24584","last_updated":"2025-08-18T16:52:22Z","snapshot_observed_at":"2026-08-09T10:10:52.166144Z","submitted_at":"2025-05-30T13:32:00Z","title":"AutoChemSchematic AI: Agentic Physics-Aware Automation for Chemical Manufacturing Scale-Up","version":3},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-08-07T12:35:27.359712Z"},"links":{"cited_paper":"/paper/2501.00309","citing_paper":"/paper/2505.24584"},"observation_digest":"sha256:cc0fed7637d95d1e5eef3b121a29bcec40fc150e81d7bacc9f535fd03460e354","observation_id":"31437aac-2a3d-456d-9f8f-39960b61ff55","resolution":{"observed_at":"2026-08-07T12:35:27.359712Z","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":"2026-08-07T12:35:27.438724Z","title":"G-retriever: Retrieval-augmented generation for textual graph understanding and question answering","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.24584","last_updated":"2025-08-18T16:52:22Z","snapshot_observed_at":"2026-08-09T10:10:52.166144Z","submitted_at":"2025-05-30T13:32:00Z","title":"AutoChemSchematic AI: Agentic Physics-Aware Automation for Chemical Manufacturing Scale-Up","version":3},"reference_index":18,"source":"arxiv_source","source_observed_at":"2026-08-07T12:35:27.438724Z"},"links":{"citing_paper":"/paper/2505.24584"},"observation_digest":"sha256:7ec32b4a6e247c88c9b97790f7a4cea82c794df8b6fcd216bbe5eb907893b0f1","observation_id":"32e5f959-0378-497f-8483-ef837721d2fa","resolution":{"observed_at":"2026-08-07T12:35:27.438724Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2211.05583","last_updated":"2026-06-08T15:07:16Z","snapshot_observed_at":"2026-07-06T14:16:44.567536Z","submitted_at":"2022-10-26T10:03:15Z","title":"Toward automatic generation of control structures for process flow diagrams with large language models","version":2},"cited_work":{"arxiv_id":"2211.05583","doi":null,"metadata_source":"pith","pith_arxiv_id":"2211.05583","snapshot_observed_at":"2026-08-07T12:35:33.733178Z","title":"Toward automatic generation of control structures for process flow diagrams with large language models","venue":"cs.CL","work_id":"f6c73542-bf96-4762-a3ec-bc256cbfe822","year":2022},"citing_paper":{"arxiv_id":"2505.24584","last_updated":"2025-08-18T16:52:22Z","snapshot_observed_at":"2026-08-09T10:10:52.166144Z","submitted_at":"2025-05-30T13:32:00Z","title":"AutoChemSchematic AI: Agentic Physics-Aware Automation for Chemical Manufacturing Scale-Up","version":3},"reference_index":19,"source":"arxiv_source","source_observed_at":"2026-08-07T12:35:27.578126Z"},"links":{"cited_paper":"/paper/2211.05583","citing_paper":"/paper/2505.24584"},"observation_digest":"sha256:472450732d14c3bf96728609a0a4912b9c4efea53afa3c83dd59d856f4a1b5e4","observation_id":"56e5f62b-5e85-4eb7-8b26-657f6bcb62d6","resolution":{"observed_at":"2026-08-07T12:35:33.784922Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T12:35:37.248208Z","title":"Retrointext: A multimodal large language model enhanced framework for retrosynthetic planning via in-context representation learning","venue":null,"work_id":"7cfb2963-a75e-4d2b-8f24-542c937e2efe","year":null},"citing_paper":{"arxiv_id":"2505.24584","last_updated":"2025-08-18T16:52:22Z","snapshot_observed_at":"2026-08-09T10:10:52.166144Z","submitted_at":"2025-05-30T13:32:00Z","title":"AutoChemSchematic AI: Agentic Physics-Aware Automation for Chemical Manufacturing Scale-Up","version":3},"reference_index":20,"source":"arxiv_source","source_observed_at":"2026-08-07T12:35:27.696506Z"},"links":{"citing_paper":"/paper/2505.24584"},"observation_digest":"sha256:157ae575643c7e2c088a334ee1afc6b29e0b40ed8639e18b4381d435d4265655","observation_id":"c82ee19c-357e-40f9-9e2d-86461e8a8daa","resolution":{"observed_at":"2026-08-07T12:35:37.310670Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2412.11388","last_updated":"2025-05-31T23:21:54Z","snapshot_observed_at":"2026-07-06T20:07:28.545657Z","submitted_at":"2024-12-16T02:28:53Z","title":"INTERACT: Enabling Interactive, Question-Driven Learning in Large Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.11388","snapshot_observed_at":"2026-08-07T12:35:27.804301Z","title":"R., and Srivastava, S","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.24584","last_updated":"2025-08-18T16:52:22Z","snapshot_observed_at":"2026-08-09T10:10:52.166144Z","submitted_at":"2025-05-30T13:32:00Z","title":"AutoChemSchematic AI: Agentic Physics-Aware Automation for Chemical Manufacturing Scale-Up","version":3},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-08-07T12:35:27.804301Z"},"links":{"cited_paper":"/paper/2412.11388","citing_paper":"/paper/2505.24584"},"observation_digest":"sha256:8b8c42d97d0288f04de8272be70c35887ca69dd42ac11a8b65d63684da80c805","observation_id":"35cb5cdc-b3e8-4788-8bc7-41d82af74bcb","resolution":{"observed_at":"2026-08-07T12:35:27.804301Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.02834","last_updated":"2024-06-23T08:45:33Z","snapshot_observed_at":"2026-08-07T23:10:12.222808Z","submitted_at":"2024-02-05T09:44:49Z","title":"Shortened LLaMA: Depth Pruning for Large Language Models with Comparison of Retraining Methods","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.02834","snapshot_observed_at":"2026-08-07T12:35:27.915557Z","title":"Shortened llama: Depth pruning for large language models with comparison of retraining methods","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.24584","last_updated":"2025-08-18T16:52:22Z","snapshot_observed_at":"2026-08-09T10:10:52.166144Z","submitted_at":"2025-05-30T13:32:00Z","title":"AutoChemSchematic AI: Agentic Physics-Aware Automation for Chemical Manufacturing Scale-Up","version":3},"reference_index":22,"source":"arxiv_source","source_observed_at":"2026-08-07T12:35:27.915557Z"},"links":{"cited_paper":"/paper/2402.02834","citing_paper":"/paper/2505.24584"},"observation_digest":"sha256:b451ad40b917dac9b8a751331d161c56baa9b5165ded48d829c53578593f1a11","observation_id":"82759771-3a7d-4ba3-8c66-2d25f3b11cd9","resolution":{"observed_at":"2026-08-07T12:35:27.915557Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.05015","last_updated":"2024-05-28T18:59:01Z","snapshot_observed_at":"2026-08-05T11:51:34.708208Z","submitted_at":"2024-02-07T16:32:58Z","title":"A Sober Look at LLMs for Material Discovery: Are They Actually Good for Bayesian Optimization Over Molecules?","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.05015","snapshot_observed_at":"2026-08-07T12:35:28.065705Z","title":"A sober look at llms for material discovery: Are they actually good for bayesian optimization over molecules? arXiv