AI4AI-Bench: Benchmarking LLM Agents in Algorithmic Design for Recursive Self-Improvement
Recursive self-improvement (RSI) asks whether an AI system can improve the process that produces AI systems, so that ...
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Recursive self-improvement (RSI) asks whether an AI system can improve the process that produces AI systems, so that ...
Naturalistic computer-use traces, passively recorded screenshots and mouse or keyboard actions, are a valuable resour...
Travel behavior research increasingly combines digital data collection with predictive modeling, yet these stages are...
Personalized interpretation of medical reports has emerged as an increasingly important need among patients. Addressi...
Customer-service LLM agents must follow organizational policy when acting on a user's behalf. Compliance failures ari...
How to efficiently finetune robot policies to learn new tasks on the fly? State of the art robotic manipulation polic...
LLM agents learn by interacting with environments, yet these environments are hand-built and static: blind to an agen...
Identity-preserving image generation becomes increasingly unreliable when a scene must contain many specified people....
Action-conditioned video world models require low-latency causal generation and reliable responses to game-native con...
Memory has become a key component of large language models, enabling them to retain information and learn from long-t...
We present 4DAnyone, a framework for reconstructing 4D humans from an uncalibrated monocular video by generating reco...
Large language models often fail to answer questions about a bounded document collection when the source documents ar...