{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:HR7J2GXFHSQJIRLHLO6K3JPSNH","short_pith_number":"pith:HR7J2GXF","schema_version":"1.0","canonical_sha256":"3c7e9d1ae53ca09445675bbcada5f269f8a23c62102cc64dc70f7c9242e7b604","source":{"kind":"arxiv","id":"2405.06682","version":3},"attestation_state":"computed","paper":{"title":"Self-Reflection in LLM Agents: Effects on Problem-Solving Performance","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Erhan Guven, Matthew Renze","submitted_at":"2024-05-05T18:56:46Z","abstract_excerpt":"In this study, we investigated the effects of self-reflection in large language models (LLMs) on problem-solving performance. We instructed nine popular LLMs to answer a series of multiple-choice questions to provide a performance baseline. For each incorrectly answered question, we instructed eight types of self-reflecting LLM agents to reflect on their mistakes and provide themselves with guidance to improve problem-solving. Then, using this guidance, each self-reflecting agent attempted to re-answer the same questions. Our results indicate that LLM agents are able to significantly improve t"},"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":"2405.06682","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-05-05T18:56:46Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"2c845174207aa03687e4051befc7ff3f044d11d2493d372a7110d565c615b7f2","abstract_canon_sha256":"30cd0b23a6e656935d7f182248bee6bbe8aeec8d00365b6f38faa3319434f535"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:30:53.685177Z","signature_b64":"CJtP7rqaGHvsFe0DgkdmmGDxRrzbvzvIEuSAoWfbE0F9totqKFGqOrVjQx/SB7R2v2vPZFMRASiMugl9uI1XBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3c7e9d1ae53ca09445675bbcada5f269f8a23c62102cc64dc70f7c9242e7b604","last_reissued_at":"2026-07-05T10:30:53.684228Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:30:53.684228Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Self-Reflection in LLM Agents: Effects on Problem-Solving Performance","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Erhan Guven, Matthew Renze","submitted_at":"2024-05-05T18:56:46Z","abstract_excerpt":"In this study, we investigated the effects of self-reflection in large language models (LLMs) on problem-solving performance. We instructed nine popular LLMs to answer a series of multiple-choice questions to provide a performance baseline. For each incorrectly answered question, we instructed eight types of self-reflecting LLM agents to reflect on their mistakes and provide themselves with guidance to improve problem-solving. Then, using this guidance, each self-reflecting agent attempted to re-answer the same questions. Our results indicate that LLM agents are able to significantly improve t"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2405.06682","kind":"arxiv","version":3},"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/2405.06682/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":"2405.06682","created_at":"2026-07-05T10:30:53.684343+00:00"},{"alias_kind":"arxiv_version","alias_value":"2405.06682v3","created_at":"2026-07-05T10:30:53.684343+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2405.06682","created_at":"2026-07-05T10:30:53.684343+00:00"},{"alias_kind":"pith_short_12","alias_value":"HR7J2GXFHSQJ","created_at":"2026-07-05T10:30:53.684343+00:00"},{"alias_kind":"pith_short_16","alias_value":"HR7J2GXFHSQJIRLH","created_at":"2026-07-05T10:30:53.684343+00:00"},{"alias_kind":"pith_short_8","alias_value":"HR7J2GXF","created_at":"2026-07-05T10:30:53.684343+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":28,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.28840","citing_title":"How Consistent Are LLM Agents? Measuring Behavioral Reproducibility in Multi-Step Tool-Calling Pipelines","ref_index":10,"is_internal_anchor":false},{"citing_arxiv_id":"2607.01061","citing_title":"Agentic generation of verifiable rules for deterministic, self-expanding reaction classification","ref_index":20,"is_internal_anchor":false},{"citing_arxiv_id":"2606.04503","citing_title":"Smart Picks in the Dark: Towards Efficient RLVR for Reasoning via Tracing Metacognitive Pivots","ref_index":20,"is_internal_anchor":false},{"citing_arxiv_id":"2605.31492","citing_title":"LinTree: Improving LLM Reasoning with Explicitly Structured Search Histories","ref_index":19,"is_internal_anchor":false},{"citing_arxiv_id":"2605.31010","citing_title":"MoG: Mixture of Experts for Graph-based Retrieval-Augmented Generation","ref_index":29,"is_internal_anchor":false},{"citing_arxiv_id":"2606.30989","citing_title":"Wait, am I