{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:JIH6W2XTAZXLR2GL3M7TAJW3L6","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"425bba65d4caa90e2f87005984a730eeec185e2baf4de82ccabb00e667d53f31","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2026-04-22T04:55:31Z","title_canon_sha256":"35468607909beeaadde9538b3c9c7c718b7afec5a4ea2b1b58febce8b9dcecda"},"schema_version":"1.0","source":{"id":"2604.20183","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2604.20183","created_at":"2026-06-03T01:05:50Z"},{"alias_kind":"arxiv_version","alias_value":"2604.20183v2","created_at":"2026-06-03T01:05:50Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2604.20183","created_at":"2026-06-03T01:05:50Z"},{"alias_kind":"pith_short_12","alias_value":"JIH6W2XTAZXL","created_at":"2026-06-03T01:05:50Z"},{"alias_kind":"pith_short_16","alias_value":"JIH6W2XTAZXLR2GL","created_at":"2026-06-03T01:05:50Z"},{"alias_kind":"pith_short_8","alias_value":"JIH6W2XT","created_at":"2026-06-03T01:05:50Z"}],"graph_snapshots":[{"event_id":"sha256:1ce249cf17926fbab7c3b6b17e041f7462e5acf815c4a9767577a2ee7eebd2e9","target":"graph","created_at":"2026-06-03T01:05:50Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":4,"items":[{"attestation":"unclaimed","claim_id":"C1","kind":"strongest_claim","source":"verdict.strongest_claim","status":"machine_extracted","text":"DCM-Agent achieves an average performance improvement of 11%-21% across seven optimization benchmarks, with a knowledge inheritance phenomenon where memory constructed by larger models guides smaller models toward superior performance."},{"attestation":"unclaimed","claim_id":"C2","kind":"weakest_assumption","source":"verdict.weakest_assumption","status":"machine_extracted","text":"That historical solutions from prior problems can be reliably clustered and distilled into generalizable, non-overfitting guidance that transfers to new optimization instances with multi-paradigm ambiguity."},{"attestation":"unclaimed","claim_id":"C3","kind":"one_line_summary","source":"verdict.one_line_summary","status":"machine_extracted","text":"DCM-Agent improves LLM performance on multi-paradigm optimization problems by 11-21% via dual-cluster memory construction and dynamic inference guidance."},{"attestation":"unclaimed","claim_id":"C4","kind":"headline","source":"verdict.pith_extraction.headline","status":"machine_extracted","text":"Dual clusters of historical solutions let LLMs resolve conflicting modeling paradigms in optimization problems."}],"snapshot_sha256":"7e8154bdcaecc49918e6089df59cfb30bd76fb6402a863bc2f29b706664565b1"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[{"findings_count":0,"name":"ai_meta_artifact","ran_at":"2026-05-21T15:34:58.751657Z","status":"completed","version":"1.0.0"},{"findings_count":0,"name":"doi_compliance","ran_at":"2026-05-20T02:14:30.263284Z","status":"completed","version":"1.0.0"}],"endpoint":"/pith/2604.20183/integrity.json","findings":[],"snapshot_sha256":"869f8ff737f3b0d89b9ad828ce5008896efc343a5d6036473ac7689749e6aad5","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Large Language Models (LLMs) often struggle with structural ambiguity in optimization problems, where a single problem admits multiple related but conflicting modeling paradigms, hindering effective solution generation. To address this, we propose Dual-Cluster Memory Agent (DCM-Agent) to enhance performance by leveraging historical solutions in a training-free manner. Central to this is Dual-Cluster Memory Construction. This agent assigns historical solutions to modeling and coding clusters, then distills each cluster's content into three structured types: Approach, Checklist, and Pitfall. Thi","authors_text":"Bifan Wei, Boxuan Zhang, Jun Liu, Lingling Zhang, Xinyu Zhang, Yuchen Wan, Zesheng Yang","cross_cats":[],"headline":"Dual clusters of historical solutions let LLMs resolve conflicting modeling paradigms in optimization problems.","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2026-04-22T04:55:31Z","title":"Dual-Cluster Memory Agent: Resolving Multi-Paradigm Ambiguity in Optimization Problem Solving"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2604.20183","kind":"arxiv","version":2},"verdict":{"created_at":"2026-05-10T00:35:26.824440Z","id":"9a3a5405-9877-4ed3-a1de-f8d133860cbe","model_set":{"reader":"grok-4.3"},"one_line_summary":"DCM-Agent improves LLM performance on multi-paradigm optimization problems by 11-21% via dual-cluster memory construction and dynamic inference guidance.","pipeline_version":"pith-pipeline@v0.9.0","pith_extraction_headline":"Dual clusters of historical solutions let LLMs resolve conflicting modeling paradigms in optimization problems.","strongest_claim":"DCM-Agent achieves an average performance improvement of 11%-21% across seven optimization benchmarks, with a knowledge inheritance phenomenon where memory constructed by larger models guides smaller models toward superior performance.","weakest_assumption":"That historical solutions from prior problems can be reliably clustered and distilled into generalizable, non-overfitting guidance that transfers to new optimization instances with multi-paradigm ambiguity."}},"verdict_id":"9a3a5405-9877-4ed3-a1de-f8d133860cbe"}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:428e900fa80b97f9cc7b38f19123b9722e0e84a945b93ef9c9f5bfeb67a91dfd","target":"record","created_at":"2026-06-03T01:05:50Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"425bba65d4caa90e2f87005984a730eeec185e2baf4de82ccabb00e667d53f31","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2026-04-22T04:55:31Z","title_canon_sha256":"35468607909beeaadde9538b3c9c7c718b7afec5a4ea2b1b58febce8b9dcecda"},"schema_version":"1.0","source":{"id":"2604.20183","kind":"arxiv","version":2}},"canonical_sha256":"4a0feb6af3066eb8e8cbdb3f3026db5f9eabb3fb95b7b825d45a0c9eaad31801","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"4a0feb6af3066eb8e8cbdb3f3026db5f9eabb3fb95b7b825d45a0c9eaad31801","first_computed_at":"2026-06-03T01:05:50.563514Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-06-03T01:05:50.563514Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"lNbrhvg56c/ydtx0dziMoZhHXyvC5XyG61Dk56BYx6/eN15z6gMum68Ykr1BmxLs/2frtRxPrhZInOyehU45Cg==","signature_status":"signed_v1","signed_at":"2026-06-03T01:05:50.563984Z","signed_message":"canonical_sha256_bytes"},"source_id":"2604.20183","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:428e900fa80b97f9cc7b38f19123b9722e0e84a945b93ef9c9f5bfeb67a91dfd","sha256:1ce249cf17926fbab7c3b6b17e041f7462e5acf815c4a9767577a2ee7eebd2e9"],"state_sha256":"586da26e31b6a5b9ec8e7499ec8dcffc285ef768d9efdb7de4c27622135cdca9"}