{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:4RDRJ4RD7Y5V5AGLPEFHHJPUSK","short_pith_number":"pith:4RDRJ4RD","canonical_record":{"source":{"id":"1706.00536","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2017-06-02T02:10:39Z","cross_cats_sorted":[],"title_canon_sha256":"4113f0e74c032328bb6f7064a3b781ddd8b31fcbf9016885a2e95fcb52cc4849","abstract_canon_sha256":"b8b339d6d8b5628942a0bd74c0c48cb69a84c78200fc00a2a0204376edf7c26d"},"schema_version":"1.0"},"canonical_sha256":"e44714f223fe3b5e80cb790a73a5f492bda269fee65ccfd9c0f13a41a518d6b8","source":{"kind":"arxiv","id":"1706.00536","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1706.00536","created_at":"2026-05-18T00:27:00Z"},{"alias_kind":"arxiv_version","alias_value":"1706.00536v2","created_at":"2026-05-18T00:27:00Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1706.00536","created_at":"2026-05-18T00:27:00Z"},{"alias_kind":"pith_short_12","alias_value":"4RDRJ4RD7Y5V","created_at":"2026-05-18T12:31:00Z"},{"alias_kind":"pith_short_16","alias_value":"4RDRJ4RD7Y5V5AGL","created_at":"2026-05-18T12:31:00Z"},{"alias_kind":"pith_short_8","alias_value":"4RDRJ4RD","created_at":"2026-05-18T12:31:00Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:4RDRJ4RD7Y5V5AGLPEFHHJPUSK","target":"record","payload":{"canonical_record":{"source":{"id":"1706.00536","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2017-06-02T02:10:39Z","cross_cats_sorted":[],"title_canon_sha256":"4113f0e74c032328bb6f7064a3b781ddd8b31fcbf9016885a2e95fcb52cc4849","abstract_canon_sha256":"b8b339d6d8b5628942a0bd74c0c48cb69a84c78200fc00a2a0204376edf7c26d"},"schema_version":"1.0"},"canonical_sha256":"e44714f223fe3b5e80cb790a73a5f492bda269fee65ccfd9c0f13a41a518d6b8","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:27:00.506280Z","signature_b64":"tfkg9rnNxzl78v88bv4Y1AhWww604//G0LhE7K2JHHRNVf6xtPxLJ5Ch+n35B/GfBfcR4H4MYcsuzFaNtsuUCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e44714f223fe3b5e80cb790a73a5f492bda269fee65ccfd9c0f13a41a518d6b8","last_reissued_at":"2026-05-18T00:27:00.505501Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:27:00.505501Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1706.00536","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":"2026-05-18T00:27:00Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"0BRKHEOqwnVJCCPrXLmUyEij87j9As2elgBphLr2fvGmOynzyy1pks5rrCdiwLeESBsM8dIbDfTD31NZfi2CBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-30T11:03:19.496981Z"},"content_sha256":"ffe297a232e9ca77812d207d1c09d29e4c082ac53fdfd29a8b228e42d6fb9202","schema_version":"1.0","event_id":"sha256:ffe297a232e9ca77812d207d1c09d29e4c082ac53fdfd29a8b228e42d6fb9202"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:4RDRJ4RD7Y5V5AGLPEFHHJPUSK","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Modeling Latent Attention Within Neural Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Christopher Grimm, David Abel, Dilip Arumugam, Lawson L.S. Wong, Michael L. Littman, Siddharth Karamcheti","submitted_at":"2017-06-02T02:10:39Z","abstract_excerpt":"Deep neural networks are able to solve tasks across a variety of domains and modalities of data. Despite many empirical successes, we lack the ability to clearly understand and interpret the learned internal mechanisms that contribute to such effective behaviors or, more critically, failure modes. In this work, we present a general method for visualizing an arbitrary neural network's inner mechanisms and their power and limitations. Our dataset-centric method produces visualizations of how a trained network attends to components of its inputs. The computed \"attention masks\" support improved in"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1706.00536","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":""},"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":"2026-05-18T00:27:00Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"nqRrvVBKL0TLrpusWL2VCCaCcJ/WtRzOIgXytlxwFdEhYV0h9+xL8YD2CFZwNraQJIBK8s0i0oj6dTamnvs5DA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-30T11:03:19.497469Z"},"content_sha256":"dd63eec4aa3eb4812b20c5125c8c655b014d82ce9c71d5d03b7c90f69a54cb22","schema_version":"1.0","event_id":"sha256:dd63eec4aa3eb4812b20c5125c8c655b014d82ce9c71d5d03b7c90f69a54cb22"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/4RDRJ4RD7Y5V5AGLPEFHHJPUSK/bundle.json","state_url":"https://pith.science