{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:MLLLAUXZLP2CZTKXA7XJ4BCDRY","short_pith_number":"pith:MLLLAUXZ","schema_version":"1.0","canonical_sha256":"62d6b052f95bf42ccd5707ee9e04438e0ab374b2b65c32831b521720569b06ba","source":{"kind":"arxiv","id":"2410.20285","version":6},"attestation_state":"computed","paper":{"title":"SWE-Search: Enhancing Software Agents with Monte Carlo Tree Search and Iterative Refinement","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Albert \\\"Orwall, Anirudh Goyal, Antonis Antoniades, Kexun Zhang, William Wang, Yuxi Xie","submitted_at":"2024-10-26T22:45:56Z","abstract_excerpt":"Software engineers operating in complex and dynamic environments must continuously adapt to evolving requirements, learn iteratively from experience, and reconsider their approaches based on new insights. However, current large language model (LLM)-based software agents often follow linear, sequential processes that prevent backtracking and exploration of alternative solutions, limiting their ability to rethink their strategies when initial approaches prove ineffective. To address these challenges, we propose SWE-Search, a multi-agent framework that integrates Monte Carlo Tree Search (MCTS) wi"},"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":"2410.20285","kind":"arxiv","version":6},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.AI","submitted_at":"2024-10-26T22:45:56Z","cross_cats_sorted":[],"title_canon_sha256":"066762ec829ac95ca7f34360664db63471268bea6f9f0c2da0e7d29863c2b562","abstract_canon_sha256":"e034ca7ecc182dba9e4e16ffc242fe9a5f023b1203d4ebd1c954a57d56e4ed89"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:43:02.753094Z","signature_b64":"xyn3wFx8qu+rwnc3tuhW0Idbp2pjx+Oi7RGm+DyqfppPsr9gXfi/tE1elVT4DdbocVZXDfHYXXiK+0CavrIUBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"62d6b052f95bf42ccd5707ee9e04438e0ab374b2b65c32831b521720569b06ba","last_reissued_at":"2026-07-05T10:43:02.752619Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:43:02.752619Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"SWE-Search: Enhancing Software Agents with Monte Carlo Tree Search and Iterative Refinement","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Albert \\\"Orwall, Anirudh Goyal, Antonis Antoniades, Kexun Zhang, William Wang, Yuxi Xie","submitted_at":"2024-10-26T22:45:56Z","abstract_excerpt":"Software engineers operating in complex and dynamic environments must continuously adapt to evolving requirements, learn iteratively from experience, and reconsider their approaches based on new insights. However, current large language model (LLM)-based software agents often follow linear, sequential processes that prevent backtracking and exploration of alternative solutions, limiting their ability to rethink their strategies when initial approaches prove ineffective. To address these challenges, we propose SWE-Search, a multi-agent framework that integrates Monte Carlo Tree Search (MCTS) wi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.20285","kind":"arxiv","version":6},"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/2410.20285/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":"2410.20285","created_at":"2026-07-05T10:43:02.752674+00:00"},{"alias_kind":"arxiv_version","alias_value":"2410.20285v6","created_at":"2026-07-05T10:43:02.752674+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.20285","created_at":"2026-07-05T10:43:02.752674+00:00"},{"alias_kind":"pith_short_12","alias_value":"MLLLAUXZLP2C","created_at":"2026-07-05T10:43:02.752674+00:00"},{"alias_kind":"pith_short_16","alias_value":"MLLLAUXZLP2CZTKX","created_at":"2026-07-05T10:43:02.752674+00:00"},{"alias_kind":"pith_short_8","alias_value":"MLLLAUXZ","created_at":"2026-07-05T10:43:02.752674+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":15,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.12191","citing_title":"Agentic