{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:M5VNWOCU5TPKCTD45KX2J4TCF7","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":"87b3e57bc9b2e368ca6b372b286e041dec6d86aabbba29d4f594e970a244459b","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-10-23T13:20:57Z","title_canon_sha256":"3714835707f9c7f78e4f216785ad9b5941f6db0aa70d07a6a899df2192636d0e"},"schema_version":"1.0","source":{"id":"2510.20535","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2510.20535","created_at":"2026-07-30T01:19:14Z"},{"alias_kind":"arxiv_version","alias_value":"2510.20535v2","created_at":"2026-07-30T01:19:14Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2510.20535","created_at":"2026-07-30T01:19:14Z"},{"alias_kind":"pith_short_12","alias_value":"M5VNWOCU5TPK","created_at":"2026-07-30T01:19:14Z"},{"alias_kind":"pith_short_16","alias_value":"M5VNWOCU5TPKCTD4","created_at":"2026-07-30T01:19:14Z"},{"alias_kind":"pith_short_8","alias_value":"M5VNWOCU","created_at":"2026-07-30T01:19:14Z"}],"graph_snapshots":[{"event_id":"sha256:655c213761344e0954a543b65059cd960a53f5cb9224c38ad0fd300fb621e14b","target":"graph","created_at":"2026-07-30T01:19:14Z","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/2510.20535/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Recent techniques such as retrieval-augmented generation or chain-of-thought reasoning have led to longer contexts and increased inference costs. Context compression techniques can reduce these costs, but the most effective approaches require fine-tuning the target model or even modifying its architecture. This can degrade its general abilities when not used for this specific purpose. Here we explore an alternative approach: an encoder that compresses the context into continuous representations which replace token embeddings in decoder LLMs. First, we perform a systematic study of training str","authors_text":"Edouard Grave, Hippolyte Pilchen, Patrick P\\'erez","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-10-23T13:20:57Z","title":"ARC-Encoder: learning compressed text representations for large language models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2510.20535","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":"sha256:8c9aae07e0d0761e596978685949474d2bda468a94ccaad209fe3fe44027adf5","target":"record","created_at":"2026-07-30T01:19:14Z","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":"87b3e57bc9b2e368ca6b372b286e041dec6d86aabbba29d4f594e970a244459b","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-10-23T13:20:57Z","title_canon_sha256":"3714835707f9c7f78e4f216785ad9b5941f6db0aa70d07a6a899df2192636d0e"},"schema_version":"1.0","source":{"id":"2510.20535","kind":"arxiv","version":2}},"canonical_sha256":"676adb3854ecdea14c7ceaafa4f2622fd59538be4e956b68fc67b62d4bd9ee20","receipt":{"builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"676adb3854ecdea14c7ceaafa4f2622fd59538be4e956b68fc67b62d4bd9ee20","first_computed_at":"2026-07-30T01:19:14.822044Z","kind":"pith_receipt","last_reissued_at":"2026-07-30T01:19:14.822044Z","receipt_version":"0.3","signature_status":"unsigned_v0"},"source_id":"2510.20535","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:8c9aae07e0d0761e596978685949474d2bda468a94ccaad209fe3fe44027adf5","sha256:655c213761344e0954a543b65059cd960a53f5cb9224c38ad0fd300fb621e14b"],"state_sha256":"9ccd37422aebe88fb193b23e382dbe1a0b75d7e7aa265bba98682c5d1ad1b206"}