/pith/4RDRJ4RD7Y5V5AGLPEFHHJPUSK/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/4RDRJ4RD7Y5V5AGLPEFHHJPUSK/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":"2026-05-30T11:03:19Z","links":{"resolver":"https://pith.science/pith/4RDRJ4RD7Y5V5AGLPEFHHJPUSK","bundle":"https://pith.science/pith/4RDRJ4RD7Y5V5AGLPEFHHJPUSK/bundle.json","state":"https://pith.science/pith/4RDRJ4RD7Y5V5AGLPEFHHJPUSK/state.json","well_known_bundle":"https://pith.science/.well-known/pith/4RDRJ4RD7Y5V5AGLPEFHHJPUSK/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:4RDRJ4RD7Y5V5AGLPEFHHJPUSK","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":"b8b339d6d8b5628942a0bd74c0c48cb69a84c78200fc00a2a0204376edf7c26d","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2017-06-02T02:10:39Z","title_canon_sha256":"4113f0e74c032328bb6f7064a3b781ddd8b31fcbf9016885a2e95fcb52cc4849"},"schema_version":"1.0","source":{"id":"1706.00536","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1706.00536","created_at":"2026-05-18T00:27:00Z"},{"alias_kind":"arxiv_version","alias_value":"1706.00536v2","created_at":"2026-05-18T00:27:00Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1706.00536","created_at":"2026-05-18T00:27:00Z"},{"alias_kind":"pith_short_12","alias_value":"4RDRJ4RD7Y5V","created_at":"2026-05-18T12:31:00Z"},{"alias_kind":"pith_short_16","alias_value":"4RDRJ4RD7Y5V5AGL","created_at":"2026-05-18T12:31:00Z"},{"alias_kind":"pith_short_8","alias_value":"4RDRJ4RD","created_at":"2026-05-18T12:31:00Z"}],"graph_snapshots":[{"event_id":"sha256:dd63eec4aa3eb4812b20c5125c8c655b014d82ce9c71d5d03b7c90f69a54cb22","target":"graph","created_at":"2026-05-18T00:27:00Z","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"},"paper":{"abstract_excerpt":"Deep neural networks are able to solve tasks across a variety of domains and modalities of data. Despite many empirical successes, we lack the ability to clearly understand and interpret the learned internal mechanisms that contribute to such effective behaviors or, more critically, failure modes. In this work, we present a general method for visualizing an arbitrary neural network's inner mechanisms and their power and limitations. Our dataset-centric method produces visualizations of how a trained network attends to components of its inputs. The computed \"attention masks\" support improved in","authors_text":"Christopher Grimm, David Abel, Dilip Arumugam, Lawson L.S. Wong, Michael L. Littman, Siddharth Karamcheti","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2017-06-02T02:10:39Z","title":"Modeling Latent Attention Within Neural Networks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1706.00536","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:ffe297a232e9ca77812d207d1c09d29e4c082ac53fdfd29a8b228e42d6fb9202","target":"record","created_at":"2026-05-18T00:27:00Z","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":"b8b339d6d8b5628942a0bd74c0c48cb69a84c78200fc00a2a0204376edf7c26d","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2017-06-02T02:10:39Z","title_canon_sha256":"4113f0e74c032328bb6f7064a3b781ddd8b31fcbf9016885a2e95fcb52cc4849"},"schema_version":"1.0","source":{"id":"1706.00536","kind":"arxiv","version":2}},"canonical_sha256":"e44714f223fe3b5e80cb790a73a5f492bda269fee65ccfd9c0f13a41a518d6b8","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"e44714f223fe3b5e80cb790a73a5f492bda269fee65ccfd9c0f13a41a518d6b8","first_computed_at":"2026-05-18T00:27:00.505501Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:27:00.505501Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"tfkg9rnNxzl78v88bv4Y1AhWww604//G0LhE7K2JHHRNVf6xtPxLJ5Ch+n35B/GfBfcR4H4MYcsuzFaNtsuUCQ==","signature_status":"signed_v1","signed_at":"2026-05-18T00:27:00.506280Z","signed_message":"canonical_sha256_bytes"},"source_id":"1706.00536","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:ffe297a232e9ca77812d207d1c09d29e4c082ac53fdfd29a8b228e42d6fb9202","sha256:dd63eec4aa3eb4812b20c5125c8c655b014d82ce9c71d5d03b7c90f69a54cb22"],"state_sha256":"1f9cae72a136cf0eb0111d2293eba285651c0b88269e39830b00a0c269921943"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"3uqCH/Ek7/j4OKEPZ2rvpVoqZkohaa3Tldn5rAKwN3SVJneZ71NKaY6q+yPqAsrkGgyXxEgDz1kwlWx9vTrCCg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-30T11:03:19.501185Z","bundle_sha256":"7a6609f491e96095754c7419797254afbcb85ac8c34a47ce055709c3969b53ac"}}