{"as_of":"2026-08-09T00:44:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:a952edaf4aff4a41957a932e51e7f675e637cd6f9b113ee73f07c2c3589a2d6d","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":16,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":16,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-08T06:32:00.761636+00:00","state":"measured"},{"denominator":16,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":16,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T15:15:52.359951Z","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":"2026-07-04T09:59:45.539148Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2404.01077","last_updated":"2024-12-02T08:47:24Z","snapshot_observed_at":"2026-07-06T17:53:55.825572Z","submitted_at":"2024-04-01T12:19:08Z","title":"Efficient Prompting Methods for Large Language Models: A Survey","version":2},"cited_work":{"arxiv_id":"2404.01077","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2404.01077","snapshot_observed_at":"2026-07-04T09:59:45.539148Z","title":"arXiv preprint arXiv:2404.01077 , year=","venue":null,"work_id":"efdeee1a-44d2-403e-8e7a-c68bdc865bff","year":2024},"citing_paper":{"arxiv_id":"2410.20791","last_updated":"2026-04-06T19:29:25Z","snapshot_observed_at":"2026-08-03T01:41:19.546733Z","submitted_at":"2024-10-28T07:16:00Z","title":"From Cool Demos to Production-Ready FMware: Core Challenges and a Technology Roadmap","version":3},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-05-23T19:07:21.016824Z"},"links":{"cited_paper":"/paper/2404.01077","citing_paper":"/paper/2410.20791"},"observation_digest":"sha256:43e0d7c13791b648ba664e1e6a9bc30704414d4037e4633c20fe862883b3c131","observation_id":"f934b144-9853-40ae-86b3-7094c03a3f45","resolution":{"observed_at":"2026-05-23T19:08:20.945968Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2404.01077","last_updated":"2024-12-02T08:47:24Z","snapshot_observed_at":"2026-07-06T17:53:55.825572Z","submitted_at":"2024-04-01T12:19:08Z","title":"Efficient Prompting Methods for Large Language Models: A Survey","version":2},"cited_work":{"arxiv_id":"2404.01077","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2404.01077","snapshot_observed_at":"2026-07-04T09:59:45.539148Z","title":"arXiv preprint arXiv:2404.01077 , year=","venue":null,"work_id":"efdeee1a-44d2-403e-8e7a-c68bdc865bff","year":2024},"citing_paper":{"arxiv_id":"2502.14925","last_updated":"2026-04-09T19:58:28Z","snapshot_observed_at":"2026-08-05T17:56:07.944638Z","submitted_at":"2025-02-19T23:15:23Z","title":"CODEPROMPTZIP: Code-specific Prompt Compression for Retrieval-Augmented Generation in Coding Tasks with LMs","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-05-23T01:59:18.546405Z"},"links":{"cited_paper":"/paper/2404.01077","citing_paper":"/paper/2502.14925"},"observation_digest":"sha256:fe255c228d483af556b05f8bdb31fbbfbe0946a6fc16007b5e504f175bd4da92","observation_id":"6e6e00ad-ff19-4ae1-a808-b64054722cf7","resolution":{"observed_at":"2026-05-23T02:02:23.065100Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2404.01077","last_updated":"2024-12-02T08:47:24Z","snapshot_observed_at":"2026-07-06T17:53:55.825572Z","submitted_at":"2024-04-01T12:19:08Z","title":"Efficient Prompting Methods for Large Language Models: A Survey","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.01077","snapshot_observed_at":"2026-08-07T15:15:52.359951Z","title":"Efficient prompting methods for large language models: A survey.arXiv preprint arXiv:2404.01077, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.15774","last_updated":"2025-05-21T17:26:11Z","snapshot_observed_at":"2026-08-07T15:09:42.504814Z","submitted_at":"2025-05-21T17:26:11Z","title":"Beyond Hard and Soft: Hybrid Context Compression for Balancing Local and Global Information