{"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"}