{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:E5GC4KI4744YXI7GZLELQ7GVFV","short_pith_number":"pith:E5GC4KI4","schema_version":"1.0","canonical_sha256":"274c2e291cff398ba3e6cac8b87cd52d6bbc09df68c0077fd7a2d7fcdf6fa558","source":{"kind":"arxiv","id":"2505.05177","version":1},"attestation_state":"computed","paper":{"title":"MARK: Memory Augmented Refinement of Knowledge","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.AI","authors_text":"Anish Ganguli, Debleena Banerjee, Prabal Deb","submitted_at":"2025-05-08T12:28:00Z","abstract_excerpt":"Large Language Models (LLMs) assist in specialized tasks but struggle to align with evolving domain knowledge without costly fine-tuning. Domain knowledge consists of: Knowledge: Immutable facts (e.g., 'A stone is solid') and generally accepted principles (e.g., ethical standards); Refined Memory: Evolving insights shaped by business needs and real-world changes. However, a significant gap often exists between a domain expert's deep, nuanced understanding and the system's domain knowledge, which can hinder accurate information retrieval and application. Our Memory-Augmented Refinement of Knowl"},"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":"2505.05177","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2025-05-08T12:28:00Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"7aea3c4b609f9c271b7109e495254d7eea71c8dc9a0c2ea50ab2b7d2a7ff8497","abstract_canon_sha256":"daa44974ec3c122f3bc91f3eccaa5533893b962740ba9356e86689e4816bfc95"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:00:20.236322Z","signature_b64":"bmETiivWT1O+39ovdKn5nNZ229E4fM6MZRKTvK+0bw4F4X+wguAe3fuJQRpTC9UiKtZEtM+UiLFCsG7SO1PZBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"274c2e291cff398ba3e6cac8b87cd52d6bbc09df68c0077fd7a2d7fcdf6fa558","last_reissued_at":"2026-07-05T11:00:20.235794Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:00:20.235794Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"MARK: Memory Augmented Refinement of Knowledge","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.AI","authors_text":"Anish Ganguli, Debleena Banerjee, Prabal Deb","submitted_at":"2025-05-08T12:28:00Z","abstract_excerpt":"Large Language Models (LLMs) assist in specialized tasks but struggle to align with evolving domain knowledge without costly fine-tuning. Domain knowledge consists of: Knowledge: Immutable facts (e.g., 'A stone is solid') and generally accepted principles (e.g., ethical standards); Refined Memory: Evolving insights shaped by business needs and real-world changes. However, a significant gap often exists between a domain expert's deep, nuanced understanding and the system's domain knowledge, which can hinder accurate information retrieval and application. Our Memory-Augmented Refinement of Knowl"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.05177","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/2505.05177/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":"2505.05177","created_at":"2026-07-05T11:00:20.235855+00:00"},{"alias_kind":"arxiv_version","alias_value":"2505.05177v1","created_at":"2026-07-05T11:00:20.235855+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.05177","created_at":"2026-07-05T11:00:20.235855+00:00"},{"alias_kind":"pith_short_12","alias_value":"E5GC4KI4744Y","created_at":"2026-07-05T11:00:20.235855+00:00"},{"alias_kind":"pith_short_16","alias_value":"E5GC4KI4744YXI7G","created_at":"2026-07-05T11:00:20.235855+00:00"},{"alias_kind":"pith_short_8","alias_value":"E5GC4KI4","created_at":"2026-07-05T11:00:20.235855+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2512.10696","citing_title":"Remember Me, Refine Me: A Dynamic Procedural Memory Framework for Experience-Driven Agent Evolution","ref_index":3,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/E5GC4KI4744YXI7GZLELQ7GVFV","json":"https://pith.science/pith/E5GC4KI4744YXI7GZLELQ7GVFV.json","graph_json":"https://pith.science/api/pith-number/E5GC4KI4744YXI7GZLELQ7GVFV/graph.json","events_json":"https://pith.science/api/pith-number/E5GC4KI4744YXI7GZLELQ7GVFV/events.json","paper":"https://pith.science/paper/E5GC4KI4"},"agent_actions":{"view_html":"https://pith.science/pith/E5GC4KI4744YXI7GZLELQ7GVFV","download_json":"https://pith.science/pith/E5GC4KI4744YXI7GZLELQ7GVFV.json","view_paper":"https://pith.science/paper/E5GC4KI4","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2505.05177&json=true","fetch_graph":"https://pith.science/api/pith-number/E5GC4KI4744YXI7GZLELQ7GVFV/graph.json","fetch_events":"https://pith.science/api/pith-number/E5GC4KI4744YXI7GZLELQ7GVFV/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/E5GC4KI4744YXI7GZLELQ7GVFV/action/timestamp_anchor","attest_storage":"https://pith.science/pith/E5GC4KI4744YXI7GZLELQ7GVFV/action/storage_attestation","attest_author":"https://pith.science/pith/E5GC4KI4744YXI7GZLELQ7GVFV/action/author_attestation","sign_citation":"https://pith.science/pith/E5GC4KI4744YXI7GZLELQ7GVFV/action/citation_signature","submit_replication":"https://pith.science/pith/E5GC4KI4744YXI7GZLELQ7GVFV/action/replication_record"}},"created_at":"2026-07-05T11:00:20.235855+00:00","updated_at":"2026-07-05T11:00:20.235855+00:00"}