{"as_of":"2026-08-09T14:33:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:4328e75fabcc1a02be9f6fe37cc72a21ef1b95f8c612efa0c0c93e95c5b6fcdb","coverage":[{"denominator":68,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":68,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-03T11:54:22.116161Z","state":"measured"},{"denominator":68,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":68,"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/2607.29196/citation-record","integrity":"/paper/2607.29196/integrity","json":"/paper/2607.29196/citation-record.json","paper":"/paper/2607.29196"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T11:54:15.919993Z","title":"Alibaba Cloud Model Studio : Model list","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.29196","last_updated":"2026-07-31T09:15:56Z","snapshot_observed_at":"2026-08-08T12:46:47.360602Z","submitted_at":"2026-07-31T09:15:56Z","title":"Hy-MultiTurn: A Six-Dimensional Benchmark for Deep Multi-Turn Dialogue Understanding","version":1},"reference_index":1,"source":"arxiv_source","source_observed_at":"2026-08-03T11:54:15.919993Z"},"links":{"citing_paper":"/paper/2607.29196"},"observation_digest":"sha256:4dbb1ffa52e4537096f503540c74a753dfe98eb1a9af34ec0f9c810c535449ae","observation_id":"f0cf0ce2-41e3-4d47-94db-1bd806f57502","resolution":{"observed_at":"2026-08-03T11:54:15.919993Z","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":"2026-08-03T11:54:16.042870Z","title":"Claude Platform : Models overview","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.29196","last_updated":"2026-07-31T09:15:56Z","snapshot_observed_at":"2026-08-08T12:46:47.360602Z","submitted_at":"2026-07-31T09:15:56Z","title":"Hy-MultiTurn: A Six-Dimensional Benchmark for Deep Multi-Turn Dialogue Understanding","version":1},"reference_index":2,"source":"arxiv_source","source_observed_at":"2026-08-03T11:54:16.042870Z"},"links":{"citing_paper":"/paper/2607.29196"},"observation_digest":"sha256:118d4a0ef2b6ef1cc7ccde89e40da14c9d18828e84a02b642bc2917fcc42d7e6","observation_id":"3036ea58-a18b-4e08-97a5-cef77ef32811","resolution":{"observed_at":"2026-08-03T11:54:16.042870Z","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":"2026-08-03T11:54:16.153590Z","title":"MT-Bench-101 : A fine-grained benchmark for evaluating large language models in multi-turn dialogues","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.29196","last_updated":"2026-07-31T09:15:56Z","snapshot_observed_at":"2026-08-08T12:46:47.360602Z","submitted_at":"2026-07-31T09:15:56Z","title":"Hy-MultiTurn: A Six-Dimensional Benchmark for Deep Multi-Turn Dialogue Understanding","version":1},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-08-03T11:54:16.153590Z"},"links":{"citing_paper":"/paper/2607.29196"},"observation_digest":"sha256:ff50f058789df8d051e5a97a5279161dbb0b49db4e2761b790fa5eab1b5fa456","observation_id":"14bbb043-3dbf-43a8-9f36-822884196317","resolution":{"observed_at":"2026-08-03T11:54:16.153590Z","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":"2026-08-03T11:54:16.411559Z","title":"Doubao Seed Models : Model documentation","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.29196","last_updated":"2026-07-31T09:15:56Z","snapshot_observed_at":"2026-08-08T12:46:47.360602Z","submitted_at":"2026-07-31T09:15:56Z","title":"Hy-MultiTurn: A Six-Dimensional Benchmark for Deep Multi-Turn Dialogue Understanding","version":1},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-08-03T11:54:16.411559Z"},"links":{"citing_paper":"/paper/2607.29196"},"observation_digest":"sha256:3c5d2d200ec1754caf9e4578c1ab6eaa5f57175ad79e7f6dd17d01689cb2fce4","observation_id":"d9b935d4-89b7-464c-9b93-81d198d4a8ec","resolution":{"observed_at":"2026-08-03T11:54:16.411559Z","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":"2026-08-03T11:54:16.444735Z","title":"DeepSeek API Docs : Models and pricing","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.29196","last_updated":"2026-07-31T09:15:56Z","snapshot_observed_at":"2026-08-08T12:46:47.360602Z","submitted_at":"2026-07-31T09:15:56Z","title":"Hy-MultiTurn: A Six-Dimensional Benchmark for Deep Multi-Turn Dialogue Understanding","version":1},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-08-03T11:54:16.444735Z"},"links":{"citing_paper":"/paper/2607.29196"},"observation_digest":"sha256:5674f45a8a8b1e732eac97724aabbf04d12d70dde25c6255a5553bdec6d17b9d","observation_id":"7651d1cd-8161-475a-a40c-395242d3ee08","resolution":{"observed_at":"2026-08-03T11:54:16.444735Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2404.04475","last_updated":"2025-03-10T09:27:03Z","snapshot_observed_at":"2026-07-06T17:56:23.317089Z","submitted_at":"2024-04-06T02:29:02Z","title":"Length-Controlled AlpacaEval: A Simple Way to Debias Automatic Evaluators","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.04475","snapshot_observed_at":"2026-08-03T11:54:16.537617Z","title":"Hashimoto","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.29196","last_updated":"2026-07-31T09:15:56Z","snapshot_observed_at":"2026-08-08T12:46:47.360602Z","submitted_at":"2026-07-31T09:15:56Z","title":"Hy-MultiTurn: A Six-Dimensional Benchmark for Deep Multi-Turn Dialogue Understanding","version":1},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-08-03T11:54:16.537617Z"},"links":{"cited_paper":"/paper/2404.04475","citing_paper":"/paper/2607.29196"},"observation_digest":"sha256:6035a1078aeeaa2e9239c11c452ad6281870496fe8097793091d3ea755306267","observation_id":"a833b0b0-d3ad-4cd8-9de6-45b8972cce1e","resolution":{"observed_at":"2026-08-03T11:54:16.537617Z","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":"2026-08-03T11:54:16.559431Z","title":"FairMT-Bench : Benchmarking fairness for multi-turn dialogue in conversational LLMs","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.29196","last_updated":"2026-07-31T09:15:56Z","snapshot_observed_at":"2026-08-08T12:46:47.360602Z","submitted_at":"2026-07-31T09:15:56Z","title":"Hy-MultiTurn: A Six-Dimensional Benchmark for Deep Multi-Turn Dialogue Understanding","version":1},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-08-03T11:54:16.559431Z"},"links":{"citing_paper":"/paper/2607.29196"},"observation_digest":"sha256:2448d8a7e043cad66b676143e8f6e0324eba464de019bb0382ecffad4714871d","observation_id":"1912a822-75cb-469e-9841-64c175ed4af3","resolution":{"observed_at":"2026-08-03T11:54:16.559431Z","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":"2026-08-03T11:54:16.629039Z","title":"Gemini API : Models","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.29196","last_updated":"2026-07-31T09:15:56Z","snapshot_observed_at":"2026-08-08T12:46:47.360602Z","submitted_at":"2026-07-31T09:15:56Z","title":"Hy-MultiTurn: A Six-Dimensional Benchmark for Deep Multi-Turn Dialogue Understanding","version":1},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-08-03T11:54:16.629039Z"},"links":{"citing_paper":"/paper/2607.29196"},"observation_digest":"sha256:c8f7d8d246d87ca9361738427d9d97589746031f35b9c5f3e3b8ed8176136988","observation_id":"603b541b-59bc-4471-8346-954d49da2f21","resolution":{"observed_at":"2026-08-03T11:54:16.629039Z","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":"2026-08-03T11:54:16.923948Z","title":"RULER : What's the real context size of your long-context language models? In COLM, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.29196","last_updated":"2026-07-31T09:15:56Z","snapshot_observed_at":"2026-08-08T12:46:47.360602Z","submitted_at":"2026-07-31T09:15:56Z","title":"Hy-MultiTurn: A Six-Dimensional Benchmark for Deep Multi-Turn Dialogue Understanding","version":1},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-08-03T11:54:16.923948Z"},"links":{"citing_paper":"/paper/2607.29196"},"observation_digest":"sha256:731d3383faf911619913de8c24cd2aabee8ad7ecf4ce4d05ce2af1adf1dc97e9","observation_id":"44f2f3a2-e09c-4f37-ae59-dc1b28bd6b3e","resolution":{"observed_at":"2026-08-03T11:54:16.923948Z","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":"2026-08-03T11:54:16.974012Z","title":"FollowBench : A multi-level fine-grained constraints following benchmark","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.29196","last_updated":"2026-07-31T09:15:56Z","snapshot_observed_at":"2026-08-08T12:46:47.360602Z","submitted_at":"2026-07-31T09:15:56Z","title":"Hy-MultiTurn: A Six-Dimensional Benchmark for Deep Multi-Turn Dialogue Understanding","version":1},"reference_index":18,"source":"arxiv_source","source_observed_at":"2026-08-03T11:54:16.974012Z"},"links":{"citing_paper":"/paper/2607.29196"},"observation_digest":"sha256:06ac38e4ed425f0b5292717020cd92ce2a7af3d0beab320402abb1aec3b0ac3f","observation_id":"66ffa153-03d6-482a-9884-32961603557e","resolution":{"observed_at":"2026-08-03T11:54:16.974012Z","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":"10.52202/079017-3381","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"BABILong : Testing the limits of LLMs with long context reasoning-in-a-haystack","venue":null,"work_id":"913cd194-fce4-4c85-b8b8-811b9b549569","year":2024},"citing_paper":{"arxiv_id":"2607.29196","last_updated":"2026-07-31T09:15:56Z","snapshot_observed_at":"2026-08-08T12:46:47.360602Z","submitted_at":"2026-07-31T09:15:56Z","title":"Hy-MultiTurn: A Six-Dimensional Benchmark for Deep Multi-Turn Dialogue Understanding","version":1},"reference_index":19,"source":"arxiv_source","source_observed_at":"2026-08-03T11:54:17.019149Z"},"links":{"citing_paper":"/paper/2607.29196"},"observation_digest":"sha256:ba59acf9c0288668417a0442747edec0d230c1ac99e8bccfab3044ca19f8d108","observation_id":"3f177cc6-b26d-4473-a42d-157813d9b8a4","resolution":{"observed_at":"2026-08-03T11:59:11.755013Z","resolver_source":"doi","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T11:54:17.079136Z","title":"MT-Eval : A multi-turn capabilities evaluation benchmark for large language models","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.29196","last_updated":"2026-07-31T09:15:56Z","snapshot_observed_at":"2026-08-08T12:46:47.360602Z","submitted_at":"2026-07-31T09:15:56Z","title":"Hy-MultiTurn: A Six-Dimensional Benchmark for Deep Multi-Turn Dialogue Understanding","version":1},"reference_index":20,"source":"arxiv_source","source_observed_at":"2026-08-03T11:54:17.079136Z"},"links":{"citing_paper":"/paper/2607.29196"},"observation_digest":"sha256:138465ee195db6de374b0c872e549f2491b62515d39d908743bca5d36a6f4a7a","observation_id":"4dac1aa8-5421-400a-bc94-1ada3ffefbe2","resolution":{"observed_at":"2026-08-03T11:54:17.079136Z","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":"2026-08-03T11:54:17.128350Z","title":"LLMs get lost in multi-turn conversation","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.29196","last_updated":"2026-07-31T09:15:56Z","snapshot_observed_at":"2026-08-08T12:46:47.360602Z","submitted_at":"2026-07-31T09:15:56Z","title":"Hy-MultiTurn: A Six-Dimensional Benchmark for Deep Multi-Turn Dialogue Understanding","version":1},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-08-03T11:54:17.128350Z"},"links":{"citing_paper":"/paper/2607.29196"},"observation_digest":"sha256:46483fd8ee17914aa440a6fcbffdf59c8299b67ddf98ec4e32e59f991c270ca1","observation_id":"cb74a53b-6413-47a3-b470-73366c7fbefa","resolution":{"observed_at":"2026-08-03T11:54:17.128350Z","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":"2026-08-03T11:54:17.180556Z","title":"Gonzalez, and Ion Stoica","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.29196","last_updated":"2026-07-31T09:15:56Z","snapshot_observed_at":"2026-08-08T12:46:47.360602Z","submitted_at":"2026-07-31T09:15:56Z","title":"Hy-MultiTurn: A Six-Dimensional Benchmark for Deep Multi-Turn Dialogue Understanding","version":1},"reference_index":23,"source":"arxiv_source","source_observed_at":"2026-08-03T11:54:17.180556Z"},"links":{"citing_paper":"/paper/2607.29196"},"observation_digest":"sha256:2fd1be1aef6f817d8497c566a6ad76fc7c76404982daaf6f1eca37cacb64ebb4","observation_id":"356376a1-a280-4d92-9ea9-2752420a64f7","resolution":{"observed_at":"2026-08-03T11:54:17.180556Z","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":"2026-08-03T11:54:17.357756Z","title":"WildBench : Benchmarking LLMs with challenging tasks from real users in the wild","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.29196","last_updated":"2026-07-31T09:15:56Z","snapshot_observed_at":"2026-08-08T12:46:47.360602Z","submitted_at":"2026-07-31T09:15:56Z","title":"Hy-MultiTurn: A Six-Dimensional Benchmark for Deep Multi-Turn Dialogue Understanding","version":1},"reference_index":26,"source":"arxiv_source","source_observed_at":"2026-08-03T11:54:17.357756Z"},"links":{"citing_paper":"/paper/2607.29196"},"observation_digest":"sha256:7ac57a5119255727278428cb7344bd9c2a9cb9680f8a4ceddbfc651e440b3c6f","observation_id":"8c8549df-01c6-4de3-8f02-dc5573d5c8f6","resolution":{"observed_at":"2026-08-03T11:54:17.357756Z","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":"2026-08-03T11:54:17.413746Z","title":"Lost in the middle: How language models use long contexts","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.29196","last_updated":"2026-07-31T09:15:56Z","snapshot_observed_at":"2026-08-08T12:46:47.360602Z","submitted_at":"2026-07-31T09:15:56Z","title":"Hy-MultiTurn: A Six-Dimensional Benchmark for Deep Multi-Turn Dialogue Understanding","version":1},"reference_index":27,"source":"arxiv_source","source_observed_at":"2026-08-03T11:54:17.413746Z"},"links":{"citing_paper":"/paper/2607.29196"},"observation_digest":"sha256:fa5b4380d932a8d4ba1cfec1107f25d501efd6672c17d905086b9b405c8cc52b","observation_id":"27475f7e-b596-469f-8cf3-24a2093494e1","resolution":{"observed_at":"2026-08-03T11:54:17.413746Z","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":"2026-08-03T11:54:17.559077Z","title":"AgentBench : Evaluating LLMs as agents","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.29196","last_updated":"2026-07-31T09:15:56Z","snapshot_observed_at":"2026-08-08T12:46:47.360602Z","submitted_at":"2026-07-31T09:15:56Z","title":"Hy-MultiTurn: A Six-Dimensional Benchmark for Deep Multi-Turn Dialogue