{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:2GZ2IA3KYZ6LG6RKOCHEBX67QE","short_pith_number":"pith:2GZ2IA3K","schema_version":"1.0","canonical_sha256":"d1b3a4036ac67cb37a2a708e40dfdf8133e6151dea6476c7cd2ae387b07aa094","source":{"kind":"arxiv","id":"2607.17486","version":1},"attestation_state":"computed","paper":{"title":"SALT: Salience-Aware Lexical Trie for Long-Context Compression","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.PF","authors_text":"Hyunjin Yi, Joydhriti Choudhury, Oteo Mamo, Shangqian Gao, Weikuan Yu","submitted_at":"2026-07-20T02:20:52Z","abstract_excerpt":"As large language models (LLMs) process increasingly longer prompts, computation and KV-cache memory costs have emerged as major bottlenecks in inference systems. Existing input-level prompt compression methods address this, but rank each sentence by a scalar relevance score, treating the document as an unstructured pool of words and sentences. Under tight budgets, this causes theme collapse, where the dominant theme(s) of a document consumes the budget, discarding less-frequent yet task-relevant themes. Preserving thematic coverage instead requires allocating the budget across recurring theme"},"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":"2607.17486","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.PF","submitted_at":"2026-07-20T02:20:52Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"2ee746c339d3e2447772b12c5245c00f318cfe7c6aeebc07198309a7d6575302","abstract_canon_sha256":"a70c4d732678bdf6b7370bb6af6d93a0045eb2c1f094e6a717e2cc492de96a57"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-21T01:21:48.052688Z","signature_b64":"KHEB6bXeIQT4lRkg3ehTLn56R0VE/2ynnbWVAu4Gwas/bk8+frWByb4wU2kjyc6G4/gip9agyGCLEsSZXPpsAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d1b3a4036ac67cb37a2a708e40dfdf8133e6151dea6476c7cd2ae387b07aa094","last_reissued_at":"2026-07-21T01:21:48.051770Z","signature_status":"signed_v1","first_computed_at":"2026-07-21T01:21:48.051770Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"SALT: Salience-Aware Lexical Trie for Long-Context Compression","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.PF","authors_text":"Hyunjin Yi, Joydhriti Choudhury, Oteo Mamo, Shangqian Gao, Weikuan Yu","submitted_at":"2026-07-20T02:20:52Z","abstract_excerpt":"As large language models (LLMs) process increasingly longer prompts, computation and KV-cache memory costs have emerged as major bottlenecks in inference systems. Existing input-level prompt compression methods address this, but rank each sentence by a scalar relevance score, treating the document as an unstructured pool of words and sentences. Under tight budgets, this causes theme collapse, where the dominant theme(s) of a document consumes the budget, discarding less-frequent yet task-relevant themes. Preserving thematic coverage instead requires allocating the budget across recurring theme"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.17486","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/2607.17486/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":"2607.17486","created_at":"2026-07-21T01:21:48.052206+00:00"},{"alias_kind":"arxiv_version","alias_value":"2607.17486v1","created_at":"2026-07-21T01:21:48.052206+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.17486","created_at":"2026-07-21T01:21:48.052206+00:00"},{"alias_kind":"pith_short_12","alias_value":"2GZ2IA3KYZ6L","created_at":"2026-07-21T01:21:48.052206+00:00"},{"alias_kind":"pith_short_16","alias_value":"2GZ2IA3KYZ6LG6RK","created_at":"2026-07-21T01:21:48.052206+00:00"},{"alias_kind":"pith_short_8","alias_value":"2GZ2IA3K","created_at":"2026-07-21T01:21:48.052206+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/2GZ2IA3KYZ6LG6RKOCHEBX67QE","json":"https://pith.science/pith/2GZ2IA3KYZ6LG6RKOCHEBX67QE.json","graph_json":"https://pith.science/api/pith-number/2GZ2IA3KYZ6LG6RKOCHEBX67QE/graph.json","events_json":"https://pith.science/api/pith-number/2GZ2IA3KYZ6LG6RKOCHEBX67QE/events.json","paper":"https://pith.science/paper/2GZ2IA3K"},"agent_actions":{"view_html":"https://pith.science/pith/2GZ2IA3KYZ6LG6RKOCHEBX67QE","download_json":"https://pith.science/pith/2GZ2IA3KYZ6LG6RKOCHEBX67QE.json","view_paper":"https://pith.science/paper/2GZ2IA3K","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2607.17486&json=true","fetch_graph":"https://pith.science/api/pith-number/2GZ2IA3KYZ6LG6RKOCHEBX67QE/graph.json","fetch_events":"https://pith.science/api/pith-number/2GZ2IA3KYZ6LG6RKOCHEBX67QE/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/2GZ2IA3KYZ6LG6RKOCHEBX67QE/action/timestamp_anchor","attest_storage":"https://pith.science/pith/2GZ2IA3KYZ6LG6RKOCHEBX67QE/action/storage_attestation","attest_author":"https://pith.science/pith/2GZ2IA3KYZ6LG6RKOCHEBX67QE/action/author_attestation","sign_citation":"https://pith.science/pith/2GZ2IA3KYZ6LG6RKOCHEBX67QE/action/citation_signature","submit_replication":"https://pith.science/pith/2GZ2IA3KYZ6LG6RKOCHEBX67QE/action/replication_record"}},"created_at":"2026-07-21T01:21:48.052206+00:00","updated_at":"2026-07-21T01:21:48.052206+00:00"}