{"as_of":"2026-08-10T12:17:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:aa4457960f483b67b841f2a35d88b2f6e47ebceebd5ca7d3ee85ec5d6f0b35ec","coverage":[{"denominator":35,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":35,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T15:04:35.125546Z","state":"measured"},{"denominator":36,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":36,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-10T06:31:04.303077+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-05T04:50:31.897556Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"pith","source_observed_at":"2026-08-05T04:50:34.194718Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2506.11052","last_updated":"2025-05-22T09:33:55Z","snapshot_observed_at":"2026-08-09T16:48:02.490392Z","submitted_at":"2025-05-22T09:33:55Z","title":"ACCORD: Autoregressive Constraint-satisfying Generation for COmbinatorial Optimization with Routing and Dynamic attention","version":1},"cited_work":{"arxiv_id":"2506.11052","doi":null,"metadata_source":"pith","pith_arxiv_id":"2506.11052","snapshot_observed_at":"2026-08-05T04:50:34.194718Z","title":"ACCORD: Autoregressive Constraint-satisfying Generation for COmbinatorial Optimization with Routing and Dynamic attention","venue":"cs.LG","work_id":"f18430b4-adb3-4e07-9bfc-650262155717","year":2025},"citing_paper":{"arxiv_id":"2509.05946","last_updated":"2025-09-07T06:46:03Z","snapshot_observed_at":"2026-08-07T19:21:09.227690Z","submitted_at":"2025-09-07T06:46:03Z","title":"Large Language Models for Next-Generation Wireless Network Management: A Survey and Tutorial","version":1},"reference_index":111,"source":"pdf_text","source_observed_at":"2026-08-05T04:50:31.897556Z"},"links":{"cited_paper":"/paper/2506.11052","citing_paper":"/paper/2509.05946"},"observation_digest":"sha256:a99832f59778c42b10d0868fd5025d5deb3a9799d9c2a6aa85621ba97bd7bae7","observation_id":"d0546838-4bc7-4e9e-93ef-4496ddae5ce0","resolution":{"observed_at":"2026-08-05T04:50:34.199583Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2506.11052/citation-record","integrity":"/paper/2506.11052/integrity","json":"/paper/2506.11052/citation-record.json","paper":"/paper/2506.11052"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"2503.01877","last_updated":"2025-03-27T10:38:45Z","snapshot_observed_at":"2026-08-07T17:45:56.074435Z","submitted_at":"2025-02-26T15:20:01Z","title":"Starjob: Dataset for LLM-Driven Job Shop Scheduling","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2503.01877","snapshot_observed_at":"2026-08-07T15:04:34.057453Z","title":"Abgaryan, T","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.11052","last_updated":"2025-05-22T09:33:55Z","snapshot_observed_at":"2026-08-09T16:48:02.490392Z","submitted_at":"2025-05-22T09:33:55Z","title":"ACCORD: Autoregressive Constraint-satisfying Generation for COmbinatorial Optimization with Routing and Dynamic attention","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-07T15:04:34.057453Z"},"links":{"cited_paper":"/paper/2503.01877","citing_paper":"/paper/2506.11052"},"observation_digest":"sha256:d3a850bffc6ea3f3213096e7f338fcf8993d7ed2461bb54abf63f5c8c11722fe","observation_id":"1d507a07-b564-46cf-b8cf-a1b7a617b270","resolution":{"observed_at":"2026-08-07T15:04:34.057453Z","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":"2026-08-07T15:04:37.326071Z","title":"Shifting bottleneck procedures for job shop scheduling","venue":null,"work_id":"4617052d-e425-45e2-b5a1-129848222b28","year":1988},"citing_paper":{"arxiv_id":"2506.11052","last_updated":"2025-05-22T09:33:55Z","snapshot_observed_at":"2026-08-09T16:48:02.490392Z","submitted_at":"2025-05-22T09:33:55Z","title":"ACCORD: Autoregressive Constraint-satisfying Generation for COmbinatorial Optimization with Routing and Dynamic attention","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-07T15:04:34.090372Z"},"links":{"citing_paper":"/paper/2506.11052"},"observation_digest":"sha256:cf77b53ba59d9ab0afaff195f9ee5ada1b28df7ae2c826ed4ad52f67ca91bbfc","observation_id":"73502c0e-e98a-45ab-8ce6-80282b963bfa","resolution":{"observed_at":"2026-08-07T15:04:37.332191Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:04:37.310147Z","title":"Llama 3 model card, 