Understanding","version":1},"reference_index":29,"source":"arxiv_source","source_observed_at":"2026-08-03T11:54:17.559077Z"},"links":{"citing_paper":"/paper/2607.29196"},"observation_digest":"sha256:0fa27b89e0c9a34a0969022f92155b9970591fce3cf16b4ce023aed79bb64487","observation_id":"4d45c391-a5f7-4eee-b4f9-324475eba6fe","resolution":{"observed_at":"2026-08-03T11:54:17.559077Z","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":"10.18653/v1/2025.emnlp-main.1160","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":null,"venue":null,"work_id":"12710cb8-ab0e-4c08-b887-0a8756b74c71","year":2025},"citing_paper":{"arxiv_id":"2607.29196","last_updated":"2026-07-31T09:15:56Z","snapshot_observed_at":"2026-08-08T12:46:47.360602Z","submitted_at":"2026-07-31T09:15:56Z","title":"Hy-MultiTurn: A Six-Dimensional Benchmark for Deep Multi-Turn Dialogue Understanding","version":1},"reference_index":30,"source":"arxiv_source","source_observed_at":"2026-08-03T11:54:17.602223Z"},"links":{"citing_paper":"/paper/2607.29196"},"observation_digest":"sha256:49e41b6db066735a744d88c3b258f4afb4b1a4886bfa08759bc355f15dfcd805","observation_id":"f89319d8-1197-4fa1-a93e-a123ac6a6de0","resolution":{"observed_at":"2026-08-03T11:59:11.236171Z","resolver_source":"doi","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T11:54:17.702021Z","title":"Kimi API Platform : Model list","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.29196","last_updated":"2026-07-31T09:15:56Z","snapshot_observed_at":"2026-08-08T12:46:47.360602Z","submitted_at":"2026-07-31T09:15:56Z","title":"Hy-MultiTurn: A Six-Dimensional Benchmark for Deep Multi-Turn Dialogue Understanding","version":1},"reference_index":31,"source":"arxiv_source","source_observed_at":"2026-08-03T11:54:17.702021Z"},"links":{"citing_paper":"/paper/2607.29196"},"observation_digest":"sha256:e24b55f0ac048a0339f9f427775978185d006ef997e86cdcdeac2f79e234a59b","observation_id":"d2cbc0fe-42c2-40ad-a71c-ddbb8be256c7","resolution":{"observed_at":"2026-08-03T11:54:17.702021Z","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":"2026-08-03T11:54:17.768866Z","title":"OpenAI Platform : Models","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.29196","last_updated":"2026-07-31T09:15:56Z","snapshot_observed_at":"2026-08-08T12:46:47.360602Z","submitted_at":"2026-07-31T09:15:56Z","title":"Hy-MultiTurn: A Six-Dimensional Benchmark for Deep Multi-Turn Dialogue Understanding","version":1},"reference_index":32,"source":"arxiv_source","source_observed_at":"2026-08-03T11:54:17.768866Z"},"links":{"citing_paper":"/paper/2607.29196"},"observation_digest":"sha256:b92389b962df4d4287c0d00791be546ca07b64ac4e1bfc89a820b1784838f092","observation_id":"629ab9e0-4056-45b6-8456-a44fcfe905c9","resolution":{"observed_at":"2026-08-03T11:54:17.768866Z","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":"2026-08-03T11:54:18.233562Z","title":"Hy3 and Hy3 Preview : Model repositories","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.29196","last_updated":"2026-07-31T09:15:56Z","snapshot_observed_at":"2026-08-08T12:46:47.360602Z","submitted_at":"2026-07-31T09:15:56Z","title":"Hy-MultiTurn: A Six-Dimensional Benchmark for Deep Multi-Turn Dialogue Understanding","version":1},"reference_index":36,"source":"arxiv_source","source_observed_at":"2026-08-03T11:54:18.233562Z"},"links":{"citing_paper":"/paper/2607.29196"},"observation_digest":"sha256:c31484e3933ddbe5c332942fdbf024978f0b70ff60561b0736cc6e570e69620b","observation_id":"5a1e23ee-283c-43d4-90a7-dec9d6524afa","resolution":{"observed_at":"2026-08-03T11:54:18.233562Z","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":"10.18653/v1/2025.findings-emnlp.992","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"CMT-Eval : A novel chinese multi-turn dialogue evaluation dataset addressing real-world conversational challenges","venue":null,"work_id":"fb005955-5bba-4883-9b7a-d6a4a7aadc86","year":2025},"citing_paper":{"arxiv_id":"2607.29196","last_updated":"2026-07-31T09:15:56Z","snapshot_observed_at":"2026-08-08T12:46:47.360602Z","submitted_at":"2026-07-31T09:15:56Z","title":"Hy-MultiTurn: A Six-Dimensional Benchmark for Deep Multi-Turn Dialogue Understanding","version":1},"reference_index":37,"source":"arxiv_source","source_observed_at":"2026-08-03T11:54:18.265099Z"},"links":{"citing_paper":"/paper/2607.29196"},"observation_digest":"sha256:40a881824ca13625862a87e09e5055bbe8a5f7478423ecc0e7454e4c09e59224","observation_id":"d19327f9-2842-4873-8fcb-988fe0aa84bf","resolution":{"observed_at":"2026-08-03T11:59:10.910704Z","resolver_source":"doi","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T11:54:18.345350Z","title":"MINT : Evaluating LLMs in multi-turn interaction with tools and language feedback","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.29196","last_updated":"2026-07-31T09:15:56Z","snapshot_observed_at":"2026-08-08T12:46:47.360602Z","submitted_at":"2026-07-31T09:15:56Z","title":"Hy-MultiTurn: A Six-Dimensional Benchmark for Deep Multi-Turn Dialogue Understanding","version":1},"reference_index":38,"source":"arxiv_source","source_observed_at":"2026-08-03T11:54:18.345350Z"},"links":{"citing_paper":"/paper/2607.29196"},"observation_digest":"sha256:8939de8ebdd6e43767a60e81f3b951d7a52a1967977ae86c01de992155b22b6b","observation_id":"473381da-46c6-4d6b-8945-b2c2423d20b1","resolution":{"observed_at":"2026-08-03T11:54:18.345350Z","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":"2026-08-03T11:54:18.451885Z","title":"Benchmarking complex instruction-following with multiple constraints composition","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.29196","last_updated":"2026-07-31T09:15:56Z","snapshot_observed_at":"2026-08-08T12:46:47.360602Z","submitted_at":"2026-07-31T09:15:56Z","title":"Hy-MultiTurn: A Six-Dimensional Benchmark for Deep Multi-Turn Dialogue Understanding","version":1},"reference_index":39,"source":"arxiv_source","source_observed_at":"2026-08-03T11:54:18.451885Z"},"links":{"citing_paper":"/paper/2607.29196"},"observation_digest":"sha256:752ebad6aa747be19fee9caa8a1ab091ac8fe5b58926581b1a73235fdf3daea6","observation_id":"261179f1-6dc4-4b9c-aa60-3b2d90ab4d01","resolution":{"observed_at":"2026-08-03T11:54:18.451885Z","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":"2026-08-03T11:54:18.560017Z","title":"LongMemEval : Benchmarking chat assistants on long-term interactive memory","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.29196","last_updated":"2026-07-31T09:15:56Z","snapshot_observed_at":"2026-08-08T12:46:47.360602Z","submitted_at":"2026-07-31T09:15:56Z","title":"Hy-MultiTurn: A Six-Dimensional Benchmark for Deep Multi-Turn Dialogue Understanding","version":1},"reference_index":40,"source":"arxiv_source","source_observed_at":"2026-08-03T11:54:18.560017Z"},"links":{"citing_paper":"/paper/2607.29196"},"observation_digest":"sha256:e05821b1ff288e032ef52f0be88c9f1445895304883d94c26136e70cd162202c","observation_id":"44b14624-421e-491f-a8e4-dc2900169b7b","resolution":{"observed_at":"2026-08-03T11:54:18.560017Z","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":"2026-08-03T11:54:18.613923Z","title":"xAI API : Models (grok 4.5)","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.29196","last_updated":"2026-07-31T09:15:56Z","snapshot_observed_at":"2026-08-08T12:46:47.360602Z","submitted_at":"2026-07-31T09:15:56Z","title":"Hy-MultiTurn: A Six-Dimensional Benchmark for Deep Multi-Turn Dialogue Understanding","version":1},"reference_index":41,"source":"arxiv_source","source_observed_at":"2026-08-03T11:54:18.613923Z"},"links":{"citing_paper":"/paper/2607.29196"},"observation_digest":"sha256:13590d80486ffb48e12f236b5e596d4a6433f7cbc8bdde3363f678f17eea3217","observation_id":"9a66bbfe-6b56-46ae-b77e-b38b002879c1","resolution":{"observed_at":"2026-08-03T11:54:18.613923Z","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":"2026-08-03T11:54:18.721000Z","title":"Evaluating large language models at evaluating instruction following","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.29196","last_updated":"2026-07-31T09:15:56Z","snapshot_observed_at":"2026-08-08T12:46:47.360602Z","submitted_at":"2026-07-31T09:15:56Z","title":"Hy-MultiTurn: A Six-Dimensional Benchmark for Deep Multi-Turn Dialogue Understanding","version":1},"reference_index":42,"source":"arxiv_source","source_observed_at":"2026-08-03T11:54:18.721000Z"},"links":{"citing_paper":"/paper/2607.29196"},"observation_digest":"sha256:5825c9dcfd74a9b97e9ba3e1acfc02f1afeb8d2d01ca2ab03765bcc1fbcbf440","observation_id":"412a0b53-379d-465a-bf0e-f6e05dca639f","resolution":{"observed_at":"2026-08-03T11:54:18.721000Z","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":"2026-08-03T11:54:18.888898Z","title":"IHEval : Evaluating language models on following the instruction hierarchy","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.29196","last_updated":"2026-07-31T09:15:56Z","snapshot_observed_at":"2026-08-08T12:46:47.360602Z","submitted_at":"2026-07-31T09:15:56Z","title":"Hy-MultiTurn: A Six-Dimensional Benchmark for Deep Multi-Turn Dialogue Understanding","version":1},"reference_index":44,"source":"arxiv_source","source_observed_at":"2026-08-03T11:54:18.888898Z"},"links":{"citing_paper":"/paper/2607.29196"},"observation_digest":"sha256:e53a27e6361600bc56a5695aca070cb463a659e661bfbf148d8c53685d4213f2","observation_id":"f16b115b-90fe-40c0-8776-b6dc2acfc2e8","resolution":{"observed_at":"2026-08-03T11:54:18.888898Z","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":"2026-08-03T11:54:19.008449Z","title":"Judging LLM -as-a-judge with MT-Bench and chatbot arena","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.29196","last_updated":"2026-07-31T09:15:56Z","snapshot_observed_at":"2026-08-08T12:46:47.360602Z","submitted_at":"2026-07-31T09:15:56Z","title":"Hy-MultiTurn: A Six-Dimensional Benchmark for Deep Multi-Turn Dialogue Understanding","version":1},"reference_index":45,"source":"arxiv_source","source_observed_at":"2026-08-03T11:54:19.008449Z"},"links":{"citing_paper":"/paper/2607.29196"},"observation_digest":"sha256:ecc70c24bd57cb69a86b2408cea49a560d95251e64bbbdc2eaa7ce9c78d3a19a","observation_id":"74defae4-0169-4498-85d2-ef0f1bb937c2","resolution":{"observed_at":"2026-08-03T11:54:19.008449Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2311.07911","last_updated":"2023-11-14T05:13:55Z","snapshot_observed_at":"2026-07-06T16:47:08.877195Z","submitted_at":"2023-11-14T05:13:55Z","title":"Instruction-Following Evaluation for Large Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2311.07911","snapshot_observed_at":"2026-08-03T11:54:19.138575Z","title":"Instruction-following evaluation for large language models, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.29196","last_updated":"2026-07-31T09:15:56Z","snapshot_observed_at":"2026-08-08T12:46:47.360602Z","submitted_at":"2026-07-31T09:15:56Z","title":"Hy-MultiTurn: A Six-Dimensional Benchmark for Deep Multi-Turn Dialogue Understanding","version":1},"reference_index":47,"source":"arxiv_source","source_observed_at":"2026-08-03T11:54:19.138575Z"},"links":{"cited_paper":"/paper/2311.07911","citing_paper":"/paper/2607.29196"},"observation_digest":"sha256:642fc79fd479badac0da80c7efb32fff27c96e1587c5c64c4624e435ab5152e4","observation_id":"6598612d-71c5-4a06-ba10-f913903dca20","resolution":{"observed_at":"2026-08-03T11:54:19.138575Z","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":"2026-08-03T11:54:19.193063Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.29196","last_updated":"2026-07-31T09:15:56Z","snapshot_observed_at":"2026-08-08T12:46:47.360602Z","submitted_at":"2026-07-31T09:15:56Z","title":"Hy-MultiTurn: A Six-Dimensional Benchmark for Deep Multi-Turn Dialogue Understanding","version":1},"reference_index":48,"source":"arxiv_source","source_observed_at":"2026-08-03T11:54:19.193063Z"},"links":{"citing_paper":"/paper/2607.29196"},"observation_digest":"sha256:ddd89b66f22f3d41929da39ce7ce332c4e2c9bc2621f3a0ef97fd838dcab02c7","observation_id":"95e2234a-e055-442d-9436-52831b1a6544","resolution":{"observed_at":"2026-08-03T11:54:19.193063Z","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":"2026-08-03T11:54:19.334819Z","title":"2024 , doi=","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.29196","last_updated":"2026-07-31T09:15:56Z","snapshot_observed_at":"2026-08-08T12:46:47.360602Z","submitted_at":"2026-07-31T09:15:56Z","title":"Hy-MultiTurn: A Six-Dimensional Benchmark for Deep Multi-Turn Dialogue Understanding","version":1},"reference_index":49,"source":"arxiv_source","source_observed_at":"2026-08-03T11:54:19.334819Z"},"links":{"citing_paper":"/paper/2607.29196"},"observation_digest":"sha256:7670bc01160ad473fcc1d0bfea1dff37e4a5db9f5baa30341e01c2ebf39146da","observation_id":"1c203874-00cb-471a-998d-4c739198b33d","resolution":{"observed_at":"2026-08-03T11:54:19.334819Z","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":"2026-08-03T11:54:19.445605Z","title":"2024 , doi=","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.29196","last_updated":"2026-07-31T09:15:56Z","snapshot_observed_at":"2026-08-08T12:46:47.360602Z","submitted_at":"2026-07-31T09:15:56Z","title":"Hy-MultiTurn: A Six-Dimensional Benchmark for Deep Multi-Turn Dialogue Understanding","version":1},"reference_index":50,"source":"arxiv_source","source_observed_at":"2026-08-03T11:54:19.445605Z"},"links":{"citing_paper":"/paper/2607.29196"},"observation_digest":"sha256:7838b2e0ca237cdef58608d6ecc6039cc9a4792787ef9d7677f22dbf2707b66e","observation_id":"f7682089-71b1-42e1-8b75-7dfa8245a5a6","resolution":{"observed_at":"2026-08-03T11:54:19.445605Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.15553","last_updated":"2024-11-13T04:26:13Z","snapshot_observed_at":"2026-07-06T19:36:45.701678Z","submitted_at":"2024-10-21T00:59:47Z","title":"Multi-IF: Benchmarking LLMs on Multi-Turn and Multilingual Instructions Following","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.15553","snapshot_observed_at":"2026-08-03T11:54:19.499279Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.29196","last_updated":"2026-07-31T09:15:56Z","snapshot_observed_at":"2026-08-08T12:46:47.360602Z","submitted_at":"2026-07-31T09:15:56Z","title":"Hy-MultiTurn: A Six-Dimensional Benchmark for Deep Multi-Turn Dialogue