preprint arXiv:2402.05015, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.24584","last_updated":"2025-08-18T16:52:22Z","snapshot_observed_at":"2026-08-09T10:10:52.166144Z","submitted_at":"2025-05-30T13:32:00Z","title":"AutoChemSchematic AI: Agentic Physics-Aware Automation for Chemical Manufacturing Scale-Up","version":3},"reference_index":23,"source":"arxiv_source","source_observed_at":"2026-08-07T12:35:28.065705Z"},"links":{"cited_paper":"/paper/2402.05015","citing_paper":"/paper/2505.24584"},"observation_digest":"sha256:a42c7b546753d0bceddf92099edf4123193dec54d533f46da39997dd1ca70f52","observation_id":"d3d558a8-f38c-4f05-bcc1-046c400b6132","resolution":{"observed_at":"2026-08-07T12:35:28.065705Z","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":"2026-08-07T12:35:28.220920Z","title":"H., Gonzalez, J., Zhang, H., and Stoica, I","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.24584","last_updated":"2025-08-18T16:52:22Z","snapshot_observed_at":"2026-08-09T10:10:52.166144Z","submitted_at":"2025-05-30T13:32:00Z","title":"AutoChemSchematic AI: Agentic Physics-Aware Automation for Chemical Manufacturing Scale-Up","version":3},"reference_index":24,"source":"arxiv_source","source_observed_at":"2026-08-07T12:35:28.220920Z"},"links":{"citing_paper":"/paper/2505.24584"},"observation_digest":"sha256:6fa57a516772b50d08b7944acfcc52f7176158c4b33a47e3aedfb77f541b5390","observation_id":"53e66473-8266-4b3e-8356-3bd0f97887d5","resolution":{"observed_at":"2026-08-07T12:35:28.220920Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2501.10069","last_updated":"2025-04-27T08:55:07Z","snapshot_observed_at":"2026-08-03T02:39:23.642980Z","submitted_at":"2025-01-17T09:42:48Z","title":"A Survey on LLM Test-Time Compute via Search: Tasks, LLM Profiling, Search Algorithms, and Relevant Frameworks","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.10069","snapshot_observed_at":"2026-08-07T12:35:28.325869Z","title":"A survey on llm test-time compute via search: Tasks, llm profiling, search algorithms, and relevant frameworks","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2505.24584","last_updated":"2025-08-18T16:52:22Z","snapshot_observed_at":"2026-08-09T10:10:52.166144Z","submitted_at":"2025-05-30T13:32:00Z","title":"AutoChemSchematic AI: Agentic Physics-Aware Automation for Chemical Manufacturing Scale-Up","version":3},"reference_index":25,"source":"arxiv_source","source_observed_at":"2026-08-07T12:35:28.325869Z"},"links":{"cited_paper":"/paper/2501.10069","citing_paper":"/paper/2505.24584"},"observation_digest":"sha256:aee2ecf655f50dd7eb07bc79058502eab0369e0b1474d219781c51179a41bfaf","observation_id":"4a9f9166-e000-46ce-b89f-e663b8216b5e","resolution":{"observed_at":"2026-08-07T12:35:28.325869Z","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":"2026-08-07T12:35:28.440274Z","title":"Cppo: Accelerating the training of group relative policy optimization-based reasoning models","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2505.24584","last_updated":"2025-08-18T16:52:22Z","snapshot_observed_at":"2026-08-09T10:10:52.166144Z","submitted_at":"2025-05-30T13:32:00Z","title":"AutoChemSchematic AI: Agentic Physics-Aware Automation for Chemical Manufacturing Scale-Up","version":3},"reference_index":26,"source":"arxiv_source","source_observed_at":"2026-08-07T12:35:28.440274Z"},"links":{"citing_paper":"/paper/2505.24584"},"observation_digest":"sha256:2476f62f41aad4c48537e7725dbdccf64a49150765b97a261bc0ef90ce054c30","observation_id":"8f8875a9-29eb-49ef-b995-cb6bbef1072c","resolution":{"observed_at":"2026-08-07T12:35:28.440274Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2412.19437","last_updated":"2025-02-18T17:26:38Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2024-12-27T04:03:16Z","title":"DeepSeek-V3 Technical Report","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.19437","snapshot_observed_at":"2026-08-07T12:35:28.553636Z","title":"Deepseek-v3 technical report","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.24584","last_updated":"2025-08-18T16:52:22Z","snapshot_observed_at":"2026-08-09T10:10:52.166144Z","submitted_at":"2025-05-30T13:32:00Z","title":"AutoChemSchematic AI: Agentic Physics-Aware Automation for Chemical Manufacturing Scale-Up","version":3},"reference_index":27,"source":"arxiv_source","source_observed_at":"2026-08-07T12:35:28.553636Z"},"links":{"cited_paper":"/paper/2412.19437","citing_paper":"/paper/2505.24584"},"observation_digest":"sha256:daf0e351c38eb8466b6ded877ce97a9ac4a4e4a6019b6eb6de64f103ecdb2915","observation_id":"9eba21a6-bbc2-4aae-bfc5-4b27aa964249","resolution":{"observed_at":"2026-08-07T12:35:28.553636Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2503.13447","last_updated":"2025-03-17T17:59:54Z","snapshot_observed_at":"2026-08-07T16:57:01.042252Z","submitted_at":"2025-03-17T17:59:54Z","title":"MetaScale: Test-Time Scaling with Evolving Meta-Thoughts","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2503.13447","snapshot_observed_at":"2026-08-07T12:35:28.663379Z","title":"Y., Wang, F., Zhang, S., Poon, H., and Chen, M","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2505.24584","last_updated":"2025-08-18T16:52:22Z","snapshot_observed_at":"2026-08-09T10:10:52.166144Z","submitted_at":"2025-05-30T13:32:00Z","title":"AutoChemSchematic AI: Agentic Physics-Aware Automation for Chemical Manufacturing Scale-Up","version":3},"reference_index":28,"source":"arxiv_source","source_observed_at":"2026-08-07T12:35:28.663379Z"},"links":{"cited_paper":"/paper/2503.13447","citing_paper":"/paper/2505.24584"},"observation_digest":"sha256:961aa3ae9bfaa0da25f2c7091715d612707c542a459060806978533806a34416","observation_id":"d624e507-dcae-490a-89fc-fbe49fdeb2b7","resolution":{"observed_at":"2026-08-07T12:35:28.663379Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2503.23077","last_updated":"2025-08-13T15:48:46Z","snapshot_observed_at":"2026-08-07T16:28:35.345205Z","submitted_at":"2025-03-29T13:27:46Z","title":"Efficient Inference for Large Reasoning Models: A Survey","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2503.23077","snapshot_observed_at":"2026-08-07T12:35:28.772127Z","title":"Efficient inference for large reasoning models: A survey","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2505.24584","last_updated":"2025-08-18T16:52:22Z","snapshot_observed_at":"2026-08-09T10:10:52.166144Z","submitted_at":"2025-05-30T13:32:00Z","title":"AutoChemSchematic AI: Agentic Physics-Aware Automation for Chemical Manufacturing