2024","venue":null,"work_id":"e62bd0d5-b5e5-46ad-a691-8c8e0b100753","year":2024},"citing_paper":{"arxiv_id":"2506.11052","last_updated":"2025-05-22T09:33:55Z","snapshot_observed_at":"2026-08-09T16:48:02.490392Z","submitted_at":"2025-05-22T09:33:55Z","title":"ACCORD: Autoregressive Constraint-satisfying Generation for COmbinatorial Optimization with Routing and Dynamic attention","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-07T15:04:34.118424Z"},"links":{"citing_paper":"/paper/2506.11052"},"observation_digest":"sha256:5a4f0660d6318c32b9337e3287524b3f728b7e67e57fd2a5a4bdcccca1e48b6d","observation_id":"63bfc661-ef33-4494-9d27-db81f2e35374","resolution":{"observed_at":"2026-08-07T15:04:37.314915Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:04:37.294046Z","title":"Unsloth: Accelerated fine-tuning for large language models, 2024","venue":null,"work_id":"311ea4dc-c907-4af2-acc5-e727c9220af5","year":2024},"citing_paper":{"arxiv_id":"2506.11052","last_updated":"2025-05-22T09:33:55Z","snapshot_observed_at":"2026-08-09T16:48:02.490392Z","submitted_at":"2025-05-22T09:33:55Z","title":"ACCORD: Autoregressive Constraint-satisfying Generation for COmbinatorial Optimization with Routing and Dynamic attention","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-07T15:04:34.152826Z"},"links":{"citing_paper":"/paper/2506.11052"},"observation_digest":"sha256:b05f5cac416844b19eb677cbf11db998646fb58823a68e073bc259e7a4a24929","observation_id":"2771daa7-34b6-44f9-93d3-a68571a6967d","resolution":{"observed_at":"2026-08-07T15:04:37.298857Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2503.06917","last_updated":"2026-05-05T13:20:09Z","snapshot_observed_at":"2026-08-09T16:48:19.000443Z","submitted_at":"2025-03-10T04:58:18Z","title":"Sample-Efficient Optimization over Generative Priors via Coarse Learnability","version":5},"cited_work":{"arxiv_id":"2503.06917","doi":null,"metadata_source":"pith","pith_arxiv_id":"2503.06917","snapshot_observed_at":"2026-08-07T15:04:35.316664Z","title":"Sample-Efficient Optimization over Generative Priors via Coarse Learnability","venue":"cs.LG","work_id":"782781ab-1fd4-45c7-a46a-ee096883a612","year":2025},"citing_paper":{"arxiv_id":"2506.11052","last_updated":"2025-05-22T09:33:55Z","snapshot_observed_at":"2026-08-09T16:48:02.490392Z","submitted_at":"2025-05-22T09:33:55Z","title":"ACCORD: Autoregressive Constraint-satisfying Generation for COmbinatorial Optimization with Routing and Dynamic attention","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-07T15:04:34.183726Z"},"links":{"cited_paper":"/paper/2503.06917","citing_paper":"/paper/2506.11052"},"observation_digest":"sha256:7b73716490b260e98e3883bbbb363c0c027288a57ca43f94ecdfeb2b0949e803","observation_id":"e0faa89d-8733-4ae1-8f85-04c1b8b72f0d","resolution":{"observed_at":"2026-08-07T15:04:35.347100Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:04:34.214900Z","title":"Language models are few-shot learners","venue":null,"work_id":null,"year":1901},"citing_paper":{"arxiv_id":"2506.11052","last_updated":"2025-05-22T09:33:55Z","snapshot_observed_at":"2026-08-09T16:48:02.490392Z","submitted_at":"2025-05-22T09:33:55Z","title":"ACCORD: Autoregressive Constraint-satisfying Generation for COmbinatorial Optimization with Routing and Dynamic attention","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-07T15:04:34.214900Z"},"links":{"citing_paper":"/paper/2506.11052"},"observation_digest":"sha256:bff3c759e2b63c813080c019398c8ef681404d474ad3aa2e45fc492e01810750","observation_id":"dee90f7d-2f80-4cc8-aede-e360059c3771","resolution":{"observed_at":"2026-08-07T15:04:34.214900Z","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":"2026-08-07T15:04:34.247182Z","title":"Palm: Scaling language modeling with pathways","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.11052","last_updated":"2025-05-22T09:33:55Z","snapshot_observed_at":"2026-08-09T16:48:02.490392Z","submitted_at":"2025-05-22T09:33:55Z","title":"ACCORD: Autoregressive Constraint-satisfying Generation for COmbinatorial Optimization with Routing and Dynamic attention","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-07T15:04:34.247182Z"},"links":{"citing_paper":"/paper/2506.11052"},"observation_digest":"sha256:a3004b88557afb0a31d7250da777c4ceeb79869541ee2d1639ebe7fd25a5cbd2","observation_id":"b976059c-684b-42e9-bfae-fc245707f71a","resolution":{"observed_at":"2026-08-07T15:04:34.247182Z","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":"2026-08-07T15:04:37.218279Z","title":"Benchmarks