{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2021:G6CZIOTU7ZNLZAL4MX6JFB43XD","short_pith_number":"pith:G6CZIOTU","canonical_record":{"source":{"id":"2111.00134","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NE","submitted_at":"2021-10-30T01:05:40Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"787176a49ff2dd3328f55d64c57682b3829875cec3f47da5762518d20a21f2f7","abstract_canon_sha256":"35c7ea6dc7ac605c222692ff8902a8a3c2d4ec9219cb790645eeaab3ba09e8b7"},"schema_version":"1.0"},"canonical_sha256":"3785943a74fe5abc817c65fc92879bb8e25510d225a3849e66a0f2d152b3393c","source":{"kind":"arxiv","id":"2111.00134","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2111.00134","created_at":"2026-07-05T04:17:37Z"},{"alias_kind":"arxiv_version","alias_value":"2111.00134v3","created_at":"2026-07-05T04:17:37Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2111.00134","created_at":"2026-07-05T04:17:37Z"},{"alias_kind":"pith_short_12","alias_value":"G6CZIOTU7ZNL","created_at":"2026-07-05T04:17:37Z"},{"alias_kind":"pith_short_16","alias_value":"G6CZIOTU7ZNLZAL4","created_at":"2026-07-05T04:17:37Z"},{"alias_kind":"pith_short_8","alias_value":"G6CZIOTU","created_at":"2026-07-05T04:17:37Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2021:G6CZIOTU7ZNLZAL4MX6JFB43XD","target":"record","payload":{"canonical_record":{"source":{"id":"2111.00134","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NE","submitted_at":"2021-10-30T01:05:40Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"787176a49ff2dd3328f55d64c57682b3829875cec3f47da5762518d20a21f2f7","abstract_canon_sha256":"35c7ea6dc7ac605c222692ff8902a8a3c2d4ec9219cb790645eeaab3ba09e8b7"},"schema_version":"1.0"},"canonical_sha256":"3785943a74fe5abc817c65fc92879bb8e25510d225a3849e66a0f2d152b3393c","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:17:37.041299Z","signature_b64":"D2U/LDss66tduVJx9kX3lUrPYxAM5K43jOfH+/E6Yry7PZnwvWbNc4taxX93a0lMtNLnPqi8TpsDNx9z+A9gCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3785943a74fe5abc817c65fc92879bb8e25510d225a3849e66a0f2d152b3393c","last_reissued_at":"2026-07-05T04:17:37.040726Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:17:37.040726Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2111.00134","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T04:17:37Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"NEBWYJEAOKnM8+QBtptasIT2FA7/2R+5zHplGZ1s8SbB4PmYbiVtixl5NoqtQFwgbRr3RCpkp7jCABf4eY2YDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-23T03:56:05.934997Z"},"content_sha256":"17d8f59e45e7656a7c5b9cfb8eaccfed4626c7062a6e34815f7e8596bf3ac8ba","schema_version":"1.0","event_id":"sha256:17d8f59e45e7656a7c5b9cfb8eaccfed4626c7062a6e34815f7e8596bf3ac8ba"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2021:G6CZIOTU7ZNLZAL4MX6JFB43XD","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Context Meta-Reinforcement Learning via Neuromodulation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.NE","authors_text":"Andrea Soltoggio, Eseoghene Ben-Iwhiwhu, Jeffery Dick, Nicholas A. Ketz, Praveen K. Pilly","submitted_at":"2021-10-30T01:05:40Z","abstract_excerpt":"Meta-reinforcement learning (meta-RL) algorithms enable agents to adapt quickly to tasks from few samples in dynamic environments. Such a feat is achieved through dynamic representations in an agent's policy network (obtained via reasoning about task context, model parameter updates, or both). However, obtaining rich dynamic representations for fast adaptation beyond simple benchmark problems is challenging due to the burden placed on the policy network to accommodate different policies. This paper addresses the challenge by introducing neuromodulation as a modular component to augment a stand"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2111.00134","kind":"arxiv","version":3},"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/2111.00134/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":"2026-07-05T04:17:37Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"vxVg060hF26hke/ncT2JfEYN5IOM+So373DZDv5oIRhhjfVvRjKObw7x+OgoJzMWfRZwTThX1A25W8uK6AFfCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-23T03:56:05.935520Z"},"content_sha256":"3a6836d99f0514f7970133f76c3bc94e292db9d644c1d668e7548666340acb3d","schema_version":"1.0","event_id":"sha256:3a6836d99f0514f7970133f76c3bc94e292db9d644c1d668e7548666340acb3d"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/G6CZIOTU7ZNLZAL4MX6JFB43XD/bundle.json","state_url":"https://pith.science/pith/G6CZIOTU7ZNLZAL4MX6JFB43XD/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/G6CZIOTU7ZNLZAL4MX6JFB43XD/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":"2026-08-23T03:56:05Z","links":{"resolver":"https://pith.science/pith/G6CZIOTU7ZNLZAL4MX6JFB43XD","bundle":"https://pith.science/pith/G6CZIOTU7ZNLZAL4MX6JFB43XD/bundle.json","state":"https://pith.science/pith/G6CZIOTU7ZNLZAL4MX6JFB43XD/state.json","well_known_bundle":"https://pith.science/.well-known/pith/G6CZIOTU7ZNLZAL4MX6JFB43XD/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:G6CZIOTU7ZNLZAL4MX6JFB43XD","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":"35c7ea6dc7ac605c222692ff8902a8a3c2d4ec9219cb790645eeaab3ba09e8b7","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NE","submitted_at":"2021-10-30T01:05:40Z","title_canon_sha256":"787176a49ff2dd3328f55d64c57682b3829875cec3f47da5762518d20a21f2f7"},"schema_version":"1.0","source":{"id":"2111.00134","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2111.00134","created_at":"2026-07-05T04:17:37Z"},{"alias_kind":"arxiv_version","alias_value":"2111.00134v3","created_at":"2026-07-05T04:17:37Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2111.00134","created_at":"2026-07-05T04:17:37Z"},{"alias_kind":"pith_short_12","alias_value":"G6CZIOTU7ZNL","created_at":"2026-07-05T04:17:37Z"},{"alias_kind":"pith_short_16","alias_value":"G6CZIOTU7ZNLZAL4","created_at":"2026-07-05T04:17:37Z"},{"alias_kind":"pith_short_8","alias_value":"G6CZIOTU","created_at":"2026-07-05T04:17:37Z"}],"graph_snapshots":[{"event_id":"sha256:3a6836d99f0514f7970133f76c3bc94e292db9d644c1d668e7548666340acb3d","target":"graph","created_at":"2026-07-05T04:17:37Z","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/2111.00134/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Meta-reinforcement learning (meta-RL) algorithms enable agents to adapt quickly to tasks from few samples in dynamic environments. Such a feat is achieved through dynamic representations in an agent's policy network (obtained via reasoning about task context, model parameter updates, or both). However, obtaining rich dynamic representations for fast adaptation beyond simple benchmark problems is challenging due to the burden placed on the policy network to accommodate different policies. This paper addresses the challenge by introducing neuromodulation as a modular component to augment a stand","authors_text":"Andrea Soltoggio, Eseoghene Ben-Iwhiwhu, Jeffery Dick, Nicholas A. Ketz, Praveen K. Pilly","cross_cats":["cs.AI","cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NE","submitted_at":"2021-10-30T01:05:40Z","title":"Context Meta-Reinforcement Learning via Neuromodulation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2111.00134","kind":"arxiv","version":3},"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":"sha256:17d8f59e45e7656a7c5b9cfb8eaccfed4626c7062a6e34815f7e8596bf3ac8ba","target":"record","created_at":"2026-07-05T04:17:37Z","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":"35c7ea6dc7ac605c222692ff8902a8a3c2d4ec9219cb790645eeaab3ba09e8b7","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NE","submitted_at":"2021-10-30T01:05:40Z","title_canon_sha256":"787176a49ff2dd3328f55d64c57682b3829875cec3f47da5762518d20a21f2f7"},"schema_version":"1.0","source":{"id":"2111.00134","kind":"arxiv","version":3}},"canonical_sha256":"3785943a74fe5abc817c65fc92879bb8e25510d225a3849e66a0f2d152b3393c","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"3785943a74fe5abc817c65fc92879bb8e25510d225a3849e66a0f2d152b3393c","first_computed_at":"2026-07-05T04:17:37.040726Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T04:17:37.040726Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"D2U/LDss66tduVJx9kX3lUrPYxAM5K43jOfH+/E6Yry7PZnwvWbNc4taxX93a0lMtNLnPqi8TpsDNx9z+A9gCA==","signature_status":"signed_v1","signed_at":"2026-07-05T04:17:37.041299Z","signed_message":"canonical_sha256_bytes"},"source_id":"2111.00134","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:17d8f59e45e7656a7c5b9cfb8eaccfed4626c7062a6e34815f7e8596bf3ac8ba","sha256:3a6836d99f0514f7970133f76c3bc94e292db9d644c1d668e7548666340acb3d"],"state_sha256":"6e1ac4b57def6631c6ec42dc18edc7dd9b63ad9cb05f3ebb4d889ff8c6b2bb3d"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"pjbeBtxT5THvaSGg48JdTrbIpVpcepI5zPXuIyqIGQ2UGvEvTXw+dzA9lBwS7TNPCz/o/Svm6GRXSYrAfFz4Aw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-23T03:56:05.939998Z","bundle_sha256":"d8632447726f570f82e474e13744597e1f9a6c5c9c8c1730594392568990c6e0"}}