{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:LHIFZPQB6CSQZPW3FJPZLIJ7CF","short_pith_number":"pith:LHIFZPQB","canonical_record":{"source":{"id":"2502.05164","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-02-07T18:48:25Z","cross_cats_sorted":["cond-mat.dis-nn"],"title_canon_sha256":"aeed6b594b0f8801efe161dbbc45c9202c5183d9079f8de104a1ba9f9d3407e2","abstract_canon_sha256":"e22ecffce09592dbd5b79769ec2e38890f6d7685657860ed564c7e31fefe1457"},"schema_version":"1.0"},"canonical_sha256":"59d05cbe01f0a50cbedb2a5f95a13f1177920c83ad5fa1390c4a9cadbc36d169","source":{"kind":"arxiv","id":"2502.05164","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2502.05164","created_at":"2026-07-05T11:16:54Z"},{"alias_kind":"arxiv_version","alias_value":"2502.05164v2","created_at":"2026-07-05T11:16:54Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.05164","created_at":"2026-07-05T11:16:54Z"},{"alias_kind":"pith_short_12","alias_value":"LHIFZPQB6CSQ","created_at":"2026-07-05T11:16:54Z"},{"alias_kind":"pith_short_16","alias_value":"LHIFZPQB6CSQZPW3","created_at":"2026-07-05T11:16:54Z"},{"alias_kind":"pith_short_8","alias_value":"LHIFZPQB","created_at":"2026-07-05T11:16:54Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:LHIFZPQB6CSQZPW3FJPZLIJ7CF","target":"record","payload":{"canonical_record":{"source":{"id":"2502.05164","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-02-07T18:48:25Z","cross_cats_sorted":["cond-mat.dis-nn"],"title_canon_sha256":"aeed6b594b0f8801efe161dbbc45c9202c5183d9079f8de104a1ba9f9d3407e2","abstract_canon_sha256":"e22ecffce09592dbd5b79769ec2e38890f6d7685657860ed564c7e31fefe1457"},"schema_version":"1.0"},"canonical_sha256":"59d05cbe01f0a50cbedb2a5f95a13f1177920c83ad5fa1390c4a9cadbc36d169","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:16:54.779528Z","signature_b64":"HbJSLfzL/kldROavfneC2FYPZipdB1IYwWyod4WY7yeWJ7u2W04/ZQDTSGbZuGlQH4YvlFoertWdlEtBc6hICg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"59d05cbe01f0a50cbedb2a5f95a13f1177920c83ad5fa1390c4a9cadbc36d169","last_reissued_at":"2026-07-05T11:16:54.779007Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:16:54.779007Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2502.05164","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T11:16:54Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"lUDY8IMSR2Wo2D/N5hzS3HLII9VWHMdGzKsyiETxKDrZ8Vy2jnsZzpWUFr7KIpOCyB6IVPL3p9HOg687Gro2Cw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T18:40:49.210189Z"},"content_sha256":"809ee414a0b8f4dbeb22ff7901a6bb160e12fc495f9ef901bcb64cad030afb1c","schema_version":"1.0","event_id":"sha256:809ee414a0b8f4dbeb22ff7901a6bb160e12fc495f9ef901bcb64cad030afb1c"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:LHIFZPQB6CSQZPW3FJPZLIJ7CF","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"In-context denoising with one-layer transformers: connections between attention and associative memory retrieval","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cond-mat.dis-nn"],"primary_cat":"cs.LG","authors_text":"Alberto Bietti, Anirvan M. Sengupta, Matthew Smart","submitted_at":"2025-02-07T18:48:25Z","abstract_excerpt":"We introduce in-context denoising, a task that refines the connection between attention-based architectures and dense associative memory (DAM) networks, also known as modern Hopfield networks. Using a Bayesian framework, we show theoretically and empirically that certain restricted denoising problems can be solved optimally even by a single-layer transformer. We demonstrate that a trained attention layer processes each denoising prompt by performing a single gradient descent update on a context-aware DAM energy landscape, where context tokens serve as associative memories and the query token a"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.05164","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/2502.05164/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":"2026-07-05T11:16:54Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"7xATnAJgBWKsymLTfQ2cw5GBDrNaKsDu7wQhDfzO4JkgGVYgexWK89TUWvLQYDYNOOO8rB9TJcX363p7ppXLDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T18:40:49.211060Z"},"content_sha256":"d0b33e78fc32c0045c4a7bf48822532684e0d15200b511fe69043488d29ac930","schema_version":"1.0","event_id":"sha256:d0b33e78fc32c0045c4a7bf48822532684e0d15200b511fe69043488d29ac930"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/LHIFZPQB6CSQZPW3FJPZLIJ7CF/bundle.json","state_url":"https://pith.science/pith/LHIFZPQB6CSQZPW3FJPZLIJ7CF/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/LHIFZPQB6CSQZPW3FJPZLIJ7CF/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":"2026-08-09T18:40:49Z","links":{"resolver":"https://pith.science/pith/LHIFZPQB6CSQZPW3FJPZLIJ7CF","bundle":"https://pith.science/pith/LHIFZPQB6CSQZPW3FJPZLIJ7CF/bundle.json","state":"https://pith.science/pith/LHIFZPQB6CSQZPW3FJPZLIJ7CF/state.json","well_known_bundle":"https://pith.science/.well-known/pith/LHIFZPQB6CSQZPW3FJPZLIJ7CF/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:LHIFZPQB6CSQZPW3FJPZLIJ7CF","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":"e22ecffce09592dbd5b79769ec2e38890f6d7685657860ed564c7e31fefe1457","cross_cats_sorted":["cond-mat.dis-nn"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-02-07T18:48:25Z","title_canon_sha256":"aeed6b594b0f8801efe161dbbc45c9202c5183d9079f8de104a1ba9f9d3407e2"},"schema_version":"1.0","source":{"id":"2502.05164","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2502.05164","created_at":"2026-07-05T11:16:54Z"},{"alias_kind":"arxiv_version","alias_value":"2502.05164v2","created_at":"2026-07-05T11:16:54Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.05164","created_at":"2026-07-05T11:16:54Z"},{"alias_kind":"pith_short_12","alias_value":"LHIFZPQB6CSQ","created_at":"2026-07-05T11:16:54Z"},{"alias_kind":"pith_short_16","alias_value":"LHIFZPQB6CSQZPW3","created_at":"2026-07-05T11:16:54Z"},{"alias_kind":"pith_short_8","alias_value":"LHIFZPQB","created_at":"2026-07-05T11:16:54Z"}],"graph_snapshots":[{"event_id":"sha256:d0b33e78fc32c0045c4a7bf48822532684e0d15200b511fe69043488d29ac930","target":"graph","created_at":"2026-07-05T11:16:54Z","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/2502.05164/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"We introduce in-context denoising, a task that refines the connection between attention-based architectures and dense associative memory (DAM) networks, also known as modern Hopfield networks. Using a Bayesian framework, we show theoretically and empirically that certain restricted denoising problems can be solved optimally even by a single-layer transformer. We demonstrate that a trained attention layer processes each denoising prompt by performing a single gradient descent update on a context-aware DAM energy landscape, where context tokens serve as associative memories and the query token a","authors_text":"Alberto Bietti, Anirvan M. Sengupta, Matthew Smart","cross_cats":["cond-mat.dis-nn"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-02-07T18:48:25Z","title":"In-context denoising with one-layer transformers: connections between attention and associative memory retrieval"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.05164","kind":"arxiv","version":2},"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":"sha256:809ee414a0b8f4dbeb22ff7901a6bb160e12fc495f9ef901bcb64cad030afb1c","target":"record","created_at":"2026-07-05T11:16:54Z","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":"e22ecffce09592dbd5b79769ec2e38890f6d7685657860ed564c7e31fefe1457","cross_cats_sorted":["cond-mat.dis-nn"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-02-07T18:48:25Z","title_canon_sha256":"aeed6b594b0f8801efe161dbbc45c9202c5183d9079f8de104a1ba9f9d3407e2"},"schema_version":"1.0","source":{"id":"2502.05164","kind":"arxiv","version":2}},"canonical_sha256":"59d05cbe01f0a50cbedb2a5f95a13f1177920c83ad5fa1390c4a9cadbc36d169","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"59d05cbe01f0a50cbedb2a5f95a13f1177920c83ad5fa1390c4a9cadbc36d169","first_computed_at":"2026-07-05T11:16:54.779007Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:16:54.779007Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"HbJSLfzL/kldROavfneC2FYPZipdB1IYwWyod4WY7yeWJ7u2W04/ZQDTSGbZuGlQH4YvlFoertWdlEtBc6hICg==","signature_status":"signed_v1","signed_at":"2026-07-05T11:16:54.779528Z","signed_message":"canonical_sha256_bytes"},"source_id":"2502.05164","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:809ee414a0b8f4dbeb22ff7901a6bb160e12fc495f9ef901bcb64cad030afb1c","sha256:d0b33e78fc32c0045c4a7bf48822532684e0d15200b511fe69043488d29ac930"],"state_sha256":"205f0761d242ddeb5ce26b49254321607a64f41679d8fb857cdc51f58b687ca4"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Bgd/bV3p/iT8a9IBrfgV7sPUu95BnIqYlqDZwhNncxb+9c+k9j+vm9CWOQb1guaBq1mM9xN/XSwi2vPcfNutDA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-09T18:40:49.226559Z","bundle_sha256":"105ff1ae5a4208b79498527b0b9c78bde8cbc0f0542e327bf71f694f85e3c7bf"}}