{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:OAVPFXTVFDYYSQDMFBSGRQSWZE","short_pith_number":"pith:OAVPFXTV","schema_version":"1.0","canonical_sha256":"702af2de7528f189406c286468c256c9360022ef9908151ed7917b897c005a94","source":{"kind":"arxiv","id":"2606.17454","version":2},"attestation_state":"computed","paper":{"title":"Dissecting model behavior through agent trajectories","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.AI","authors_text":"Anoop Deoras, Gaurav Gupta, Jun Huan, Vatshank Chaturvedi","submitted_at":"2026-06-16T03:17:03Z","abstract_excerpt":"AI agent performance is not just a modeling problem, it is fundamentally a systems problem. The advanced capabilities of models are realized through agent harnesses. Therefore, a gap between model assumptions and harness behavior can easily prevent the model's full capabilities from translating into agent performance. We formalize this as the `intent-execution' gap: the mismatch between what the model intends and what the harness executes, and vice versa. We argue that minimizing this intent-execution gap is as important as other aspects of harness design such as tools and execution loops. To "},"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":"2606.17454","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2026-06-16T03:17:03Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"a7a23a89a1d4858e602793f5ed3b047b9137bc888cabbe8a08286b8dc0533cc5","abstract_canon_sha256":"3bdd43f577f04683b86e19a9025f225d923a160e557d03e092df48cc9ab4fe15"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-19T16:11:38.558143Z","signature_b64":"8Xw71ci6Ulln5NGTykDvuni5KPjTG+RAAj1Hhz9L7wmJC7JEBDEOn31KiXmilDt6wAcSrBkuMErxyCLUio2oDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"702af2de7528f189406c286468c256c9360022ef9908151ed7917b897c005a94","last_reissued_at":"2026-06-19T16:11:38.557786Z","signature_status":"signed_v1","first_computed_at":"2026-06-19T16:11:38.557786Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Dissecting model behavior through agent trajectories","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.AI","authors_text":"Anoop Deoras, Gaurav Gupta, Jun Huan, Vatshank Chaturvedi","submitted_at":"2026-06-16T03:17:03Z","abstract_excerpt":"AI agent performance is not just a modeling problem, it is fundamentally a systems problem. The advanced capabilities of models are realized through agent harnesses. Therefore, a gap between model assumptions and harness behavior can easily prevent the model's full capabilities from translating into agent performance. We formalize this as the `intent-execution' gap: the mismatch between what the model intends and what the harness executes, and vice versa. We argue that minimizing this intent-execution gap is as important as other aspects of harness design such as tools and execution loops. To "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.17454","kind":"arxiv","version":2},"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/2606.17454/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":"2606.17454","created_at":"2026-06-19T16:11:38.557850+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.17454v2","created_at":"2026-06-19T16:11:38.557850+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.17454","created_at":"2026-06-19T16:11:38.557850+00:00"},{"alias_kind":"pith_short_12","alias_value":"OAVPFXTVFDYY","created_at":"2026-06-19T16:11:38.557850+00:00"},{"alias_kind":"pith_short_16","alias_value":"OAVPFXTVFDYYSQDM","created_at":"2026-06-19T16:11:38.557850+00:00"},{"alias_kind":"pith_short_8","alias_value":"OAVPFXTV","created_at":"2026-06-19T16:11:38.557850+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/OAVPFXTVFDYYSQDMFBSGRQSWZE","json":"https://pith.science/pith/OAVPFXTVFDYYSQDMFBSGRQSWZE.json","graph_json":"https://pith.science/api/pith-number/OAVPFXTVFDYYSQDMFBSGRQSWZE/graph.json","events_json":"https://pith.science/api/pith-number/OAVPFXTVFDYYSQDMFBSGRQSWZE/events.json","paper":"https://pith.science/paper/OAVPFXTV"},"agent_actions":{"view_html":"https://pith.science/pith/OAVPFXTVFDYYSQDMFBSGRQSWZE","download_json":"https://pith.science/pith/OAVPFXTVFDYYSQDMFBSGRQSWZE.json","view_paper":"https://pith.science/paper/OAVPFXTV","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.17454&json=true","fetch_graph":"https://pith.science/api/pith-number/OAVPFXTVFDYYSQDMFBSGRQSWZE/graph.json","fetch_events":"https://pith.science/api/pith-number/OAVPFXTVFDYYSQDMFBSGRQSWZE/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/OAVPFXTVFDYYSQDMFBSGRQSWZE/action/timestamp_anchor","attest_storage":"https://pith.science/pith/OAVPFXTVFDYYSQDMFBSGRQSWZE/action/storage_attestation","attest_author":"https://pith.science/pith/OAVPFXTVFDYYSQDMFBSGRQSWZE/action/author_attestation","sign_citation":"https://pith.science/pith/OAVPFXTVFDYYSQDMFBSGRQSWZE/action/citation_signature","submit_replication":"https://pith.science/pith/OAVPFXTVFDYYSQDMFBSGRQSWZE/action/replication_record"}},"created_at":"2026-06-19T16:11:38.557850+00:00","updated_at":"2026-06-19T16:11:38.557850+00:00"}