{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:GQRLM5JVVG3JD5FM53COHQFWOD","short_pith_number":"pith:GQRLM5JV","canonical_record":{"source":{"id":"2302.05342","kind":"arxiv","version":4},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-02-10T15:57:20Z","cross_cats_sorted":[],"title_canon_sha256":"d6f933fded2968b0b78a4b1d05d94332cb1db57b46e9ebcd480c05e73f19b112","abstract_canon_sha256":"277cb27e8168622e832ac175ac1496580a3470a386042eabf431097576228df5"},"schema_version":"1.0"},"canonical_sha256":"3422b67535a9b691f4aceec4e3c0b670ca0dc5590357f5c1bba129b4b0ec14fe","source":{"kind":"arxiv","id":"2302.05342","version":4},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2302.05342","created_at":"2026-07-05T08:37:03Z"},{"alias_kind":"arxiv_version","alias_value":"2302.05342v4","created_at":"2026-07-05T08:37:03Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2302.05342","created_at":"2026-07-05T08:37:03Z"},{"alias_kind":"pith_short_12","alias_value":"GQRLM5JVVG3J","created_at":"2026-07-05T08:37:03Z"},{"alias_kind":"pith_short_16","alias_value":"GQRLM5JVVG3JD5FM","created_at":"2026-07-05T08:37:03Z"},{"alias_kind":"pith_short_8","alias_value":"GQRLM5JV","created_at":"2026-07-05T08:37:03Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:GQRLM5JVVG3JD5FM53COHQFWOD","target":"record","payload":{"canonical_record":{"source":{"id":"2302.05342","kind":"arxiv","version":4},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-02-10T15:57:20Z","cross_cats_sorted":[],"title_canon_sha256":"d6f933fded2968b0b78a4b1d05d94332cb1db57b46e9ebcd480c05e73f19b112","abstract_canon_sha256":"277cb27e8168622e832ac175ac1496580a3470a386042eabf431097576228df5"},"schema_version":"1.0"},"canonical_sha256":"3422b67535a9b691f4aceec4e3c0b670ca0dc5590357f5c1bba129b4b0ec14fe","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:37:03.536036Z","signature_b64":"sgJrmYNdV4rlZTFL2c0YbmLGwY0WFPq4B3GMUaFW9q0Y92TDVXJkNYEPKBNmcfU+geUB9MDulJfcy+YqM13TDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3422b67535a9b691f4aceec4e3c0b670ca0dc5590357f5c1bba129b4b0ec14fe","last_reissued_at":"2026-07-05T08:37:03.535590Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:37:03.535590Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2302.05342","source_version":4,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T08:37:03Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"O+ECKf6Dru/b2hhonma1+M/cXee9cGVQ8hVW7r4fLy9RWE5dVu72MGZxuhdCUa0wtaCBUG61msytJa50a0Z8Cg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T14:14:09.513366Z"},"content_sha256":"99957114d5a0f6a2bbe27b4abc7922ab3fa7334689de885d748b8f2343514969","schema_version":"1.0","event_id":"sha256:99957114d5a0f6a2bbe27b4abc7922ab3fa7334689de885d748b8f2343514969"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:GQRLM5JVVG3JD5FM53COHQFWOD","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Combining Reconstruction and Contrastive Methods for Multimodal Representations in RL","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Fabian Otto, Gerhard Neumann, Philipp Becker, Sebastian Mossburger","submitted_at":"2023-02-10T15:57:20Z","abstract_excerpt":"Learning self-supervised representations using reconstruction or contrastive losses improves performance and sample complexity of image-based and multimodal reinforcement learning (RL). Here, different self-supervised loss functions have distinct advantages and limitations depending on the information density of the underlying sensor modality. Reconstruction provides strong learning signals but is susceptible to distractions and spurious information. While contrastive approaches can ignore those, they may fail to capture all relevant details and can lead to representation collapse. For multimo"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2302.05342","kind":"arxiv","version":4},"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/2302.05342/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":"2026-07-05T08:37:03Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Tnp+8CcHrFaYsUKJ4WFAt1Zvz6FORE2YKDNnYrq1Udx10h/4nvCpr+XY0Vo/pI280nz6q8raYSk0jMAcb9KwDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T14:14:09.513898Z"},"content_sha256":"1bf587a2d06754df3f83f939e2967be2c85635a971f2e545aeefee1710d30cb1","schema_version":"1.0","event_id":"sha256:1bf587a2d06754df3f83f939e2967be2c85635a971f2e545aeefee1710d30cb1"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/GQRLM5JVVG3JD5FM53COHQFWOD/bundle.json","state_url":"https://pith.science/pith/GQRLM5JVVG3JD5FM53COHQFWOD/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/GQRLM5JVVG3JD5FM53COHQFWOD/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":"2026-08-09T14:14:09Z","links":{"resolver":"https://pith.science/pith/GQRLM5JVVG3JD5FM53COHQFWOD","bundle":"https://pith.science/pith/GQRLM5JVVG3JD5FM53COHQFWOD/bundle.json","state":"https://pith.science/pith/GQRLM5JVVG3JD5FM53COHQFWOD/state.json","well_known_bundle":"https://pith.science/.well-known/pith/GQRLM5JVVG3JD5FM53COHQFWOD/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:GQRLM5JVVG3JD5FM53COHQFWOD","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":"277cb27e8168622e832ac175ac1496580a3470a386042eabf431097576228df5","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-02-10T15:57:20Z","title_canon_sha256":"d6f933fded2968b0b78a4b1d05d94332cb1db57b46e9ebcd480c05e73f19b112"},"schema_version":"1.0","source":{"id":"2302.05342","kind":"arxiv","version":4}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2302.05342","created_at":"2026-07-05T08:37:03Z"},{"alias_kind":"arxiv_version","alias_value":"2302.05342v4","created_at":"2026-07-05T08:37:03Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2302.05342","created_at":"2026-07-05T08:37:03Z"},{"alias_kind":"pith_short_12","alias_value":"GQRLM5JVVG3J","created_at":"2026-07-05T08:37:03Z"},{"alias_kind":"pith_short_16","alias_value":"GQRLM5JVVG3JD5FM","created_at":"2026-07-05T08:37:03Z"},{"alias_kind":"pith_short_8","alias_value":"GQRLM5JV","created_at":"2026-07-05T08:37:03Z"}],"graph_snapshots":[{"event_id":"sha256:1bf587a2d06754df3f83f939e2967be2c85635a971f2e545aeefee1710d30cb1","target":"graph","created_at":"2026-07-05T08:37:03Z","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/2302.05342/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Learning self-supervised representations using reconstruction or contrastive losses improves performance and sample complexity of image-based and multimodal reinforcement learning (RL). Here, different self-supervised loss functions have distinct advantages and limitations depending on the information density of the underlying sensor modality. Reconstruction provides strong learning signals but is susceptible to distractions and spurious information. While contrastive approaches can ignore those, they may fail to capture all relevant details and can lead to representation collapse. For multimo","authors_text":"Fabian Otto, Gerhard Neumann, Philipp Becker, Sebastian Mossburger","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-02-10T15:57:20Z","title":"Combining Reconstruction and Contrastive Methods for Multimodal Representations in RL"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2302.05342","kind":"arxiv","version":4},"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":"sha256:99957114d5a0f6a2bbe27b4abc7922ab3fa7334689de885d748b8f2343514969","target":"record","created_at":"2026-07-05T08:37:03Z","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":"277cb27e8168622e832ac175ac1496580a3470a386042eabf431097576228df5","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-02-10T15:57:20Z","title_canon_sha256":"d6f933fded2968b0b78a4b1d05d94332cb1db57b46e9ebcd480c05e73f19b112"},"schema_version":"1.0","source":{"id":"2302.05342","kind":"arxiv","version":4}},"canonical_sha256":"3422b67535a9b691f4aceec4e3c0b670ca0dc5590357f5c1bba129b4b0ec14fe","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"3422b67535a9b691f4aceec4e3c0b670ca0dc5590357f5c1bba129b4b0ec14fe","first_computed_at":"2026-07-05T08:37:03.535590Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:37:03.535590Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"sgJrmYNdV4rlZTFL2c0YbmLGwY0WFPq4B3GMUaFW9q0Y92TDVXJkNYEPKBNmcfU+geUB9MDulJfcy+YqM13TDQ==","signature_status":"signed_v1","signed_at":"2026-07-05T08:37:03.536036Z","signed_message":"canonical_sha256_bytes"},"source_id":"2302.05342","source_kind":"arxiv","source_version":4}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:99957114d5a0f6a2bbe27b4abc7922ab3fa7334689de885d748b8f2343514969","sha256:1bf587a2d06754df3f83f939e2967be2c85635a971f2e545aeefee1710d30cb1"],"state_sha256":"e9ec625f736d1359e74c2f662fa0c57d2bcfcca6a487b2170667e168ca42a71b"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Ml9wUWMyx0VS1NfMX3GEGWe2BfVsKrEo2Vdha4hxLWMVmc3X8ra5wRBOsrk7Fmq+csHMYJQzATI//P0a52vLDw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-09T14:14:09.519504Z","bundle_sha256":"d6a1e6508574be7348a3767879deeae4a7ee8e480d07c969d1e72db216dc8dae"}}