Scale-Up","version":3},"reference_index":29,"source":"arxiv_source","source_observed_at":"2026-08-07T12:35:28.772127Z"},"links":{"cited_paper":"/paper/2503.23077","citing_paper":"/paper/2505.24584"},"observation_digest":"sha256:caba79a0d1637fbbc5fd771dd9c190f422e19eca36f519196bd5b57e50968d46","observation_id":"92436c6d-4843-4ee7-bd4e-94f520a26559","resolution":{"observed_at":"2026-08-07T12:35:28.772127Z","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":"2026-08-07T12:35:37.061416Z","title":"W., and Yang, Y","venue":null,"work_id":"5397bfaa-6b2d-4aad-a6c9-220c7a880d2b","year":2024},"citing_paper":{"arxiv_id":"2505.24584","last_updated":"2025-08-18T16:52:22Z","snapshot_observed_at":"2026-08-09T10:10:52.166144Z","submitted_at":"2025-05-30T13:32:00Z","title":"AutoChemSchematic AI: Agentic Physics-Aware Automation for Chemical Manufacturing Scale-Up","version":3},"reference_index":30,"source":"arxiv_source","source_observed_at":"2026-08-07T12:35:28.847721Z"},"links":{"citing_paper":"/paper/2505.24584"},"observation_digest":"sha256:2d62a63e3f74b685d9818d477000c8031a766a6800b1cb0e785533f32460b8a1","observation_id":"f3ad670e-d356-4b49-91f3-2cf1d0aa6346","resolution":{"observed_at":"2026-08-07T12:35:37.173502Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2405.04304","last_updated":"2024-11-07T12:59:39Z","snapshot_observed_at":"2026-08-09T07:26:06.310167Z","submitted_at":"2024-05-07T13:27:52Z","title":"Dynamic Speculation Lookahead Accelerates Speculative Decoding of Large Language Models","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.04304","snapshot_observed_at":"2026-08-07T12:35:28.937606Z","title":"Dynamic speculation lookahead accelerates speculative decoding of large language models","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.24584","last_updated":"2025-08-18T16:52:22Z","snapshot_observed_at":"2026-08-09T10:10:52.166144Z","submitted_at":"2025-05-30T13:32:00Z","title":"AutoChemSchematic AI: Agentic Physics-Aware Automation for Chemical Manufacturing Scale-Up","version":3},"reference_index":31,"source":"arxiv_source","source_observed_at":"2026-08-07T12:35:28.937606Z"},"links":{"cited_paper":"/paper/2405.04304","citing_paper":"/paper/2505.24584"},"observation_digest":"sha256:ac5e0aef58349caf8e6cea38a08e76b69a91708cb8d51ebf15eaaee0e3dbdd2e","observation_id":"e5b0615e-ba43-4bfe-adc9-e302d256909e","resolution":{"observed_at":"2026-08-07T12:35:28.937606Z","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":"2026-08-07T12:35:36.866943Z","title":"Dwsim: Open source process simulator, 2025","venue":null,"work_id":"6d34d7db-f93a-497b-b79f-89e51aae6274","year":2025},"citing_paper":{"arxiv_id":"2505.24584","last_updated":"2025-08-18T16:52:22Z","snapshot_observed_at":"2026-08-09T10:10:52.166144Z","submitted_at":"2025-05-30T13:32:00Z","title":"AutoChemSchematic AI: Agentic Physics-Aware Automation for Chemical Manufacturing Scale-Up","version":3},"reference_index":32,"source":"arxiv_source","source_observed_at":"2026-08-07T12:35:29.027222Z"},"links":{"citing_paper":"/paper/2505.24584"},"observation_digest":"sha256:32beff39a832968248c44fe229a1d3eaa993d45dad2a84a2c1e5be71b26e3cb6","observation_id":"cdb80368-62fb-4203-9e1c-8f7d0ee8418a","resolution":{"observed_at":"2026-08-07T12:35:36.956698Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T12:35:36.640952Z","title":"text-embedding-3-small model","venue":null,"work_id":"1bf5ad44-7417-48fa-82b8-90efa0b825e4","year":2024},"citing_paper":{"arxiv_id":"2505.24584","last_updated":"2025-08-18T16:52:22Z","snapshot_observed_at":"2026-08-09T10:10:52.166144Z","submitted_at":"2025-05-30T13:32:00Z","title":"AutoChemSchematic AI: Agentic Physics-Aware Automation for Chemical Manufacturing Scale-Up","version":3},"reference_index":33,"source":"arxiv_source","source_observed_at":"2026-08-07T12:35:29.142505Z"},"links":{"citing_paper":"/paper/2505.24584"},"observation_digest":"sha256:4b4de2ff4546cf29bc9cc0e639e41d9db3f66e403d23917f816a26d3e77a4a17","observation_id":"16176cfb-8004-4b75-b75a-f6dca626d298","resolution":{"observed_at":"2026-08-07T12:35:36.737697Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T12:35:36.420782Z","title":"A chemically-guided generative diffusion model for materials synthesis planning","venue":null,"work_id":"e614c40a-6b2c-481d-8daa-a5308babc150","year":2024},"citing_paper":{"arxiv_id":"2505.24584","last_updated":"2025-08-18T16:52:22Z","snapshot_observed_at":"2026-08-09T10:10:52.166144Z","submitted_at":"2025-05-30T13:32:00Z","title":"AutoChemSchematic AI: Agentic Physics-Aware Automation for Chemical Manufacturing Scale-Up","version":3},"reference_index":34,"source":"arxiv_source","source_observed_at":"2026-08-07T12:35:29.226708Z"},"links":{"citing_paper":"/paper/2505.24584"},"observation_digest":"sha256:f067e9d49a8b6414fdd6fd67189231957468218cc48f2440bfc664e7e608e95a","observation_id":"72fa2498-a6a2-4052-a927-0f89c94c77a9","resolution":{"observed_at":"2026-08-07T12:35:36.557064Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2405.04437","last_updated":"2025-01-29T04:10:41Z","snapshot_observed_at":"2026-08-05T02:13:00.249260Z","submitted_at":"2024-05-07T16:00:32Z","title":"vAttention: Dynamic Memory Management for Serving LLMs without PagedAttention","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.04437","snapshot_observed_at":"2026-08-07T12:35:29.351522Z","title":"vattention: Dynamic memory management for serving llms without pagedattention","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.24584","last_updated":"2025-08-18T16:52:22Z","snapshot_observed_at":"2026-08-09T10:10:52.166144Z","submitted_at":"2025-05-30T13:32:00Z","title":"AutoChemSchematic AI: Agentic Physics-Aware Automation for Chemical Manufacturing Scale-Up","version":3},"reference_index":35,"source":"arxiv_source","source_observed_at":"2026-08-07T12:35:29.351522Z"},"links":{"cited_paper":"/paper/2405.04437","citing_paper":"/paper/2505.24584"},"observation_digest":"sha256:b4e761e5d58d624abdd91cfe35ebda88007475c34d96b82caf66969e3c3977ae","observation_id":"e187508e-9ad4-4c8d-8242-60c8b694c8f3","resolution":{"observed_at":"2026-08-07T12:35:29.351522Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2503.07572","last_updated":"2025-03-10T17:40:43Z","snapshot_observed_at":"2026-08-07T17:16:00.148793Z","submitted_at":"2025-03-10T17:40:43Z","title":"Optimizing Test-Time Compute via Meta Reinforcement Fine-Tuning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2503.07572","snapshot_observed_at":"2026-08-07T12:35:29.461138Z","title":"Y., Setlur, A., Tunstall, L., Beeching, E","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2505.24584","last_updated":"2025-08-18T16:52:22Z","snapshot_observed_at":"2026-08-09T10:10:52.166144Z","submitted_at":"2025-05-30T13:32:00Z","title":"AutoChemSchematic