for shop scheduling problems","venue":null,"work_id":"5ced4cb6-a43a-4d8d-aec0-f5dccb075af9","year":1998},"citing_paper":{"arxiv_id":"2506.11052","last_updated":"2025-05-22T09:33:55Z","snapshot_observed_at":"2026-08-09T16:48:02.490392Z","submitted_at":"2025-05-22T09:33:55Z","title":"ACCORD: Autoregressive Constraint-satisfying Generation for COmbinatorial Optimization with Routing and Dynamic attention","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-07T15:04:34.274037Z"},"links":{"citing_paper":"/paper/2506.11052"},"observation_digest":"sha256:14fc0d622d630a20c36c38b1d737db08a30ec1e07265d5544538edff8a230ec0","observation_id":"2e8fabf7-b987-4e8d-9c3f-f590cdf220c1","resolution":{"observed_at":"2026-08-07T15:04:37.259334Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:04:37.066901Z","title":"Genetic algorithms in scheduling","venue":null,"work_id":"9af5b599-3ae6-4ff0-a281-5fd1c6aaf3f4","year":1996},"citing_paper":{"arxiv_id":"2506.11052","last_updated":"2025-05-22T09:33:55Z","snapshot_observed_at":"2026-08-09T16:48:02.490392Z","submitted_at":"2025-05-22T09:33:55Z","title":"ACCORD: Autoregressive Constraint-satisfying Generation for COmbinatorial Optimization with Routing and Dynamic attention","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-07T15:04:34.302528Z"},"links":{"citing_paper":"/paper/2506.11052"},"observation_digest":"sha256:1310775cec79fa75066686c54a584c78f80c609890bc297f3044e22b71f7f01c","observation_id":"64ad90aa-00dc-4c06-a134-73a8da7bdf28","resolution":{"observed_at":"2026-08-07T15:04:37.123280Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:04:36.942028Z","title":"Google’s or-tools","venue":null,"work_id":"30d08eb3-0f0a-48db-b724-660abbdc4fff","year":2024},"citing_paper":{"arxiv_id":"2506.11052","last_updated":"2025-05-22T09:33:55Z","snapshot_observed_at":"2026-08-09T16:48:02.490392Z","submitted_at":"2025-05-22T09:33:55Z","title":"ACCORD: Autoregressive Constraint-satisfying Generation for COmbinatorial Optimization with Routing and Dynamic attention","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-07T15:04:34.333756Z"},"links":{"citing_paper":"/paper/2506.11052"},"observation_digest":"sha256:6bd4043b3fe848c77383debee79260fe969100cbf5a3e091cc03ea95cf2b93aa","observation_id":"d3beb8db-fbdd-4923-a675-a0b95cb96869","resolution":{"observed_at":"2026-08-07T15:04:36.999339Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:04:36.798478Z","title":"LoRA: Low-rank adaptation of large language models","venue":null,"work_id":"dac6fb46-87ed-44a7-a786-987caf22a927","year":2022},"citing_paper":{"arxiv_id":"2506.11052","last_updated":"2025-05-22T09:33:55Z","snapshot_observed_at":"2026-08-09T16:48:02.490392Z","submitted_at":"2025-05-22T09:33:55Z","title":"ACCORD: Autoregressive Constraint-satisfying Generation for COmbinatorial Optimization with Routing and Dynamic attention","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-07T15:04:34.367389Z"},"links":{"citing_paper":"/paper/2506.11052"},"observation_digest":"sha256:30ab2515593256cdaa00b839f71f86460a343161afacc5a35de0279c7901c4fa","observation_id":"780f13cb-b01e-47c8-b1de-45f505ac75e6","resolution":{"observed_at":"2026-08-07T15:04:36.868884Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:04:36.685928Z","title":"Huang, P","venue":null,"work_id":"54d389ad-026b-4da4-bc30-58aff0c78de6","year":2022},"citing_paper":{"arxiv_id":"2506.11052","last_updated":"2025-05-22T09:33:55Z","snapshot_observed_at":"2026-08-09T16:48:02.490392Z","submitted_at":"2025-05-22T09:33:55Z","title":"ACCORD: Autoregressive Constraint-satisfying Generation for COmbinatorial Optimization with Routing and Dynamic attention","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-07T15:04:34.402334Z"},"links":{"citing_paper":"/paper/2506.11052"},"observation_digest":"sha256:6ab9ba3a861e0541ece0d8488632e7ba4941520ccef08a7405e5e61ef274cdfd","observation_id":"ba23f5ea-3e72-4930-9da8-1a89c891c749","resolution":{"observed_at":"2026-08-07T15:04:36.726846Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:04:36.583802Z","title":"Huang et al","venue":null,"work_id":"0b91deb9-a7cb-4573-88d3-8c84d678c978","year":2024},"citing_paper":{"arxiv_id":"2506.11052","last_updated":"2025-05-22T09:33:55Z","snapshot_observed_at":"2026-08-09T16:48:02.490392Z","submitted_at":"2025-05-22T09:33:55Z","title":"ACCORD: