Effective de-escalation is critical for law enforcement safety and community trust, yet traditional training methods lack scalability and realism. While Large Language Models (LLMs) enable dynamic, open-ended simulations, their substantial computational footprint renders them impractical for deployment on the lightweight, portable hardware required for immersive field training. Small Language Mode
Human action recognition is pivotal in computer vision, with applications ranging from surveillance to human-robot interaction. Despite the effectiveness of supervised skeleton-based methods, their reliance on exhaustive annotation limits generalization to novel actions. Zero-Shot Skeleton Action Recognition (ZSAR) emerges as a promising paradigm, yet it faces challenges due to the spectral bias o
Recent LLM-based agents have closed substantial portions of the scientific discovery loop in software-only machine-learning research, in chemistry, and in biology. Extending the same loop to high-fidelity physical simulators is harder, because solver completion does not imply physical validity and many failure modes appear only in field-level imagery rather than in solver logs. We present AI CFD S
Autonomous Machine Learning Engineering (MLE) requires agents to perform sustained, iterative optimization over long horizons. While recent LLM-based agents show promise, current prompt-based agents for MLE suffer from behavioral stagnation due to frozen parameters. Although Reinforcement Learning (RL) offers a remedy, applying it to MLE is hindered by prohibitive execution latency and inefficient
Reinforcement learning algorithms assume that observations satisfy the Markov property, yet real-world sensors frequently violate this assumption through correlated noise, latency, or partial observability. Standard performance metrics conflate Markov breakdowns with other sources of suboptimality, leaving practitioners without tools to detect such violations. This paper introduces a prediction-ba
Capital is concentrating around a small set of AI-adjacent names rather than spreading across many rounds. The pattern points to large, strategically structured checks at the top of the market, with little evidence of broad early-stage dispersion.
Capital is concentrating around a small set of AI-adjacent names rather than spreading across many rounds. The pattern points to large, strategically structured checks at the top of the market, with little evidence of broad early-stage dispersion.
Capital is concentrating around a small set of AI-adjacent names rather than spreading across many rounds. The pattern points to large, strategically structured checks at the top of the market, with little evidence of broad early-stage dispersion.
Named moves are sparse, but the desk still matters when a person or role shift signals institutional reorganization. The current bundle is too thin to show a broader hiring or departure pattern.
Named moves are sparse, but the desk still matters when a person or role shift signals institutional reorganization. The current bundle is too thin to show a broader hiring or departure pattern.
Named moves are sparse, but the desk still matters when a person or role shift signals institutional reorganization. The current bundle is too thin to show a broader hiring or departure pattern.
Fresh benchmark entries span intelligence, multimodal coding, and verified software repair, keeping evaluation pressure on both general and task-specific systems. The mix matters because it tracks where score gains are still being recorded, and where the ceiling is moving.
Fresh benchmark entries span intelligence, multimodal coding, and verified software repair, keeping evaluation pressure on both general and task-specific systems. The mix matters because it tracks where score gains are still being recorded, and where the ceiling is moving.
Fresh benchmark entries span intelligence, multimodal coding, and verified software repair, keeping evaluation pressure on both general and task-specific systems. The mix matters because it tracks where score gains are still being recorded, and where the ceiling is moving.