Standard supervised training for deepfake detection treats all samples with uniform importance, which can be suboptimal for learning robust and generalizable features. In this work, we propose a novel Tutor-Student Reinforcement Learning (TSRL) framework to dynamically optimize the training curriculum. Our method models the training process as a Markov Decision Process where a ``Tutor'' agent lear
Large language models (LLMs) are increasingly applied to scientific research, yet existing evaluations often fail to reflect the fine-grained capabilities required in practice. Most benchmarks are manually curated or domain-generic, limiting scalability and alignment with real scientific use cases. In this paper, we propose a new framework named SciCustom to address the problem. It enables the cus
Reinforcement learning for legged locomotion has matured into a stack of multi-component reward functions and physics-engine benchmarks whose morphologies are uniformly derived from real commercial hardware. Game NPCs, however, are bound by stylistic constraints absent from sim-to-real robotics and routinely take the form of creatures with no real-robot counterpart. We introduce ARC-RL, a suite of
Automatic report labeling facilitates the identification of clinical findings from unstructured text and enables large-scale annotation for medical imaging research. Existing rule-based labelers struggle with the diverse descriptions in clinical reports, while fine-tuning pre-trained language models (PLMs) requires large amounts of labeled data that are often unavailable in clinical settings. In t
Can a single LLM-based optimization system match specialized tools across fundamentally different domains? We show that when optimization problems are formulated as improving a text artifact evaluated by a scoring function, a single AI-based optimization system-supporting single-task search, multi-task search with cross-problem transfer, and generalization to unseen inputs-achieves state-of-the-ar
Capital remains concentrated in large, established names rather than fresh startup formation. The cycle points to late-stage scale and balance-sheet strength, with one company absorbing the clearest share of attention.
Capital remains concentrated in large, established names rather than fresh startup formation. The cycle points to late-stage scale and balance-sheet strength, with one company absorbing the clearest share of attention.
Capital remains concentrated in large, established names rather than fresh startup formation. The cycle points to late-stage scale and balance-sheet strength, with one company absorbing the clearest share of attention.
Named moves cluster around senior operator and technical roles, with one high-profile lawyer joining the mix. The signal is organizational: teams are still reshaping around infrastructure, analysis, and legal defense.
Named moves cluster around senior operator and technical roles, with one high-profile lawyer joining the mix. The signal is organizational: teams are still reshaping around infrastructure, analysis, and legal defense.
Named moves cluster around senior operator and technical roles, with one high-profile lawyer joining the mix. The signal is organizational: teams are still reshaping around infrastructure, analysis, and legal defense.
Three entries on the same evaluation family give a narrow but readable snapshot of capability tracking. The interest is in coverage across difficulty levels, not a broad leaderboard shake-up.
Three entries on the same evaluation family give a narrow but readable snapshot of capability tracking. The interest is in coverage across difficulty levels, not a broad leaderboard shake-up.
Three entries on the same evaluation family give a narrow but readable snapshot of capability tracking. The interest is in coverage across difficulty levels, not a broad leaderboard shake-up.