{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:SLLG6GGT3TQPOKYNW7UGAIZONG","short_pith_number":"pith:SLLG6GGT","schema_version":"1.0","canonical_sha256":"92d66f18d3dce0f72b0db7e860232e698942a3688686f6a707bc2c7637bd5fb3","source":{"kind":"arxiv","id":"2607.22651","version":1},"attestation_state":"computed","paper":{"title":"ARdena: Scenario-driven control of real-time LLM agents","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Domagoj Matijevi\\'c, Luka Borozan","submitted_at":"2026-06-26T08:58:33Z","abstract_excerpt":"Large language models (LLMs) have enabled increasingly capable conversational agents, but reliably controlling their behavior in real-time interactive environments remains a significant challenge. Existing approaches often rely on model fine-tuning or alignment procedures that are difficult to adapt to changing interaction requirements. This paper introduces layered scenario-driven LLM control, a framework that enables runtime behavior control through structured prompting. By combining persistent context with scenario-specific constraints, the approach allows agent behavior to be modified duri"},"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":"2607.22651","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2026-06-26T08:58:33Z","cross_cats_sorted":[],"title_canon_sha256":"a0d91f865496fc1cad60fdf852ee541f639b2fb6e6f7dc2d0d761a4ca5b8e879","abstract_canon_sha256":"4d2edf911399eb23202d5cb22443e53e25579718c0c6850e5bb172baef7e6168"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-28T00:21:47.222119Z","signature_b64":"57qZausQKpMaHiXHjYXQwtiZH3tHbLAig7SUwPaslZYZdlej7NRLGVniGBJOWMOp7EcjHX/317aFVkB097ZqCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"92d66f18d3dce0f72b0db7e860232e698942a3688686f6a707bc2c7637bd5fb3","last_reissued_at":"2026-07-28T00:21:47.221194Z","signature_status":"signed_v1","first_computed_at":"2026-07-28T00:21:47.221194Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"ARdena: Scenario-driven control of real-time LLM agents","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Domagoj Matijevi\\'c, Luka Borozan","submitted_at":"2026-06-26T08:58:33Z","abstract_excerpt":"Large language models (LLMs) have enabled increasingly capable conversational agents, but reliably controlling their behavior in real-time interactive environments remains a significant challenge. Existing approaches often rely on model fine-tuning or alignment procedures that are difficult to adapt to changing interaction requirements. This paper introduces layered scenario-driven LLM control, a framework that enables runtime behavior control through structured prompting. By combining persistent context with scenario-specific constraints, the approach allows agent behavior to be modified duri"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.22651","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/2607.22651/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":"2607.22651","created_at":"2026-07-28T00:21:47.221652+00:00"},{"alias_kind":"arxiv_version","alias_value":"2607.22651v1","created_at":"2026-07-28T00:21:47.221652+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.22651","created_at":"2026-07-28T00:21:47.221652+00:00"},{"alias_kind":"pith_short_12","alias_value":"SLLG6GGT3TQP","created_at":"2026-07-28T00:21:47.221652+00:00"},{"alias_kind":"pith_short_16","alias_value":"SLLG6GGT3TQPOKYN","created_at":"2026-07-28T00:21:47.221652+00:00"},{"alias_kind":"pith_short_8","alias_value":"SLLG6GGT","created_at":"2026-07-28T00:21:47.221652+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/SLLG6GGT3TQPOKYNW7UGAIZONG","json":"https://pith.science/pith/SLLG6GGT3TQPOKYNW7UGAIZONG.json","graph_json":"https://pith.science/api/pith-number/SLLG6GGT3TQPOKYNW7UGAIZONG/graph.json","events_json":"https://pith.science/api/pith-number/SLLG6GGT3TQPOKYNW7UGAIZONG/events.json","paper":"https://pith.science/paper/SLLG6GGT"},"agent_actions":{"view_html":"https://pith.science/pith/SLLG6GGT3TQPOKYNW7UGAIZONG","download_json":"https://pith.science/pith/SLLG6GGT3TQPOKYNW7UGAIZONG.json","view_paper":"https://pith.science/paper/SLLG6GGT","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2607.22651&json=true","fetch_graph":"https://pith.science/api/pith-number/SLLG6GGT3TQPOKYNW7UGAIZONG/graph.json","fetch_events":"https://pith.science/api/pith-number/SLLG6GGT3TQPOKYNW7UGAIZONG/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/SLLG6GGT3TQPOKYNW7UGAIZONG/action/timestamp_anchor","attest_storage":"https://pith.science/pith/SLLG6GGT3TQPOKYNW7UGAIZONG/action/storage_attestation","attest_author":"https://pith.science/pith/SLLG6GGT3TQPOKYNW7UGAIZONG/action/author_attestation","sign_citation":"https://pith.science/pith/SLLG6GGT3TQPOKYNW7UGAIZONG/action/citation_signature","submit_replication":"https://pith.science/pith/SLLG6GGT3TQPOKYNW7UGAIZONG/action/replication_record"}},"created_at":"2026-07-28T00:21:47.221652+00:00","updated_at":"2026-07-28T00:21:47.221652+00:00"}