{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:AWNKUGLSI7ZSPS7SNNDAFSISH3","short_pith_number":"pith:AWNKUGLS","canonical_record":{"source":{"id":"2607.17981","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-07-20T14:15:03Z","cross_cats_sorted":[],"title_canon_sha256":"56223bff89bd3b6da8a2512e9ad0af51bb1039a5398d040b9bb0baf699726c86","abstract_canon_sha256":"7c63756e36c4154af43607d69d4a4ae2cc619ec7ef22ad0ac6d1d6d7dd2521b7"},"schema_version":"1.0"},"canonical_sha256":"059aaa197247f327cbf26b4602c9123ee9f2d93cb9fbe7dd109007b96a3441d3","source":{"kind":"arxiv","id":"2607.17981","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2607.17981","created_at":"2026-07-21T02:22:10Z"},{"alias_kind":"arxiv_version","alias_value":"2607.17981v1","created_at":"2026-07-21T02:22:10Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.17981","created_at":"2026-07-21T02:22:10Z"},{"alias_kind":"pith_short_12","alias_value":"AWNKUGLSI7ZS","created_at":"2026-07-21T02:22:10Z"},{"alias_kind":"pith_short_16","alias_value":"AWNKUGLSI7ZSPS7S","created_at":"2026-07-21T02:22:10Z"},{"alias_kind":"pith_short_8","alias_value":"AWNKUGLS","created_at":"2026-07-21T02:22:10Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:AWNKUGLSI7ZSPS7SNNDAFSISH3","target":"record","payload":{"canonical_record":{"source":{"id":"2607.17981","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-07-20T14:15:03Z","cross_cats_sorted":[],"title_canon_sha256":"56223bff89bd3b6da8a2512e9ad0af51bb1039a5398d040b9bb0baf699726c86","abstract_canon_sha256":"7c63756e36c4154af43607d69d4a4ae2cc619ec7ef22ad0ac6d1d6d7dd2521b7"},"schema_version":"1.0"},"canonical_sha256":"059aaa197247f327cbf26b4602c9123ee9f2d93cb9fbe7dd109007b96a3441d3","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-21T02:22:10.353257Z","signature_b64":"+Dz4UZswHqxWPMb/JwknqTrlEDnPsCVvYT2VIJe9xtl0V3ZR8iA+6Vju6W/vu02RzwTZCvG13J6gBuhXX8PABA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"059aaa197247f327cbf26b4602c9123ee9f2d93cb9fbe7dd109007b96a3441d3","last_reissued_at":"2026-07-21T02:22:10.352447Z","signature_status":"signed_v1","first_computed_at":"2026-07-21T02:22:10.352447Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2607.17981","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-21T02:22:10Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"VruRkvzy6ixLs2JSlS/1pCj7kELSDs4RH6kHbc9oe4RoI5QB9h1GQidpjo9/EkyYjNdWbDSbBLyWpqoETbGfDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T12:19:08.677428Z"},"content_sha256":"c18830365fd4162ec625f2712e2075904d04856b77d8dd7258f790fb5dd9e3f6","schema_version":"1.0","event_id":"sha256:c18830365fd4162ec625f2712e2075904d04856b77d8dd7258f790fb5dd9e3f6"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:AWNKUGLSI7ZSPS7SNNDAFSISH3","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Information-Based Exploration via Random Features for Reinforcement Learning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Odalric-Ambrym Maillard, Waris Radji","submitted_at":"2026-07-20T14:15:03Z","abstract_excerpt":"Representation learning has enabled classical exploration strategies to be extended to deep Reinforcement Learning (RL), but often makes algorithms more complex and theoretical guarantees harder to establish. We introduce Random Feature Information Gain (RFIG), grounded in Bayesian kernel methods theory, which uses random Fourier features to approximate information gain and compute exploration bonuses in non-countable spaces. We provide error bounds on information gain approximation and avoid the black-box aspects of neural network-based uncertainty estimation, for optimism-based exploration. "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.17981","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/2607.17981/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":"2026-07-21T02:22:10Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"XpOY8AIp18ScAShB37siUg6YivselbE+cbUwjywBfyJCKeRvje5HJ4FziPqdlKh/V7c+Kjsr3GVMyfPnNyHpBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T12:19:08.678378Z"},"content_sha256":"177896276b75fd3fad1519f92c9ed3f6994e47ce573369777a7e54529c1833c4","schema_version":"1.0","event_id":"sha256:177896276b75fd3fad1519f92c9ed3f6994e47ce573369777a7e54529c1833c4"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/AWNKUGLSI7ZSPS7SNNDAFSISH3/bundle.json","state_url":"https://pith.science/pith/AWNKUGLSI7ZSPS7SNNDAFSISH3/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/AWNKUGLSI7ZSPS7SNNDAFSISH3/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":"2026-08-04T12:19:08Z","links":{"resolver":"https://pith.science/pith/AWNKUGLSI7ZSPS7SNNDAFSISH3","bundle":"https://pith.science/pith/AWNKUGLSI7ZSPS7SNNDAFSISH3/bundle.json","state":"https://pith.science/pith/AWNKUGLSI7ZSPS7SNNDAFSISH3/state.json","well_known_bundle":"https://pith.science/.well-known/pith/AWNKUGLSI7ZSPS7SNNDAFSISH3/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:AWNKUGLSI7ZSPS7SNNDAFSISH3","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":"7c63756e36c4154af43607d69d4a4ae2cc619ec7ef22ad0ac6d1d6d7dd2521b7","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-07-20T14:15:03Z","title_canon_sha256":"56223bff89bd3b6da8a2512e9ad0af51bb1039a5398d040b9bb0baf699726c86"},"schema_version":"1.0","source":{"id":"2607.17981","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2607.17981","created_at":"2026-07-21T02:22:10Z"},{"alias_kind":"arxiv_version","alias_value":"2607.17981v1","created_at":"2026-07-21T02:22:10Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.17981","created_at":"2026-07-21T02:22:10Z"},{"alias_kind":"pith_short_12","alias_value":"AWNKUGLSI7ZS","created_at":"2026-07-21T02:22:10Z"},{"alias_kind":"pith_short_16","alias_value":"AWNKUGLSI7ZSPS7S","created_at":"2026-07-21T02:22:10Z"},{"alias_kind":"pith_short_8","alias_value":"AWNKUGLS","created_at":"2026-07-21T02:22:10Z"}],"graph_snapshots":[{"event_id":"sha256:177896276b75fd3fad1519f92c9ed3f6994e47ce573369777a7e54529c1833c4","target":"graph","created_at":"2026-07-21T02:22:10Z","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/2607.17981/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Representation learning has enabled classical exploration strategies to be extended to deep Reinforcement Learning (RL), but often makes algorithms more complex and theoretical guarantees harder to establish. We introduce Random Feature Information Gain (RFIG), grounded in Bayesian kernel methods theory, which uses random Fourier features to approximate information gain and compute exploration bonuses in non-countable spaces. We provide error bounds on information gain approximation and avoid the black-box aspects of neural network-based uncertainty estimation, for optimism-based exploration. ","authors_text":"Odalric-Ambrym Maillard, Waris Radji","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-07-20T14:15:03Z","title":"Information-Based Exploration via Random Features for Reinforcement Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.17981","kind":"arxiv","version":1},"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:c18830365fd4162ec625f2712e2075904d04856b77d8dd7258f790fb5dd9e3f6","target":"record","created_at":"2026-07-21T02:22:10Z","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":"7c63756e36c4154af43607d69d4a4ae2cc619ec7ef22ad0ac6d1d6d7dd2521b7","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-07-20T14:15:03Z","title_canon_sha256":"56223bff89bd3b6da8a2512e9ad0af51bb1039a5398d040b9bb0baf699726c86"},"schema_version":"1.0","source":{"id":"2607.17981","kind":"arxiv","version":1}},"canonical_sha256":"059aaa197247f327cbf26b4602c9123ee9f2d93cb9fbe7dd109007b96a3441d3","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"059aaa197247f327cbf26b4602c9123ee9f2d93cb9fbe7dd109007b96a3441d3","first_computed_at":"2026-07-21T02:22:10.352447Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-21T02:22:10.352447Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"+Dz4UZswHqxWPMb/JwknqTrlEDnPsCVvYT2VIJe9xtl0V3ZR8iA+6Vju6W/vu02RzwTZCvG13J6gBuhXX8PABA==","signature_status":"signed_v1","signed_at":"2026-07-21T02:22:10.353257Z","signed_message":"canonical_sha256_bytes"},"source_id":"2607.17981","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:c18830365fd4162ec625f2712e2075904d04856b77d8dd7258f790fb5dd9e3f6","sha256:177896276b75fd3fad1519f92c9ed3f6994e47ce573369777a7e54529c1833c4"],"state_sha256":"4d0c57038fe5e8cc28e137071c9840771790705e4cd2777180127c56e9ad87a2"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"3D3n9im4+nr+ctLFnTiX2wuXHGuuYurqFrKnVh3ZxXCRh158FSm+m+eVezUcJj7WsuDxzXfysKJvTyVh8xMVDQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-04T12:19:08.686141Z","bundle_sha256":"c7dd43169b12f094fd06dbdbac3238f927ff0d0092113f6afde47769e6e67a23"}}