World action models generate actions together with visual predictions of their consequences. These paired outputs create the potential for planning by sampling multiple actions from one state, comparing their imagined outcomes, and choosing the action with the most promising predicted outcome. However, how to use imagi...
Jian-Hao Yuan, Yuan-Yuan Yuan, Benjamin Ramtoula et al.· 0 citations
Large Language Models (LLMs) increasingly ship with explicit"thinking modes", yet their counterpart,"no-thinking", has received far less attention. We study LLMs'no-thinking behavior along two axes. a. How to measure no-thinking? Prior work typically defines no-thinking through proxies such as a disabled thinking mode...
In decentralized consumer-to-consumer (C2C) marketplaces, people list goods, negotiate with strangers, and rate one another, so trust rests on reputation. Large language model (LLM) agents now act for users, raising risks to their money, privacy, and reputation. We introduce BazaarBench, a simulated C2C marketplace and...
Ziyan Wang, Shu-Qing Shi, James Oldfield et al.· 0 citations
Test-time scaling improves language model reasoning by spending additional compute at inference. However, both classes of existing methods often fail to continue improving over long timescales. Parallel methods repeatedly sample independent answers from the model, scaling poorly on problems the model is unlikely to sol...
Joseph Rance, Fabio Pizzati, Juil Sock et al.· 0 citations
This work proposes $D^2-Monitor, in which a lightweight probe runs on every sample, producing a base prediction and a hesitation estimate that decides whether to escalate the sample to a more expressive probe, and exhibits the best trade-off between effectiveness and efficiency relative to 8 baselines.
Ao Liu, Yu-Peng Chen, James Oldfield et al.· 1 citation
PartiCam is presented, a training-free Particle filtering rooted method for improved Camera controlled video generation that builds on Sequential Monte-Carlo (SMC) guidance, but introduces a local refinement stage based on particle filtered resampling.
Amine Ouasfi, Runjia Li, Junlin Han et al.· 0 citations
LatticeMind is presented, a conflict-aware structured memory that handles contradiction at write time, which maintains explicit item status, applies cheap symbolic conflict checks, and invokes LLM reconciliation only for unresolved semantic cases.
Heng Zhou, Lian Zhang, Yutao Fan et al.· 0 citations
Policymakers should urgently obtain more visibility into the automation of AI R&D, develop ways to steer and constrain an intelligence explosion, and prepare society to adapt to an intelligence explosion's impacts.
Alan Chan, Christoph Winter, A. Barto et al.· 0 citations
StraTA is a simple framework that introduces an explicit trajectory-level strategy into agentic reinforcement learning (RL) and trains strategy generation and action execution jointly with a hierarchical GRPO-style rollout design, further enhanced by diverse strategy rollout and critical self-judgment.
Xiangyuan Xue, Yifan Zhou, Zidong Wang et al.· arXiv.org· 1 citation
This work introduces WROP (World Reasoning with Object Permanence), a data infrastructure of 150 hand-designed cognitive science inspired tasks, divided into six cognitive categories, and builds Blender generators that randomize speed, lighting, camera angle, and other nuisance parameters while preserving each task's c...
Hao-Tian Zhang, Feng-Yuan Yu, Dezhi Luo et al.· 0 citations
Adversarial Probing for Implicit VulnErabilities (AdvPIE), a multimodal agentic framework to expose implicit vulnerabilities without requiring access to the parameters of target models, is proposed.
Chang-Sha Ma, Junlin Han, Shuo Chen et al.· 0 citations
This work introduces Data Journalist Agent (Data2Story), a multi-agent framework that orchestrates specialized roles into a single virtual newsroom, enabling more evidence-based, transparent, and verifiable reporting.
K. Lin, EI Batu, Yu-Hong Shi et al.· arXiv.org· 0 citations
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