{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:CSD3M2RQ4P5JZSRFDC2RQOIRE6","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":"64ffec4f25cf3984a8fd250e29c015f7c2037ac0aaf20c373d03ef80b3751984","cross_cats_sorted":["cs.AI","math.IT"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2026-02-15T07:47:09Z","title_canon_sha256":"27d434b7ce6228df00db703b9cc0b547db6f13e08cc701c9a1a73e78be623dde"},"schema_version":"1.0","source":{"id":"2602.14033","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2602.14033","created_at":"2026-06-09T01:05:14Z"},{"alias_kind":"arxiv_version","alias_value":"2602.14033v1","created_at":"2026-06-09T01:05:14Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2602.14033","created_at":"2026-06-09T01:05:14Z"},{"alias_kind":"pith_short_12","alias_value":"CSD3M2RQ4P5J","created_at":"2026-06-09T01:05:14Z"},{"alias_kind":"pith_short_16","alias_value":"CSD3M2RQ4P5JZSRF","created_at":"2026-06-09T01:05:14Z"},{"alias_kind":"pith_short_8","alias_value":"CSD3M2RQ","created_at":"2026-06-09T01:05:14Z"}],"graph_snapshots":[{"event_id":"sha256:f2b4d0072d6000fd4a0224ac8bf64c81dc70e7359b198e71c66250aea6a81db4","target":"graph","created_at":"2026-06-09T01:05:14Z","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/2602.14033/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Future sixth-generation (6G) mobile networks will demand artificial intelligence (AI) agents that are not only autonomous and efficient, but also capable of real-time adaptation in dynamic environments and transparent in their decisionmaking. However, prevailing agentic AI approaches in networking, exhibit significant shortcomings in this regard. Conventional deep reinforcement learning (DRL)-based agents lack explainability and often suffer from brittle adaptation, including catastrophic forgetting of past knowledge under non-stationary conditions. In this paper, we propose an alternative sol","authors_text":"Falko Dressler, Martin Maier, Osman Tugay Basaran","cross_cats":["cs.AI","math.IT"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2026-02-15T07:47:09Z","title":"BRAIN: Bayesian Reasoning via Active Inference for Agentic and Embodied Intelligence in Mobile Networks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2602.14033","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":"sha256:3b730bc9cbfce94898e8d2bb004569c774be26e2b8dcba9bf52519964a8279da","target":"record","created_at":"2026-06-09T01:05:14Z","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":"64ffec4f25cf3984a8fd250e29c015f7c2037ac0aaf20c373d03ef80b3751984","cross_cats_sorted":["cs.AI","math.IT"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2026-02-15T07:47:09Z","title_canon_sha256":"27d434b7ce6228df00db703b9cc0b547db6f13e08cc701c9a1a73e78be623dde"},"schema_version":"1.0","source":{"id":"2602.14033","kind":"arxiv","version":1}},"canonical_sha256":"1487b66a30e3fa9cca2518b518391127a281512eac48abe3d2b42883056e130a","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"1487b66a30e3fa9cca2518b518391127a281512eac48abe3d2b42883056e130a","first_computed_at":"2026-06-09T01:05:14.388833Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-06-09T01:05:14.388833Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"4UX/+KbsmHzwx2aTAQYImnLtlv+U3hFuAkdJtZ1W+U5N3ha8bpDLUCmGBwTEVrYNEnyuvAD33G+5cSeBnAjgCQ==","signature_status":"signed_v1","signed_at":"2026-06-09T01:05:14.389334Z","signed_message":"canonical_sha256_bytes"},"source_id":"2602.14033","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:3b730bc9cbfce94898e8d2bb004569c774be26e2b8dcba9bf52519964a8279da","sha256:f2b4d0072d6000fd4a0224ac8bf64c81dc70e7359b198e71c66250aea6a81db4"],"state_sha256":"50bea7990b1c4b158b35c1b06f7a6e66e333f6f4b12e7fb70b3b7f60b9c2414a"}