This work treats the warmup pool as a resampled random factor and adds token-matched irrelevant-filler controls, oracle-certified diagnostics, caution-pricing metrics, and quantified audits of the baseline ports to answer the question when does experience memory help a closed-loop LLM driving agent.
P pairwise convolutional ranking framework is introduced that learns directly from whole-brain T1-weighted MRI by making comparative rather than absolute predictions, which performs well even with small samples (N=108).
Marjan Rashidi, Robert Bain, Craig E. L. Stark et al.· 0 citations
CUA-Recovery is introduced, a benchmark comprising test and training suites across multiple desktop applications using ReRail, a framework for synthesizing long-horizon tasks and trajectories in a persistent desktop environment that shows that agents struggle both to recognize inherited errors and to complete the task.
Xue Hu, Hao-Ming Li, Yuxuan Wang et al.· 0 citations
Large language models (LLMs) can influence user trust and decision-making through subtle linguistic cues, yet the behavioral effects of manipulative outputs remain poorly quantified. We introduce a behavioral computing framework for measuring \textit{behavioral vulnerability}, defined as users'susceptibility to manipul...
Sadia Asif, Israel Antonio Rosales Laguan, Shumaila Asif et al.· 0 citations
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TEX, a collaborative test-time scaling framework based on execution-driven cross-validation that parallelizes agents to generate tests and patches, cross-executing each test against every patch to form a feedback matrix, enabling objective validation without verbose trace sharing.
Akash Gokul, Srijan Bansal, Ying-Bo Zhou et al.· 0 citations
The development of embodied models of animal behavior is a major goal of the neuro-AI community. Embodied models will enhance our ability to make direct comparisons between biological and artificial agents. To make such comparisons effective, though, we need embodied models that actually move like animals. Previous wor...
Aidan Sirbu, Charles Y. Zhang, Yuanjia Yang et al.· 0 citations
ReuF2F is introduced, a Lean 4 benchmark of 244 formalized open problems drawn from Research Experiences for Undergraduates (REU), spanning entry-level open research problems across a broad range of mathematical subfields and adopts a standard coding-agent evaluation setting.
Ze-Yu Zheng, Yi-Yan Liu, Yu-Yang Zhao et al.· 0 citations
Multi-Level Intermediate Representation (MLIR) underlies modern ML compiler infrastructure (TensorFlow, JAX/StableHLO, PyTorch Inductor, IREE), yet appears only in trace amounts in code-LM pretraining corpora. MLIR is also extensible by design: new dialects ship per application domain, so a fine-tuned model per dialect...
Vision-Language-Action (VLA) models provide a natural language interface to robot control, but the mapping from language to behavior is often brittle and unintuitive: semantically similar instructions can induce drastically different behaviors, while some capabilities may not be elicitable through prompting alone. As a...
Hyun Joe Jeong, Gokul Swamy, Andrea Bajcsy· 0 citations
A benign image paired with a benign caption can become harmful only in combination, making detection fundamentally a cross-modal reasoning problem. Existing multimodal harm benchmarks evaluate only classification accuracy, leaving unclear whether models identify the cross-modal mechanism that produces harm. We introduc...
Anisha Saha, Varsha Suresh, Teodora Kamova et al.· 0 citations
Existing theory of momentum assumes that gradients arrive at every parameter at a roughly constant rate, an assumption violated in practice by heavy-tailed data distributions and modern architectures. We theoretically analyze the dynamics of two tractable models of momentum under sparse updates: a least squares model w...
A machine-learning approach called CheMeleon provides a practical way to develop new drugs or other molecules with much less detailed, expensive experimentation.
Research Manager Mike Walker reflects on a career spanning hacker culture, cybersecurity, and AI, sharing lessons about trust, teamwork, leadership, and why solving difficult problems requires curiosity, judgment, agility—and an understanding of the rules. The post Learn the rules: The hacker mindset appeared first on Microsoft Research.
Learn how Google Antigravity and our Cheminformatics Skills accelerate drug discovery workflows, search compounds, and propose novel rigidified macrocycles.
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