{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:UG5FKO4WCGESIVD53VOWXGCOB2","short_pith_number":"pith:UG5FKO4W","schema_version":"1.0","canonical_sha256":"a1ba553b96118924547ddd5d6b984e0ea2233ef6482120d75116a3e5819e02d7","source":{"kind":"arxiv","id":"2604.19775","version":2},"attestation_state":"computed","paper":{"title":"From Actions to Understanding: Conformal Interpretability of Temporal Concepts in LLM Agents","license":"http://creativecommons.org/licenses/by/4.0/","headline":"Conformal prediction on step-wise rewards reveals linearly separable temporal concepts in LLM agent activations that align with task success.","cross_cats":["cs.CL","cs.ET","cs.MA","cs.RO"],"primary_cat":"cs.AI","authors_text":"Adam D. Cobb, Alexander M. Berenbeim, Anirban Roy, Colin Samplawski, Daniel Elenius, Krishiv Agarwal, Manoj Acharya, Nathaniel D. Bastian, Ramneet Kaur, Susmit Jha, Trilok Padhi, Ugur Kursuncu","submitted_at":"2026-03-27T22:29:01Z","abstract_excerpt":"Large Language Models (LLMs) are increasingly deployed as autonomous agents capable of reasoning, planning, and acting within interactive environments. Despite their growing capability to perform multi-step reasoning and decision-making tasks, internal mechanisms guiding their sequential behavior remain opaque. This paper presents a framework for interpreting the temporal evolution of concepts in LLM agents through a step-wise conformal lens. We introduce the conformal interpretability framework for temporal tasks, which combines step-wise reward modeling with conformal prediction to statistic"},"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":true},"canonical_record":{"source":{"id":"2604.19775","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2026-03-27T22:29:01Z","cross_cats_sorted":["cs.CL","cs.ET","cs.MA","cs.RO"],"title_canon_sha256":"8c848ffb436922030b91cde08c7d626e626f02ab207366f6154b86f3d008a624","abstract_canon_sha256":"d43d4b65470617b7b2d0f4c5a7daf5818c822aebffc10f429f3fad4ee5967346"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-03T00:16:53.820754Z","signature_b64":"nZlmdtBc4LD8kIcIfG/sAlTxBl1Ki3FvvghCOYTBxP/s4asKkAqnAEqm5f1js3LzYU/2t8OLqXbT4ibWKsGICg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a1ba553b96118924547ddd5d6b984e0ea2233ef6482120d75116a3e5819e02d7","last_reissued_at":"2026-07-03T00:16:53.820305Z","signature_status":"signed_v1","first_computed_at":"2026-07-03T00:16:53.820305Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"From Actions to Understanding: Conformal Interpretability of Temporal Concepts in LLM Agents","license":"http://creativecommons.org/licenses/by/4.0/","headline":"Conformal prediction on step-wise rewards reveals linearly separable temporal concepts in LLM agent activations that align with task success.","cross_cats":["cs.CL","cs.ET","cs.MA","cs.RO"],"primary_cat":"cs.AI","authors_text":"Adam D. Cobb, Alexander M. Berenbeim, Anirban Roy, Colin Samplawski, Daniel Elenius, Krishiv Agarwal, Manoj Acharya, Nathaniel D. Bastian, Ramneet Kaur, Susmit Jha, Trilok Padhi, Ugur Kursuncu","submitted_at":"2026-03-27T22:29:01Z","abstract_excerpt":"Large Language Models (LLMs) are increasingly deployed as autonomous agents capable of reasoning, planning, and acting within interactive environments. Despite their growing capability to perform multi-step reasoning and decision-making tasks, internal mechanisms guiding their sequential behavior remain opaque. This paper presents a framework for interpreting the temporal evolution of concepts in LLM agents through a step-wise conformal lens. We introduce the conformal interpretability framework for temporal tasks, which combines step-wise reward modeling with conformal prediction to statistic"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"Experimental results on two simulated interactive environments, namely ScienceWorld and AlfWorld, demonstrate that these temporal concepts are linearly separable, revealing interpretable structures aligned with task success.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"That step-wise reward modeling combined with conformal prediction can reliably and accurately label the model's internal representations at each step as successful or failing without introducing significant labeling noise or bias.