{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:T2EACM5ZFWMAM7OJXWG5NXWG6W","short_pith_number":"pith:T2EACM5Z","schema_version":"1.0","canonical_sha256":"9e880133b92d98067dc9bd8dd6dec6f59d44e3a5fc2b4cd0b3b6e1a0b07fa6b3","source":{"kind":"arxiv","id":"2405.13966","version":1},"attestation_state":"computed","paper":{"title":"On the Brittle Foundations of ReAct Prompting for Agentic Large Language Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CL"],"primary_cat":"cs.AI","authors_text":"Mudit Verma, Siddhant Bhambri, Subbarao Kambhampati","submitted_at":"2024-05-22T20:05:49Z","abstract_excerpt":"The reasoning abilities of Large Language Models (LLMs) remain a topic of debate. Some methods such as ReAct-based prompting, have gained popularity for claiming to enhance sequential decision-making abilities of agentic LLMs. However, it is unclear what is the source of improvement in LLM reasoning with ReAct based prompting. In this paper we examine these claims of ReAct based prompting in improving agentic LLMs for sequential decision-making. By introducing systematic variations to the input prompt we perform a sensitivity analysis along the claims of ReAct and find that the performance is "},"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":"2405.13966","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2024-05-22T20:05:49Z","cross_cats_sorted":["cs.CL"],"title_canon_sha256":"31aca7ccc4c2a6173181899056cd3c98f6bde39121a54099f2086cf0aa77782c","abstract_canon_sha256":"f1d32d21ba5ef411ed3ec4bcb2073c8a85f149de0f411905bc6f2174cf947db1"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:22:23.714266Z","signature_b64":"3gi3yHktA2R5MMckyCYHzq5fFcXcl8FloICcferp6wcyBVx7vr+FTja+7mkiw06EFqf8wTn75hnDBlwwAqnMBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9e880133b92d98067dc9bd8dd6dec6f59d44e3a5fc2b4cd0b3b6e1a0b07fa6b3","last_reissued_at":"2026-07-05T08:22:23.713773Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:22:23.713773Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"On the Brittle Foundations of ReAct Prompting for Agentic Large Language Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CL"],"primary_cat":"cs.AI","authors_text":"Mudit Verma, Siddhant Bhambri, Subbarao Kambhampati","submitted_at":"2024-05-22T20:05:49Z","abstract_excerpt":"The reasoning abilities of Large Language Models (LLMs) remain a topic of debate. Some methods such as ReAct-based prompting, have gained popularity for claiming to enhance sequential decision-making abilities of agentic LLMs. However, it is unclear what is the source of improvement in LLM reasoning with ReAct based prompting. In this paper we examine these claims of ReAct based prompting in improving agentic LLMs for sequential decision-making. By introducing systematic variations to the input prompt we perform a sensitivity analysis along the claims of ReAct and find that the performance is "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2405.13966","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/2405.13966/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":"2405.13966","created_at":"2026-07-05T08:22:23.713835+00:00"},{"alias_kind":"arxiv_version","alias_value":"2405.13966v1","created_at":"2026-07-05T08:22:23.713835+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2405.13966","created_at":"2026-07-05T08:22:23.713835+00:00"},{"alias_kind":"pith_short_12","alias_value":"T2EACM5ZFWMA","created_at":"2026-07-05T08:22:23.713835+00:00"},{"alias_kind":"pith_short_16","alias_value":"T2EACM5ZFWMAM7OJ","created_at":"2026-07-05T08:22:23.713835+00:00"},{"alias_kind":"pith_short_8","alias_value":"T2EACM5Z","created_at":"2026-07-05T08:22:23.713835+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.26935","citing_title":"Where Do CoT Training Gains Land in LLM based Agents?","ref_index":20,"is_internal_anchor":false},{"citing_arxiv_id":"2605.06040","citing_title":"Novelty-based Tree-of-Thought Search for LLM Reasoning and Planning","ref_index":24,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/T2EACM5ZFWMAM7OJXWG5NXWG6W","json":"https://pith.science/pith/T2EACM5ZFWMAM7OJXWG5NXWG6W.json","graph_json":"https://pith.science/api/pith-number/T2EACM5ZFWMAM7OJXWG5NXWG6W/graph.json","events_json":"https://pith.science/api/pith-number/T2EACM5ZFWMAM7OJXWG5NXWG6W/events.json","paper":"https://pith.science/paper/T2EACM5Z"},"agent_actions":{"view_html":"https://pith.science/pith/T2EACM5ZFWMAM7OJXWG5NXWG6W","download_json":"https://pith.science/pith/T2EACM5ZFWMAM7OJXWG5NXWG6W.json","view_paper":"https://pith.science/paper/T2EACM5Z","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2405.13966&json=true","fetch_graph":"https://pith.science/api/pith-number/T2EACM5ZFWMAM7OJXWG5NXWG6W/graph.json","fetch_events":"https://pith.science/api/pith-number/T2EACM5ZFWMAM7OJXWG5NXWG6W/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/T2EACM5ZFWMAM7OJXWG5NXWG6W/action/timestamp_anchor","attest_storage":"https://pith.science/pith/T2EACM5ZFWMAM7OJXWG5NXWG6W/action/storage_attestation","attest_author":"https://pith.science/pith/T2EACM5ZFWMAM7OJXWG5NXWG6W/action/author_attestation","sign_citation":"https://pith.science/pith/T2EACM5ZFWMAM7OJXWG5NXWG6W/action/citation_signature","submit_replication":"https://pith.science/pith/T2EACM5ZFWMAM7OJXWG5NXWG6W/action/replication_record"}},"created_at":"2026-07-05T08:22:23.713835+00:00","updated_at":"2026-07-05T08:22:23.713835+00:00"}