AI: Agentic Physics-Aware Automation for Chemical Manufacturing Scale-Up","version":3},"reference_index":36,"source":"arxiv_source","source_observed_at":"2026-08-07T12:35:29.461138Z"},"links":{"cited_paper":"/paper/2503.07572","citing_paper":"/paper/2505.24584"},"observation_digest":"sha256:2fb2ecfd73e722da682612e1e5d9acee698311e4243585e5b5823289f0b3fdad","observation_id":"7ffc267b-be9d-44f8-bf7a-2c865a31dcf0","resolution":{"observed_at":"2026-08-07T12:35:29.461138Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.18779","last_updated":"2024-10-24T14:31:52Z","snapshot_observed_at":"2026-07-06T19:39:06.300432Z","submitted_at":"2024-10-24T14:31:52Z","title":"A Little Help Goes a Long Way: Efficient LLM Training by Leveraging Small LMs","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.18779","snapshot_observed_at":"2026-08-07T12:35:29.600966Z","title":"S., Sadhanala, V., Rostamizadeh, A., Chakrabarti, A., Jitkrittum, W., Feinberg, V., Kim, S., Harutyunyan, H., Saunshi, N., Nado, Z., et al","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.24584","last_updated":"2025-08-18T16:52:22Z","snapshot_observed_at":"2026-08-09T10:10:52.166144Z","submitted_at":"2025-05-30T13:32:00Z","title":"AutoChemSchematic AI: Agentic Physics-Aware Automation for Chemical Manufacturing Scale-Up","version":3},"reference_index":37,"source":"arxiv_source","source_observed_at":"2026-08-07T12:35:29.600966Z"},"links":{"cited_paper":"/paper/2410.18779","citing_paper":"/paper/2505.24584"},"observation_digest":"sha256:b3988259bd4be15b37b2272da1cf706ccc67d9502c4f194eb2f65271ccaaf68c","observation_id":"4ffcb539-a804-4f74-8f51-b90df1c1d30c","resolution":{"observed_at":"2026-08-07T12:35:29.600966Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.00161","last_updated":"2024-10-07T15:07:09Z","snapshot_observed_at":"2026-08-05T04:28:36.671006Z","submitted_at":"2024-09-30T19:09:13Z","title":"KV-Compress: Paged KV-Cache Compression with Variable Compression Rates per Attention Head","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.00161","snapshot_observed_at":"2026-08-07T12:35:29.734974Z","title":"Kv-compress: Paged kv-cache compression with variable compression rates per attention head","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.24584","last_updated":"2025-08-18T16:52:22Z","snapshot_observed_at":"2026-08-09T10:10:52.166144Z","submitted_at":"2025-05-30T13:32:00Z","title":"AutoChemSchematic AI: Agentic Physics-Aware Automation for Chemical Manufacturing Scale-Up","version":3},"reference_index":38,"source":"arxiv_source","source_observed_at":"2026-08-07T12:35:29.734974Z"},"links":{"cited_paper":"/paper/2410.00161","citing_paper":"/paper/2505.24584"},"observation_digest":"sha256:871f425cf1fcec567bff01e916d0887579df630d59fb334304edc5f3ab5ca3a7","observation_id":"3ed4e409-268a-4878-a8a2-f13484d531f0","resolution":{"observed_at":"2026-08-07T12:35:29.734974Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2501.17771","last_updated":"2025-08-17T16:56:30Z","snapshot_observed_at":"2026-08-05T02:49:13.264759Z","submitted_at":"2025-01-29T17:05:33Z","title":"2SSP: A Two-Stage Framework for Structured Pruning of LLMs","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.17771","snapshot_observed_at":"2026-08-07T12:35:29.820564Z","title":"2ssp: A two-stage framework for structured pruning of llms","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2505.24584","last_updated":"2025-08-18T16:52:22Z","snapshot_observed_at":"2026-08-09T10:10:52.166144Z","submitted_at":"2025-05-30T13:32:00Z","title":"AutoChemSchematic AI: Agentic Physics-Aware Automation for Chemical Manufacturing Scale-Up","version":3},"reference_index":39,"source":"arxiv_source","source_observed_at":"2026-08-07T12:35:29.820564Z"},"links":{"cited_paper":"/paper/2501.17771","citing_paper":"/paper/2505.24584"},"observation_digest":"sha256:0075b533337bef6dfd8b3aa9ba2278b6edd6c72ee8404d2bc27f0bf3ec031885","observation_id":"0d8881ff-1e62-45fb-8f32-b50615c737e5","resolution":{"observed_at":"2026-08-07T12:35:29.820564Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1707.06347","last_updated":"2017-08-28T09:20:06Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2017-07-20T02:32:33Z","title":"Proximal Policy Optimization Algorithms","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1707.06347","snapshot_observed_at":"2026-08-07T12:35:29.943690Z","title":"Proximal policy optimization algorithms","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2505.24584","last_updated":"2025-08-18T16:52:22Z","snapshot_observed_at":"2026-08-09T10:10:52.166144Z","submitted_at":"2025-05-30T13:32:00Z","title":"AutoChemSchematic AI: Agentic Physics-Aware Automation for Chemical Manufacturing Scale-Up","version":3},"reference_index":40,"source":"arxiv_source","source_observed_at":"2026-08-07T12:35:29.943690Z"},"links":{"cited_paper":"/paper/1707.06347","citing_paper":"/paper/2505.24584"},"observation_digest":"sha256:e5680c432943931780f5c1df31412a68aeca9c543ceb33f297f34d5f9b6a2579","observation_id":"2ff1aa35-2c75-45d6-ac3a-1ca792bddb02","resolution":{"observed_at":"2026-08-07T12:35:29.943690Z","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":"2026-08-07T12:35:36.217777Z","title":null,"venue":null,"work_id":"a006e01e-b7ea-413a-a416-b4a7ecaa3f69","year":2023},"citing_paper":{"arxiv_id":"2505.24584","last_updated":"2025-08-18T16:52:22Z","snapshot_observed_at":"2026-08-09T10:10:52.166144Z","submitted_at":"2025-05-30T13:32:00Z","title":"AutoChemSchematic AI: Agentic Physics-Aware Automation for Chemical Manufacturing Scale-Up","version":3},"reference_index":41,"source":"arxiv_source","source_observed_at":"2026-08-07T12:35:30.011837Z"},"links":{"citing_paper":"/paper/2505.24584"},"observation_digest":"sha256:e3eab61420c9b54e01f357c6972a98a380794fb942c4c1d1bd38e90cccdfe691","observation_id":"921e76a3-3ecb-4b1e-9060-8292866f75ec","resolution":{"observed_at":"2026-08-07T12:35:36.351478Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T12:35:30.113385Z","title":"Flashattention-3: Fast and accurate attention with asynchrony and low-precision","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.24584","last_updated":"2025-08-18T16:52:22Z","snapshot_observed_at":"2026-08-09T10:10:52.166144Z","submitted_at":"2025-05-30T13:32:00Z","title":"AutoChemSchematic AI: Agentic Physics-Aware Automation for Chemical