{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:KNUREQJPUVESFQ3RL4EI3GXNXO","short_pith_number":"pith:KNUREQJP","schema_version":"1.0","canonical_sha256":"536912412fa54922c3715f088d9aedbb9a3008ec492eb8d76cb5fe1c9d187903","source":{"kind":"arxiv","id":"2404.16112","version":1},"attestation_state":"computed","paper":{"title":"Mamba-360: Survey of State Space Models as Transformer Alternative for Long Sequence Modelling: Methods, Applications, and Challenges","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.AI","cs.CV","cs.MM","eess.IV"],"primary_cat":"cs.LG","authors_text":"Badri Narayana Patro, Vijay Srinivas Agneeswaran","submitted_at":"2024-04-24T18:10:31Z","abstract_excerpt":"Sequence modeling is a crucial area across various domains, including Natural Language Processing (NLP), speech recognition, time series forecasting, music generation, and bioinformatics. Recurrent Neural Networks (RNNs) and Long Short Term Memory Networks (LSTMs) have historically dominated sequence modeling tasks like Machine Translation, Named Entity Recognition (NER), etc. However, the advancement of transformers has led to a shift in this paradigm, given their superior performance. Yet, transformers suffer from $O(N^2)$ attention complexity and challenges in handling inductive bias. Sever"},"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":"2404.16112","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2024-04-24T18:10:31Z","cross_cats_sorted":["cs.AI","cs.CV","cs.MM","eess.IV"],"title_canon_sha256":"cab3d7f755a8d13f29997b167182ab82a45424735e2db10cc9e19b0c2e6742f2","abstract_canon_sha256":"b0ece713ddcc9f9f18cfb7b14a2e8069c71932fd36fc5b50bc1828abd2a948fa"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:12:01.266134Z","signature_b64":"N0Y4OrN205/pWGrJbNXrIHDalZd4TxjERLU16uG+/ZKxENezn9QyVG9Asx4lqMK2Poymfe3zUm0H+wSSpwDADg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"536912412fa54922c3715f088d9aedbb9a3008ec492eb8d76cb5fe1c9d187903","last_reissued_at":"2026-07-05T08:12:01.265677Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:12:01.265677Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Mamba-360: Survey of State Space Models as Transformer Alternative for Long Sequence Modelling: Methods, Applications, and Challenges","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.AI","cs.CV","cs.MM","eess.IV"],"primary_cat":"cs.LG","authors_text":"Badri Narayana Patro, Vijay Srinivas Agneeswaran","submitted_at":"2024-04-24T18:10:31Z","abstract_excerpt":"Sequence modeling is a crucial area across various domains, including Natural Language Processing (NLP), speech recognition, time series forecasting, music generation, and bioinformatics. Recurrent Neural Networks (RNNs) and Long Short Term Memory Networks (LSTMs) have historically dominated sequence modeling tasks like Machine Translation, Named Entity Recognition (NER), etc. However, the advancement of transformers has led to a shift in this paradigm, given their superior performance. Yet, transformers suffer from $O(N^2)$ attention complexity and challenges in handling inductive bias. Sever"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2404.16112","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/2404.16112/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":"2404.16112","created_at":"2026-07-05T08:12:01.265739+00:00"},{"alias_kind":"arxiv_version","alias_value":"2404.16112v1","created_at":"2026-07-05T08:12:01.265739+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2404.16112","created_at":"2026-07-05T08:12:01.265739+00:00"},{"alias_kind":"pith_short_12","alias_value":"KNUREQJPUVES","created_at":"2026-07-05T08:12:01.265739+00:00"},{"alias_kind":"pith_short_16","alias_value":"KNUREQJPUVESFQ3R","created_at":"2026-07-05T08:12:01.265739+00:00"},{"alias_kind":"pith_short_8","alias_value":"KNUREQJP","created_at":"2026-07-05T08:12:01.265739+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":5,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2408.01129","citing_title":"A Survey of Mamba","ref_index":143,"is_internal_anchor":false},{"citing_arxiv_id":"2503.18970","citing_title":"Advancing Intelligent Sequence Modeling: Evolution, Trade-offs, and Applications of State-Space Architectures from S4 to Mamba","ref_index":1,"is_internal_anchor":false},{"citing_arxiv_id":"2604.28161","citing_title":"RopeDreamer: A Kinematic Recurrent State Space Model for Dynamics of Flexible Deformable Linear Objects","ref_index":25,"is_internal_anchor":false},{"citing_arxiv_id":"2604.08050","citing_title":"ABMAMBA: Multimodal Large Language Model with Aligned Hierarchical Bidirectional Scan for Efficient Video Captioning","ref_index":46,"is_internal_anchor":false},{"citing_arxiv_id":"2604.14724","citing_title":"HAMSA: Scanning-Free Vision State Space Models via SpectralPulseNet","ref_index":42,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/KNUREQJPUVESFQ3RL4EI3GXNXO","json":"https://pith.science/pith/KNUREQJPUVESFQ3RL4EI3GXNXO.json","graph_json":"https://pith.science/api/pith-number/KNUREQJPUVESFQ3RL4EI3GXNXO/graph.json","events_json":"https://pith.science/api/pith-number/KNUREQJPUVESFQ3RL4EI3GXNXO/events.json","paper":"https://pith.science/paper/KNUREQJP"},"agent_actions":{"view_html":"https://pith.science/pith/KNUREQJPUVESFQ3RL4EI3GXNXO","download_json":"https://pith.science/pith/KNUREQJPUVESFQ3RL4EI3GXNXO.json","view_paper":"https://pith.science/paper/KNUREQJP","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2404.16112&json=true","fetch_graph":"https://pith.science/api/pith-number/KNUREQJPUVESFQ3RL4EI3GXNXO/graph.json","fetch_events":"https://pith.science/api/pith-number/KNUREQJPUVESFQ3RL4EI3GXNXO/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/KNUREQJPUVESFQ3RL4EI3GXNXO/action/timestamp_anchor","attest_storage":"https://pith.science/pith/KNUREQJPUVESFQ3RL4EI3GXNXO/action/storage_attestation","attest_author":"https://pith.science/pith/KNUREQJPUVESFQ3RL4EI3GXNXO/action/author_attestation","sign_citation":"https://pith.science/pith/KNUREQJPUVESFQ3RL4EI3GXNXO/action/citation_signature","submit_replication":"https://pith.science/pith/KNUREQJPUVESFQ3RL4EI3GXNXO/action/replication_record"}},"created_at":"2026-07-05T08:12:01.265739+00:00","updated_at":"2026-07-05T08:12:01.265739+00:00"}