Retention","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-07T15:15:52.359951Z"},"links":{"cited_paper":"/paper/2404.01077","citing_paper":"/paper/2505.15774"},"observation_digest":"sha256:359ea8aa11cbc5180068667a1e0f5bcff1e4c98bbdf35b42a6fe43df85055b2b","observation_id":"a60ca776-5527-44d1-9f42-342092d3dbba","resolution":{"observed_at":"2026-08-07T15:15:52.359951Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2404.01077","last_updated":"2024-12-02T08:47:24Z","snapshot_observed_at":"2026-07-06T17:53:55.825572Z","submitted_at":"2024-04-01T12:19:08Z","title":"Efficient Prompting Methods for Large Language Models: A Survey","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.01077","snapshot_observed_at":"2026-08-07T13:51:28.142183Z","title":"Efficient prompting methods for large language models: A survey","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.20809","last_updated":"2025-05-27T07:16:40Z","snapshot_observed_at":"2026-08-07T21:54:12.571328Z","submitted_at":"2025-05-27T07:16:40Z","title":"Improved Representation Steering for Language Models","version":1},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-08-07T13:51:28.142183Z"},"links":{"cited_paper":"/paper/2404.01077","citing_paper":"/paper/2505.20809"},"observation_digest":"sha256:5b07f7292461ae8dda48acd8577d42c4fab02ed00e5dd3e08487d53716f07134","observation_id":"d7e33eb9-ae13-4d9e-abf5-7371e8ae0dae","resolution":{"observed_at":"2026-08-07T13:51:28.142183Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2404.01077","last_updated":"2024-12-02T08:47:24Z","snapshot_observed_at":"2026-07-06T17:53:55.825572Z","submitted_at":"2024-04-01T12:19:08Z","title":"Efficient Prompting Methods for Large Language Models: A Survey","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.01077","snapshot_observed_at":"2026-08-06T23:59:04.118839Z","title":"Efficient prompting methods for large language models: A survey","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.17320","last_updated":"2025-06-18T15:54:17Z","snapshot_observed_at":"2026-08-08T17:29:53.109376Z","submitted_at":"2025-06-18T15:54:17Z","title":"MAARTA:Multi-Agentic Adaptive Radiology Teaching Assistant","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-06T23:59:04.118839Z"},"links":{"cited_paper":"/paper/2404.01077","citing_paper":"/paper/2506.17320"},"observation_digest":"sha256:b7da224a2fe4d9b8d27248d9e1098a8cf1c7988d83f15fb8b0e728648ddc1797","observation_id":"89b38246-6a44-40ee-8285-e49792d6e606","resolution":{"observed_at":"2026-08-06T23:59:04.118839Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2404.01077","last_updated":"2024-12-02T08:47:24Z","snapshot_observed_at":"2026-07-06T17:53:55.825572Z","submitted_at":"2024-04-01T12:19:08Z","title":"Efficient Prompting Methods for Large Language Models: A Survey","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.01077","snapshot_observed_at":"2026-08-06T23:21:35.925270Z","title":"In Proceedings of the 13th International Work- shop on Semantic Evaluation , pages 54–63, Min- neapolis, Minnesota, USA","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2506.18576","last_updated":"2025-06-23T12:28:13Z","snapshot_observed_at":"2026-08-08T02:01:30.778917Z","submitted_at":"2025-06-23T12:28:13Z","title":"A Modular Taxonomy for Hate Speech Definitions and Its Impact on Zero-Shot LLM Classification Performance","version":1},"reference_index":2019,"source":"pdf_text","source_observed_at":"2026-08-06T23:21:35.925270Z"},"links":{"cited_paper":"/paper/2404.01077","citing_paper":"/paper/2506.18576"},"observation_digest":"sha256:6c93bc9083eb688edf2c59cea86d77d0e9f7faf199d1965d1f56dd9538c6d907","observation_id":"7fc4d2e9-804c-448a-a848-083968139365","resolution":{"observed_at":"2026-08-06T23:21:35.925270Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2404.01077","last_updated":"2024-12-02T08:47:24Z","snapshot_observed_at":"2026-07-06T17:53:55.825572Z","submitted_at":"2024-04-01T12:19:08Z","title":"Efficient Prompting Methods for Large Language Models: A Survey","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.01077","snapshot_observed_at":"2026-08-06T20:21:21.987576Z","title":"Chang, et al., ”Efficient Prompting Methods for Large Language Models: A