Manufacturing Scale-Up","version":3},"reference_index":42,"source":"arxiv_source","source_observed_at":"2026-08-07T12:35:30.113385Z"},"links":{"citing_paper":"/paper/2505.24584"},"observation_digest":"sha256:0481a183bd21bd341c5bf871096cf0c41eb4bfe9cb766431adb63060e7db71e4","observation_id":"94fedf7d-3d80-450c-bb13-d0015f604452","resolution":{"observed_at":"2026-08-07T12:35:30.113385Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.03300","last_updated":"2024-04-27T15:25:53Z","snapshot_observed_at":"2026-08-06T14:58:42.911363Z","submitted_at":"2024-02-05T18:55:32Z","title":"DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.03300","snapshot_observed_at":"2026-08-07T12:35:30.205428Z","title":"Deepseekmath: Pushing the limits of mathematical reasoning in open language models","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.24584","last_updated":"2025-08-18T16:52:22Z","snapshot_observed_at":"2026-08-09T10:10:52.166144Z","submitted_at":"2025-05-30T13:32:00Z","title":"AutoChemSchematic AI: Agentic Physics-Aware Automation for Chemical Manufacturing Scale-Up","version":3},"reference_index":43,"source":"arxiv_source","source_observed_at":"2026-08-07T12:35:30.205428Z"},"links":{"cited_paper":"/paper/2402.03300","citing_paper":"/paper/2505.24584"},"observation_digest":"sha256:96e6e0a1f41f01a09477b29cd1396307d96137844399091c4cbbed2bae0f9cb9","observation_id":"b4536e51-15bb-4f6e-9907-54c7df403b9b","resolution":{"observed_at":"2026-08-07T12:35:30.205428Z","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":"2026-08-07T12:35:30.306695Z","title":"When to solve, when to verify: Compute-optimal problem solving and generative verification for llm reasoning","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2505.24584","last_updated":"2025-08-18T16:52:22Z","snapshot_observed_at":"2026-08-09T10:10:52.166144Z","submitted_at":"2025-05-30T13:32:00Z","title":"AutoChemSchematic AI: Agentic Physics-Aware Automation for Chemical Manufacturing Scale-Up","version":3},"reference_index":44,"source":"arxiv_source","source_observed_at":"2026-08-07T12:35:30.306695Z"},"links":{"citing_paper":"/paper/2505.24584"},"observation_digest":"sha256:f969521cb8dacf8ae78a5299b4bea91ca19ffb1aa6a62c2e1c76b04eca0ee475","observation_id":"a0fad02c-07ac-4f7f-b988-023b191dce79","resolution":{"observed_at":"2026-08-07T12:35:30.306695Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2408.03314","last_updated":"2024-08-06T17:35:05Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2024-08-06T17:35:05Z","title":"Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.03314","snapshot_observed_at":"2026-08-07T12:35:30.382241Z","title":"Scaling llm test-time compute optimally can be more effective than scaling model parameters","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.24584","last_updated":"2025-08-18T16:52:22Z","snapshot_observed_at":"2026-08-09T10:10:52.166144Z","submitted_at":"2025-05-30T13:32:00Z","title":"AutoChemSchematic AI: Agentic Physics-Aware Automation for Chemical Manufacturing Scale-Up","version":3},"reference_index":45,"source":"arxiv_source","source_observed_at":"2026-08-07T12:35:30.382241Z"},"links":{"cited_paper":"/paper/2408.03314","citing_paper":"/paper/2505.24584"},"observation_digest":"sha256:6b825da594a3b6a8e53d1092639119e34afa233a83cd99a52c108060fb647049","observation_id":"94f06975-4095-4b07-ad37-14cad0d1f41b","resolution":{"observed_at":"2026-08-07T12:35:30.382241Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.10980","last_updated":"2024-12-09T03:01:35Z","snapshot_observed_at":"2026-08-08T03:59:52.552235Z","submitted_at":"2024-02-15T21:33:07Z","title":"ChemReasoner: Heuristic Search over a Large Language Model's Knowledge Space using Quantum-Chemical Feedback","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.10980","snapshot_observed_at":"2026-08-07T12:35:30.450795Z","title":"W., Edwards, C., Agarwal, K., Olarte, M","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.24584","last_updated":"2025-08-18T16:52:22Z","snapshot_observed_at":"2026-08-09T10:10:52.166144Z","submitted_at":"2025-05-30T13:32:00Z","title":"AutoChemSchematic AI: Agentic Physics-Aware Automation for Chemical Manufacturing Scale-Up","version":3},"reference_index":46,"source":"arxiv_source","source_observed_at":"2026-08-07T12:35:30.450795Z"},"links":{"cited_paper":"/paper/2402.10980","citing_paper":"/paper/2505.24584"},"observation_digest":"sha256:45617a1190f09433660a4bc305a0ee9f3184b19a04e25f0cebe29f9c6756f0e6","observation_id":"1bb73d9f-80ef-4710-bc27-6a5f9ac290c9","resolution":{"observed_at":"2026-08-07T12:35:30.450795Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2412.05937","last_updated":"2024-12-08T13:36:42Z","snapshot_observed_at":"2026-07-06T20:03:29.510169Z","submitted_at":"2024-12-08T13:36:42Z","title":"Accelerating Manufacturing Scale-Up from Material Discovery Using Agentic Web Navigation and Retrieval-Augmented AI for Process Engineering Schematics Design","version":1},"cited_work":{"arxiv_id":"2412.05937","doi":null,"metadata_source":"pith","pith_arxiv_id":"2412.05937","snapshot_observed_at":"2026-08-07T12:35:33.264459Z","title":"Accelerating Manufacturing Scale-Up from Material Discovery Using Agentic Web Navigation and Retrieval-Augmented AI for Process Engineering Schematics Design","venue":"cs.LG","work_id":"068c631a-0be6-46e8-b9c2-7a3122b6d69b","year":2024},"citing_paper":{"arxiv_id":"2505.24584","last_updated":"2025-08-18T16:52:22Z","snapshot_observed_at":"2026-08-09T10:10:52.166144Z","submitted_at":"2025-05-30T13:32:00Z","title":"AutoChemSchematic AI: Agentic Physics-Aware Automation for Chemical Manufacturing Scale-Up","version":3},"reference_index":47,"source":"arxiv_source","source_observed_at":"2026-08-07T12:35:30.578188Z"},"links":{"cited_paper":"/paper/2412.05937","citing_paper":"/paper/2505.24584"},"observation_digest":"sha256:156b288cc9421f413bd664009750551c5293cd63ab7e8007a20ef271b6136072","observation_id":"5bdb2fcc-956b-4e20-885b-a04b380ef094","resolution":{"observed_at":"2026-08-07T12:35:33.330708Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2306.11695","last_updated":"2024-05-06T17:47:01Z","snapshot_observed_at":"2026-07-06T15:44:42.776459Z","submitted_at":"2023-06-20T17:18:20Z","title":"A Simple and Effective Pruning Approach for Large Language Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2306.11695","snapshot_observed_at":"2026-08-07T12:35:30.669649Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.24584","last_updated":"2025-08-18T16:52:22Z","snapshot_observed_at":"2026-08-09T10:10:52.166144Z","submitted_at":"2025-05-30T13:32:00Z","title":"AutoChemSchematic