Examines memorization phenomena that occur during deployment of foundation model agents in practical applications.

Focuses on security aspects of deep neural networks, providing taxonomy and mitigation strategies for adversarial attacks.

First comprehensive framework for evaluating LLM mediators in real-time, emotional, socio-cognitive scenarios.

Improves embedding quality without extra training by using the unembedding matrix as a feature lens for text embeddings.

Presents an autonomous system for formalizing mathematical proofs, specifically targeting Erdős problems. Demonstrates automated proof formalization capabilities in the Lean theorem prover.

Provides a comprehensive taxonomy, benchmarks, and future directions for autonomous AI research agents. Examines systematic approaches to developing agents capable of conducting independent research.

Omnimodal world models explicitly designed for Physical AI and robotics applications. Enables improved simulation and control for robotic systems through multimodal understanding.

GPT-style Transformer trained on 2 billion motion frames enabling zero-shot dynamic humanoid control for tasks like soccer, dancing, and digging on real Unitree G1 robots without fine-tuning. Breaks the agility-generalization trade-off in humanoid robotics.

New IROS benchmark providing 500+ teleoperation episodes and physically accurate simulation assets for training policies that outperform human origami experts.

First foundation model for zero-shot grasping trained on billions of simulated grasps, enabling generalized manipulation without task-specific training.