{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:FRJ5SPOMXFJXWMVYPOL3NEJBWM","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":"1fb4da8c9c704bf4f63eff9d8b4a0ec743b3d81e575e8ed3791d6e721bc66ad6","cross_cats_sorted":["cs.SY"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.SY","submitted_at":"2026-05-13T20:10:35Z","title_canon_sha256":"18ac75e40c1e901ddce0a09e903056e9618d2d9b42baee3d53d7873cfd9377fc"},"schema_version":"1.0","source":{"id":"2605.14085","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.14085","created_at":"2026-05-17T23:39:12Z"},{"alias_kind":"arxiv_version","alias_value":"2605.14085v1","created_at":"2026-05-17T23:39:12Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.14085","created_at":"2026-05-17T23:39:12Z"},{"alias_kind":"pith_short_12","alias_value":"FRJ5SPOMXFJX","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_16","alias_value":"FRJ5SPOMXFJXWMVY","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_8","alias_value":"FRJ5SPOM","created_at":"2026-05-18T12:33:37Z"}],"graph_snapshots":[{"event_id":"sha256:f9fc6a4eec20c09f3d958f8f4e8581da7bb053a2c1a4e400fd1e448e701320f5","target":"graph","created_at":"2026-05-17T23:39:12Z","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":4,"items":[{"attestation":"unclaimed","claim_id":"C1","kind":"strongest_claim","source":"verdict.strongest_claim","status":"machine_extracted","text":"By iterating a user-defined cost that captures deception, resources, and smoothness, and optionally includes coupling terms between agents, the framework yields stochastic policies that balance the tradeoff between optimal paths and deceptive deviation."},{"attestation":"unclaimed","claim_id":"C2","kind":"weakest_assumption","source":"verdict.weakest_assumption","status":"machine_extracted","text":"That short-horizon optimizations within a receding loop can maintain effective deception without the global view of full-horizon methods, particularly when environmental changes occur."},{"attestation":"unclaimed","claim_id":"C3","kind":"one_line_summary","source":"verdict.one_line_summary","status":"machine_extracted","text":"A receding-horizon planner uses Boltzmann distributions over short trajectories to generate tunable deceptive paths for multiple agents."},{"attestation":"unclaimed","claim_id":"C4","kind":"headline","source":"verdict.pith_extraction.headline","status":"machine_extracted","text":"Receding-horizon optimization with Boltzmann policies generates tunable stochastic deceptive paths for single and multiple agents."}],"snapshot_sha256":"4033b98b682b9860f089a4835d306fefbbb3154318e80b2d9bc383958b5f57b9"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"abstract_excerpt":"Deceptive path planning enables autonomous agents to obscure their true goals from observers by deviating from an expected optimal path. Prior work largely solves full-horizon, end-to-end optimization for single agents, which is expensive to recompute online and difficult to scale or adapt en route. We propose a unified framework for deceptive path planning using a Boltzmann distribution, computing over short-horizon candidate trajectories within a receding-horizon loop. By param- By iterating a user-defined cost that captures deception, resources, and smoothness, and optionally includes coupl","authors_text":"Brian M. Sadler, Rick S. Blum, Xubin Fang","cross_cats":["cs.SY"],"headline":"Receding-horizon optimization with Boltzmann policies generates tunable stochastic deceptive paths for single and multiple agents.","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.SY","submitted_at":"2026-05-13T20:10:35Z","title":"Receding Horizon Multi-Agent Deceptive Path Planner"},"references":{"count":28,"internal_anchors":0,"resolved_work":28,"sample":[{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":1,"title":"Toward a systems- and control-oriented agent framework,","work_id":"34bd9c35-219a-4e52-906c-25b987f13493","year":2005},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":2,"title":"Mission-Driven Trajectory Homotopy to Explore Dynamic Coverage of USV–UA V Sys- tems,","work_id":"accd8404-d131-43b6-a2b3-f9aca524e90e","year":2025},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":3,"title":"A Universal Reactive Approach for Graph-Based Persistent Path Planning Problems With Temporal Logic Constraints,","work_id":"b18aab38-b54e-4e43-81be-47cb026bce9e","year":2025},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":4,"title":"Distributed Search Planning in 3-D Environments With a Dynamically Varying Number of Agents,","work_id":"b870c928-8748-4ff9-aab4-3d9615b6f4d6","year":2023},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":5,"title":"Decentralized Motion Planning for Multiagent Collaboration Under Coupled LTL Task Specifications,","work_id":"5c4125b8-3fd2-4f45-8f2f-3586b438e148","year":2022}],"snapshot_sha256":"2746765469a05e1ab15784ede6bed2be9d1f0b6e9d9d474f6608ddad28fe8202"},"source":{"id":"2605.14085","kind":"arxiv","version":1},"verdict":{"created_at":"2026-05-15T05:20:56.105703Z","id":"0646a103-b9e6-4fca-accd-ad5c54256cc5","model_set":{"reader":"grok-4.3"},"one_line_summary":"A receding-horizon planner uses Boltzmann distributions over short trajectories to generate tunable deceptive paths for multiple agents.","pipeline_version":"pith-pipeline@v0.9.0","pith_extraction_headline":"Receding-horizon optimization with Boltzmann policies generates tunable stochastic deceptive paths for single and multiple agents.","strongest_claim":"By iterating a user-defined cost that captures deception, resources, and smoothness, and optionally includes coupling terms between agents, the framework yields stochastic policies that balance the tradeoff between optimal paths and deceptive deviation.","weakest_assumption":"That short-horizon optimizations within a receding loop can maintain effective deception without the global view of full-horizon methods, particularly when environmental changes occur."}},"verdict_id":"0646a103-b9e6-4fca-accd-ad5c54256cc5"}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:53a0b4d30357fbb9c9d8bcb048d56c1d520453b627b3661f11c02799394c60b3","target":"record","created_at":"2026-05-17T23:39:12Z","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":"1fb4da8c9c704bf4f63eff9d8b4a0ec743b3d81e575e8ed3791d6e721bc66ad6","cross_cats_sorted":["cs.SY"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.SY","submitted_at":"2026-05-13T20:10:35Z","title_canon_sha256":"18ac75e40c1e901ddce0a09e903056e9618d2d9b42baee3d53d7873cfd9377fc"},"schema_version":"1.0","source":{"id":"2605.14085","kind":"arxiv","version":1}},"canonical_sha256":"2c53d93dccb9537b32b87b97b69121b30cbc298f4c813a431e040b3a0066662b","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"2c53d93dccb9537b32b87b97b69121b30cbc298f4c813a431e040b3a0066662b","first_computed_at":"2026-05-17T23:39:12.270649Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:39:12.270649Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"L6E25tkEjAjFE/1l60d2nNj0oPmX8wA7n2OEHBfiC1XZdTMy8GFlvBFwsRmOdjsRRxC3/KpPlmBsWxPCAl0qCg==","signature_status":"signed_v1","signed_at":"2026-05-17T23:39:12.271253Z","signed_message":"canonical_sha256_bytes"},"source_id":"2605.14085","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:53a0b4d30357fbb9c9d8bcb048d56c1d520453b627b3661f11c02799394c60b3","sha256:f9fc6a4eec20c09f3d958f8f4e8581da7bb053a2c1a4e400fd1e448e701320f5"],"state_sha256":"f7dbcf8d7efbb8c5f9734f86e663c118fb462566bc5712f3ed206114d5c551d9"}