Autoregressive Constraint-satisfying Generation for COmbinatorial Optimization with Routing and Dynamic attention","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-07T15:04:34.437247Z"},"links":{"citing_paper":"/paper/2506.11052"},"observation_digest":"sha256:60882a36fb06a310f8e870bf90742455fc38d1168812535eb51896b12c8b1a5e","observation_id":"df2d6353-7e00-40c9-a7de-90415ec7befc","resolution":{"observed_at":"2026-08-07T15:04:36.669821Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:04:36.441580Z","title":"Self-guiding exploration for combinatorial problems","venue":null,"work_id":"5b529efb-7d38-4ed4-bff6-968ea04f5cb8","year":2024},"citing_paper":{"arxiv_id":"2506.11052","last_updated":"2025-05-22T09:33:55Z","snapshot_observed_at":"2026-08-09T16:48:02.490392Z","submitted_at":"2025-05-22T09:33:55Z","title":"ACCORD: Autoregressive Constraint-satisfying Generation for COmbinatorial Optimization with Routing and Dynamic attention","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-07T15:04:34.474989Z"},"links":{"citing_paper":"/paper/2506.11052"},"observation_digest":"sha256:5443f3f10acfeda7ed54423cf43991406c9144f28f8d4069be7bf5dacf2c8d48","observation_id":"0a7fdc26-127f-4e4d-bc90-b2c12743f8bf","resolution":{"observed_at":"2026-08-07T15:04:36.523497Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:04:34.522850Z","title":"A rank stabilization scaling factor for fine-tuning with lora, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.11052","last_updated":"2025-05-22T09:33:55Z","snapshot_observed_at":"2026-08-09T16:48:02.490392Z","submitted_at":"2025-05-22T09:33:55Z","title":"ACCORD: Autoregressive Constraint-satisfying Generation for COmbinatorial Optimization with Routing and Dynamic attention","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-07T15:04:34.522850Z"},"links":{"citing_paper":"/paper/2506.11052"},"observation_digest":"sha256:c4793bbf6bea8e790f008e0686d06779caf4c54932936ea30cc2dc1bc64a87f4","observation_id":"f5b6fa50-4c71-4bc3-b3aa-3428d7a79ab3","resolution":{"observed_at":"2026-08-07T15:04:34.522850Z","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":"2026-08-07T15:04:36.257492Z","title":"Khalil, Hanjun Dai, Yuyu Zhang, Bistra Dilkina, and Le Song","venue":null,"work_id":"06ed6eed-d6c4-408f-a500-41cbddcc19b9","year":2017},"citing_paper":{"arxiv_id":"2506.11052","last_updated":"2025-05-22T09:33:55Z","snapshot_observed_at":"2026-08-09T16:48:02.490392Z","submitted_at":"2025-05-22T09:33:55Z","title":"ACCORD: Autoregressive Constraint-satisfying Generation for COmbinatorial Optimization with Routing and Dynamic attention","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-07T15:04:34.548939Z"},"links":{"citing_paper":"/paper/2506.11052"},"observation_digest":"sha256:20cdaa3f761dd7bdde72b1076629221a09e06f8af68c83a4e0a3dcf1f33f2a4d","observation_id":"a4f9a88e-919c-430c-8401-3880a4b84876","resolution":{"observed_at":"2026-08-07T15:04:36.359936Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:04:36.144142Z","title":"Attention, learn to solve routing problems! In International Conference on Learning Representations , 2019","venue":null,"work_id":"030f6160-29c5-4bec-9bce-5dc63f5f3127","year":2019},"citing_paper":{"arxiv_id":"2506.11052","last_updated":"2025-05-22T09:33:55Z","snapshot_observed_at":"2026-08-09T16:48:02.490392Z","submitted_at":"2025-05-22T09:33:55Z","title":"ACCORD: Autoregressive Constraint-satisfying Generation for COmbinatorial Optimization with Routing and Dynamic attention","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-07T15:04:34.563396Z"},"links":{"citing_paper":"/paper/2506.11052"},"observation_digest":"sha256:8c5a494a21d2946bcbffe6d5b5ba2611c0539e68dd863153bc0880a3e994520a","observation_id":"07e4f57e-792b-4e02-84e6-ec97322b7d9a","resolution":{"observed_at":"2026-08-07T15:04:36.181540Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:04:36.067949Z","title":"Complexity of machine scheduling problems","venue":null,"work_id":"54c3f911-13af-4015-a53c-bbc362a9a00d","year":1979},"citing_paper":{"arxiv_id":"2506.11052","last_updated":"2025-05-22T09:33:55Z","snapshot_observed_at":"2026-08-09T16:48:02.490392Z","submitted_at":"2025-05-22T09:33:55Z","title":"ACCORD: Autoregressive Constraint-satisfying Generation for COmbinatorial Optimization with Routing and Dynamic