AlignSAE is introduced, a method that aligns SAE features with a predefined ontology through a "pre-train, then post-train"curriculum and achieves disentanglement improvements of $9$--$\infty\times$ over standard SAEs, and further supports multi-hop reasoning and a mechanistic probe of grokking-like generalization dyna...
Minglai Yang, Xin-Yu Guo, Zheng-Liang Shi et al.· arXiv.org· 4 citations
CyberArena is a dynamic, symmetric adversarial benchmark built around multi-round attack-and-defense combat, and introduces a dynamical model that maps match dynamics into unified, multidimensional scores across offensive, defensive, and domain-specific capabilities.
Bangya Liu, Jianhong Tu, Yu-Jing Pan et al.· 0 citations
In-context learning (ICL) allows language models to adapt their behavior from instructions and demonstrations in a prompt, but accuracy alone does not reveal how contextual information controls their predictions. We study the acquisition and trainability of ICL with a controlled diagnostic that separates copying a near...
Minglai Yang, Xin-Yu Guo, Ren-Xiong Wang et al.· 0 citations
Long-horizon, semi-verifiable agentic tasks often rely on rubric-based evaluation, but the evidence needed to assess these tasks can be spread across the environment state and the execution trace. A judge may overlook this evidence or accept unsupported claims, including an agent's own reports of success. When rubric v...
Reza Sanayei, Razvan-Gabriel Dumitru, Sun-Jie Hou et al.· 0 citations
RSI-Forge is introduced, a multi-agent pipeline that turns published papers into executable environments for self-improvement and provides a scalable approach to constructing research environments for training and evaluating self-improving agents.
Ren-Xiong Wang, Darvin Yi, Abril Herrlein et al.· 2 citations
Reliable Enterprise Agent Deployment (READY), a framework for qualifying AI agents for deployment on enterprise workflows, and provides a basis for comparing agent systems, setting oversight requirements, and making evidence-based deployment decisions.
Veronica Chatrath, Bryan Zhu, Jing-Xuan Fan et al.· 0 citations
This work proposes Rubric Dropout, a one-line fix borrowed from neuron dropout that randomly drops a subset of the rubric's criteria before computing the reward, so the policy never optimizes the same rubric twice.
Minglai Yang, Xinyu Guo, Utkarsh Tyagi et al.· 0 citations
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