Understanding","version":1},"reference_index":51,"source":"arxiv_source","source_observed_at":"2026-08-03T11:54:19.499279Z"},"links":{"cited_paper":"/paper/2410.15553","citing_paper":"/paper/2607.29196"},"observation_digest":"sha256:6e854e7216735e526e2fbea4095c01326f2e8796f0b31e599d0a1e45fed6bab1","observation_id":"3ce27f70-24a4-4c64-89b2-5b147d3a0374","resolution":{"observed_at":"2026-08-03T11:54:19.499279Z","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":"2026-08-03T11:54:19.553312Z","title":"2026 , url=","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.29196","last_updated":"2026-07-31T09:15:56Z","snapshot_observed_at":"2026-08-08T12:46:47.360602Z","submitted_at":"2026-07-31T09:15:56Z","title":"Hy-MultiTurn: A Six-Dimensional Benchmark for Deep Multi-Turn Dialogue Understanding","version":1},"reference_index":52,"source":"arxiv_source","source_observed_at":"2026-08-03T11:54:19.553312Z"},"links":{"citing_paper":"/paper/2607.29196"},"observation_digest":"sha256:097e7dee030e3839b4c59346e39920f2792a63f88a31c92f96317b47d89133ab","observation_id":"6ad40d67-b1b2-47e9-9524-66dc83fa7371","resolution":{"observed_at":"2026-08-03T11:54:19.553312Z","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":"2026-08-03T11:54:19.658771Z","title":"2024 , url=","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.29196","last_updated":"2026-07-31T09:15:56Z","snapshot_observed_at":"2026-08-08T12:46:47.360602Z","submitted_at":"2026-07-31T09:15:56Z","title":"Hy-MultiTurn: A Six-Dimensional Benchmark for Deep Multi-Turn Dialogue Understanding","version":1},"reference_index":53,"source":"arxiv_source","source_observed_at":"2026-08-03T11:54:19.658771Z"},"links":{"citing_paper":"/paper/2607.29196"},"observation_digest":"sha256:3fe61aaa147587950632a6dd8c605de090d3bed2023cc1440c8bd25b25af49be","observation_id":"9fe6b26e-9b3f-413f-880b-cf076fce5627","resolution":{"observed_at":"2026-08-03T11:54:19.658771Z","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":"2026-08-03T11:54:19.768540Z","title":"2024 , doi=","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.29196","last_updated":"2026-07-31T09:15:56Z","snapshot_observed_at":"2026-08-08T12:46:47.360602Z","submitted_at":"2026-07-31T09:15:56Z","title":"Hy-MultiTurn: A Six-Dimensional Benchmark for Deep Multi-Turn Dialogue Understanding","version":1},"reference_index":54,"source":"arxiv_source","source_observed_at":"2026-08-03T11:54:19.768540Z"},"links":{"citing_paper":"/paper/2607.29196"},"observation_digest":"sha256:254e50015430fac2d145d3508e925cfad0eef4115dd65237e56b1f605fb17052","observation_id":"8490e5a6-efec-4200-9b9c-6b77fd8b4f2b","resolution":{"observed_at":"2026-08-03T11:54:19.768540Z","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":"2026-08-03T11:54:19.822413Z","title":", booktitle=","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.29196","last_updated":"2026-07-31T09:15:56Z","snapshot_observed_at":"2026-08-08T12:46:47.360602Z","submitted_at":"2026-07-31T09:15:56Z","title":"Hy-MultiTurn: A Six-Dimensional Benchmark for Deep Multi-Turn Dialogue Understanding","version":1},"reference_index":55,"source":"arxiv_source","source_observed_at":"2026-08-03T11:54:19.822413Z"},"links":{"citing_paper":"/paper/2607.29196"},"observation_digest":"sha256:8982649df2ae16e62ee2404fcdbfe7bbb83267822ff0d8dc74ee64e36aa065b9","observation_id":"ec380178-7336-48b2-b2b0-a99ee1226891","resolution":{"observed_at":"2026-08-03T11:54:19.822413Z","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":"2026-08-03T11:54:19.880510Z","title":"2025 , doi=","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.29196","last_updated":"2026-07-31T09:15:56Z","snapshot_observed_at":"2026-08-08T12:46:47.360602Z","submitted_at":"2026-07-31T09:15:56Z","title":"Hy-MultiTurn: A Six-Dimensional Benchmark for Deep Multi-Turn Dialogue Understanding","version":1},"reference_index":56,"source":"arxiv_source","source_observed_at":"2026-08-03T11:54:19.880510Z"},"links":{"citing_paper":"/paper/2607.29196"},"observation_digest":"sha256:6949cfa291592902cd943cfd8ad1c6f72d1715c2bd74851746fa16a72970ef83","observation_id":"ea13f156-fe1a-42fa-a5ab-b8b3fec5e3d6","resolution":{"observed_at":"2026-08-03T11:54:19.880510Z","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":"2026-08-03T11:54:20.006529Z","title":"and Yue, Summer and Xing, Chen , booktitle=","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.29196","last_updated":"2026-07-31T09:15:56Z","snapshot_observed_at":"2026-08-08T12:46:47.360602Z","submitted_at":"2026-07-31T09:15:56Z","title":"Hy-MultiTurn: A Six-Dimensional Benchmark for Deep Multi-Turn Dialogue Understanding","version":1},"reference_index":57,"source":"arxiv_source","source_observed_at":"2026-08-03T11:54:20.006529Z"},"links":{"citing_paper":"/paper/2607.29196"},"observation_digest":"sha256:417e43c9a968f15f39821ae96040110980d238ed23aa776d59675e953471cdb2","observation_id":"ccc75343-87c4-4b25-b198-625431b2b9b1","resolution":{"observed_at":"2026-08-03T11:54:20.006529Z","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":"2026-08-03T11:54:20.045358Z","title":"2025 , publisher=","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.29196","last_updated":"2026-07-31T09:15:56Z","snapshot_observed_at":"2026-08-08T12:46:47.360602Z","submitted_at":"2026-07-31T09:15:56Z","title":"Hy-MultiTurn: A Six-Dimensional Benchmark for Deep Multi-Turn Dialogue Understanding","version":1},"reference_index":58,"source":"arxiv_source","source_observed_at":"2026-08-03T11:54:20.045358Z"},"links":{"citing_paper":"/paper/2607.29196"},"observation_digest":"sha256:c795fc36eec88951b07bda37413082a2427e53eef1bf2797a56bb804d3ffd069","observation_id":"11e4d667-1bda-4031-b82c-dffdd013b812","resolution":{"observed_at":"2026-08-03T11:54:20.045358Z","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":"10.18653/v1/2023.acl-short.4","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"ACL Short Papers , year=","venue":null,"work_id":"361c534a-369d-4894-8db0-ceda4c155ca1","year":2023},"citing_paper":{"arxiv_id":"2607.29196","last_updated":"2026-07-31T09:15:56Z","snapshot_observed_at":"2026-08-08T12:46:47.360602Z","submitted_at":"2026-07-31T09:15:56Z","title":"Hy-MultiTurn: A Six-Dimensional Benchmark for Deep Multi-Turn Dialogue Understanding","version":1},"reference_index":59,"source":"arxiv_source","source_observed_at":"2026-08-03T11:54:20.086287Z"},"links":{"citing_paper":"/paper/2607.29196"},"observation_digest":"sha256:963f45da6143ac289c3a76c0f5608381be19ff7d13833e3d8b9ba69ee0b0d5db","observation_id":"1e18a762-7b4d-426c-9b8f-4260c74beae7","resolution":{"observed_at":"2026-08-03T11:59:12.218601Z","resolver_source":"doi","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T11:54:20.132395Z","title":"TACL , volume=","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.29196","last_updated":"2026-07-31T09:15:56Z","snapshot_observed_at":"2026-08-08T12:46:47.360602Z","submitted_at":"2026-07-31T09:15:56Z","title":"Hy-MultiTurn: A Six-Dimensional Benchmark for Deep Multi-Turn Dialogue Understanding","version":1},"reference_index":60,"source":"arxiv_source","source_observed_at":"2026-08-03T11:54:20.132395Z"},"links":{"citing_paper":"/paper/2607.29196"},"observation_digest":"sha256:292f20757385e620025ee184543b2798aa20225762e33a302af5ba75e0faf8cb","observation_id":"84a5a9e6-6b63-40d0-b986-1b213fa56d81","resolution":{"observed_at":"2026-08-03T11:54:20.132395Z","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":"2026-08-03T11:54:20.192447Z","title":"2025 , doi=","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.29196","last_updated":"2026-07-31T09:15:56Z","snapshot_observed_at":"2026-08-08T12:46:47.360602Z","submitted_at":"2026-07-31T09:15:56Z","title":"Hy-MultiTurn: A Six-Dimensional Benchmark for Deep Multi-Turn Dialogue Understanding","version":1},"reference_index":61,"source":"arxiv_source","source_observed_at":"2026-08-03T11:54:20.192447Z"},"links":{"citing_paper":"/paper/2607.29196"},"observation_digest":"sha256:7f2dadd184705bf7d8dc0cb9182cfb4183f78aa034018d3b10b7fddc6da79b5c","observation_id":"0ff98886-80a2-4e70-bc53-3359ec0e3565","resolution":{"observed_at":"2026-08-03T11:54:20.192447Z","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":"2026-08-03T11:54:20.238557Z","title":"EMNLP Findings , year=","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.29196","last_updated":"2026-07-31T09:15:56Z","snapshot_observed_at":"2026-08-08T12:46:47.360602Z","submitted_at":"2026-07-31T09:15:56Z","title":"Hy-MultiTurn: A Six-Dimensional Benchmark for Deep Multi-Turn Dialogue Understanding","version":1},"reference_index":62,"source":"arxiv_source","source_observed_at":"2026-08-03T11:54:20.238557Z"},"links":{"citing_paper":"/paper/2607.29196"},"observation_digest":"sha256:de28791219928c0e45f52f86265a31aa3b52516fc614cc45f29a2f6f37fe5e86","observation_id":"0c6f0438-6623-4b24-a9e8-8e7f29f87cf6","resolution":{"observed_at":"2026-08-03T11:54:20.238557Z","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":"2026-08-03T11:54:20.342561Z","title":"2025 , url=","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.29196","last_updated":"2026-07-31T09:15:56Z","snapshot_observed_at":"2026-08-08T12:46:47.360602Z","submitted_at":"2026-07-31T09:15:56Z","title":"Hy-MultiTurn: A Six-Dimensional Benchmark for Deep Multi-Turn Dialogue Understanding","version":1},"reference_index":63,"source":"arxiv_source","source_observed_at":"2026-08-03T11:54:20.342561Z"},"links":{"citing_paper":"/paper/2607.29196"},"observation_digest":"sha256:810d26a4f50036fced47ff9cb6e6a022f6e442facf7fd6b545278d9179108b54","observation_id":"3e1e7f3f-85ad-456b-afc7-a08bed963865","resolution":{"observed_at":"2026-08-03T11:54:20.342561Z","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":"10.18653/v1/2026.acl-long.433","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T11:59:11.825698Z","title":"One Battle After Another: Probing","venue":null,"work_id":"3aceca4d-29be-4c75-8542-4fc6046ca8fe","year":2026},"citing_paper":{"arxiv_id":"2607.29196","last_updated":"2026-07-31T09:15:56Z","snapshot_observed_at":"2026-08-08T12:46:47.360602Z","submitted_at":"2026-07-31T09:15:56Z","title":"Hy-MultiTurn: A Six-Dimensional Benchmark for Deep Multi-Turn Dialogue Understanding","version":1},"reference_index":64,"source":"arxiv_source","source_observed_at":"2026-08-03T11:54:20.448065Z"},"links":{"citing_paper":"/paper/2607.29196"},"observation_digest":"sha256:920746c48d21aad1dfe3c5cc8e2b922949c5517ef9f4d84f3c2c978b8a8fb704","observation_id":"0fb63212-b825-4aed-af41-f80ac3d69a1b","resolution":{"observed_at":"2026-08-03T11:59:11.909726Z","resolver_source":"doi","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T11:54:20.485039Z","title":"ACL , year=","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.29196","last_updated":"2026-07-31T09:15:56Z","snapshot_observed_at":"2026-08-08T12:46:47.360602Z","submitted_at":"2026-07-31T09:15:56Z","title":"Hy-MultiTurn: A Six-Dimensional Benchmark for Deep Multi-Turn Dialogue Understanding","version":1},"reference_index":65,"source":"arxiv_source","source_observed_at":"2026-08-03T11:54:20.485039Z"},"links":{"citing_paper":"/paper/2607.29196"},"observation_digest":"sha256:d0a1c69bf7e0917181919f5b312d967ded34f8f72ba7904ffca8f9855f8eeead","observation_id":"2f64be0f-f0ac-4694-bf3c-e4213f1548a3","resolution":{"observed_at":"2026-08-03T11:54:20.485039Z","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":"2026-08-03T11:54:20.538257Z","title":"2023 , eprint=","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.29196","last_updated":"2026-07-31T09:15:56Z","snapshot_observed_at":"2026-08-08T12:46:47.360602Z","submitted_at":"2026-07-31T09:15:56Z","title":"Hy-MultiTurn: A Six-Dimensional Benchmark for Deep Multi-Turn Dialogue Understanding","version":1},"reference_index":66,"source":"arxiv_source","source_observed_at":"2026-08-03T11:54:20.538257Z"},"links":{"citing_paper":"/paper/2607.29196"},"observation_digest":"sha256:393db5168ef9149360dc23badd501324cc2f9799f90437a394e5d73ad0a00455","observation_id":"a17c039c-748a-46fc-b388-7252a5c1637d","resolution":{"observed_at":"2026-08-03T11:54:20.538257Z","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":"2026-08-03T11:54:20.650070Z","title":"2024 , doi=","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.29196","last_updated":"2026-07-31T09:15:56Z","snapshot_observed_at":"2026-08-08T12:46:47.360602Z","submitted_at":"2026-07-31T09:15:56Z","title":"Hy-MultiTurn: A Six-Dimensional Benchmark for Deep Multi-Turn Dialogue Understanding","version":1},"reference_index":67,"source":"arxiv_source","source_observed_at":"2026-08-03T11:54:20.650070Z"},"links":{"citing_paper":"/paper/2607.29196"},"observation_digest":"sha256:b4fcae6fceb862bce86d51e443fc41af48f027678fdfc62e142bdb9c601a7f37","observation_id":"dde7cb24-6ccc-4263-b316-b5e9b5f6586e","resolution":{"observed_at":"2026-08-03T11:54:20.650070Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2508.06709","last_updated":"2025-08-08T21:22:12Z","snapshot_observed_at":"2026-08-09T13:02:32.592874Z","submitted_at":"2025-08-08T21:22:12Z","title":"Play Favorites: A Statistical Method to Measure Self-Bias in LLM-as-a-Judge","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2508.06709","snapshot_observed_at":"2026-08-03T11:54:20.710057Z","title":"Play Favorites: A Statistical Method to Measure Self-Bias in","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.29196","last_updated":"2026-07-31T09:15:56Z","snapshot_observed_at":"2026-08-08T12:46:47.360602Z","submitted_at":"2026-07-31T09:15:56Z","title":"Hy-MultiTurn: A Six-Dimensional Benchmark for Deep Multi-Turn Dialogue Understanding","version":1},"reference_index":68,"source":"arxiv_source","source_observed_at":"2026-08-03T11:54:20.710057Z"},"links":{"cited_paper":"/paper/2508.06709","citing_paper":"/paper/2607.29196"},"observation_digest":"sha256:b407e9e21906166f1d8e640f03bf0381f626544d3a9cbd10bb4eb3a937208646","observation_id":"fa650952-5d5a-4684-b682-9a9d4e91de11","resolution":{"observed_at":"2026-08-03T11:54:20.710057Z","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":"2026-08-03T11:54:20.763870Z","title":"2024 , pages=","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.29196","last_updated":"2026-07-31T09:15:56Z","snapshot_observed_at":"2026-08-08T12:46:47.360602Z","submitted_at":"2026-07-31T09:15:56Z","title":"Hy-MultiTurn: A Six-Dimensional Benchmark for Deep Multi-Turn Dialogue