arXiv Computer Vision
Calibrate What You SHIP: Post-Selection Risk Control for Verifier-Guided Text-to-Image Generation
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arXiv AI
Oct 28
CASEL: Consequence-Aware Selective Execution for Reliable Long-Horizon Tool-Using Agents
CASEL (Consequence-Aware Selective Execution Layer) is a runtime allocation policy for tool‑using language agents that chooses among direct execution, verification, sandboxing, clarification, approval, and abstention for each proposed tool call. It combines calibrated failure probability, state‑dependent consequence, reversibility, ambiguity, and evidence freshness to minimize a per‑mode cost. In controlled simulations, CASEL cuts harmful‑side‑effect trajectories from 14.75 % to 7.02 % and reduces mean normalized harm from 0.184 to 0.123 compared to an uncertainty‑ranked gate.
arXiv Machine Learning
Sep 10
Conditional Validity for Adaptive Modality Acquisition: When the Policy Chooses Its Own Calibration Group
The paper introduces RouteCert, a method for ensuring risk control in multimodal systems that acquire inputs adaptively. It shows that conditional calibration can remain valid even when the acquisition policy determines the calibration group, and provides two finite‑sample constructions: threshold‑free routing with terminal‑pattern calibration and simultaneous validation of policy‑pattern pairs. Experiments on a clinical ECG task and masked multimodal benchmarks demonstrate that RouteCert achieves low disagreement rates and competitive answered fractions while validating each acquisition stage separately.
arXiv AI
Jul 8
PolicyShiftGuard: Benchmarking and Improving Policy-Adaptive Image Guardrails
arXiv:2607. 05910v1 Announce Type: cross Abstract: Image guardrails are typically trained and evaluated under a fixed safety policy, implicitly treating safety as an intrinsic property of an image.
arXiv AI
Jun 30
Budgeted Act-or-Defer Multi-Agent LLM Deliberation with Local Reliability Bounds
arXiv:2606. 29654v1 Announce Type: new Abstract: Multi-agent deliberation among LLMs can improve reasoning, but deployment requires deciding when the current answer is reliable enough to act on and when it should be escalated to human review.
arXiv Machine Learning
Aug 18
Guaranteed Adaptive Modality Acquisition: When the Policy Chooses Its Own Calibration Group
arXiv:2608. 15520v1 Announce Type: new Abstract: A multimodal system may begin inference holding only some of its inputs and may acquire the rest at a cost.
arXiv Machine Learning
Aug 3
MARGIN: Runtime Confidence Calibration for Multi-Agent Foundation Model Coordination
arXiv:2605. 22949v3 Announce Type: replace Abstract: Foundation-model pools are increasingly used as black-box responders in coordinated systems where a coordinator must decide which response to trust.