{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:MLE5FI4NFAD74EDBMXNAZXKQN5","short_pith_number":"pith:MLE5FI4N","canonical_record":{"source":{"id":"2605.28919","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-05-27T17:59:14Z","cross_cats_sorted":["cs.AI","cs.CL"],"title_canon_sha256":"5736f90218cc82e7cd66b87a15126e6cc678c5c9c0457e84bab4de57e39dc4f4","abstract_canon_sha256":"a39264b558f38e9a0f1983bdd5d68e1a520c7458f8fd8cb2f3e5ae1c58e63713"},"schema_version":"1.0"},"canonical_sha256":"62c9d2a38d2807fe106165da0cdd506f7b65120f070f17e060593f9ce21aeed0","source":{"kind":"arxiv","id":"2605.28919","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.28919","created_at":"2026-05-29T00:04:16Z"},{"alias_kind":"arxiv_version","alias_value":"2605.28919v1","created_at":"2026-05-29T00:04:16Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.28919","created_at":"2026-05-29T00:04:16Z"},{"alias_kind":"pith_short_12","alias_value":"MLE5FI4NFAD7","created_at":"2026-05-29T00:04:16Z"},{"alias_kind":"pith_short_16","alias_value":"MLE5FI4NFAD74EDB","created_at":"2026-05-29T00:04:16Z"},{"alias_kind":"pith_short_8","alias_value":"MLE5FI4N","created_at":"2026-05-29T00:04:16Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:MLE5FI4NFAD74EDBMXNAZXKQN5","target":"record","payload":{"canonical_record":{"source":{"id":"2605.28919","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-05-27T17:59:14Z","cross_cats_sorted":["cs.AI","cs.CL"],"title_canon_sha256":"5736f90218cc82e7cd66b87a15126e6cc678c5c9c0457e84bab4de57e39dc4f4","abstract_canon_sha256":"a39264b558f38e9a0f1983bdd5d68e1a520c7458f8fd8cb2f3e5ae1c58e63713"},"schema_version":"1.0"},"canonical_sha256":"62c9d2a38d2807fe106165da0cdd506f7b65120f070f17e060593f9ce21aeed0","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-29T00:04:16.548347Z","signature_b64":"Ffpi5XBvEQUvx02pmATnvfX904JiHzi1OIOrQmcHSmk8TzQYmiiFQTrklvpJCgJ4cCbZa4+3/9HWW13JoL+rBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"62c9d2a38d2807fe106165da0cdd506f7b65120f070f17e060593f9ce21aeed0","last_reissued_at":"2026-05-29T00:04:16.547689Z","signature_status":"signed_v1","first_computed_at":"2026-05-29T00:04:16.547689Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2605.28919","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-29T00:04:16Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Hce8Ytyx5APuZAiOBbK4r6QvOpIj1j82kCv4rWhaiMpJOdhoJJ89ttHXdYVRpBVq28dzRAzWAxh19OSlMMxtDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-29T21:56:54.539611Z"},"content_sha256":"bdab423b6b454878fde35278efbe0979b56b1093375d1025cb845600c905ad59","schema_version":"1.0","event_id":"sha256:bdab423b6b454878fde35278efbe0979b56b1093375d1025cb845600c905ad59"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:MLE5FI4NFAD74EDBMXNAZXKQN5","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"CosmicFish-HRM: Adaptive Reasoning via Hierarchical Recurrent Mechanisms in Compact Language Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CL"],"primary_cat":"cs.LG","authors_text":"Venkat Akhil Lakkapragada","submitted_at":"2026-05-27T17:59:14Z","abstract_excerpt":"Large language models have achieved strong reasoning capabilities, though often at the cost of massive parameter counts and expensive inference. In this work, we explore a different direction: adaptive reasoning depth in compact language models. We present CosmicFish-HRM, a compact language model built around a Hierarchical Reasoning Module (HRM) that dynamically allocates computational effort during inference. Instead of applying fixed computation to every input, the model iterates through high-level and low-level reasoning cycles and learns when to halt based on input complexity. CosmicFish-"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.28919","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/2605.28919/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-29T00:04:16Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"pBf771PdEDMDiD8rDuet+lgUpJrfeT6YqDEe5YFfq0na5v4mS7cWQkQmlSvqOXe3JnUEOouybFtaAdgkZL2rDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-29T21:56:54.539983Z"},"content_sha256":"4b433370cf67ba41b1cc58ffd6c3815dc56d3f4be3c83f8331168707538772cf","schema_version":"1.0","event_id":"sha256:4b433370cf67ba41b1cc58ffd6c3815dc56d3f4be3c83f8331168707538772cf"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/MLE5FI4NFAD74EDBMXNAZXKQN5/bundle.json","state_url":"https://pith.science/pith/MLE5FI4NFAD74EDBMXNAZXKQN5/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/MLE5FI4NFAD74EDBMXNAZXKQN5/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-06-29T21:56:54Z","links":{"resolver":"https://pith.science/pith/MLE5FI4NFAD74EDBMXNAZXKQN5","bundle":"https://pith.science/pith/MLE5FI4NFAD74EDBMXNAZXKQN5/bundle.json","state":"https://pith.science/pith/MLE5FI4NFAD74EDBMXNAZXKQN5/state.json","well_known_bundle":"https://pith.science/.well-known/pith/MLE5FI4NFAD74EDBMXNAZXKQN5/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:MLE5FI4NFAD74EDBMXNAZXKQN5","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":"a39264b558f38e9a0f1983bdd5d68e1a520c7458f8fd8cb2f3e5ae1c58e63713","cross_cats_sorted":["cs.AI","cs.CL"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-05-27T17:59:14Z","title_canon_sha256":"5736f90218cc82e7cd66b87a15126e6cc678c5c9c0457e84bab4de57e39dc4f4"},"schema_version":"1.0","source":{"id":"2605.28919","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.28919","created_at":"2026-05-29T00:04:16Z"},{"alias_kind":"arxiv_version","alias_value":"2605.28919v1","created_at":"2026-05-29T00:04:16Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.28919","created_at":"2026-05-29T00:04:16Z"},{"alias_kind":"pith_short_12","alias_value":"MLE5FI4NFAD7","created_at":"2026-05-29T00:04:16Z"},{"alias_kind":"pith_short_16","alias_value":"MLE5FI4NFAD74EDB","created_at":"2026-05-29T00:04:16Z"},{"alias_kind":"pith_short_8","alias_value":"MLE5FI4N","created_at":"2026-05-29T00:04:16Z"}],"graph_snapshots":[{"event_id":"sha256:4b433370cf67ba41b1cc58ffd6c3815dc56d3f4be3c83f8331168707538772cf","target":"graph","created_at":"2026-05-29T00:04:16Z","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/2605.28919/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Large language models have achieved strong reasoning capabilities, though often at the cost of massive parameter counts and expensive inference. In this work, we explore a different direction: adaptive reasoning depth in compact language models. We present CosmicFish-HRM, a compact language model built around a Hierarchical Reasoning Module (HRM) that dynamically allocates computational effort during inference. Instead of applying fixed computation to every input, the model iterates through high-level and low-level reasoning cycles and learns when to halt based on input complexity. CosmicFish-","authors_text":"Venkat Akhil Lakkapragada","cross_cats":["cs.AI","cs.CL"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-05-27T17:59:14Z","title":"CosmicFish-HRM: Adaptive Reasoning via Hierarchical Recurrent Mechanisms in Compact Language Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.28919","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:bdab423b6b454878fde35278efbe0979b56b1093375d1025cb845600c905ad59","target":"record","created_at":"2026-05-29T00:04:16Z","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":"a39264b558f38e9a0f1983bdd5d68e1a520c7458f8fd8cb2f3e5ae1c58e63713","cross_cats_sorted":["cs.AI","cs.CL"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-05-27T17:59:14Z","title_canon_sha256":"5736f90218cc82e7cd66b87a15126e6cc678c5c9c0457e84bab4de57e39dc4f4"},"schema_version":"1.0","source":{"id":"2605.28919","kind":"arxiv","version":1}},"canonical_sha256":"62c9d2a38d2807fe106165da0cdd506f7b65120f070f17e060593f9ce21aeed0","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"62c9d2a38d2807fe106165da0cdd506f7b65120f070f17e060593f9ce21aeed0","first_computed_at":"2026-05-29T00:04:16.547689Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-29T00:04:16.547689Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Ffpi5XBvEQUvx02pmATnvfX904JiHzi1OIOrQmcHSmk8TzQYmiiFQTrklvpJCgJ4cCbZa4+3/9HWW13JoL+rBQ==","signature_status":"signed_v1","signed_at":"2026-05-29T00:04:16.548347Z","signed_message":"canonical_sha256_bytes"},"source_id":"2605.28919","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:bdab423b6b454878fde35278efbe0979b56b1093375d1025cb845600c905ad59","sha256:4b433370cf67ba41b1cc58ffd6c3815dc56d3f4be3c83f8331168707538772cf"],"state_sha256":"a04ec8999a141d3aecafa338c758e446623334c6a2d85c52a05eda38a7b1fbfa"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"fXykt8Vz2SciTXcpN3YtRA84wj3zRzRVq9d/tKOYcQ89dM/DXhJ671AWx7rewRfvAbhdpz6mrVJmM/ZmZzZhBw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-29T21:56:54.542214Z","bundle_sha256":"fc76ca971a57ae6a0d149ee550e4c3a4d2c0b6d4fb66ee93aabd39069fa970dc"}}