{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:E4EN5GUOCGDEPMKWBRFBZ65XDR","short_pith_number":"pith:E4EN5GUO","schema_version":"1.0","canonical_sha256":"2708de9a8e118647b1560c4a1cfbb71c48dcac3004b96088ceca1a39c5a127bb","source":{"kind":"arxiv","id":"2501.00504","version":2},"attestation_state":"computed","paper":{"title":"The Algonauts Project 2025 Challenge: How the Human Brain Makes Sense of Multimodal Movies","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"q-bio.NC","authors_text":"Alessandro T. Gifford, Aude Oliva, Basile Pinsard, Domenic Bersch, Gemma Roig, Julie Boyle, Lune Bellec, Marie St-Laurent, Radoslaw M. Cichy","submitted_at":"2024-12-31T15:28:31Z","abstract_excerpt":"There is growing symbiosis between artificial and biological intelligence sciences: neural principles inspire new intelligent machines, which are in turn used to advance our theoretical understanding of the brain. To promote further collaboration between biological and artificial intelligence researchers, we introduce the 2025 edition of the Algonauts Project challenge: How the Human Brain Makes Sense of Multimodal Movies (https://algonautsproject.com/). In collaboration with the Courtois Project on Neuronal Modelling (CNeuroMod), this edition aims to bring forth a new generation of brain enco"},"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":"2501.00504","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"q-bio.NC","submitted_at":"2024-12-31T15:28:31Z","cross_cats_sorted":[],"title_canon_sha256":"3b1a815b3c1b312d08fb03ce65df57159b9205e8fd49a47c3f3730cfeeddfec2","abstract_canon_sha256":"257e9404033d61ff5a41b5d157b6eea69ba8f397f0fb47bf87fbf99118025f77"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:57:24.839268Z","signature_b64":"79SEWfTpfxYgEfGoNH690w2uCFHOCGA28WjJ3i7dpO65MDd3BBiKEo2k4rNo1lt4vILBA8O8MJGlPmLeSln/Cw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2708de9a8e118647b1560c4a1cfbb71c48dcac3004b96088ceca1a39c5a127bb","last_reissued_at":"2026-07-05T09:57:24.838845Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:57:24.838845Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"The Algonauts Project 2025 Challenge: How the Human Brain Makes Sense of Multimodal Movies","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"q-bio.NC","authors_text":"Alessandro T. Gifford, Aude Oliva, Basile Pinsard, Domenic Bersch, Gemma Roig, Julie Boyle, Lune Bellec, Marie St-Laurent, Radoslaw M. Cichy","submitted_at":"2024-12-31T15:28:31Z","abstract_excerpt":"There is growing symbiosis between artificial and biological intelligence sciences: neural principles inspire new intelligent machines, which are in turn used to advance our theoretical understanding of the brain. To promote further collaboration between biological and artificial intelligence researchers, we introduce the 2025 edition of the Algonauts Project challenge: How the Human Brain Makes Sense of Multimodal Movies (https://algonautsproject.com/). In collaboration with the Courtois Project on Neuronal Modelling (CNeuroMod), this edition aims to bring forth a new generation of brain enco"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.00504","kind":"arxiv","version":2},"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/2501.00504/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":"2501.00504","created_at":"2026-07-05T09:57:24.838903+00:00"},{"alias_kind":"arxiv_version","alias_value":"2501.00504v2","created_at":"2026-07-05T09:57:24.838903+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.00504","created_at":"2026-07-05T09:57:24.838903+00:00"},{"alias_kind":"pith_short_12","alias_value":"E4EN5GUOCGDE","created_at":"2026-07-05T09:57:24.838903+00:00"},{"alias_kind":"pith_short_16","alias_value":"E4EN5GUOCGDEPMKW","created_at":"2026-07-05T09:57:24.838903+00:00"},{"alias_kind":"pith_short_8","alias_value":"E4EN5GUO","created_at":"2026-07-05T09:57:24.838903+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":4,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2607.05171","citing_title":"RABBiT: Rapidly adaptive BOLD foundation model via brain-tuning for accurate zero-shot and few-shot prediction of speech-elicited responses in the brain","ref_index":12,"is_internal_anchor":true},{"citing_arxiv_id":"2605.29850","citing_title":"MIRAGE: Adaptive Multimodal Gating for Whole-Brain fMRI Encoding","ref_index":14,"is_internal_anchor":false},{"citing_arxiv_id":"2605.08495","citing_title":"NeuralBench: A Unifying Framework to Benchmark NeuroAI Models","ref_index":207,"is_internal_anchor":false},{"citing_arxiv_id":"2605.04326","citing_title":"A foundation model of vision, audition, and language for in-silico neuroscience","ref_index":46,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/E4EN5GUOCGDEPMKWBRFBZ65XDR","json":"https://pith.science/pith/E4EN5GUOCGDEPMKWBRFBZ65XDR.json","graph_json":"https://pith.science/api/pith-number/E4EN5GUOCGDEPMKWBRFBZ65XDR/graph.json","events_json":"https://pith.science/api/pith-number/E4EN5GUOCGDEPMKWBRFBZ65XDR/events.json","paper":"https://pith.science/paper/E4EN5GUO"},"agent_actions":{"view_html":"https://pith.science/pith/E4EN5GUOCGDEPMKWBRFBZ65XDR","download_json":"https://pith.science/pith/E4EN5GUOCGDEPMKWBRFBZ65XDR.json","view_paper":"https://pith.science/paper/E4EN5GUO","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2501.00504&json=true","fetch_graph":"https://pith.science/api/pith-number/E4EN5GUOCGDEPMKWBRFBZ65XDR/graph.json","fetch_events":"https://pith.science/api/pith-number/E4EN5GUOCGDEPMKWBRFBZ65XDR/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/E4EN5GUOCGDEPMKWBRFBZ65XDR/action/timestamp_anchor","attest_storage":"https://pith.science/pith/E4EN5GUOCGDEPMKWBRFBZ65XDR/action/storage_attestation","attest_author":"https://pith.science/pith/E4EN5GUOCGDEPMKWBRFBZ65XDR/action/author_attestation","sign_citation":"https://pith.science/pith/E4EN5GUOCGDEPMKWBRFBZ65XDR/action/citation_signature","submit_replication":"https://pith.science/pith/E4EN5GUOCGDEPMKWBRFBZ65XDR/action/replication_record"}},"created_at":"2026-07-05T09:57:24.838903+00:00","updated_at":"2026-07-05T09:57:24.838903+00:00"}