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"A conformal interpretability method labels LLM agent states step-by-step and extracts linearly separable temporal concept directions aligned with task success on ScienceWorld and AlfWorld.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"Conformal prediction on step-wise rewards reveals linearly separable temporal concepts in LLM agent activations that align with task success.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"d26c2a7f647f90e6640e787170c532dd0249317e2a26088039fd410d6bdda83a"},"source":{"id":"2604.19775","kind":"arxiv","version":2},"verdict":{"id":"16d24958-d593-471e-857f-c03d0cd6f1b6","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-14T22:29:45.126090Z","strongest_claim":"Experimental results on two simulated interactive environments, namely ScienceWorld and AlfWorld, demonstrate that these temporal concepts are linearly separable, revealing interpretable structures aligned with task success.","one_line_summary":"A conformal interpretability method labels LLM agent states step-by-step and extracts linearly separable temporal concept directions aligned with task success on ScienceWorld and AlfWorld.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"That step-wise reward modeling combined with conformal prediction can reliably and accurately label the model's internal representations at each step as successful or failing without introducing significant labeling noise or bias.","pith_extraction_headline":"Conformal prediction on step-wise rewards reveals linearly separable temporal concepts in LLM agent activations that align with task success."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2604.19775/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":2,"snapshot_sha256":"78717e0a6caa1db3e225a4dd4dd05072431ebf0eda47e7353fdf15e8b51a822b"},"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":"2604.19775","created_at":"2026-07-03T00:16:53.820380+00:00"},{"alias_kind":"arxiv_version","alias_value":"2604.19775v2","created_at":"2026-07-03T00:16:53.820380+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2604.19775","created_at":"2026-07-03T00:16:53.820380+00:00"},{"alias_kind":"pith_short_12","alias_value":"UG5FKO4WCGES","created_at":"2026-07-03T00:16:53.820380+00:00"},{"alias_kind":"pith_short_16","alias_value":"UG5FKO4WCGESIVD5","created_at":"2026-07-03T00:16:53.820380+00:00"},{"alias_kind":"pith_short_8","alias_value":"UG5FKO4W","created_at":"2026-07-03T00:16:53.820380+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2607.06503","citing_title":"Doomed from the Start: Early Abort of LLM Agent Episodes via a Recall-Controlled Probe Cascade","ref_index":19,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":2,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/UG5FKO4WCGESIVD53VOWXGCOB2","json":"https://pith.science/pith/UG5FKO4WCGESIVD53VOWXGCOB2.json","graph_json":"https://pith.science/api/pith-number/UG5FKO4WCGESIVD53VOWXGCOB2/graph.json","events_json":"https://pith.science/api/pith-number/UG5FKO4WCGESIVD53VOWXGCOB2/events.json","paper":"https://pith.science/paper/UG5FKO4W"},"agent_actions":{"view_html":"https://pith.science/pith/UG5FKO4WCGESIVD53VOWXGCOB2","download_json":"https://pith.science/pith/UG5FKO4WCGESIVD53VOWXGCOB2.json","view_paper":"https://pith.science/paper/UG5FKO4W","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2604.19775&json=true","fetch_graph":"https://pith.science/api/pith-number/UG5FKO4WCGESIVD53VOWXGCOB2/graph.json","fetch_events":"https://pith.science/api/pith-number/UG5FKO4WCGESIVD53VOWXGCOB2/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/UG5FKO4WCGESIVD53VOWXGCOB2/action/timestamp_anchor","attest_storage":"https://pith.science/pith/UG5FKO4WCGESIVD53VOWXGCOB2/action/storage_attestation","attest_author":"https://pith.science/pith/UG5FKO4WCGESIVD53VOWXGCOB2/action/author_attestation","sign_citation":"https://pith.science/pith/UG5FKO4WCGESIVD53VOWXGCOB2/action/citation_signature","submit_replication":"https://pith.science/pith/UG5FKO4WCGESIVD53VOWXGCOB2/action/replication_record"}},"created_at":"2026-07-03T00:16:53.820380+00:00","updated_at":"2026-07-03T00:16:53.820380+00:00"}