{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:3VF4BJHGG5MXHTZKYRNFY3LUIV","short_pith_number":"pith:3VF4BJHG","schema_version":"1.0","canonical_sha256":"dd4bc0a4e6375973cf2ac45a5c6d74456a976e44c1be4e9c2ce56990767d43d5","source":{"kind":"arxiv","id":"2605.29005","version":1},"attestation_state":"computed","paper":{"title":"LoRe: Adaptive Interaction-Evaluation Routing with Per-Step Interaction Budgets for Iterative Graph Solvers","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Heng Fan, Jintao Li, Yong-Yi Wang, Zheng-An Wang","submitted_at":"2026-05-27T19:00:57Z","abstract_excerpt":"Diffusion-based neural solvers for combinatorial optimization repeatedly re-evaluate dense edge/factor interactions, making inference expensive in wall-clock time and often memory-bound at scale. Inspired by the computational methodologies of many-body physics, we introduce LoRe, a training-free, inference-time drop-in wrapper that enforces per-step interaction-evaluation budgeting: at each iteration, it evaluates only a fixed fraction of interactions by dynamically routing computation to high-conflict or high-uncertainty interactions, instead of using a fixed sparsification (e.g., static kNN "},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2605.29005","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-05-27T19:00:57Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"6f3fb0565da6989e0b58350da549b9074fe23853ffe3a61ee1cabcb47a625247","abstract_canon_sha256":"f76d66f257478e7c823cd85f8c37dfc7cd405823d8c2ae72faf5fb561bbe3179"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-29T01:04:43.282965Z","signature_b64":"bZDwBk7esBmyLAoUJ2yK1vuo2Mc61lhpvEJvBN+8XuqPOo/cfVckVcMQNG0ij7hcD1CTHu9r4kPbQKkTJlPCCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"dd4bc0a4e6375973cf2ac45a5c6d74456a976e44c1be4e9c2ce56990767d43d5","last_reissued_at":"2026-05-29T01:04:43.282518Z","signature_status":"signed_v1","first_computed_at":"2026-05-29T01:04:43.282518Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"LoRe: Adaptive Interaction-Evaluation Routing with Per-Step Interaction Budgets for Iterative Graph Solvers","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Heng Fan, Jintao Li, Yong-Yi Wang, Zheng-An Wang","submitted_at":"2026-05-27T19:00:57Z","abstract_excerpt":"Diffusion-based neural solvers for combinatorial optimization repeatedly re-evaluate dense edge/factor interactions, making inference expensive in wall-clock time and often memory-bound at scale. Inspired by the computational methodologies of many-body physics, we introduce LoRe, a training-free, inference-time drop-in wrapper that enforces per-step interaction-evaluation budgeting: at each iteration, it evaluates only a fixed fraction of interactions by dynamically routing computation to high-conflict or high-uncertainty interactions, instead of using a fixed sparsification (e.g., static kNN "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.29005","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2605.29005/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2605.29005","created_at":"2026-05-29T01:04:43.282592+00:00"},{"alias_kind":"arxiv_version","alias_value":"2605.29005v1","created_at":"2026-05-29T01:04:43.282592+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.29005","created_at":"2026-05-29T01:04:43.282592+00:00"},{"alias_kind":"pith_short_12","alias_value":"3VF4BJHGG5MX","created_at":"2026-05-29T01:04:43.282592+00:00"},{"alias_kind":"pith_short_16","alias_value":"3VF4BJHGG5MXHTZK","created_at":"2026-05-29T01:04:43.282592+00:00"},{"alias_kind":"pith_short_8","alias_value":"3VF4BJHG","created_at":"2026-05-29T01:04:43.282592+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/3VF4BJHGG5MXHTZKYRNFY3LUIV","json":"https://pith.science/pith/3VF4BJHGG5MXHTZKYRNFY3LUIV.json","graph_json":"https://pith.science/api/pith-number/3VF4BJHGG5MXHTZKYRNFY3LUIV/graph.json","events_json":"https://pith.science/api/pith-number/3VF4BJHGG5MXHTZKYRNFY3LUIV/events.json","paper":"https://pith.science/paper/3VF4BJHG"},"agent_actions":{"view_html":"https://pith.science/pith/3VF4BJHGG5MXHTZKYRNFY3LUIV","download_json":"https://pith.science/pith/3VF4BJHGG5MXHTZKYRNFY3LUIV.json","view_paper":"https://pith.science/paper/3VF4BJHG","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2605.29005&json=true","fetch_graph":"https://pith.science/api/pith-number/3VF4BJHGG5MXHTZKYRNFY3LUIV/graph.json","fetch_events":"https://pith.science/api/pith-number/3VF4BJHGG5MXHTZKYRNFY3LUIV/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/3VF4BJHGG5MXHTZKYRNFY3LUIV/action/timestamp_anchor","attest_storage":"https://pith.science/pith/3VF4BJHGG5MXHTZKYRNFY3LUIV/action/storage_attestation","attest_author":"https://pith.science/pith/3VF4BJHGG5MXHTZKYRNFY3LUIV/action/author_attestation","sign_citation":"https://pith.science/pith/3VF4BJHGG5MXHTZKYRNFY3LUIV/action/citation_signature","submit_replication":"https://pith.science/pith/3VF4BJHGG5MXHTZKYRNFY3LUIV/action/replication_record"}},"created_at":"2026-05-29T01:04:43.282592+00:00","updated_at":"2026-05-29T01:04:43.282592+00:00"}