attention","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-07T15:04:34.577750Z"},"links":{"citing_paper":"/paper/2506.11052"},"observation_digest":"sha256:c6f5fa72ab0fdb6c3987019a46c289dfde719859ed7004e985f45eb5d4fe70df","observation_id":"21631915-7c9a-4a1e-bbfd-7b2899cfbe0d","resolution":{"observed_at":"2026-08-07T15:04:36.101516Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2310.19046","last_updated":"2024-04-26T06:24:59Z","snapshot_observed_at":"2026-08-02T19:01:58.611954Z","submitted_at":"2023-10-29T15:44:52Z","title":"Large Language Models as Evolutionary Optimizers","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.19046","snapshot_observed_at":"2026-08-07T15:04:34.607658Z","title":"Large language models as evolutionary optimizers","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.11052","last_updated":"2025-05-22T09:33:55Z","snapshot_observed_at":"2026-08-09T16:48:02.490392Z","submitted_at":"2025-05-22T09:33:55Z","title":"ACCORD: Autoregressive Constraint-satisfying Generation for COmbinatorial Optimization with Routing and Dynamic attention","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-07T15:04:34.607658Z"},"links":{"cited_paper":"/paper/2310.19046","citing_paper":"/paper/2506.11052"},"observation_digest":"sha256:ad550c9c05b2c14c891bcf1954a4640e147ae77d7edb3bf798ff45f0fe81b22b","observation_id":"3ae6fd86-6e4a-4f93-9c74-48d0f7b5c7b1","resolution":{"observed_at":"2026-08-07T15:04:34.607658Z","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":"2026-08-07T15:04:34.638777Z","title":"Self-refine: Iterative refinement with self-feedback","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.11052","last_updated":"2025-05-22T09:33:55Z","snapshot_observed_at":"2026-08-09T16:48:02.490392Z","submitted_at":"2025-05-22T09:33:55Z","title":"ACCORD: Autoregressive Constraint-satisfying Generation for COmbinatorial Optimization with Routing and Dynamic attention","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-07T15:04:34.638777Z"},"links":{"citing_paper":"/paper/2506.11052"},"observation_digest":"sha256:dfd888c4081d8fdbc7147c26963099784e3849a99f72e1a78a0ec793aabd78fb","observation_id":"5273e091-a1d4-4397-994f-958c18a0f34a","resolution":{"observed_at":"2026-08-07T15:04:34.638777Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.01997","last_updated":"2024-05-03T10:54:14Z","snapshot_observed_at":"2026-07-06T18:09:17.538646Z","submitted_at":"2024-05-03T10:54:14Z","title":"Exploring Combinatorial Problem Solving with Large Language Models: A Case Study on the Travelling Salesman Problem Using GPT-3.5 Turbo","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.01997","snapshot_observed_at":"2026-08-07T15:04:34.673466Z","title":"Exploring combinatorial problem solving with large language models: A case study on the traveling salesman problem using gpt-3.5 turbo","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.11052","last_updated":"2025-05-22T09:33:55Z","snapshot_observed_at":"2026-08-09T16:48:02.490392Z","submitted_at":"2025-05-22T09:33:55Z","title":"ACCORD: Autoregressive Constraint-satisfying Generation for COmbinatorial Optimization with Routing and Dynamic attention","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-07T15:04:34.673466Z"},"links":{"cited_paper":"/paper/2405.01997","citing_paper":"/paper/2506.11052"},"observation_digest":"sha256:ff745e28a24969fb26802718ddd683b22841fc272f307c4252b638457798501a","observation_id":"dba18f8b-5585-4920-9f28-9ececfd58dc2","resolution":{"observed_at":"2026-08-07T15:04:34.673466Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.02611","last_updated":"2025-03-01T12:46:25Z","snapshot_observed_at":"2026-08-09T16:48:07.552220Z","submitted_at":"2024-02-04T20:56:09Z","title":"FCoReBench: Can Large Language Models Solve Challenging First-Order Combinatorial Reasoning Problems?","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.02611","snapshot_observed_at":"2026-08-07T15:04:34.700548Z","title":"Puzzlebench: Can llms solve challenging first-order combinatorial reasoning problems? arXiv preprint arXiv:2402.02611, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.11052","last_updated":"2025-05-22T09:33:55Z","snapshot_observed_at":"2026-08-09T16:48:02.490392Z","submitted_at":"2025-05-22T09:33:55Z","title":"ACCORD: Autoregressive Constraint-satisfying Generation for COmbinatorial Optimization with Routing and Dynamic