Environment Engineering for Large Language Models: A Survey of Environment Modeling, Synthesis, Evaluation, and Application","ref_index":249,"is_internal_anchor":false},{"citing_arxiv_id":"2607.01597","citing_title":"A Single Patch Is Not Enough: Deterministic Fusion of Repair Candidates","ref_index":13,"is_internal_anchor":false},{"citing_arxiv_id":"2606.14061","citing_title":"LLM Agents Can See Code Repositories","ref_index":1,"is_internal_anchor":false},{"citing_arxiv_id":"2605.18851","citing_title":"STRIDE: Learnable Stepwise Language Feedback for LLM Reasoning","ref_index":35,"is_internal_anchor":false},{"citing_arxiv_id":"2605.18747","citing_title":"Code as Agent Harness","ref_index":166,"is_internal_anchor":false},{"citing_arxiv_id":"2601.12538","citing_title":"Agentic Reasoning for Large Language Models","ref_index":132,"is_internal_anchor":false},{"citing_arxiv_id":"2603.22048","citing_title":"Dynamic analysis enhances issue resolution","ref_index":2,"is_internal_anchor":false},{"citing_arxiv_id":"2511.20857","citing_title":"Evo-Memory: Benchmarking LLM Agent Test-time Learning with Self-Evolving Memory","ref_index":220,"is_internal_anchor":false},{"citing_arxiv_id":"2605.12925","citing_title":"AgentLens: Revealing The Lucky Pass Problem in SWE-Agent Evaluation","ref_index":1,"is_internal_anchor":false},{"citing_arxiv_id":"2605.10674","citing_title":"Step Rejection Fine-Tuning: A Practical Distillation Recipe","ref_index":5,"is_internal_anchor":false},{"citing_arxiv_id":"2604.08089","citing_title":"GALA: Multimodal Graph Alignment for Bug Localization in Automated Program Repair","ref_index":2,"is_internal_anchor":false},{"citing_arxiv_id":"2604.05481","citing_title":"On the Role of Fault Localization Context for LLM-Based Program Repair","ref_index":2,"is_internal_anchor":false},{"citing_arxiv_id":"2604.04580","citing_title":"Beyond Fixed Tests: Repository-Level Issue Resolution as Coevolution of Code and Behavioral Constraints","ref_index":5,"is_internal_anchor":false},{"citing_arxiv_id":"2604.16790","citing_title":"Bias in the Loop: Auditing LLM-as-a-Judge for Software Engineering","ref_index":2,"is_internal_anchor":false},{"citing_arxiv_id":"2604.17016","citing_title":"HELO-APR: Enhancing Low-Resource Program Repair through Cross-Lingual Knowledge Transfer","ref_index":1,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/MLLLAUXZLP2CZTKXA7XJ4BCDRY","json":"https://pith.science/pith/MLLLAUXZLP2CZTKXA7XJ4BCDRY.json","graph_json":"https://pith.science/api/pith-number/MLLLAUXZLP2CZTKXA7XJ4BCDRY/graph.json","events_json":"https://pith.science/api/pith-number/MLLLAUXZLP2CZTKXA7XJ4BCDRY/events.json","paper":"https://pith.science/paper/MLLLAUXZ"},"agent_actions":{"view_html":"https://pith.science/pith/MLLLAUXZLP2CZTKXA7XJ4BCDRY","download_json":"https://pith.science/pith/MLLLAUXZLP2CZTKXA7XJ4BCDRY.json","view_paper":"https://pith.science/paper/MLLLAUXZ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2410.20285&json=true","fetch_graph":"https://pith.science/api/pith-number/MLLLAUXZLP2CZTKXA7XJ4BCDRY/graph.json","fetch_events":"https://pith.science/api/pith-number/MLLLAUXZLP2CZTKXA7XJ4BCDRY/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/MLLLAUXZLP2CZTKXA7XJ4BCDRY/action/timestamp_anchor","attest_storage":"https://pith.science/pith/MLLLAUXZLP2CZTKXA7XJ4BCDRY/action/storage_attestation","attest_author":"https://pith.science/pith/MLLLAUXZLP2CZTKXA7XJ4BCDRY/action/author_attestation","sign_citation":"https://pith.science/pith/MLLLAUXZLP2CZTKXA7XJ4BCDRY/action/citation_signature","submit_replication":"https://pith.science/pith/MLLLAUXZLP2CZTKXA7XJ4BCDRY/action/replication_record"}},"created_at":"2026-07-05T10:43:02.752674+00:00","updated_at":"2026-07-05T10:43:02.752674+00:00"}