{"data":{"event":{"id":"c35e4a89-4010-4138-ae75-95d00e80a7d6","slug":"toward-a-decision-assurance-layer-for-ai-assisted-flight-planning-in-air-feb62b9469","title":"Toward a Decision-Assurance Layer for AI-Assisted Flight Planning in Air Traffic Management","short_summary":"arXiv:2609.13552v1 Announce Type: new \nAbstract: Generative AI is increasingly being used informally in Air Traffic Management (ATM) for tasks such as flight plan generation, trajectory interpretation, and constraint checking. Although these tools can reduce workload and accelerate planning, their non-deterministic outputs create safety and operational risks in human-in-the-loop settings. This paper proposes the AI Trust and Assurance Layer (ATAL), a model-agnostic decision assurance architecture that evaluates whether AI-generated flight-planning outputs are sufficiently reliable for operatio","full_description":"arXiv:2609.13552v1 Announce Type: new \nAbstract: Generative AI is increasingly being used informally in Air Traffic Management (ATM) for tasks such as flight plan generation, trajectory interpretation, and constraint checking. Although these tools can reduce workload and accelerate planning, their non-deterministic outputs create safety and operational risks in human-in-the-loop settings. This paper proposes the AI Trust and Assurance Layer (ATAL), a model-agnostic decision assurance architecture that evaluates whether AI-generated flight-planning outputs are sufficiently reliable for operational use. ATAL combines semantic stability under prompt variation, operational consistency of structured outputs, and normative constraint validation against domain rules, and maps these signals to a Decision Readiness Level (DRL) for human operators. An ATM-inspired experimental study shows how unsafe, inconsistent, or misleading outputs can be identified before influencing flight-plan validation or execution. Although demonstrated in aviation, the framework is also transferable to other safety-critical decision-support domains that require human oversight under regulatory constraints.\n\nUpdate (2026-09-17): Evaluating whether AI models would sabotage AI safety research. Evaluating whether AI models would sabotage AI safety research\n\nUpdate (2026-09-16): Position: AI Is Not Ready for Strategic Conflicts. arXiv:2609.16189v1 Announce Type: new \nAbstract: Open-ended strategic wargames are high-stakes LM-based social simulations: they model adversaries, institutions, escalation, plan brittleness, doctrine, and crisis response. Language models (LMs) are attractive because they can play agents, generate scenario branches, adjudicate ambiguous actions, and summarize lessons, but the same affordances make open-ended roles dangerous: model language determines both what an actor attempts and what becomes simulated reality. This position paper argues that no LM-enabled wargame should inform planning, doctrine, policy, or crisis response without an auditable safety case, and that the proper use of open-ended wargames today is to stress-test decision-influencing LM agents. We identify five failure modes: decision laundering, adjudication opacity, role collapse, escalation-through-adjudication, and failure of strategic imagination. Ordinary benchmarks cannot establish safety for these settings. Wargames can expose failures as stress tests; they are not themselves safety cases for consequential use.\n\nUpdate (2026-09-17): OpenAI sets plan to disclose safety incidents and reveals more issues - BBC. OpenAI sets plan to disclose safety incidents and reveals more issues  BBC\n\nUpdate (2026-09-21): Risk-Aware Occupancy for Safety-Oriented End-to-End Autonomous Driving. arXiv:2609.21470v1 Announce Type: new \nAbstract: Sparse representation formulates the environment perception for the end-to-end driving system as a set of discrete elements like objects and lane lines. This formulation meets safety risks in crowded, occluded scenes dealing with unstructured obstacles, uncertain regions, and intricate interactions. In this paper, we propose a dense representation, risk-aware occupancy, to characterize planning-relevant risks in an explicit and uniform manner. It jointly encodes global scene occupancy, map-derived traffic constraints, and future dynamic agent occupancy into a unified BEV map. The unified BEV map captures the risk evidence for trajectory planning in both spatial and temporal dimensions. We design an E2E network, ROIDrive, to realize risk-aware occupancy. It predicts risk-aware occupancy with an independent branch and injects it into planning queries for safety-oriented trajectory generation. In addition, to quantify the safety problem, we introduce RiskOcc4D-nuScenes built upon nuscenes and occ3d-nuscenes. Our risk-aware occupancy yields rel","ledger_type":"benefit","primary_domain_id":"8f1af1b9-7302-4b51-aae9-fdfac04d158a","event_status":"provisional","event_date":"2026-09-15T00:00:00.000Z","discovery_date":"2026-09-15T00:00:00.000Z","first_published_date":"2026-09-15T00:00:00.000Z","last_reviewed_date":"2026-09-21T00:00:00.000Z","geographic_scope":"International","affected_population":null,"base_impact_tier":1,"base_score":"1.00","attribution_multiplier":"0.1000","evidence_multiplier":"0.5000","realization_multiplier":"0.2000","durability_multiplier":"0.5000","current_event_score":"0.005000","confidence_level":"low","score_explanation":"Ingest update: independent source raised evidence; added a follow-up source. Score remains provisional until review. Follow-up ingest: independent source raised evidence; added a follow-up source. Follow-up ingest: independent source raised evidence; added a follow-up source. 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Although these tools can reduce workload and accelerate planning, their non-deterministic outputs create safety and operational risks in human-in-the-loop settings. This paper proposes the AI Trust and Assurance Layer (ATAL), a model-agnostic decision assurance architectur","content_hash":null,"source_reliability_class":"medium","is_primary_source":true,"is_independent":true,"is_peer_reviewed":false,"archived_url":null,"created_at":"2026-09-15T04:00:56.500Z"},{"id":"546cb5e9-6bd7-430e-af43-3b79d1f86c54","event_id":"c35e4a89-4010-4138-ae75-95d00e80a7d6","url":"https://arxiv.org/abs/2609.16189","canonical_url":"https://arxiv.org/abs/2609.16189","source_type":"preprint","publisher":"arXiv cs.AI","author":null,"publication_date":"2026-09-16T00:00:00.000Z","retrieved_at":"2026-09-17T03:00:58.112Z","title":"Position: AI Is Not Ready for Strategic Conflicts","excerpt":"arXiv:2609.16189v1 Announce Type: new \nAbstract: Open-ended strategic wargames are high-stakes LM-based social simulations: they model adversaries, institutions, escalation, plan brittleness, doctrine, and crisis response. 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