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.
By Yanyan Zhang, Fei Shen, Ting Yan, Hao Zhang
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.
By Melika Baghi
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.
By Mingyang Song, Luxin Xu, Haoyu Sun, Minzhou Pan, Yu Cheng, Bo Li
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.
By Mengdie Flora Wang, Haochen Xie, Guanghui Wang, Devin Zhang, Jae Oh Woo
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.
By Melika Baghi
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.
By Joss Armstrong
The paper introduces CARE, a method for certifying accelerated inference in vision‑language‑action models. CARE uses paired rollouts on a calibration set to guarantee that the risk of acceleration‑induced failure stays below a user‑specified budget, selecting the fastest certified accelerator or falling back to the reference policy. Experiments on LIBERO suites show that CARE achieves 9.0–10.8× speedups while preserving at least 85.8% of reference‑solved episodes with 95% confidence, and it outperforms non‑guaranteed selectors in both budget adherence and rollout efficiency.
By Rui Liu, Tong Zheng, Jindong Gu, Zhipeng Wang
While vision-language-action (VLA) models have advanced rapidly, running them at every control step remains expensive. Prior work accelerates VLA inference using techniques like action chunking and vi...
arXiv:2610.02828v1 Announce Type: new
Abstract: Adaptive LLM reinforcement-learning post-training changes multiple training actuators online, including rollout temperature, group size, clipping, KL r...
By Miaobo Hu, Shuhao Hu, Xiaobo Guo, Xin Wang, Bokun Wang, Daren Zha, Jun Xiao
The paper introduces Audit-First VAPO, a method that separates directional admission from magnitude control for selective updates under imperfect verification. It employs an observation-only accept‑appeal‑abstain policy with a finite secondary‑verification budget, freezing the action trace before clean labels are added, and uses finite‑sample bounds to certify harmful risk, coverage, and verifier‑call rate. Experiments on Qwen3.5‑0.8B and GSM8K show that RC‑VAPO achieves high accuracy while maintaining low selected harmful risk and modest verifier cost compared to baseline methods.
By Miaobo Hu, Shuhao Hu, Xiaobo Guo, Xin Wang, Bokun Wang, Rui Chen, Daren Zha
The paper introduces loss‑conditioned state execution, a model‑agnostic technique that decides whether to apply a world model’s proposed state change or keep the current state based on whether the change reduces downstream loss. It formalizes state movability as the existence of a loss‑reducing feasible correction and constructs loss‑specific proposals from predictive distributions, executing them only when a groupwise lower confidence bound on loss improvement is positive. Experiments on forecasting and dynamics benchmarks show that the method accepts updates for a subset of cases, achieving lower bounded loss than persistence or always executing the proposal, and highlights that event predictability and loss‑based decisions must be evaluated separately.
By Jintao Xu, Zhengyu Chen, Ben Zhang, Yongzhi Qi, Jianshen Zhang
arXiv:2606. 20364v1 Announce Type: new Abstract: A companion study established a de-biased, cross-model VLM-as-3D-judge that reliably ranks single-image-to-3D mesh quality where cheap geometry and CLIP proxies fall short.
By Ali Asaria, Tony Salomone, Deep Gandhi