{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:JTML4XVFPGMFT73RYCCONZLHYR","short_pith_number":"pith:JTML4XVF","schema_version":"1.0","canonical_sha256":"4cd8be5ea5799859ff71c084e6e567c465a878dfd0ef1a820ffdebc936cea254","source":{"kind":"arxiv","id":"2401.14016","version":3},"attestation_state":"computed","paper":{"title":"Towards Uncertainty-Aware Language Agent","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Ehsan Shareghi, Jiuzhou Han, Wray Buntine","submitted_at":"2024-01-25T08:48:21Z","abstract_excerpt":"While Language Agents have achieved promising success by placing Large Language Models at the core of a more versatile design that dynamically interacts with the external world, the existing approaches neglect the notion of uncertainty during these interactions. We present the Uncertainty-Aware Language Agent (UALA), a framework that orchestrates the interaction between the agent and the external world using uncertainty quantification. Compared with other well-known counterparts like ReAct, our extensive experiments across 3 representative tasks (HotpotQA, StrategyQA, MMLU) and various LLM siz"},"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":"2401.14016","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-01-25T08:48:21Z","cross_cats_sorted":[],"title_canon_sha256":"d095f873884396d2cc6818490975a39a88a8035df2c40897c1758d12a8cb13a9","abstract_canon_sha256":"42a67e973be6ddad4ce47625b8403dd3084ba3fd4659c0cbdcc568090e4991c9"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:24:59.724821Z","signature_b64":"j2Ur7euUQPqFV8l7lzvGcXEDKUR9wiqum8WrWhUCBA1GlcYXg1qTbGiUi/s1jvyKpNSk04Z4VTJ7MRZHlTqcBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4cd8be5ea5799859ff71c084e6e567c465a878dfd0ef1a820ffdebc936cea254","last_reissued_at":"2026-07-05T08:24:59.724262Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:24:59.724262Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Towards Uncertainty-Aware Language Agent","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Ehsan Shareghi, Jiuzhou Han, Wray Buntine","submitted_at":"2024-01-25T08:48:21Z","abstract_excerpt":"While Language Agents have achieved promising success by placing Large Language Models at the core of a more versatile design that dynamically interacts with the external world, the existing approaches neglect the notion of uncertainty during these interactions. We present the Uncertainty-Aware Language Agent (UALA), a framework that orchestrates the interaction between the agent and the external world using uncertainty quantification. Compared with other well-known counterparts like ReAct, our extensive experiments across 3 representative tasks (HotpotQA, StrategyQA, MMLU) and various LLM siz"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2401.14016","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/2401.14016/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":"2401.14016","created_at":"2026-07-05T08:24:59.724322+00:00"},{"alias_kind":"arxiv_version","alias_value":"2401.14016v3","created_at":"2026-07-05T08:24:59.724322+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2401.14016","created_at":"2026-07-05T08:24:59.724322+00:00"},{"alias_kind":"pith_short_12","alias_value":"JTML4XVFPGMF","created_at":"2026-07-05T08:24:59.724322+00:00"},{"alias_kind":"pith_short_16","alias_value":"JTML4XVFPGMFT73R","created_at":"2026-07-05T08:24:59.724322+00:00"},{"alias_kind":"pith_short_8","alias_value":"JTML4XVF","created_at":"2026-07-05T08:24:59.724322+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":3,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.12587","citing_title":"Strategic Decision Support for AI Agents","ref_index":29,"is_internal_anchor":false},{"citing_arxiv_id":"2605.24756","citing_title":"Proper Scoring Rules for Agentic Uncertainty Quantification","ref_index":16,"is_internal_anchor":false},{"citing_arxiv_id":"2605.26835","citing_title":"Helicase: Uncertainty-Guided Supply Chain Knowledge Graph Construction with Autonomous Multi-Agent LLMs","ref_index":9,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/JTML4XVFPGMFT73RYCCONZLHYR","json":"https://pith.science/pith/JTML4XVFPGMFT73RYCCONZLHYR.json","graph_json":"https://pith.science/api/pith-number/JTML4XVFPGMFT73RYCCONZLHYR/graph.json","events_json":"https://pith.science/api/pith-number/JTML4XVFPGMFT73RYCCONZLHYR/events.json","paper":"https://pith.science/paper/JTML4XVF"},"agent_actions":{"view_html":"https://pith.science/pith/JTML4XVFPGMFT73RYCCONZLHYR","download_json":"https://pith.science/pith/JTML4XVFPGMFT73RYCCONZLHYR.json","view_paper":"https://pith.science/paper/JTML4XVF","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2401.14016&json=true","fetch_graph":"https://pith.science/api/pith-number/JTML4XVFPGMFT73RYCCONZLHYR/graph.json","fetch_events":"https://pith.science/api/pith-number/JTML4XVFPGMFT73RYCCONZLHYR/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/JTML4XVFPGMFT73RYCCONZLHYR/action/timestamp_anchor","attest_storage":"https://pith.science/pith/JTML4XVFPGMFT73RYCCONZLHYR/action/storage_attestation","attest_author":"https://pith.science/pith/JTML4XVFPGMFT73RYCCONZLHYR/action/author_attestation","sign_citation":"https://pith.science/pith/JTML4XVFPGMFT73RYCCONZLHYR/action/citation_signature","submit_replication":"https://pith.science/pith/JTML4XVFPGMFT73RYCCONZLHYR/action/replication_record"}},"created_at":"2026-07-05T08:24:59.724322+00:00","updated_at":"2026-07-05T08:24:59.724322+00:00"}