{"paper":{"title":"Intent-aligned Autonomous Spacecraft Guidance via Reasoning Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"A spacecraft guidance framework uses intermediate behavior abstractions to align foundation model predictions with safe trajectory optimization.","cross_cats":["cs.AI","cs.SY","math.OC"],"primary_cat":"eess.SY","authors_text":"Simone D'Amico, Yuji Takubo","submitted_at":"2026-04-19T00:25:54Z","abstract_excerpt":"Future spacecraft operations require autonomy that can interpret high-level mission intent while preserving safety. However, existing trajectory optimization still relies heavily on expert-crafted formulations and does not support intent-conditioned decision-making. This paper proposes an intent-aligned spacecraft guidance framework that links high-level reasoning and safe trajectory optimization through explicit intermediate abstractions, based on behavior sequences and waypoint constraints. A foundation model first predicts an intent-aligned behavior plan, a waypoint generation model then co"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"Numerical experiments in close-proximity operation scenarios demonstrate that the proposed pipeline achieves over 90% SCP convergence and yields a 1.5× higher rate of generating trajectories that satisfy the top intent-prioritized performance criteria than heuristic decision-making.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"That the intermediate abstractions of behavior sequences and waypoint constraints can reliably translate high-level intent predictions into constraints that preserve both safety and intent alignment during optimization.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"A pipeline links foundation-model intent reasoning to safe trajectory optimization via behavior sequences and waypoint constraints, achieving over 90% convergence and 1.5x better intent satisfaction in close-proximity tests.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"A spacecraft guidance framework uses intermediate behavior abstractions to align foundation model predictions with safe trajectory optimization.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"ae73b6b1621ea91074007d7a72d6747d68ef8a6b530e4bda8bf39d407922a0f9"},"source":{"id":"2604.17176","kind":"arxiv","version":2},"verdict":{"id":"4c71184e-29c5-487b-bf28-c0aa2eb89c55","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-10T06:37:00.473628Z","strongest_claim":"Numerical experiments in close-proximity operation scenarios demonstrate that the proposed pipeline achieves over 90% SCP convergence and yields a 1.5× higher rate of generating trajectories that satisfy the top intent-prioritized performance criteria than heuristic decision-making.","one_line_summary":"A pipeline links foundation-model intent reasoning to safe trajectory optimization via behavior sequences and waypoint constraints, achieving over 90% convergence and 1.5x better intent satisfaction in close-proximity tests.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"That the intermediate abstractions of behavior sequences and waypoint constraints can reliably translate high-level intent predictions into constraints that preserve both safety and intent alignment during optimization.","pith_extraction_headline":"A spacecraft guidance framework uses intermediate behavior abstractions to align foundation model predictions with safe trajectory optimization."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2604.17176/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"}