Survey,” arXiv preprint arXiv:2404.01077 , 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.03223","last_updated":"2025-07-03T23:44:50Z","snapshot_observed_at":"2026-08-06T23:56:27.012210Z","submitted_at":"2025-07-03T23:44:50Z","title":"SI-Agent: An Agentic Framework for Feedback-Driven Generation and Tuning of Human-Readable System Instructions for Large Language Models","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-06T20:21:21.987576Z"},"links":{"cited_paper":"/paper/2404.01077","citing_paper":"/paper/2507.03223"},"observation_digest":"sha256:be1211621ecc09b6c5eb0c70ad57e0be11f03c6e859d217e9928b5cbd1cb7af3","observation_id":"5fca162a-3463-441f-8eb9-a4c88a2b70b0","resolution":{"observed_at":"2026-08-06T20:21:21.987576Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2404.01077","last_updated":"2024-12-02T08:47:24Z","snapshot_observed_at":"2026-07-06T17:53:55.825572Z","submitted_at":"2024-04-01T12:19:08Z","title":"Efficient Prompting Methods for Large Language Models: A Survey","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.01077","snapshot_observed_at":"2026-08-06T15:37:24.863525Z","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":"2026-08-07T23:33:15.536065Z","submitted_at":"2025-07-21T11:28:09Z","title":"Step-level Verifier-guided Hybrid Test-Time Scaling for Large Language Models","version":3},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-08-06T15:37:24.863525Z"},"links":{"cited_paper":"/paper/2404.01077","citing_paper":"/paper/2507.15512"},"observation_digest":"sha256:86bfebb6a14a96bef1ead384edf2d7b246d4b1004e140b532319d550436fdb8c","observation_id":"0888cbf8-91aa-4517-a4cf-70e55c111605","resolution":{"observed_at":"2026-08-06T15:37:24.863525Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2404.01077","last_updated":"2024-12-02T08:47:24Z","snapshot_observed_at":"2026-07-06T17:53:55.825572Z","submitted_at":"2024-04-01T12:19:08Z","title":"Efficient Prompting Methods for Large Language Models: A Survey","version":2},"cited_work":{"arxiv_id":"2404.01077","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2404.01077","snapshot_observed_at":"2026-07-04T09:59:45.539148Z","title":"arXiv preprint arXiv:2404.01077 , year=","venue":null,"work_id":"efdeee1a-44d2-403e-8e7a-c68bdc865bff","year":2024},"citing_paper":{"arxiv_id":"2510.15079","last_updated":"2026-04-07T05:37:01Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-10-16T18:48:12Z","title":"Assessing Coherency and Consistency of Code Execution Reasoning by Large Language Models","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-05-18T05:59:00.400429Z"},"links":{"cited_paper":"/paper/2404.01077","citing_paper":"/paper/2510.15079"},"observation_digest":"sha256:daa7a459d8d63505e0ed91e63cd560cf04a026f423b32d870eeffdfea28feee7","observation_id":"f454de97-b3ee-4342-982d-cf58491fce0f","resolution":{"observed_at":"2026-05-18T06:00:57.265483Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2404.01077","last_updated":"2024-12-02T08:47:24Z","snapshot_observed_at":"2026-07-06T17:53:55.825572Z","submitted_at":"2024-04-01T12:19:08Z","title":"Efficient Prompting Methods for Large Language Models: A Survey","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.01077","snapshot_observed_at":"2026-08-03T20:29:13.345645Z","title":"Efficient prompting methods for large language models: A survey.arXiv preprint arXiv:2404.01077, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2511.19829","last_updated":"2026-06-07T06:44:13Z","snapshot_observed_at":"2026-08-03T20:29:13.018160Z","submitted_at":"2025-11-25T01:41:13Z","title":"Knowing How to Edit: Reliable Evaluation Signals for Diagnosing and Optimizing Prompts at Query Level","version":3},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-03T20:29:13.345645Z"},"links":{"cited_paper":"/paper/2404.01077","citing_paper":"/paper/2511.19829"},"observation_digest":"sha256:4e3f3cbd0bc1b19c47528fef031af357afc91945cd888d69a9c63e43dd50697d","observation_id":"6276c615-b950-4740-8e91-c94d9da3544e","resolution":{"observed_at":"2026-08-03T20:29:13.345645Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2404.01077","last_updated":"2024-12-02T08:47:24Z","snapshot_observed_at":"2026-07-06T17:53:55.825572Z","submitted_at":"2024-04-01T12:19:08Z","title":"Efficient