AI: Agentic Physics-Aware Automation for Chemical Manufacturing Scale-Up","version":3},"reference_index":48,"source":"arxiv_source","source_observed_at":"2026-08-07T12:35:30.669649Z"},"links":{"cited_paper":"/paper/2306.11695","citing_paper":"/paper/2505.24584"},"observation_digest":"sha256:da831e7d46317dc39b31b201753d0769732fde8e77911fed5f23525efadb2b2f","observation_id":"f8127382-1fd0-4ac1-93d1-d9ed08ff5de2","resolution":{"observed_at":"2026-08-07T12:35:30.669649Z","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":"2026-08-07T12:35:30.786335Z","title":"The curse of depth in large language models","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2505.24584","last_updated":"2025-08-18T16:52:22Z","snapshot_observed_at":"2026-08-09T10:10:52.166144Z","submitted_at":"2025-05-30T13:32:00Z","title":"AutoChemSchematic AI: Agentic Physics-Aware Automation for Chemical Manufacturing Scale-Up","version":3},"reference_index":49,"source":"arxiv_source","source_observed_at":"2026-08-07T12:35:30.786335Z"},"links":{"citing_paper":"/paper/2505.24584"},"observation_digest":"sha256:b083127a6f2050c04f28b9f50b9e28dd731e334aff1d3eb43c20495a74c4c269","observation_id":"7f290849-08b4-4290-8309-109077570a42","resolution":{"observed_at":"2026-08-07T12:35:30.786335Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2502.07780","last_updated":"2026-07-15T12:46:32Z","snapshot_observed_at":"2026-08-09T06:26:59.031313Z","submitted_at":"2025-02-11T18:59:35Z","title":"DarwinLM: Evolutionary Structured Pruning of Large Language Models","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.07780","snapshot_observed_at":"2026-08-07T12:35:30.868615Z","title":"Darwinlm: Evolutionary structured pruning of large language models","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2505.24584","last_updated":"2025-08-18T16:52:22Z","snapshot_observed_at":"2026-08-09T10:10:52.166144Z","submitted_at":"2025-05-30T13:32:00Z","title":"AutoChemSchematic AI: Agentic Physics-Aware Automation for Chemical Manufacturing Scale-Up","version":3},"reference_index":50,"source":"arxiv_source","source_observed_at":"2026-08-07T12:35:30.868615Z"},"links":{"cited_paper":"/paper/2502.07780","citing_paper":"/paper/2505.24584"},"observation_digest":"sha256:20473bb412d650f5c018176f105ce8d8185af37b376dfdcf669701c07dfe5b30","observation_id":"7287e1bf-ca2c-471b-976e-a5f585d31d34","resolution":{"observed_at":"2026-08-07T12:35:30.868615Z","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":"2026-08-07T12:35:35.846190Z","title":null,"venue":null,"work_id":"3a9b7fad-93d9-4532-b146-48dea2e326d3","year":2025},"citing_paper":{"arxiv_id":"2505.24584","last_updated":"2025-08-18T16:52:22Z","snapshot_observed_at":"2026-08-09T10:10:52.166144Z","submitted_at":"2025-05-30T13:32:00Z","title":"AutoChemSchematic AI: Agentic Physics-Aware Automation for Chemical Manufacturing Scale-Up","version":3},"reference_index":51,"source":"arxiv_source","source_observed_at":"2026-08-07T12:35:30.975870Z"},"links":{"citing_paper":"/paper/2505.24584"},"observation_digest":"sha256:b598acbc842b299f4276702da3a1e51871b4f3d6dbbbdabe82dccff86d9eff35","observation_id":"e572f765-4eb7-4a0a-9d77-1e91dccd9ae0","resolution":{"observed_at":"2026-08-07T12:35:35.992200Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T12:35:31.064319Z","title":"A., Waltman, L., and Van Eck, N","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2505.24584","last_updated":"2025-08-18T16:52:22Z","snapshot_observed_at":"2026-08-09T10:10:52.166144Z","submitted_at":"2025-05-30T13:32:00Z","title":"AutoChemSchematic AI: Agentic Physics-Aware Automation for Chemical Manufacturing Scale-Up","version":3},"reference_index":52,"source":"arxiv_source","source_observed_at":"2026-08-07T12:35:31.064319Z"},"links":{"citing_paper":"/paper/2505.24584"},"observation_digest":"sha256:26031dfdc6e281b8dbbcac28511dd484eddd08ee2b2f0840097c0157c4b91d40","observation_id":"3b2e50ac-048d-4110-aca7-f4fcf5ca1e52","resolution":{"observed_at":"2026-08-07T12:35:31.064319Z","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":"2026-08-07T12:35:35.416225Z","title":"S., and Schweidtmann, A","venue":null,"work_id":"8c585b63-f061-4a53-8554-817f46d38ad0","year":2023},"citing_paper":{"arxiv_id":"2505.24584","last_updated":"2025-08-18T16:52:22Z","snapshot_observed_at":"2026-08-09T10:10:52.166144Z","submitted_at":"2025-05-30T13:32:00Z","title":"AutoChemSchematic AI: Agentic Physics-Aware Automation for Chemical Manufacturing Scale-Up","version":3},"reference_index":53,"source":"arxiv_source","source_observed_at":"2026-08-07T12:35:31.132726Z"},"links":{"citing_paper":"/paper/2505.24584"},"observation_digest":"sha256:187272fabe6e5e6e2b1bda5df7345a2c21843f669540bdf414138b49518fb656","observation_id":"d30b1468-834c-47a1-931b-b03cb75fdbf9","resolution":{"observed_at":"2026-08-07T12:35:35.569332Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2406.16976","last_updated":"2025-03-07T17:24:35Z","snapshot_observed_at":"2026-07-06T18:36:16.828338Z","submitted_at":"2024-06-23T06:22:49Z","title":"Efficient Evolutionary Search Over Chemical Space with Large Language Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.16976","snapshot_observed_at":"2026-08-07T12:35:31.216160Z","title":"Efficient evolutionary search over chemical space with large language models","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.24584","last_updated":"2025-08-18T16:52:22Z","snapshot_observed_at":"2026-08-09T10:10:52.166144Z","submitted_at":"2025-05-30T13:32:00Z","title":"AutoChemSchematic AI: Agentic Physics-Aware Automation for Chemical Manufacturing Scale-Up","version":3},"reference_index":54,"source":"arxiv_source","source_observed_at":"2026-08-07T12:35:31.216160Z"},"links":{"cited_paper":"/paper/2406.16976","citing_paper":"/paper/2505.24584"},"observation_digest":"sha256:1faeb45dcc4923949595974a28bf431d536490d36a99e8214e16e4819d74cbf1","observation_id":"a57b5ed3-3e53-44e0-a797-aa2b649b2744","resolution":{"observed_at":"2026-08-07T12:35:31.216160Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2412.06419","last_updated":"2024-12-09T11:57:16Z","snapshot_observed_at":"2026-07-06T20:03:51.758274Z","submitted_at":"2024-12-09T11:57:16Z","title":"LLM-BIP: Structured Pruning for Large Language Models with Block-Wise Forward Importance Propagation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.06419","snapshot_observed_at":"2026-08-07T12:35:31.300124Z","title":"Llm-bip: Structured pruning for large language models with