{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:6TIBPTEM2ZPCAWJJZU7U3WCDZW","short_pith_number":"pith:6TIBPTEM","canonical_record":{"source":{"id":"2502.14276","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-02-20T05:28:44Z","cross_cats_sorted":["cs.AI","cs.CL"],"title_canon_sha256":"82d5b9937b1933bf5a5a91ae145346f106cf9d7552786b17f7ccd9db9280f0e8","abstract_canon_sha256":"1cf2e9dcc64cff3692e377e5e19851259e9f352b8087ad1c05e1b15578fcd8db"},"schema_version":"1.0"},"canonical_sha256":"f4d017cc8cd65e205929cd3f4dd843cdabf6d27e50db8185fd49340ef68d66e0","source":{"kind":"arxiv","id":"2502.14276","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2502.14276","created_at":"2026-07-05T11:11:42Z"},{"alias_kind":"arxiv_version","alias_value":"2502.14276v2","created_at":"2026-07-05T11:11:42Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.14276","created_at":"2026-07-05T11:11:42Z"},{"alias_kind":"pith_short_12","alias_value":"6TIBPTEM2ZPC","created_at":"2026-07-05T11:11:42Z"},{"alias_kind":"pith_short_16","alias_value":"6TIBPTEM2ZPCAWJJ","created_at":"2026-07-05T11:11:42Z"},{"alias_kind":"pith_short_8","alias_value":"6TIBPTEM","created_at":"2026-07-05T11:11:42Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:6TIBPTEM2ZPCAWJJZU7U3WCDZW","target":"record","payload":{"canonical_record":{"source":{"id":"2502.14276","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-02-20T05:28:44Z","cross_cats_sorted":["cs.AI","cs.CL"],"title_canon_sha256":"82d5b9937b1933bf5a5a91ae145346f106cf9d7552786b17f7ccd9db9280f0e8","abstract_canon_sha256":"1cf2e9dcc64cff3692e377e5e19851259e9f352b8087ad1c05e1b15578fcd8db"},"schema_version":"1.0"},"canonical_sha256":"f4d017cc8cd65e205929cd3f4dd843cdabf6d27e50db8185fd49340ef68d66e0","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:11:42.146933Z","signature_b64":"ZVGMezaWnGyd+EgCmWmb6GiNVTqXqT3vwzlPn2zQyKeff7u1d8lZKIGb1sWQ6kCzjVP2LAtx2qC526QQHiklDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f4d017cc8cd65e205929cd3f4dd843cdabf6d27e50db8185fd49340ef68d66e0","last_reissued_at":"2026-07-05T11:11:42.146447Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:11:42.146447Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2502.14276","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T11:11:42Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ZIp3xdOIDiwJkbIzVDHMffYbdPVA/IQZAXCsY50a23Yz2+DApvAEwISGZubrk40mV/FcTl44Rc79OP5DUizTDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T16:27:50.489405Z"},"content_sha256":"2d4d9718197822812a30cf367359604e16dcf8c4aaef32b020059c73cddce22f","schema_version":"1.0","event_id":"sha256:2d4d9718197822812a30cf367359604e16dcf8c4aaef32b020059c73cddce22f"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:6TIBPTEM2ZPCAWJJZU7U3WCDZW","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"STeCa: Step-level Trajectory Calibration for LLM Agent Learning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.CL"],"primary_cat":"cs.LG","authors_text":"Chak Tou Leong, Hanlin Wang, Jian Wang, Wenjie Li","submitted_at":"2025-02-20T05:28:44Z","abstract_excerpt":"Large language model (LLM)-based agents have shown promise in tackling complex tasks by interacting dynamically with the environment. Existing work primarily focuses on behavior cloning from expert demonstrations or preference learning through exploratory trajectory sampling. However, these methods often struggle to address long-horizon tasks, where suboptimal actions accumulate step by step, causing agents to deviate from correct task trajectories. To address this, we highlight the importance of timely calibration and the need to automatically construct calibration trajectories for training a"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.14276","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/2502.14276/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T11:11:42Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Jk1S963o4pe7vkmX0mlbaY9iTmpKL02LJvcFlA0y5JnqCawUpZQUbfXIxNcN2GkCCsxFN2RUWa7jzE5Wm/N0CA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T16:27:50.489783Z"},"content_sha256":"335c86f9369e2695c58e5f204fa7cb9bf523bde505d387b2b255496d22a91557","schema_version":"1.0","event_id":"sha256:335c86f9369e2695c58e5f204fa7cb9bf523bde505d387b2b255496d22a91557"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/6TIBPTEM2ZPCAWJJZU7U3WCDZW/bundle.json","state_url":"https://pith.science/pith/6TIBPTEM2ZPCAWJJZU7U3WCDZW/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/6TIBPTEM2ZPCAWJJZU7U3WCDZW/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-05T16:27:50Z","links":{"resolver":"https://pith.science/pith/6TIBPTEM2ZPCAWJJZU7U3WCDZW","bundle":"https://pith.science/pith/6TIBPTEM2ZPCAWJJZU7U3WCDZW/bundle.json","state":"https://pith.science/pith/6TIBPTEM2ZPCAWJJZU7U3WCDZW/state.json","well_known_bundle":"https://pith.science/.well-known/pith/6TIBPTEM2ZPCAWJJZU7U3WCDZW/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:6TIBPTEM2ZPCAWJJZU7U3WCDZW","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"1cf2e9dcc64cff3692e377e5e19851259e9f352b8087ad1c05e1b15578fcd8db","cross_cats_sorted":["cs.AI","cs.CL"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-02-20T05:28:44Z","title_canon_sha256":"82d5b9937b1933bf5a5a91ae145346f106cf9d7552786b17f7ccd9db9280f0e8"},"schema_version":"1.0","source":{"id":"2502.14276","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2502.14276","created_at":"2026-07-05T11:11:42Z"},{"alias_kind":"arxiv_version","alias_value":"2502.14276v2","created_at":"2026-07-05T11:11:42Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.14276","created_at":"2026-07-05T11:11:42Z"},{"alias_kind":"pith_short_12","alias_value":"6TIBPTEM2ZPC","created_at":"2026-07-05T11:11:42Z"},{"alias_kind":"pith_short_16","alias_value":"6TIBPTEM2ZPCAWJJ","created_at":"2026-07-05T11:11:42Z"},{"alias_kind":"pith_short_8","alias_value":"6TIBPTEM","created_at":"2026-07-05T11:11:42Z"}],"graph_snapshots":[{"event_id":"sha256:335c86f9369e2695c58e5f204fa7cb9bf523bde505d387b2b255496d22a91557","target":"graph","created_at":"2026-07-05T11:11:42Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2502.14276/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Large language model (LLM)-based agents have shown promise in tackling complex tasks by interacting dynamically with the environment. Existing work primarily focuses on behavior cloning from expert demonstrations or preference learning through exploratory trajectory sampling. However, these methods often struggle to address long-horizon tasks, where suboptimal actions accumulate step by step, causing agents to deviate from correct task trajectories. To address this, we highlight the importance of timely calibration and the need to automatically construct calibration trajectories for training a","authors_text":"Chak Tou Leong, Hanlin Wang, Jian Wang, Wenjie Li","cross_cats":["cs.AI","cs.CL"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-02-20T05:28:44Z","title":"STeCa: Step-level Trajectory Calibration for LLM Agent Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.14276","kind":"arxiv","version":2},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:2d4d9718197822812a30cf367359604e16dcf8c4aaef32b020059c73cddce22f","target":"record","created_at":"2026-07-05T11:11:42Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"1cf2e9dcc64cff3692e377e5e19851259e9f352b8087ad1c05e1b15578fcd8db","cross_cats_sorted":["cs.AI","cs.CL"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-02-20T05:28:44Z","title_canon_sha256":"82d5b9937b1933bf5a5a91ae145346f106cf9d7552786b17f7ccd9db9280f0e8"},"schema_version":"1.0","source":{"id":"2502.14276","kind":"arxiv","version":2}},"canonical_sha256":"f4d017cc8cd65e205929cd3f4dd843cdabf6d27e50db8185fd49340ef68d66e0","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"f4d017cc8cd65e205929cd3f4dd843cdabf6d27e50db8185fd49340ef68d66e0","first_computed_at":"2026-07-05T11:11:42.146447Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:11:42.146447Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"ZVGMezaWnGyd+EgCmWmb6GiNVTqXqT3vwzlPn2zQyKeff7u1d8lZKIGb1sWQ6kCzjVP2LAtx2qC526QQHiklDw==","signature_status":"signed_v1","signed_at":"2026-07-05T11:11:42.146933Z","signed_message":"canonical_sha256_bytes"},"source_id":"2502.14276","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:2d4d9718197822812a30cf367359604e16dcf8c4aaef32b020059c73cddce22f","sha256:335c86f9369e2695c58e5f204fa7cb9bf523bde505d387b2b255496d22a91557"],"state_sha256":"c85d48778f23a87ccf0028a1fb0417116f9d8dec689672bd48d882c231ad832a"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"WN1KARoO3YVk+Tj5ktreQYP8cr010r50nUod+aWrEwH9B5mjEqv6o5CP7j6t0l3PTjfZHPuGp/4aMt6O/klaBQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-05T16:27:50.492891Z","bundle_sha256":"39b737354ece20b4ef57d03e67925b4c07241f60e9a2d26dddd47cc23e4f3fd7"}}