Prompting Methods for Large Language Models: A Survey","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.01077","snapshot_observed_at":"2026-08-03T09:35:18.428819Z","title":"Ef- ficient prompting methods for large language models: A survey,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2601.13481","last_updated":"2026-01-20T00:31:19Z","snapshot_observed_at":"2026-08-08T14:09:20.264779Z","submitted_at":"2026-01-20T00:31:19Z","title":"Towards Efficient and Robust Linguistic Emotion Diagnosis for Mental Health via Multi-Agent Instruction Refinement","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-03T09:35:18.428819Z"},"links":{"cited_paper":"/paper/2404.01077","citing_paper":"/paper/2601.13481"},"observation_digest":"sha256:e8cd6dad9d3ed83669e6ab8a1887858fdd97782248b0e6eb9ddd5fa18056e127","observation_id":"44ac342f-3907-4345-818d-5e929550c85a","resolution":{"observed_at":"2026-08-03T09:35:18.428819Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2404.01077","last_updated":"2024-12-02T08:47:24Z","snapshot_observed_at":"2026-07-06T17:53:55.825572Z","submitted_at":"2024-04-01T12:19:08Z","title":"Efficient Prompting Methods for Large Language Models: A Survey","version":2},"cited_work":{"arxiv_id":"2404.01077","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2404.01077","snapshot_observed_at":"2026-07-04T09:59:45.539148Z","title":"arXiv preprint arXiv:2404.01077 , year=","venue":null,"work_id":"efdeee1a-44d2-403e-8e7a-c68bdc865bff","year":2024},"citing_paper":{"arxiv_id":"2605.05974","last_updated":"2026-05-19T06:13:53Z","snapshot_observed_at":"2026-07-06T23:18:31.432422Z","submitted_at":"2026-05-07T10:19:06Z","title":"PragLocker: Protecting Agent Intellectual Property in Untrusted Deployments via Non-Portable Prompts","version":1},"reference_index":120,"source":"arxiv_source","source_observed_at":"2026-05-08T09:24:27.977204Z"},"links":{"cited_paper":"/paper/2404.01077","citing_paper":"/paper/2605.05974"},"observation_digest":"sha256:2cbe8011a2cf267ae96ef0ff3d8e642b95a2eb38c649bcee9b5effcbf0d9d35e","observation_id":"c2d0ff71-b605-4517-bd5b-e4a14f828eab","resolution":{"observed_at":"2026-05-11T20:21:10.847340Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2404.01077","last_updated":"2024-12-02T08:47:24Z","snapshot_observed_at":"2026-07-06T17:53:55.825572Z","submitted_at":"2024-04-01T12:19:08Z","title":"Efficient Prompting Methods for Large Language Models: A Survey","version":2},"cited_work":{"arxiv_id":"2404.01077","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2404.01077","snapshot_observed_at":"2026-07-04T09:59:45.539148Z","title":"arXiv preprint arXiv:2404.01077 , year=","venue":null,"work_id":"efdeee1a-44d2-403e-8e7a-c68bdc865bff","year":2024},"citing_paper":{"arxiv_id":"2605.05974","last_updated":"2026-05-19T06:13:53Z","snapshot_observed_at":"2026-07-06T23:18:31.432422Z","submitted_at":"2026-05-07T10:19:06Z","title":"PragLocker: Protecting Agent Intellectual Property in Untrusted Deployments via Non-Portable Prompts","version":2},"reference_index":120,"source":"arxiv_source","source_observed_at":"2026-05-20T23:32:48.878074Z"},"links":{"cited_paper":"/paper/2404.01077","citing_paper":"/paper/2605.05974"},"observation_digest":"sha256:dc2d98cbd5e7d8354f8045cdeab79d7de4a1694d92ec0fe62895aa80662477f4","observation_id":"ff6ee498-8da4-4b7c-bf8c-178e807aaf37","resolution":{"observed_at":"2026-05-20T23:33:50.569226Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2404.01077","last_updated":"2024-12-02T08:47:24Z","snapshot_observed_at":"2026-07-06T17:53:55.825572Z","submitted_at":"2024-04-01T12:19:08Z","title":"Efficient Prompting Methods for Large Language Models: A