block-wise forward importance propagation","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.24584","last_updated":"2025-08-18T16:52:22Z","snapshot_observed_at":"2026-08-09T10:10:52.166144Z","submitted_at":"2025-05-30T13:32:00Z","title":"AutoChemSchematic AI: Agentic Physics-Aware Automation for Chemical Manufacturing Scale-Up","version":3},"reference_index":55,"source":"arxiv_source","source_observed_at":"2026-08-07T12:35:31.300124Z"},"links":{"cited_paper":"/paper/2412.06419","citing_paper":"/paper/2505.24584"},"observation_digest":"sha256:16071914b767132aab83851ad156901134acdf45e5c2a17ebb061d2605e95fa4","observation_id":"556e86b9-6d94-473e-b923-ef1b98a019d9","resolution":{"observed_at":"2026-08-07T12:35:31.300124Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2309.14717","last_updated":"2023-10-09T07:39:04Z","snapshot_observed_at":"2026-08-03T21:40:53.000035Z","submitted_at":"2023-09-26T07:22:23Z","title":"QA-LoRA: Quantization-Aware Low-Rank Adaptation of Large Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2309.14717","snapshot_observed_at":"2026-08-07T12:35:31.362249Z","title":"Qa-lora: Quantization-aware low-rank adaptation of large language models","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.24584","last_updated":"2025-08-18T16:52:22Z","snapshot_observed_at":"2026-08-09T10:10:52.166144Z","submitted_at":"2025-05-30T13:32:00Z","title":"AutoChemSchematic AI: Agentic Physics-Aware Automation for Chemical Manufacturing Scale-Up","version":3},"reference_index":56,"source":"arxiv_source","source_observed_at":"2026-08-07T12:35:31.362249Z"},"links":{"cited_paper":"/paper/2309.14717","citing_paper":"/paper/2505.24584"},"observation_digest":"sha256:07769fece43d01483733468bdc53ec68b25a1b5ffa5b45d0847d5e9c327c4565","observation_id":"97fa5196-3d37-48a5-9453-89f73c4bf1e8","resolution":{"observed_at":"2026-08-07T12:35:31.362249Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2504.13825","last_updated":"2025-04-18T17:54:33Z","snapshot_observed_at":"2026-08-07T16:01:02.744308Z","submitted_at":"2025-04-18T17:54:33Z","title":"Feature Alignment and Representation Transfer in Knowledge Distillation for Large Language Models","version":1},"cited_work":{"arxiv_id":"2504.13825","doi":null,"metadata_source":"pith","pith_arxiv_id":"2504.13825","snapshot_observed_at":"2026-08-07T12:35:33.019975Z","title":"Feature Alignment and Representation Transfer in Knowledge Distillation for Large Language Models","venue":"cs.CL","work_id":"916af2b7-0e02-4fc6-a6f9-0ef1f0892050","year":2025},"citing_paper":{"arxiv_id":"2505.24584","last_updated":"2025-08-18T16:52:22Z","snapshot_observed_at":"2026-08-09T10:10:52.166144Z","submitted_at":"2025-05-30T13:32:00Z","title":"AutoChemSchematic AI: Agentic Physics-Aware Automation for Chemical Manufacturing Scale-Up","version":3},"reference_index":57,"source":"arxiv_source","source_observed_at":"2026-08-07T12:35:31.446834Z"},"links":{"cited_paper":"/paper/2504.13825","citing_paper":"/paper/2505.24584"},"observation_digest":"sha256:c2421f3cda98663ea7f9a924442024a4f987e991742935c3e081578ea7df2d9f","observation_id":"bfeddd8f-ac13-4703-84c2-681ee1c207d3","resolution":{"observed_at":"2026-08-07T12:35:33.068962Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T12:35:35.088290Z","title":"C., Jimenez Rezende, D., Schuurmans, D., Mordatch, I., and Cubuk, E","venue":null,"work_id":"d902df5c-8bc6-4a68-a9d9-63284be92db6","year":2024},"citing_paper":{"arxiv_id":"2505.24584","last_updated":"2025-08-18T16:52:22Z","snapshot_observed_at":"2026-08-09T10:10:52.166144Z","submitted_at":"2025-05-30T13:32:00Z","title":"AutoChemSchematic AI: Agentic Physics-Aware Automation for Chemical Manufacturing Scale-Up","version":3},"reference_index":58,"source":"arxiv_source","source_observed_at":"2026-08-07T12:35:31.545141Z"},"links":{"citing_paper":"/paper/2505.24584"},"observation_digest":"sha256:6e9a3ff097c45d4c045725d53c1182152838a55c11db36b4ad54b36532c8bdc9","observation_id":"f97ac0e8-221f-4960-87fe-b5773a4b92ed","resolution":{"observed_at":"2026-08-07T12:35:35.259195Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T12:35:31.622236Z","title":"Towards thinking-optimal scaling of test-time compute for llm reasoning","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2505.24584","last_updated":"2025-08-18T16:52:22Z","snapshot_observed_at":"2026-08-09T10:10:52.166144Z","submitted_at":"2025-05-30T13:32:00Z","title":"AutoChemSchematic AI: Agentic Physics-Aware Automation for Chemical Manufacturing Scale-Up","version":3},"reference_index":59,"source":"arxiv_source","source_observed_at":"2026-08-07T12:35:31.622236Z"},"links":{"citing_paper":"/paper/2505.24584"},"observation_digest":"sha256:b95310dabc64f8c5a8a7f526a2259980ee874fa3fc2a29e3efe4b60f9844e3f2","observation_id":"08ec376f-3ada-4284-8baa-8f2f4c75dd37","resolution":{"observed_at":"2026-08-07T12:35:31.622236Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2504.00810","last_updated":"2025-04-01T14:01:50Z","snapshot_observed_at":"2026-08-07T16:17:22.645083Z","submitted_at":"2025-04-01T14:01:50Z","title":"Z1: Efficient Test-time Scaling with Code","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2504.00810","snapshot_observed_at":"2026-08-07T12:35:31.672994Z","title":"Z1: Efficient test-time scaling with code","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2505.24584","last_updated":"2025-08-18T16:52:22Z","snapshot_observed_at":"2026-08-09T10:10:52.166144Z","submitted_at":"2025-05-30T13:32:00Z","title":"AutoChemSchematic AI: Agentic Physics-Aware Automation for Chemical Manufacturing Scale-Up","version":3},"reference_index":60,"source":"arxiv_source","source_observed_at":"2026-08-07T12:35:31.672994Z"},"links":{"cited_paper":"/paper/2504.00810","citing_paper":"/paper/2505.24584"},"observation_digest":"sha256:fad71a47eab92bf91d4e84f7fe7b2cb72bef05189c505f06858b426f16eb9bf9","observation_id":"d2c6340c-6677-49e0-b7e1-99198a651ef9","resolution":{"observed_at":"2026-08-07T12:35:31.672994Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2409.00155","last_updated":"2024-08-30T12:09:14Z","snapshot_observed_at":"2026-07-06T19:08:26.637143Z","submitted_at":"2024-08-30T12:09:14Z","title":"Common Steps in Machine Learning Might Hinder The Explainability Aims in Medicine","version":1},"cited_work":{"arxiv_id":"2409.00155","doi":null,"metadata_source":"pith","pith_arxiv_id":"2409.00155","snapshot_observed_at":"2026-08-07T12:35:32.647788Z","title":"Common Steps in Machine Learning Might Hinder The Explainability Aims in