{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:56BILXMJJM55GERYJS64E5EEA3","short_pith_number":"pith:56BILXMJ","schema_version":"1.0","canonical_sha256":"ef8285dd894b3bd312384cbdc2748406d1d57436635f75ff82f80a345cda6c60","source":{"kind":"arxiv","id":"2608.02879","version":1},"attestation_state":"computed","paper":{"title":"Interpreting Black-Box Large Language Models with Sentence-Level Energy Landscapes","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Fatemeh Seyyedsalehi, Maryam Asgarinezhad, Maryam Rezaee, Pooriya Safaei","submitted_at":"2026-08-03T21:01:41Z","abstract_excerpt":"The widespread adoption of proprietary Large Language Models (LLMs) accessed strictly through closed APIs has created a critical challenge for responsible deployment: a fundamental lack of interpretability. To address this, we propose a model-agnostic, post-hoc attribution interpreter operating at the sentence level. Our approach trains an Energy-Based Model (EBM) as a surrogate to capture the LLM's internal conceptual consistency between prompts and responses. This energy landscape guides the training of a lightweight interpreter network. Uniquely, our interpreter operates as a standalone too"},"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":"2608.02879","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2026-08-03T21:01:41Z","cross_cats_sorted":[],"title_canon_sha256":"531fde8b58f5e0567574c80b0501684aef64cea61d602b2eaa0e6e188ee40da1","abstract_canon_sha256":"d01435c3ffb6f65585eed070a012cb72da6f97211e13968d19e1fa098f8bda2e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-08-05T00:43:13.859112Z","signature_b64":"SKHnx9V06OIEmGt/f8sszQPm/JRdz6zUkndVhcde7YUwirdJ+It8aRMSJ5AC6GdgWxdDI6EuKKJ0y035dEe2DA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ef8285dd894b3bd312384cbdc2748406d1d57436635f75ff82f80a345cda6c60","last_reissued_at":"2026-08-05T00:43:13.856697Z","signature_status":"signed_v1","first_computed_at":"2026-08-05T00:43:13.856697Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Interpreting Black-Box Large Language Models with Sentence-Level Energy Landscapes","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Fatemeh Seyyedsalehi, Maryam Asgarinezhad, Maryam Rezaee, Pooriya Safaei","submitted_at":"2026-08-03T21:01:41Z","abstract_excerpt":"The widespread adoption of proprietary Large Language Models (LLMs) accessed strictly through closed APIs has created a critical challenge for responsible deployment: a fundamental lack of interpretability. To address this, we propose a model-agnostic, post-hoc attribution interpreter operating at the sentence level. Our approach trains an Energy-Based Model (EBM) as a surrogate to capture the LLM's internal conceptual consistency between prompts and responses. This energy landscape guides the training of a lightweight interpreter network. Uniquely, our interpreter operates as a standalone too"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2608.02879","kind":"arxiv","version":1},"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/2608.02879/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":"2608.02879","created_at":"2026-08-05T00:43:13.857594+00:00"},{"alias_kind":"arxiv_version","alias_value":"2608.02879v1","created_at":"2026-08-05T00:43:13.857594+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2608.02879","created_at":"2026-08-05T00:43:13.857594+00:00"},{"alias_kind":"pith_short_12","alias_value":"56BILXMJJM55","created_at":"2026-08-05T00:43:13.857594+00:00"},{"alias_kind":"pith_short_16","alias_value":"56BILXMJJM55GERY","created_at":"2026-08-05T00:43:13.857594+00:00"},{"alias_kind":"pith_short_8","alias_value":"56BILXMJ","created_at":"2026-08-05T00:43:13.857594+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/56BILXMJJM55GERYJS64E5EEA3","json":"https://pith.science/pith/56BILXMJJM55GERYJS64E5EEA3.json","graph_json":"https://pith.science/api/pith-number/56BILXMJJM55GERYJS64E5EEA3/graph.json","events_json":"https://pith.science/api/pith-number/56BILXMJJM55GERYJS64E5EEA3/events.json","paper":"https://pith.science/paper/56BILXMJ"},"agent_actions":{"view_html":"https://pith.science/pith/56BILXMJJM55GERYJS64E5EEA3","download_json":"https://pith.science/pith/56BILXMJJM55GERYJS64E5EEA3.json","view_paper":"https://pith.science/paper/56BILXMJ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2608.02879&json=true","fetch_graph":"https://pith.science/api/pith-number/56BILXMJJM55GERYJS64E5EEA3/graph.json","fetch_events":"https://pith.science/api/pith-number/56BILXMJJM55GERYJS64E5EEA3/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/56BILXMJJM55GERYJS64E5EEA3/action/timestamp_anchor","attest_storage":"https://pith.science/pith/56BILXMJJM55GERYJS64E5EEA3/action/storage_attestation","attest_author":"https://pith.science/pith/56BILXMJJM55GERYJS64E5EEA3/action/author_attestation","sign_citation":"https://pith.science/pith/56BILXMJJM55GERYJS64E5EEA3/action/citation_signature","submit_replication":"https://pith.science/pith/56BILXMJJM55GERYJS64E5EEA3/action/replication_record"}},"created_at":"2026-08-05T00:43:13.857594+00:00","updated_at":"2026-08-05T00:43:13.857594+00:00"}