Survey","version":2},"cited_work":{"arxiv_id":"2404.01077","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2404.01077","snapshot_observed_at":"2026-07-04T09:59:45.539148Z","title":"arXiv preprint arXiv:2404.01077 , year=","venue":null,"work_id":"efdeee1a-44d2-403e-8e7a-c68bdc865bff","year":2024},"citing_paper":{"arxiv_id":"2605.24138","last_updated":"2026-05-22T18:56:47Z","snapshot_observed_at":"2026-08-06T17:40:07.201362Z","submitted_at":"2026-05-22T18:56:47Z","title":"Understanding Conversational Patterns in Multi-agent Programming: A Case Study on Fibonacci Game Development","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-06-30T14:57:40.568265Z"},"links":{"cited_paper":"/paper/2404.01077","citing_paper":"/paper/2605.24138"},"observation_digest":"sha256:0628a1f78077fefddfc464c2ac05f3f5b6a2c200ec72abde7b4c45678812a659","observation_id":"145e7b85-72ff-48b1-8e19-bc0ca9040d65","resolution":{"observed_at":"2026-06-30T15:04:46.355124Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2404.01077","last_updated":"2024-12-02T08:47:24Z","snapshot_observed_at":"2026-07-06T17:53:55.825572Z","submitted_at":"2024-04-01T12:19:08Z","title":"Efficient Prompting Methods for Large Language Models: A Survey","version":2},"cited_work":{"arxiv_id":"2404.01077","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2404.01077","snapshot_observed_at":"2026-07-04T09:59:45.539148Z","title":"arXiv preprint arXiv:2404.01077 , year=","venue":null,"work_id":"efdeee1a-44d2-403e-8e7a-c68bdc865bff","year":2024},"citing_paper":{"arxiv_id":"2606.04661","last_updated":"2026-06-03T09:40:03Z","snapshot_observed_at":"2026-07-06T23:44:42.700120Z","submitted_at":"2026-06-03T09:40:03Z","title":"CRAFT: Cost-aware Refinement And Front-aware Tuning of Prompts","version":1},"reference_index":220,"source":"arxiv_source","source_observed_at":"2026-06-28T06:39:17.268337Z"},"links":{"cited_paper":"/paper/2404.01077","citing_paper":"/paper/2606.04661"},"observation_digest":"sha256:68304352a7a9703627298adb115cc4dec6ac302fe301127bfdaf496fc8e858c1","observation_id":"de4f85ee-2045-4e64-9417-a30294b2e586","resolution":{"observed_at":"2026-07-02T07:46:46.418282Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2404.01077","last_updated":"2024-12-02T08:47:24Z","snapshot_observed_at":"2026-07-06T17:53:55.825572Z","submitted_at":"2024-04-01T12:19:08Z","title":"Efficient Prompting Methods for Large Language Models: A Survey","version":2},"cited_work":{"arxiv_id":"2404.01077","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2404.01077","snapshot_observed_at":"2026-07-04T09:59:45.539148Z","title":"arXiv preprint arXiv:2404.01077 , year=","venue":null,"work_id":"efdeee1a-44d2-403e-8e7a-c68bdc865bff","year":2024},"citing_paper":{"arxiv_id":"2606.23664","last_updated":"2026-06-22T17:48:40Z","snapshot_observed_at":"2026-08-02T17:24:52.002758Z","submitted_at":"2026-06-22T17:48:40Z","title":"MAS-PromptBench: When Does Prompt Optimization Improve Multi-Agent LLM Systems?","version":1},"reference_index":45,"source":"arxiv_source","source_observed_at":"2026-06-26T09:15:50.722199Z"},"links":{"cited_paper":"/paper/2404.01077","citing_paper":"/paper/2606.23664"},"observation_digest":"sha256:4232bc7e5541eb8606526763b108fbd9e9c9a3f0629b8b8fd1911d78c28d4a72","observation_id":"d5647f70-ad13-453d-b760-939a6784146d","resolution":{"observed_at":"2026-07-04T09:59:45.541220Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2404.01077/citation-record","integrity":"/paper/2404.01077/integrity","json":"/paper/2404.01077/citation-record.json","paper":"/paper/2404.01077"},"outbound":[],"paper":{"arxiv_id":"2404.01077","last_updated":"2024-12-02T08:47:24Z","latest_version":2,"primary_category":"cs.CL","snapshot_observed_at":"2026-07-06T17:53:55.825572Z","submitted_at":"2024-04-01T12:19:08Z","title":"Efficient Prompting Methods for Large Language Models: A Survey"},"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":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"thesis":"As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 16 inbound Pith citation observations for arXiv:2404.01077."}