{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:R6V7D6HDWMLTBQPZJNZQY46YYL","short_pith_number":"pith:R6V7D6HD","schema_version":"1.0","canonical_sha256":"8fabf1f8e3b31730c1f94b730c73d8c2f9872b59a563a43ad6713c299509b17a","source":{"kind":"arxiv","id":"2102.09407","version":5},"attestation_state":"computed","paper":{"title":"Adaptive Rational Activations to Boost Deep Reinforcement Learning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Alejandro Molina, Kristian Kersting, Martin Mundt, Patrick Schramowski, Quentin Delfosse","submitted_at":"2021-02-18T14:53:12Z","abstract_excerpt":"Latest insights from biology show that intelligence not only emerges from the connections between neurons but that individual neurons shoulder more computational responsibility than previously anticipated. This perspective should be critical in the context of constantly changing distinct reinforcement learning environments, yet current approaches still primarily employ static activation functions. In this work, we motivate why rationals are suitable for adaptable activation functions and why their inclusion into neural networks is crucial. Inspired by recurrence in residual networks, we derive"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2102.09407","kind":"arxiv","version":5},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2021-02-18T14:53:12Z","cross_cats_sorted":[],"title_canon_sha256":"0b85861dabaab5f1eaab15a496eb2b00aeaf39df5869b1cfffac6f6d2dd76fdb","abstract_canon_sha256":"50875964b95b2d86ceb2843dfd8d7a8e4f082433b8bfb0f076ba0b5918eaa0fa"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:56:43.562204Z","signature_b64":"k806ohW6ImkqClUVDQULRhx1ei4dLh4oF7QDPETrr5D7pYOuYq5M+W4LbOBHmWs7h4JGiF9UC9Ps1NS/v9jqDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8fabf1f8e3b31730c1f94b730c73d8c2f9872b59a563a43ad6713c299509b17a","last_reissued_at":"2026-07-05T07:56:43.561673Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:56:43.561673Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Adaptive Rational Activations to Boost Deep Reinforcement Learning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Alejandro Molina, Kristian Kersting, Martin Mundt, Patrick Schramowski, Quentin Delfosse","submitted_at":"2021-02-18T14:53:12Z","abstract_excerpt":"Latest insights from biology show that intelligence not only emerges from the connections between neurons but that individual neurons shoulder more computational responsibility than previously anticipated. This perspective should be critical in the context of constantly changing distinct reinforcement learning environments, yet current approaches still primarily employ static activation functions. In this work, we motivate why rationals are suitable for adaptable activation functions and why their inclusion into neural networks is crucial. Inspired by recurrence in residual networks, we derive"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2102.09407","kind":"arxiv","version":5},"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/2102.09407/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2102.09407","created_at":"2026-07-05T07:56:43.561742+00:00"},{"alias_kind":"arxiv_version","alias_value":"2102.09407v5","created_at":"2026-07-05T07:56:43.561742+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2102.09407","created_at":"2026-07-05T07:56:43.561742+00:00"},{"alias_kind":"pith_short_12","alias_value":"R6V7D6HDWMLT","created_at":"2026-07-05T07:56:43.561742+00:00"},{"alias_kind":"pith_short_16","alias_value":"R6V7D6HDWMLTBQPZ","created_at":"2026-07-05T07:56:43.561742+00:00"},{"alias_kind":"pith_short_8","alias_value":"R6V7D6HD","created_at":"2026-07-05T07:56:43.561742+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":3,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.14990","citing_title":"Rational Sparse Autoencoder","ref_index":5,"is_internal_anchor":false},{"citing_arxiv_id":"2509.22562","citing_title":"Activation Function Design Sustains Plasticity in Continual Learning","ref_index":5,"is_internal_anchor":false},{"citing_arxiv_id":"2604.15414","citing_title":"Beyond Single-Model Optimization: Preserving Plasticity in Continual Reinforcement Learning","ref_index":6,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/R6V7D6HDWMLTBQPZJNZQY46YYL","json":"https://pith.science/pith/R6V7D6HDWMLTBQPZJNZQY46YYL.json","graph_json":"https://pith.science/api/pith-number/R6V7D6HDWMLTBQPZJNZQY46YYL/graph.json","events_json":"https://pith.science/api/pith-number/R6V7D6HDWMLTBQPZJNZQY46YYL/events.json","paper":"https://pith.science/paper/R6V7D6HD"},"agent_actions":{"view_html":"https://pith.science/pith/R6V7D6HDWMLTBQPZJNZQY46YYL","download_json":"https://pith.science/pith/R6V7D6HDWMLTBQPZJNZQY46YYL.json","view_paper":"https://pith.science/paper/R6V7D6HD","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2102.09407&json=true","fetch_graph":"https://pith.science/api/pith-number/R6V7D6HDWMLTBQPZJNZQY46YYL/graph.json","fetch_events":"https://pith.science/api/pith-number/R6V7D6HDWMLTBQPZJNZQY46YYL/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/R6V7D6HDWMLTBQPZJNZQY46YYL/action/timestamp_anchor","attest_storage":"https://pith.science/pith/R6V7D6HDWMLTBQPZJNZQY46YYL/action/storage_attestation","attest_author":"https://pith.science/pith/R6V7D6HDWMLTBQPZJNZQY46YYL/action/author_attestation","sign_citation":"https://pith.science/pith/R6V7D6HDWMLTBQPZJNZQY46YYL/action/citation_signature","submit_replication":"https://pith.science/pith/R6V7D6HDWMLTBQPZJNZQY46YYL/action/replication_record"}},"created_at":"2026-07-05T07:56:43.561742+00:00","updated_at":"2026-07-05T07:56:43.561742+00:00"}