Understanding","version":1},"reference_index":69,"source":"arxiv_source","source_observed_at":"2026-08-03T11:54:20.763870Z"},"links":{"citing_paper":"/paper/2607.29196"},"observation_digest":"sha256:6e70b14229cf21029c804d4522835f8fd88e2a56ea2e1dd4758da218bcac0503","observation_id":"111450b6-8723-4fd4-8784-5e7c7c74343e","resolution":{"observed_at":"2026-08-03T11:54:20.763870Z","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":"2026-08-03T11:54:20.871776Z","title":"2024 , pages=","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.29196","last_updated":"2026-07-31T09:15:56Z","snapshot_observed_at":"2026-08-08T12:46:47.360602Z","submitted_at":"2026-07-31T09:15:56Z","title":"Hy-MultiTurn: A Six-Dimensional Benchmark for Deep Multi-Turn Dialogue Understanding","version":1},"reference_index":70,"source":"arxiv_source","source_observed_at":"2026-08-03T11:54:20.871776Z"},"links":{"citing_paper":"/paper/2607.29196"},"observation_digest":"sha256:fe57db388cb2429cbb87e3354aca2cf4696fcf027d14ae319933fced4a952303","observation_id":"44e57a8b-8d57-4ef3-8593-58be3d7b9d0c","resolution":{"observed_at":"2026-08-03T11:54:20.871776Z","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":"10.18653/v1/2025.emnlp-main.471","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"2025 , pages=","venue":null,"work_id":"56797a28-1fd1-4154-9dbe-e7c8d802539f","year":2025},"citing_paper":{"arxiv_id":"2607.29196","last_updated":"2026-07-31T09:15:56Z","snapshot_observed_at":"2026-08-08T12:46:47.360602Z","submitted_at":"2026-07-31T09:15:56Z","title":"Hy-MultiTurn: A Six-Dimensional Benchmark for Deep Multi-Turn Dialogue Understanding","version":1},"reference_index":71,"source":"arxiv_source","source_observed_at":"2026-08-03T11:54:20.981497Z"},"links":{"citing_paper":"/paper/2607.29196"},"observation_digest":"sha256:797b4f8d75ad063b188a74834b985b77cd89d8ab5d454ae17b5cf2992db6b737","observation_id":"5018c1a1-57bb-48e3-aa00-867b84183756","resolution":{"observed_at":"2026-08-03T11:59:11.494999Z","resolver_source":"doi","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T11:54:21.036528Z","title":"2024 , pages=","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.29196","last_updated":"2026-07-31T09:15:56Z","snapshot_observed_at":"2026-08-08T12:46:47.360602Z","submitted_at":"2026-07-31T09:15:56Z","title":"Hy-MultiTurn: A Six-Dimensional Benchmark for Deep Multi-Turn Dialogue Understanding","version":1},"reference_index":72,"source":"arxiv_source","source_observed_at":"2026-08-03T11:54:21.036528Z"},"links":{"citing_paper":"/paper/2607.29196"},"observation_digest":"sha256:c5abba65595a4e16f45c5ae2620d38dc6226189f6fcd31f7c9b4f62a44f89e90","observation_id":"520d4e1f-1fbe-43f6-ac24-ae0b9bf60f74","resolution":{"observed_at":"2026-08-03T11:54:21.036528Z","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":"2026-08-03T11:54:21.099778Z","title":"NeurIPS , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.29196","last_updated":"2026-07-31T09:15:56Z","snapshot_observed_at":"2026-08-08T12:46:47.360602Z","submitted_at":"2026-07-31T09:15:56Z","title":"Hy-MultiTurn: A Six-Dimensional Benchmark for Deep Multi-Turn Dialogue Understanding","version":1},"reference_index":73,"source":"arxiv_source","source_observed_at":"2026-08-03T11:54:21.099778Z"},"links":{"citing_paper":"/paper/2607.29196"},"observation_digest":"sha256:953aa458dce44c4dd17eac708f81d4109a5fa968e04c573746e36492e1abf38e","observation_id":"49fd23ea-58a5-4393-958e-2a8ef63f2b9f","resolution":{"observed_at":"2026-08-03T11:54:21.099778Z","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":"2026-08-03T11:54:21.251825Z","title":"2024 , pages=","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.29196","last_updated":"2026-07-31T09:15:56Z","snapshot_observed_at":"2026-08-08T12:46:47.360602Z","submitted_at":"2026-07-31T09:15:56Z","title":"Hy-MultiTurn: A Six-Dimensional Benchmark for Deep Multi-Turn Dialogue Understanding","version":1},"reference_index":74,"source":"arxiv_source","source_observed_at":"2026-08-03T11:54:21.251825Z"},"links":{"citing_paper":"/paper/2607.29196"},"observation_digest":"sha256:aed399d6d731defb29b1e65e46f8cf686e6fa6637566bb47386a25cf358a8ad7","observation_id":"ddc43a79-eb40-46fb-acd0-552c57942b1f","resolution":{"observed_at":"2026-08-03T11:54:21.251825Z","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":"2026-08-03T11:54:21.319604Z","title":"2024 , pages=","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.29196","last_updated":"2026-07-31T09:15:56Z","snapshot_observed_at":"2026-08-08T12:46:47.360602Z","submitted_at":"2026-07-31T09:15:56Z","title":"Hy-MultiTurn: A Six-Dimensional Benchmark for Deep Multi-Turn Dialogue Understanding","version":1},"reference_index":75,"source":"arxiv_source","source_observed_at":"2026-08-03T11:54:21.319604Z"},"links":{"citing_paper":"/paper/2607.29196"},"observation_digest":"sha256:81bda8b95ac363732a6d529839570fea80679ac8faadad702342a6df44c8cbae","observation_id":"a920d421-8dc4-4892-a761-94487f9b8672","resolution":{"observed_at":"2026-08-03T11:54:21.319604Z","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":"2026-08-03T11:54:21.353611Z","title":"2025 , url=","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.29196","last_updated":"2026-07-31T09:15:56Z","snapshot_observed_at":"2026-08-08T12:46:47.360602Z","submitted_at":"2026-07-31T09:15:56Z","title":"Hy-MultiTurn: A Six-Dimensional Benchmark for Deep Multi-Turn Dialogue Understanding","version":1},"reference_index":76,"source":"arxiv_source","source_observed_at":"2026-08-03T11:54:21.353611Z"},"links":{"citing_paper":"/paper/2607.29196"},"observation_digest":"sha256:6028481701001d84c92e39f927665acc3f4196d7424ee573cfce9be73842ae52","observation_id":"3f7d2321-4a16-4707-81db-30f55d960c9a","resolution":{"observed_at":"2026-08-03T11:54:21.353611Z","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":"2026-08-03T11:54:21.409490Z","title":"ICML , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.29196","last_updated":"2026-07-31T09:15:56Z","snapshot_observed_at":"2026-08-08T12:46:47.360602Z","submitted_at":"2026-07-31T09:15:56Z","title":"Hy-MultiTurn: A Six-Dimensional Benchmark for Deep Multi-Turn Dialogue Understanding","version":1},"reference_index":77,"source":"arxiv_source","source_observed_at":"2026-08-03T11:54:21.409490Z"},"links":{"citing_paper":"/paper/2607.29196"},"observation_digest":"sha256:3693cdf305eeeaf0967c043dcab6cd5840f9bdf613b2fbb163ab6fe0a282fea6","observation_id":"d34b4f04-1c03-431a-8fb6-c59f52c4fdb4","resolution":{"observed_at":"2026-08-03T11:54:21.409490Z","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":"2026-08-03T11:54:21.525763Z","title":", booktitle=","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.29196","last_updated":"2026-07-31T09:15:56Z","snapshot_observed_at":"2026-08-08T12:46:47.360602Z","submitted_at":"2026-07-31T09:15:56Z","title":"Hy-MultiTurn: A Six-Dimensional Benchmark for Deep Multi-Turn Dialogue Understanding","version":1},"reference_index":78,"source":"arxiv_source","source_observed_at":"2026-08-03T11:54:21.525763Z"},"links":{"citing_paper":"/paper/2607.29196"},"observation_digest":"sha256:f123cdd49aaaf80420f6211763b3f4997d631d04fdf2dd8e89a9fac9d5a44d5c","observation_id":"03c31d39-a66f-49dd-81d9-318030a90c71","resolution":{"observed_at":"2026-08-03T11:54:21.525763Z","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":"2026-08-03T11:54:21.565721Z","title":"ICLR , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.29196","last_updated":"2026-07-31T09:15:56Z","snapshot_observed_at":"2026-08-08T12:46:47.360602Z","submitted_at":"2026-07-31T09:15:56Z","title":"Hy-MultiTurn: A Six-Dimensional Benchmark for Deep Multi-Turn Dialogue Understanding","version":1},"reference_index":79,"source":"arxiv_source","source_observed_at":"2026-08-03T11:54:21.565721Z"},"links":{"citing_paper":"/paper/2607.29196"},"observation_digest":"sha256:ac28c817a580bd72c13752d315d2ff1cfc5a3c16ad22bc33e5a65f96b19d29f4","observation_id":"ba903d10-a02c-4f8f-94c5-b909c9858ecf","resolution":{"observed_at":"2026-08-03T11:54:21.565721Z","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":"2026-08-03T11:54:21.635669Z","title":"2024 , url=","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.29196","last_updated":"2026-07-31T09:15:56Z","snapshot_observed_at":"2026-08-08T12:46:47.360602Z","submitted_at":"2026-07-31T09:15:56Z","title":"Hy-MultiTurn: A Six-Dimensional Benchmark for Deep Multi-Turn Dialogue Understanding","version":1},"reference_index":80,"source":"arxiv_source","source_observed_at":"2026-08-03T11:54:21.635669Z"},"links":{"citing_paper":"/paper/2607.29196"},"observation_digest":"sha256:3d710c03c0d78b5fc069e106bcbdce97bd7ad99fdfe49a5d6714cbc2c1e6409b","observation_id":"a79af1e1-4b35-4cea-8893-137854931432","resolution":{"observed_at":"2026-08-03T11:54:21.635669Z","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":"2026-08-03T11:54:21.790097Z","title":"2024 , url=","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.29196","last_updated":"2026-07-31T09:15:56Z","snapshot_observed_at":"2026-08-08T12:46:47.360602Z","submitted_at":"2026-07-31T09:15:56Z","title":"Hy-MultiTurn: A Six-Dimensional Benchmark for Deep Multi-Turn Dialogue Understanding","version":1},"reference_index":81,"source":"arxiv_source","source_observed_at":"2026-08-03T11:54:21.790097Z"},"links":{"citing_paper":"/paper/2607.29196"},"observation_digest":"sha256:1fd5aa86bdf6599295f0c129ef59e8ddbf7d54ff3d9aa14016355ef3fc3eb858","observation_id":"b2f778f6-c09c-4024-8d6c-daeaee36b10c","resolution":{"observed_at":"2026-08-03T11:54:21.790097Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2305.10250","last_updated":"2023-05-21T06:20:28Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-05-17T14:40:29Z","title":"MemoryBank: Enhancing Large Language Models with Long-Term Memory","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.10250","snapshot_observed_at":"2026-08-03T11:54:21.890923Z","title":"2305.10250 , archivePrefix=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.29196","last_updated":"2026-07-31T09:15:56Z","snapshot_observed_at":"2026-08-08T12:46:47.360602Z","submitted_at":"2026-07-31T09:15:56Z","title":"Hy-MultiTurn: A Six-Dimensional Benchmark for Deep Multi-Turn Dialogue Understanding","version":1},"reference_index":82,"source":"arxiv_source","source_observed_at":"2026-08-03T11:54:21.890923Z"},"links":{"cited_paper":"/paper/2305.10250","citing_paper":"/paper/2607.29196"},"observation_digest":"sha256:3a981f90860c86acadc494d3982488f8fba96e06bf1ebc7734135c11c57b5300","observation_id":"25d9b991-0926-405b-9e14-28231ba76393","resolution":{"observed_at":"2026-08-03T11:54:21.890923Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.19444","last_updated":"2024-05-29T18:45:55Z","snapshot_observed_at":"2026-08-04T15:41:17.375198Z","submitted_at":"2024-05-29T18:45:55Z","title":"MathChat: Benchmarking Mathematical Reasoning and Instruction Following in Multi-Turn Interactions","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.19444","snapshot_observed_at":"2026-08-03T11:54:21.954324Z","title":"2405.19444 , archivePrefix=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.29196","last_updated":"2026-07-31T09:15:56Z","snapshot_observed_at":"2026-08-08T12:46:47.360602Z","submitted_at":"2026-07-31T09:15:56Z","title":"Hy-MultiTurn: A Six-Dimensional Benchmark for Deep Multi-Turn Dialogue Understanding","version":1},"reference_index":83,"source":"arxiv_source","source_observed_at":"2026-08-03T11:54:21.954324Z"},"links":{"cited_paper":"/paper/2405.19444","citing_paper":"/paper/2607.29196"},"observation_digest":"sha256:d0233771d3b83b5215a61067ffd1cb251ffb56704841e3453025aec218c9f7a2","observation_id":"797a342e-00e3-498f-8edb-8c80e1f6b7c7","resolution":{"observed_at":"2026-08-03T11:54:21.954324Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2412.15204","last_updated":"2025-01-03T11:44:51Z","snapshot_observed_at":"2026-08-04T21:07:04.238345Z","submitted_at":"2024-12-19T18:59:17Z","title":"LongBench v2: Towards Deeper Understanding and Reasoning on Realistic Long-context Multitasks","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.15204","snapshot_observed_at":"2026-08-03T11:54:22.062287Z","title":"2412.15204 , archivePrefix=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.29196","last_updated":"2026-07-31T09:15:56Z","snapshot_observed_at":"2026-08-08T12:46:47.360602Z","submitted_at":"2026-07-31T09:15:56Z","title":"Hy-MultiTurn: A Six-Dimensional Benchmark for Deep Multi-Turn Dialogue Understanding","version":1},"reference_index":84,"source":"arxiv_source","source_observed_at":"2026-08-03T11:54:22.062287Z"},"links":{"cited_paper":"/paper/2412.15204","citing_paper":"/paper/2607.29196"},"observation_digest":"sha256:dbf7c6b7ca08f5a7d91a5cc410d568d52c231e97382352c836476de4f5b614be","observation_id":"8f1184d0-7747-431d-9ba4-9b1405d0be97","resolution":{"observed_at":"2026-08-03T11:54:22.062287Z","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":"2026-08-03T11:54:22.116161Z","title":"2025 , url=","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.29196","last_updated":"2026-07-31T09:15:56Z","snapshot_observed_at":"2026-08-08T12:46:47.360602Z","submitted_at":"2026-07-31T09:15:56Z","title":"Hy-MultiTurn: A Six-Dimensional Benchmark for Deep Multi-Turn Dialogue Understanding","version":1},"reference_index":85,"source":"arxiv_source","source_observed_at":"2026-08-03T11:54:22.116161Z"},"links":{"citing_paper":"/paper/2607.29196"},"observation_digest":"sha256:2f7df1dfbb0146c494b0b457192acb01d3fc7e09ea6f0be762ef1c030f5148bb","observation_id":"6f4a9a03-ba82-4baf-8216-f299468682ce","resolution":{"observed_at":"2026-08-03T11:54:22.116161Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2607.29196","last_updated":"2026-07-31T09:15:56Z","latest_version":1,"primary_category":"cs.CL","snapshot_observed_at":"2026-08-08T12:46:47.360602Z","submitted_at":"2026-07-31T09:15:56Z","title":"Hy-MultiTurn: A Six-Dimensional Benchmark for Deep Multi-Turn Dialogue Understanding"},"reference_resolution":{"displayed":68,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":62,"verified_exact":6,"verified_fuzzy":0},"total_outbound_references":68},"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 68 of 68 outbound references and 0 inbound Pith citation observations for arXiv:2607.29196."}