{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:KB6AIX3LJ4AOK2WNZNOCX4NN42","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":"9105eb8dea30567b7a0360242991991a7b17d7942ec8228fc9fda5d9ae7dca10","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2026-05-15T15:18:12Z","title_canon_sha256":"4d97cba6865a67c6780932904c6da2767642a3c44cd0bdd1a2a68ee91dd86092"},"schema_version":"1.0","source":{"id":"2605.16048","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.16048","created_at":"2026-05-20T00:01:50Z"},{"alias_kind":"arxiv_version","alias_value":"2605.16048v1","created_at":"2026-05-20T00:01:50Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.16048","created_at":"2026-05-20T00:01:50Z"},{"alias_kind":"pith_short_12","alias_value":"KB6AIX3LJ4AO","created_at":"2026-05-20T00:01:50Z"},{"alias_kind":"pith_short_16","alias_value":"KB6AIX3LJ4AOK2WN","created_at":"2026-05-20T00:01:50Z"},{"alias_kind":"pith_short_8","alias_value":"KB6AIX3L","created_at":"2026-05-20T00:01:50Z"}],"graph_snapshots":[{"event_id":"sha256:32944592fa0db55e2dae0f3cb4dc8b9ee00d180ab844859b559a996da6d0ed9b","target":"graph","created_at":"2026-05-20T00:01:50Z","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":[{"findings_count":0,"name":"ai_meta_artifact","ran_at":"2026-05-19T17:33:41.556824Z","status":"skipped","version":"1.0.0"},{"findings_count":0,"name":"claim_evidence","ran_at":"2026-05-19T16:41:55.530775Z","status":"completed","version":"1.0.0"}],"endpoint":"/pith/2605.16048/integrity.json","findings":[],"snapshot_sha256":"9e719061d769c3ce68e0cb5f7679ad22bc9c9c68ae6867fe5dd7b5730b12fae1","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"State Space Models (SSMs) are inherently recurrent along the sequence dimension, yet depth-recurrence - reusing the same block repeatedly across layers, as recently applied in looped transformers - has not been explored in this model family. We show that a looped SSM with $k$ parameters iterated $L$ times consistently closely matches or outperforms a standard SSM with $k \\cdot L$ independent parameters across four architectures (LRU, S5, LinOSS, LrcSSM) and six time series classification benchmarks, despite operating within a strictly smaller hypothesis space, as we formally establish. Since t","authors_text":"Daniela Rus, M\\'onika Farsang, Radu Grosu, Ramin Hasani","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2026-05-15T15:18:12Z","title":"Looped SSMs: Depth-Recurrence and Input Reshaping for Time Series Classification"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.16048","kind":"arxiv","version":1},"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":"sha256:00c2695c920ed8755a4e28256892b8c8809df8138d7d71e981408cc765c4d3ee","target":"record","created_at":"2026-05-20T00:01:50Z","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":"9105eb8dea30567b7a0360242991991a7b17d7942ec8228fc9fda5d9ae7dca10","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2026-05-15T15:18:12Z","title_canon_sha256":"4d97cba6865a67c6780932904c6da2767642a3c44cd0bdd1a2a68ee91dd86092"},"schema_version":"1.0","source":{"id":"2605.16048","kind":"arxiv","version":1}},"canonical_sha256":"507c045f6b4f00e56acdcb5c2bf1ade691bd93b97b44449a8d08f212024db309","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"507c045f6b4f00e56acdcb5c2bf1ade691bd93b97b44449a8d08f212024db309","first_computed_at":"2026-05-20T00:01:50.621007Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-20T00:01:50.621007Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"8pLyFk3cve6KE8243YHNOtWxmQUuUYGBq4e8TLa7rPPAG5lk9KKGoZKSPdTV6Mk85maBc3BsMusaa83+ijlRDA==","signature_status":"signed_v1","signed_at":"2026-05-20T00:01:50.621589Z","signed_message":"canonical_sha256_bytes"},"source_id":"2605.16048","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:00c2695c920ed8755a4e28256892b8c8809df8138d7d71e981408cc765c4d3ee","sha256:32944592fa0db55e2dae0f3cb4dc8b9ee00d180ab844859b559a996da6d0ed9b"],"state_sha256":"a54279244f5320a1987e13f5b4e0aa69eb12d37d9fa740690d3213e91c0eeb35"}