Current work on radiology report evaluation has focused primarily on LLM-based metrics for chest X-rays (CXR), leaving open whether such approaches remain robust across other modalities and anatomies, and which model and prompt configurations are best suited to serve as LLM judges. We present a comprehensive evaluation...
Federica Bologna, Jean-Philippe Corbeil, Matthew Wilkens et al.· 0 citations
Genomic Foundation Models (GFMs) typically rely on Masked Language Modeling (MLM) or Next-Token Prediction (NTP) to learn the "Laws of Nature". While effective at capturing local syntax, these generative paradigms prioritize token-level reconstruction over high-level functional context. We introduce JEPA-DNA, a model-a...
Ariel Larey, Elay Dahan, Amit Bleiweiss et al.· 0 citations
Skills are getting increasingly used in recent agentic frameworks, performing robustly with proprietary LLMs. Yet, their effectiveness for small and medium-sized open-source models (270M--80B) remains underexplored, especially under industrial, resource-constrained conditions. As such, we present a large-scale empirica...
Yangjie Xu, Lujun Li, Lama Sleem et al.· 0 citations
Large language models (LLMs) excel at natural language tasks but remain brittle in domains requiring precise logical and symbolic reasoning. Chaotic dynamical systems provide an especially demanding test because chaos is deterministic yet often misinterpreted as randomness or complexity. We introduce ChaosBench-Logic,...
Noel Thomas· 0 citations
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Regulatory compliance is an important requirement in many industrial and administrative settings. Yet compliance assessment remains difficult because regulatory requirements are distributed across structured documents and may depend on hierarchy, definitions, conditions, and cross-references, while the evidence relevan...
Jiseong Chung, Ronny Ko, Won-Yong Shin et al.· 0 citations
Pioneering companies such as Waymo have deployed electric robo-taxi services in several U.S. cities. Operating such fleets requires coordinating ride matching, vehicle repositioning, and battery charging in a stochastic environment while accounting for limited charging infrastructure. Understanding both how to design e...
Experiments show that current VLA policies struggle with zero-shot personalization, while SFT makes preferences learnable, and a controlled formulation and taxonomy of observable preferences in simulation is proposed, covering motion, skill, and user-profile choices.
Zhi-Xu Li, Ke Tang, Li-Tian Gong et al.· 0 citations
An evolutionary framework in which large language models propose circuit designs and an independent evaluator verifies and scores them is introduced and opens the door for evaluator-guided LLM co-design of new algorithmic structures for quantum computation.
An-Di Gu, Alek H. Kemeny, S. Yelin et al.· 8 citations· ⚡2
T2-Z is introduced for test-time optimization of environment latents, which augments a robotic world model with a learnable environment latent to condition world predictions, and can efficiently infer environment latents from as little as a single test-time interaction trajectory.
Chen-Yuan Zhou, Yi-Ning Hong, Huang Huang et al.· 0 citations
This work introduces CoDA, a framework in which a simulation data engine and an action policy co-evolve through a shared heterogeneous memory of environment construction, policy failures, and learning progress, and proposes Text2potential to convert these diagnoses into staged potentials for action-chunk value learning...
Tianjiao Yu, Zhi-Lin Ou, Yi-Fan Shen et al.· 0 citations
Mixture-of-experts (MoE) models have become an important approach for scaling model parameters. Modern MoE architectures increasingly combine two design choices: fine-grained expert segmentation and shared experts. The former increases expressiveness by enabling more expert combinations at a fixed activation ratio, and...
Bingcong Li, Hao Ma, Michael Muehlebach et al.· 0 citations
Recent 3D world models increasingly use features from geometry foundation models (GFMs) as predictive world states, benefiting from their rich geometric priors. However, the view dependence of these features entangles scene dynamics with camera-induced variation, making the resulting state evolution harder to predict....
Hongchang Liu, Jian Liu, Yujuan Ding et al.· 0 citations