Being Fair? Characterizing Deductive Stereotyping and Mitigating It with Fair-GCG","ref_index":80,"is_internal_anchor":false},{"citing_arxiv_id":"2606.28780","citing_title":"Multimodal Graph RAG for Long-range Visually Rich Document Understanding","ref_index":4,"is_internal_anchor":false},{"citing_arxiv_id":"2605.28144","citing_title":"Deconstructing Spatial Complexity: Hierarchical Decomposition for LLM Spatial Reasoning","ref_index":2,"is_internal_anchor":false},{"citing_arxiv_id":"2605.23518","citing_title":"VINS-120K: Ultra High-Resolution Image Editing with A Large-Scale Dataset","ref_index":37,"is_internal_anchor":false},{"citing_arxiv_id":"2409.12059","citing_title":"MeTHanol: Modularized Thinking Language Models with Intermediate Layer Thinking, Decoding and Bootstrapping Reasoning","ref_index":7,"is_internal_anchor":false},{"citing_arxiv_id":"2605.22389","citing_title":"Unified Data Selection for LLM Reasoning","ref_index":20,"is_internal_anchor":false},{"citing_arxiv_id":"2605.16205","citing_title":"Context, Reasoning, and Hierarchy: A Cost-Performance Study of Compound LLM Agent Design in an Adversarial POMDP","ref_index":18,"is_internal_anchor":false},{"citing_arxiv_id":"2605.19035","citing_title":"Trustworthy Agent Network: Trust in Agent Networks Must Be Baked In, Not Bolted On","ref_index":47,"is_internal_anchor":false},{"citing_arxiv_id":"2605.10057","citing_title":"STAR: Failure-Aware Markovian Routing for Multi-Agent Spatiotemporal Reasoning","ref_index":18,"is_internal_anchor":false},{"citing_arxiv_id":"2411.18279","citing_title":"Large Language Model-Brained GUI Agents: A Survey","ref_index":258,"is_internal_anchor":false},{"citing_arxiv_id":"2509.05489","citing_title":"Self-Aligned Reward: Towards Effective and Efficient Reasoners","ref_index":35,"is_internal_anchor":false},{"citing_arxiv_id":"2509.18847","citing_title":"Failure Makes the Agent Stronger: Enhancing Accuracy through Structured Reflection for Reliable Tool Interactions","ref_index":24,"is_internal_anchor":false},{"citing_arxiv_id":"2602.24273","citing_title":"A Minimal Agent for Automated Theorem Proving","ref_index":51,"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":151,"is_internal_anchor":false},{"citing_arxiv_id":"2604.10500","citing_title":"Visual Enhanced Depth Scaling for Multimodal Latent Reasoning","ref_index":45,"is_internal_anchor":false},{"citing_arxiv_id":"2605.10057","citing_title":"STAR: Failure-Aware Markovian Routing for Multi-Agent Spatiotemporal Reasoning","ref_index":18,"is_internal_anchor":false},{"citing_arxiv_id":"2605.10057","citing_title":"STAR: Failure-Aware Markovian Routing for Multi-Agent Spatiotemporal Reasoning","ref_index":18,"is_internal_anchor":false},{"citing_arxiv_id":"2605.09942","citing_title":"HAGE: Harnessing Agentic Memory via RL-Driven Weighted Graph Evolution","ref_index":17,"is_internal_anchor":false},{"citing_arxiv_id":"2604.10500","citing_title":"Visual Enhanced Depth Scaling for Multimodal Latent Reasoning","ref_index":45,"is_internal_anchor":false},{"citing_arxiv_id":"2604.23366","citing_title":"GSAR: Typed Grounding for Hallucination Detection and Recovery in Multi-Agent LLMs","ref_index":4,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/HR7J2GXFHSQJIRLHLO6K3JPSNH","json":"https://pith.science/pith/HR7J2GXFHSQJIRLHLO6K3JPSNH.json","graph_json":"https://pith.science/api/pith-number/HR7J2GXFHSQJIRLHLO6K3JPSNH/graph.json","events_json":"https://pith.science/api/pith-number/HR7J2GXFHSQJIRLHLO6K3JPSNH/events.json","paper":"https://pith.science/paper/HR7J2GXF"},"agent_actions":{"view_html":"https://pith.science/pith/HR7J2GXFHSQJIRLHLO6K3JPSNH","download_json":"https://pith.science/pith/HR7J2GXFHSQJIRLHLO6K3JPSNH.json","view_paper":"https://pith.science/paper/HR7J2GXF","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2405.06682&json=true","fetch_graph":"https://pith.science/api/pith-number/HR7J2GXFHSQJIRLHLO6K3JPSNH/graph.json","fetch_events":"https://pith.science/api/pith-number/HR7J2GXFHSQJIRLHLO6K3JPSNH/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/HR7J2GXFHSQJIRLHLO6K3JPSNH/action/timestamp_anchor","attest_storage":"https://pith.science/pith/HR7J2GXFHSQJIRLHLO6K3JPSNH/action/storage_attestation","attest_author":"https://pith.science/pith/HR7J2GXFHSQJIRLHLO6K3JPSNH/action/author_attestation","sign_citation":"https://pith.science/pith/HR7J2GXFHSQJIRLHLO6K3JPSNH/action/citation_signature","submit_replication":"https://pith.science/pith/HR7J2GXFHSQJIRLHLO6K3JPSNH/action/replication_record"}},"created_at":"2026-07-05T10:30:53.684343+00:00","updated_at":"2026-07-05T10:30:53.684343+00:00"}