arXiv:2607. 08233v1 Announce Type: new Abstract: A central challenge in building intelligent systems is enabling agents to jointly perceive complex inputs, form hypotheses about hidden patterns, and design informative experiments to test them.
arXiv:2505. 23399v2 Announce Type: replace Abstract: We propose GAM-Agent, a game-theoretic multi-agent framework for enhancing vision-language reasoning.
arXiv:2603. 23867v2 Announce Type: replace-cross Abstract: Vision-Language Models (VLMs) have been applied to a wide range of reasoning tasks, yet it remains unclear whether they can reason robustly under distribution shifts.
arXiv:2607. 06522v1 Announce Type: new Abstract: Vision-language models (VLMs) struggle to generalize in interactive physical reasoning, particularly under unseen tasks and environments.
ExplorationBench is a benchmark designed to evaluate AI systems’ ability to conduct scientific exploration in verifiable alien worlds, where rules are executable and can be precisely checked. It consists of two sandboxes—AlienCode and AlienLogic—each offering discovery targets, tasks, flawed manuals, environmental feedback, and tool‑call schemas. The benchmark tests whether systems can generate new hypotheses, design experiments, and iterate on results, rather than merely recalling pre‑trained knowledge, and finds that top performers can acquire and apply unfamiliar rules, though performance varies across exploration trajectories.
arXiv:2607. 01531v2 Announce Type: replace Abstract: Learning how an environment behaves from interaction is central to building agents that adapt to unfamiliar tasks.
Learning how an environment behaves from interaction is central to building agents that adapt to unfamiliar tasks. World models learned with deep networks are flexible but data-hungry and transfer poorly beyond their training distribution.
arXiv:2607. 01531v1 Announce Type: new Abstract: Learning how an environment behaves from interaction is central to building agents that adapt to unfamiliar tasks.
arXiv:2607. 28942v1 Announce Type: new Abstract: Recently Large Language Models (LLMs) have been increasingly deployed as autonomous agents in applications such as self-reflection, retrieval-augmented generation, and scientific discovery.
ExplorationBench is a new benchmark designed to evaluate AI systems’ ability to conduct scientific exploration in verifiable alien worlds. It comprises two sandbox environments—AlienCode and AlienLogic—each containing discovery targets, tasks, flawed manuals, and tool‑call schemas that force systems to formulate hypotheses, design experiments, and iterate on results. Ten AI systems were tested, revealing that while the best performers can learn and apply unfamiliar rules, their progress varies across exploration trajectories and can even regress with continued exploration.
arXiv:2606. 26460v1 Announce Type: new Abstract: AI-based scientific automation is increasingly possible by using agents to generate hypotheses, design experiments, and analyze data.
arXiv:2607. 04293v1 Announce Type: cross Abstract: Building AI Scientist agents with Large Language Models (LLMs) has recently attracted growing attention.
arXiv:2607. 12042v1 Announce Type: cross Abstract: Visual generation is increasingly ubiquitous in diverse domains, from text-to-image/video synthesis to multimodal interactive creation.