{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:HH6MRAPLHICKZBGPUDT3AWFQEE","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":"7d4a91d69446c6fc830e95b77703f53d4f713f6c9db7e29ac824898a7289f052","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2026-05-08T13:25:27Z","title_canon_sha256":"96809772eaa38e3a400b81c44488c05ef57c87596756afeaa488b347b1a97941"},"schema_version":"1.0","source":{"id":"2605.07721","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.07721","created_at":"2026-05-20T01:05:15Z"},{"alias_kind":"arxiv_version","alias_value":"2605.07721v2","created_at":"2026-05-20T01:05:15Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.07721","created_at":"2026-05-20T01:05:15Z"},{"alias_kind":"pith_short_12","alias_value":"HH6MRAPLHICK","created_at":"2026-05-20T01:05:15Z"},{"alias_kind":"pith_short_16","alias_value":"HH6MRAPLHICKZBGP","created_at":"2026-05-20T01:05:15Z"},{"alias_kind":"pith_short_8","alias_value":"HH6MRAPL","created_at":"2026-05-20T01:05:15Z"}],"graph_snapshots":[{"event_id":"sha256:85f2e6d4c38aaa6b4e8b56ace73f5703e2e0fb98ae1f2317527e9da27c96ffa9","target":"graph","created_at":"2026-05-20T01:05:15Z","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":4,"items":[{"attestation":"unclaimed","claim_id":"C1","kind":"strongest_claim","source":"verdict.strongest_claim","status":"machine_extracted","text":"MELT achieves constant-memory iterative reasoning without sacrificing LoopLM performance, using only a lightweight post-training procedure."},{"attestation":"unclaimed","claim_id":"C2","kind":"weakest_assumption","source":"verdict.weakest_assumption","status":"machine_extracted","text":"The learnable gating mechanism and the two-phase chunk-wise training (interpolated transition followed by attention-aligned distillation) are sufficient to preserve the original reasoning capabilities of the LoopLM starting model without degradation."},{"attestation":"unclaimed","claim_id":"C3","kind":"one_line_summary","source":"verdict.one_line_summary","status":"machine_extracted","text":"MELT decouples reasoning depth from memory in looped LLMs by sharing a single gated KV cache per layer and using two-phase chunk-wise distillation from Ouro, delivering constant memory use while matching or beating standard LLM performance."},{"attestation":"unclaimed","claim_id":"C4","kind":"headline","source":"verdict.pith_extraction.headline","status":"machine_extracted","text":"MELT shares one KV cache per layer across all reasoning loops and updates it with a learnable gate to keep memory constant."}],"snapshot_sha256":"5a421b0224ef6e5bb7a6013e1fcfdbb8dc30312d26fd9853a4662dafa52fefcf"},"formal_canon":{"evidence_count":2,"snapshot_sha256":"1bcfba55d480c44bd56ea2028e0a21a9622b8624dc957473e7e0045a686e7093"},"integrity":{"available":true,"clean":true,"detectors_run":[{"findings_count":0,"name":"doi_title_agreement","ran_at":"2026-05-19T16:01:18.461449Z","status":"completed","version":"1.0.0"},{"findings_count":0,"name":"doi_compliance","ran_at":"2026-05-19T11:35:08.246842Z","status":"completed","version":"1.0.0"}],"endpoint":"/pith/2605.07721/integrity.json","findings":[],"snapshot_sha256":"7723779eba02c58c015f60f8a8828a9df72e64d6737e545246e1c3996c09a939","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Recurrent LLM architectures have emerged as a promising approach for improving reasoning, as they enable multi-step computation in the embedding space without generating intermediate tokens. Models such as Ouro perform reasoning by iteratively updating internal representations while retaining a standard Key-Value (KV) cache across iterations, causing memory consumption to grow linearly with reasoning depth. Consequently, increasing the number of reasoning iterations can lead to prohibitive memory usage, limiting the practical scalability of such architectures. In this work, we propose Memory-E","authors_text":"Arash Behboodi, Arnau Padres Masdemont, Fabio Valerio Massoli, Jordi Ros-Giralt, Niccol\\`o Grillo, Victor Conchello Vendrell","cross_cats":["cs.AI","cs.LG"],"headline":"MELT shares one KV cache per layer across all reasoning loops and updates it with a learnable gate to keep memory constant.","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2026-05-08T13:25:27Z","title":"Memory-Efficient Looped Transformer: Decoupling Compute from Memory in Looped Language Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.07721","kind":"arxiv","version":2},"verdict":{"created_at":"2026-05-11T02:40:57.957349Z","id":"84dad266-6eb8-4596-8f6f-ac457eae2528","model_set":{"reader":"grok-4.3"},"one_line_summary":"MELT decouples reasoning depth from memory in looped LLMs by sharing a single gated KV cache per layer and using two-phase chunk-wise distillation from Ouro, delivering constant memory use while matching or beating standard LLM performance.","pipeline_version":"pith-pipeline@v0.9.0","pith_extraction_headline":"MELT shares one KV cache per layer across all reasoning loops and updates it with a learnable gate to keep memory constant.","strongest_claim":"MELT achieves constant-memory iterative reasoning without sacrificing LoopLM performance, using only a lightweight post-training procedure.","weakest_assumption":"The learnable gating mechanism and the two-phase chunk-wise training (interpolated transition followed by attention-aligned distillation) are sufficient to preserve the original reasoning capabilities of the LoopLM starting model without degradation."}},"verdict_id":"84dad266-6eb8-4596-8f6f-ac457eae2528"}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:5e89ae2025a337b8103268d5d222f72d4643c9f44acd03e7cd87ae55eb67adb2","target":"record","created_at":"2026-05-20T01:05:15Z","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":"7d4a91d69446c6fc830e95b77703f53d4f713f6c9db7e29ac824898a7289f052","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2026-05-08T13:25:27Z","title_canon_sha256":"96809772eaa38e3a400b81c44488c05ef57c87596756afeaa488b347b1a97941"},"schema_version":"1.0","source":{"id":"2605.07721","kind":"arxiv","version":2}},"canonical_sha256":"39fcc881eb3a04ac84cfa0e7b058b0211bfd53c6382cb41edc465cc19e5705ba","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"39fcc881eb3a04ac84cfa0e7b058b0211bfd53c6382cb41edc465cc19e5705ba","first_computed_at":"2026-05-20T01:05:15.801090Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-20T01:05:15.801090Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"gm0AmXVyBAslICZyqI4LhaQHoqC3VCOGH3tNHM/iAFVKqY5it5w6EE04jvZq65a+14lb3GUt+fIbCW9hT8YFBQ==","signature_status":"signed_v1","signed_at":"2026-05-20T01:05:15.801776Z","signed_message":"canonical_sha256_bytes"},"source_id":"2605.07721","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:5e89ae2025a337b8103268d5d222f72d4643c9f44acd03e7cd87ae55eb67adb2","sha256:85f2e6d4c38aaa6b4e8b56ace73f5703e2e0fb98ae1f2317527e9da27c96ffa9"],"state_sha256":"b579b440cb1797135b808ac4ccc003300caeb6e56e3292f77d40d43196900c5c"}