Certified Anchor-Based Adaptation (CABA) is a post‑update gate for continual adaptation that reserves historical observations, compares old and candidate policies on frozen representations, and selects the largest interpolation coefficient whose worst‑anchor forward KL divergence stays within a declared budget. The method does not require action labels or complete trajectories and is supported by a theory‑led certification framework that provides uniform behavioral‑divergence bounds under representation‑coverage and Lipschitz assumptions. In a UCI Human Activity Recognition study, CABA reduced mean forgetting from 1.01 % to 0.00 % at a budget ε = 0.05, while slightly lowering final average accuracy.
By Ahanaf Hasan Ariq, Abrar Shahid
arXiv:2610.05166v2 Announce Type: replace
Abstract: A safe action is not necessarily a viable one. A frozen vision-language-action (VLA) policy can favor a locally admissible move that leaves no poli...
By Tu Nguyen, Matthieu Zimmer, Vu Anh Vu, Ziyi Wang, Jannik Hammel Nielsen, Xuebing Zhou, Haitham Bou Ammar
arXiv:2609.37591v1 Announce Type: cross
Abstract: Test-time adaptation for vision-language navigation (TTA-VLN) enables pretrained policies to adapt online to unseen environments using only test-time...
By Yang Li, Sijia Zhang, Yihan Li, Aming WU, Zihao Zhang, Ziju Han, Yahong Han
arXiv:2608.21748v1 Announce Type: new
Abstract: Verifier-guided text-to-image systems increasingly use test-time search to select, refine, or stop among multiple candidates, yet release thresholds ar...
By Xuanhua Yin, Shunqi Mao, Wei Guo, Chuanzhi Xu, Weidong Cai
arXiv:2609.37501v1 Announce Type: cross
Abstract: We propose RegLLM, a diagnostic harness for bounded autonomy in regulated agentic workflows. It instruments six trustworthiness signals: citation val...
By Dipankar Sarkar
Large language model agents coordinate tasks via multi‑role, multi‑stage workflows that transform upstream state into intermediate artifacts such as summaries and plans. The study shows that when these artifacts are transformed—through compression, plan assimilation, or other handoff methods—the strict action‑binding constraints on upstream state can be weakened, turning mandatory requirements into optional information. In 1,296 synthetic episodes, direct handoff preserved all safety blockers, whereas transformed handoffs frequently deactivated or forbidden actions, but restoring full state fields or applying downstream verification can recover preservation.
By Yiheng Sun, Huifei Wang, Yancheng Zhu, Zhenyu Li, Zebin Zhao, Yifan Yuan
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...
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
arXiv:2607. 12216v1 Announce Type: cross Abstract: Multi-agent and memory-augmented LLM systems often place coordination content, shared state, prior discussion, tool outputs, summaries, and role instructions, inside the same finite prompt used for the current task.
By Brenda Lelis, Rodrigo Cabral-Carvalho
The paper examines how budgeted context projection can reduce the length of individual requests while affecting whether an agent completes within its budget. By analyzing a recorded ReVerPi source‑reading campaign, the authors show that retaining all intervention boundaries reveals trade‑offs that are obscured when a capped first continuation prevents companion runs, and they quantify success differences across various task subsets. The study highlights the importance of accounting for every boundary, preserving unknown outcomes, and separating completion from resource use in adaptive evaluation campaigns.
By Guangzhe Zhang
arXiv:2608.23224v1 Announce Type: cross
Abstract: Retrieval can efficiently and effectively augment a frozen vision--language--action (VLA) policy without retraining, yet retrieved text becomes a con...
By Zhiruo Zhou, Zelin Li, Xiwen Chen, Jiazhuo Li, Chenwei Wang, Huiming Chen, Xiaojun Zhu
The paper investigates how different types of privileged context affect policy drift in on‑policy self‑distillation (OPSD) for language models. By varying content (demonstration, feedback, rephrase) and source (external, self‑generated with a verifier, self‑generated without a verifier) across nine combinations, the authors train Qwen2.5‑7B on three datasets and measure target‑task accuracy, prior‑task retention, reverse KL divergence, and parameter‑update geometry. Results show that changing content leads to a larger median KL range and more aligned parameter updates than changing source, indicating that privileged context significantly influences the direction and magnitude of policy updates.
By Ravenor Davion, Nick Rui