attention","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-07T15:04:34.700548Z"},"links":{"cited_paper":"/paper/2402.02611","citing_paper":"/paper/2506.11052"},"observation_digest":"sha256:aba2711fdf50d28b5f3285c271b4ea56e04e4967b3a42648217946c1d81322bf","observation_id":"a7c44ba6-481d-4dbb-ab15-381987856a58","resolution":{"observed_at":"2026-08-07T15:04:34.700548Z","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":"2026-08-07T15:04:35.879736Z","title":"Emory Enscore, and Inyong Ham","venue":null,"work_id":"a5fe71c4-4276-4c1b-ad20-04da93bcdd9e","year":1983},"citing_paper":{"arxiv_id":"2506.11052","last_updated":"2025-05-22T09:33:55Z","snapshot_observed_at":"2026-08-09T16:48:02.490392Z","submitted_at":"2025-05-22T09:33:55Z","title":"ACCORD: Autoregressive Constraint-satisfying Generation for COmbinatorial Optimization with Routing and Dynamic attention","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-07T15:04:34.732692Z"},"links":{"citing_paper":"/paper/2506.11052"},"observation_digest":"sha256:716ba5be08e00ee19adbc263069e4156cad23e4977d408f006779a69813fd057","observation_id":"0dac8319-b030-4cd2-a748-5507833a1de1","resolution":{"observed_at":"2026-08-07T15:04:35.974432Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:04:35.748613Z","title":"Applying deep learning to the newsvendor problem","venue":null,"work_id":"1343b9f6-33d5-4d2d-adda-94f0d2f3c1f4","year":2020},"citing_paper":{"arxiv_id":"2506.11052","last_updated":"2025-05-22T09:33:55Z","snapshot_observed_at":"2026-08-09T16:48:02.490392Z","submitted_at":"2025-05-22T09:33:55Z","title":"ACCORD: Autoregressive Constraint-satisfying Generation for COmbinatorial Optimization with Routing and Dynamic attention","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-07T15:04:34.762995Z"},"links":{"citing_paper":"/paper/2506.11052"},"observation_digest":"sha256:58c2b531204863692251cc4a18c1abb9a7b9e4c8587e30810952f255836ca9b8","observation_id":"a3ad5f14-a0bf-4dec-806d-33417bf6fa57","resolution":{"observed_at":"2026-08-07T15:04:35.788180Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:04:35.722497Z","title":"Machine scheduling by mathematical programming","venue":null,"work_id":"2b0408cf-0bbd-42d7-b73b-d854da5dfb5e","year":1964},"citing_paper":{"arxiv_id":"2506.11052","last_updated":"2025-05-22T09:33:55Z","snapshot_observed_at":"2026-08-09T16:48:02.490392Z","submitted_at":"2025-05-22T09:33:55Z","title":"ACCORD: Autoregressive Constraint-satisfying Generation for COmbinatorial Optimization with Routing and Dynamic attention","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-07T15:04:34.809409Z"},"links":{"citing_paper":"/paper/2506.11052"},"observation_digest":"sha256:70325995b50f304fd1b21ba59e9d0a73a5fe61cdb6190f06b61452a81490533e","observation_id":"0a23375b-3c68-4d46-af3a-11e559fa1514","resolution":{"observed_at":"2026-08-07T15:04:35.732018Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:04:35.647267Z","title":"Benchmarks for basic scheduling problems","venue":null,"work_id":"126775f4-1486-4653-afa7-0e4efcf96242","year":1993},"citing_paper":{"arxiv_id":"2506.11052","last_updated":"2025-05-22T09:33:55Z","snapshot_observed_at":"2026-08-09T16:48:02.490392Z","submitted_at":"2025-05-22T09:33:55Z","title":"ACCORD: Autoregressive Constraint-satisfying Generation for COmbinatorial Optimization with Routing and Dynamic attention","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-07T15:04:34.841634Z"},"links":{"citing_paper":"/paper/2506.11052"},"observation_digest":"sha256:d458e2f67fe414c646ac319dcafe495027a815b0d4bdeb58c8d7667a6b1c39c4","observation_id":"372d908c-878d-49ff-96d2-d50f6de3c801","resolution":{"observed_at":"2026-08-07T15:04:35.682668Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2201.08239","last_updated":"2022-02-10T16:30:11Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2022-01-20T15:44:37Z","title":"LaMDA: Language Models for Dialog Applications","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2201.08239","snapshot_observed_at":"2026-08-07T15:04:34.874400Z","title":"Lamda: Language models for dialog applications","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.11052","last_updated":"2025-05-22T09:33:55Z","snapshot_observed_at":"2026-08-09T16:48:02.490392Z","submitted_at":"2025-05-22T09:33:55Z","title":"ACCORD: Autoregressive Constraint-satisfying Generation for COmbinatorial Optimization with