Medicine","venue":"cs.LG","work_id":"954882e4-2ca6-4a43-ba47-e051b9f009a3","year":2024},"citing_paper":{"arxiv_id":"2505.24584","last_updated":"2025-08-18T16:52:22Z","snapshot_observed_at":"2026-08-09T10:10:52.166144Z","submitted_at":"2025-05-30T13:32:00Z","title":"AutoChemSchematic AI: Agentic Physics-Aware Automation for Chemical Manufacturing Scale-Up","version":3},"reference_index":61,"source":"arxiv_source","source_observed_at":"2026-08-07T12:35:31.753568Z"},"links":{"cited_paper":"/paper/2409.00155","citing_paper":"/paper/2505.24584"},"observation_digest":"sha256:da5d01b08a077b73c3d65d561e84cc38133e7aeced0535f9034d9b4ddef4dbae","observation_id":"c067488c-3220-4369-bd17-797f38e5fcb0","resolution":{"observed_at":"2026-08-07T12:35:32.733235Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2503.24235","last_updated":"2025-05-04T15:48:08Z","snapshot_observed_at":"2026-08-07T23:06:45.603790Z","submitted_at":"2025-03-31T15:46:15Z","title":"A Survey on Test-Time Scaling in Large Language Models: What, How, Where, and How Well?","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2503.24235","snapshot_observed_at":"2026-08-07T12:35:31.894519Z","title":"What, how, where, and how well? a survey on test-time scaling in large language models","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2505.24584","last_updated":"2025-08-18T16:52:22Z","snapshot_observed_at":"2026-08-09T10:10:52.166144Z","submitted_at":"2025-05-30T13:32:00Z","title":"AutoChemSchematic AI: Agentic Physics-Aware Automation for Chemical Manufacturing Scale-Up","version":3},"reference_index":62,"source":"arxiv_source","source_observed_at":"2026-08-07T12:35:31.894519Z"},"links":{"cited_paper":"/paper/2503.24235","citing_paper":"/paper/2505.24584"},"observation_digest":"sha256:debfcdd82de5c53689af61811f9b455abb3b37381b11f23ca9a78ee043570faf","observation_id":"1e85fc5d-f9d6-4e83-b3c2-a504c6ac477f","resolution":{"observed_at":"2026-08-07T12:35:31.894519Z","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":"2026-08-07T12:35:34.800861Z","title":"Lookahead: An inference acceleration framework for large language model with lossless generation accuracy","venue":null,"work_id":"31750c0d-f987-4f27-a98c-c7e63bb6643c","year":2024},"citing_paper":{"arxiv_id":"2505.24584","last_updated":"2025-08-18T16:52:22Z","snapshot_observed_at":"2026-08-09T10:10:52.166144Z","submitted_at":"2025-05-30T13:32:00Z","title":"AutoChemSchematic AI: Agentic Physics-Aware Automation for Chemical Manufacturing Scale-Up","version":3},"reference_index":63,"source":"arxiv_source","source_observed_at":"2026-08-07T12:35:32.015549Z"},"links":{"citing_paper":"/paper/2505.24584"},"observation_digest":"sha256:ddf611b102e90bd080e54ac9dd595a28024663ac0e99deb13ab4b15b75f474aa","observation_id":"f233d782-4ee6-4d6d-819b-6cb85881895e","resolution":{"observed_at":"2026-08-07T12:35:34.959661Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2310.11451","last_updated":"2024-05-08T12:11:00Z","snapshot_observed_at":"2026-07-06T16:34:38.718198Z","submitted_at":"2023-10-17T17:58:34Z","title":"Seeking Neural Nuggets: Knowledge Transfer in Large Language Models from a Parametric Perspective","version":2},"cited_work":{"arxiv_id":"2310.11451","doi":null,"metadata_source":"pith","pith_arxiv_id":"2310.11451","snapshot_observed_at":"2026-08-07T12:35:32.464479Z","title":"Seeking Neural Nuggets: Knowledge Transfer in Large Language Models from a Parametric Perspective","venue":"cs.CL","work_id":"13dd44ba-b199-4494-b0bd-81b69ee3dbde","year":2023},"citing_paper":{"arxiv_id":"2505.24584","last_updated":"2025-08-18T16:52:22Z","snapshot_observed_at":"2026-08-09T10:10:52.166144Z","submitted_at":"2025-05-30T13:32:00Z","title":"AutoChemSchematic AI: Agentic Physics-Aware Automation for Chemical Manufacturing Scale-Up","version":3},"reference_index":64,"source":"arxiv_source","source_observed_at":"2026-08-07T12:35:32.130697Z"},"links":{"cited_paper":"/paper/2310.11451","citing_paper":"/paper/2505.24584"},"observation_digest":"sha256:a0167dc13d6e57ee69f5b9c9027c1076dcee90f8624394193868c1e4efcc1572","observation_id":"1c81f747-fa81-4c9f-8530-53abacc406a5","resolution":{"observed_at":"2026-08-07T12:35:32.531259Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T12:35:34.616685Z","title":"A survey on model compression for large language models","venue":null,"work_id":"6691a539-f785-45ee-ba4c-449f33689b04","year":2024},"citing_paper":{"arxiv_id":"2505.24584","last_updated":"2025-08-18T16:52:22Z","snapshot_observed_at":"2026-08-09T10:10:52.166144Z","submitted_at":"2025-05-30T13:32:00Z","title":"AutoChemSchematic AI: Agentic Physics-Aware Automation for Chemical Manufacturing Scale-Up","version":3},"reference_index":65,"source":"arxiv_source","source_observed_at":"2026-08-07T12:35:32.259098Z"},"links":{"citing_paper":"/paper/2505.24584"},"observation_digest":"sha256:aa5339594bace681594cb44c73bce35b08124490f8053c1e60853cebf29fc73b","observation_id":"426264f9-8ef0-47c6-bf98-4e29e4b95e56","resolution":{"observed_at":"2026-08-07T12:35:34.690351Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T12:35:32.332255Z","title":"write newline","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.24584","last_updated":"2025-08-18T16:52:22Z","snapshot_observed_at":"2026-08-09T10:10:52.166144Z","submitted_at":"2025-05-30T13:32:00Z","title":"AutoChemSchematic AI: Agentic Physics-Aware Automation for Chemical Manufacturing Scale-Up","version":3},"reference_index":66,"source":"arxiv_source","source_observed_at":"2026-08-07T12:35:32.332255Z"},"links":{"citing_paper":"/paper/2505.24584"},"observation_digest":"sha256:22a205e05a9c17030a0a9394343d4a3655fca7e11975d93426ab2cb7f2ad1a25","observation_id":"7db1c111-aeac-4d6c-b833-e62c2a602aad","resolution":{"observed_at":"2026-08-07T12:35:32.332255Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2505.24584","last_updated":"2025-08-18T16:52:22Z","latest_version":3,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-09T10:10:52.166144Z","submitted_at":"2025-05-30T13:32:00Z","title":"AutoChemSchematic AI: Agentic Physics-Aware Automation for Chemical Manufacturing Scale-Up"},"reference_resolution":{"displayed":66,"state_counts":{"malformed_identifier":0,"metadata_mismatch":4,"parse_uncertain":0,"unresolved":49,"verified_exact":4,"verified_fuzzy":9},"total_outbound_references":66},"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":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"thesis":"As of 9 August 2026, this Paper Citation Record lists 66 of 66 outbound references and 0 inbound Pith citation observations for arXiv:2505.24584."}