Routing and Dynamic attention","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-07T15:04:34.874400Z"},"links":{"cited_paper":"/paper/2201.08239","citing_paper":"/paper/2506.11052"},"observation_digest":"sha256:f0f998947bd24b3ad96f11c3ecdc2369fa207aa76639f869bf477f01397f2b71","observation_id":"80135790-0628-403d-b84d-b4fca7e651e0","resolution":{"observed_at":"2026-08-07T15:04:34.874400Z","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":"2026-08-07T15:04:35.574542Z","title":"Valmeekam, A","venue":null,"work_id":"03fa08cb-a137-41d7-8c3c-25f7227abe25","year":2022},"citing_paper":{"arxiv_id":"2506.11052","last_updated":"2025-05-22T09:33:55Z","snapshot_observed_at":"2026-08-09T16:48:02.490392Z","submitted_at":"2025-05-22T09:33:55Z","title":"ACCORD: Autoregressive Constraint-satisfying Generation for COmbinatorial Optimization with Routing and Dynamic attention","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-07T15:04:34.899328Z"},"links":{"citing_paper":"/paper/2506.11052"},"observation_digest":"sha256:e102d54d0a614e46e0439faf13747af09f670264245a38816ae9dfc3b26ed7b7","observation_id":"a6a1bf23-2785-4763-a540-09907683508d","resolution":{"observed_at":"2026-08-07T15:04:35.605388Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:04:35.500227Z","title":"Optimizing small- scale surgery scheduling with large language model","venue":null,"work_id":"e6535c52-26df-45a7-abc9-b9c37b7a1341","year":2024},"citing_paper":{"arxiv_id":"2506.11052","last_updated":"2025-05-22T09:33:55Z","snapshot_observed_at":"2026-08-09T16:48:02.490392Z","submitted_at":"2025-05-22T09:33:55Z","title":"ACCORD: Autoregressive Constraint-satisfying Generation for COmbinatorial Optimization with Routing and Dynamic attention","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-07T15:04:34.929272Z"},"links":{"citing_paper":"/paper/2506.11052"},"observation_digest":"sha256:250e904465c5df6b249aa5df27770cf3adc397dee56c04905f7384f2515f8fe3","observation_id":"e99602c4-07c7-477a-b21c-3fd705b78c3c","resolution":{"observed_at":"2026-08-07T15:04:35.525326Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2206.07682","last_updated":"2022-10-26T05:06:24Z","snapshot_observed_at":"2026-08-02T15:56:35.249569Z","submitted_at":"2022-06-15T17:32:01Z","title":"Emergent Abilities of Large Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2206.07682","snapshot_observed_at":"2026-08-07T15:04:34.965844Z","title":"Emergent abilities of large language models","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.11052","last_updated":"2025-05-22T09:33:55Z","snapshot_observed_at":"2026-08-09T16:48:02.490392Z","submitted_at":"2025-05-22T09:33:55Z","title":"ACCORD: Autoregressive Constraint-satisfying Generation for COmbinatorial Optimization with Routing and Dynamic attention","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-07T15:04:34.965844Z"},"links":{"cited_paper":"/paper/2206.07682","citing_paper":"/paper/2506.11052"},"observation_digest":"sha256:33f02d70021c3661edb46cf76bebeff317796e489de8d33b56df0daf033f59fd","observation_id":"3b01f115-33bf-4c30-98f7-053fddafcd9d","resolution":{"observed_at":"2026-08-07T15:04:34.965844Z","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":"2026-08-07T15:04:34.995345Z","title":"Chain-of-thought prompting elicits reasoning in large language models","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.11052","last_updated":"2025-05-22T09:33:55Z","snapshot_observed_at":"2026-08-09T16:48:02.490392Z","submitted_at":"2025-05-22T09:33:55Z","title":"ACCORD: Autoregressive Constraint-satisfying Generation for COmbinatorial Optimization with Routing and Dynamic attention","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-07T15:04:34.995345Z"},"links":{"citing_paper":"/paper/2506.11052"},"observation_digest":"sha256:1e31997056ad4f9b044bb8a8b6be12034c5ac2e8292a709e2b6b414ed33f0a49","observation_id":"9c83ac1c-72b7-4ecd-9a26-215cbb867d52","resolution":{"observed_at":"2026-08-07T15:04:34.995345Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2309.03409","last_updated":"2024-04-15T07:50:32Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-09-07T00:07:15Z","title":"Large Language Models as Optimizers","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2309.03409","snapshot_observed_at":"2026-08-07T15:04:35.021526Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.11052","last_updated":"2025-05-22T09:33:55Z","snapshot_observed_at":"2026-08-09T16:48:02.490392Z","submitted_at":"2025-05-22T09:33:55Z","title":"ACCORD: Autoregressive Constraint-satisfying Generation for COmbinatorial Optimization with Routing and Dynamic attention","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-07T15:04:35.021526Z"},"links":{"cited_paper":"/paper/2309.03409","citing_paper":"/paper/2506.11052"},"observation_digest":"sha256:72d13803a7067bb3ab16e04a081c6044f27334ca882078432ef06b7db4b65be5","observation_id":"a8be83e6-324d-4539-b7b7-d99482fcdecc","resolution":{"observed_at":"2026-08-07T15:04:35.021526Z","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":"2026-08-07T15:04:35.402831Z","title":"Zhang, W","venue":null,"work_id":"e92c6e08-3e44-4bea-b5a9-74bff6f42c88","year":2020},"citing_paper":{"arxiv_id":"2506.11052","last_updated":"2025-05-22T09:33:55Z","snapshot_observed_at":"2026-08-09T16:48:02.490392Z","submitted_at":"2025-05-22T09:33:55Z","title":"ACCORD: Autoregressive Constraint-satisfying Generation for COmbinatorial Optimization with Routing and Dynamic attention","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-07T15:04:35.055657Z"},"links":{"citing_paper":"/paper/2506.11052"},"observation_digest":"sha256:e857117ba82924db7060bafcb325ae624c5cdef963081a0bb3842db6435738f5","observation_id":"e6a97bfa-367f-4d29-ae3f-e7bf92daf127","resolution":{"observed_at":"2026-08-07T15:04:35.438016Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2210.03493","last_updated":"2022-10-07T12:28:21Z","snapshot_observed_at":"2026-07-06T14:01:50.333970Z","submitted_at":"2022-10-07T12:28:21Z","title":"Automatic Chain of Thought Prompting in Large Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2210.03493","snapshot_observed_at":"2026-08-07T15:04:35.091346Z","title":"Automatic chain of thought prompting in large language models","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.11052","last_updated":"2025-05-22T09:33:55Z","snapshot_observed_at":"2026-08-09T16:48:02.490392Z","submitted_at":"2025-05-22T09:33:55Z","title":"ACCORD: Autoregressive Constraint-satisfying Generation for COmbinatorial Optimization with Routing and Dynamic attention","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-07T15:04:35.091346Z"},"links":{"cited_paper":"/paper/2210.03493","citing_paper":"/paper/2506.11052"},"observation_digest":"sha256:f1d08cfa96807fc9eff35d3eff73063d2cc32ce3fe962867eb292f00ec834ad7","observation_id":"16d40969-adce-4db9-a86f-1b784c130bde","resolution":{"observed_at":"2026-08-07T15:04:35.091346Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2205.10625","last_updated":"2023-04-16T22:08:08Z","snapshot_observed_at":"2026-08-06T09:00:42.886249Z","submitted_at":"2022-05-21T15:34:53Z","title":"Least-to-Most Prompting Enables Complex Reasoning in Large Language Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2205.10625","snapshot_observed_at":"2026-08-07T15:04:35.125546Z","title":"list of list","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.11052","last_updated":"2025-05-22T09:33:55Z","snapshot_observed_at":"2026-08-09T16:48:02.490392Z","submitted_at":"2025-05-22T09:33:55Z","title":"ACCORD: Autoregressive Constraint-satisfying Generation for COmbinatorial Optimization with Routing and Dynamic attention","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-07T15:04:35.125546Z"},"links":{"cited_paper":"/paper/2205.10625","citing_paper":"/paper/2506.11052"},"observation_digest":"sha256:e43029132804cdf45ecffe0813b279753d1dae85ecdec2ec4fd9c0d755ca8a1a","observation_id":"06cb2ba2-f908-4ca0-b435-c508caea8517","resolution":{"observed_at":"2026-08-07T15:04:35.125546Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2506.11052","last_updated":"2025-05-22T09:33:55Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-09T16:48:02.490392Z","submitted_at":"2025-05-22T09:33:55Z","title":"ACCORD: Autoregressive Constraint-satisfying Generation for COmbinatorial Optimization with Routing and Dynamic attention"},"reference_resolution":{"displayed":35,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":14,"verified_exact":1,"verified_fuzzy":20},"total_outbound_references":35},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"thesis":"As of 10 August 2026, this Paper Citation Record lists 35 of 35 outbound references and 1 inbound Pith citation observation for arXiv:2506.11052."}