Benchmark with 2,159 rubric items across 300 tasks using trajectory-aware grading and 3-trial Pass^3 scoring to mitigate luck. Evaluates agent reliability in real-world robotics settings.

Reveals paraphrase fragility in VLAs causing 22-52% success drops due to task misidentification. Introduces PRIDE metric weighting success by paraphrase difficulty on LIBERO benchmark manipulation tasks.

Low-cost mobile manipulator design and training strategies for broad generalization in real-world tasks.

Closed-loop egocentric video simulator maintaining persistent 3D scene state for consistent interactions, enabling cross-embodiment transfer from human videos to robotic manipulation.

Comprehensive survey taxonomizing efficient architectures/algorithms for video world models as simulators, targeting compute bottlenecks in embodied AI, autonomous driving, and games with techniques like short-window attention for real-time long-horizon prediction.

Curated collection of 100+ works surveying shift to continuous latent spaces in LLMs/VLMs/VLAs for improved reasoning over discrete tokens, with relevance to robotics action modeling.

CaP-X is an open-source agentic robotics framework where LLMs/VLMs generate code to call perception and control APIs for execution across diverse simulated and real robots in CaP-Gym's 187 manipulation tasks. The framework includes CaP-Bench for evaluating frontier models and CaP-RL, which boosts a 7B model's success from 20% to 72% with minimal sim-to-real gap.

An upgrade over LoRA for parameter-efficient fine-tuning, enabling better performance in LLMs by decomposing weights into magnitude and direction components.

Seminal work showing how training on AI-generated data leads to 'model collapse' in neural networks, with urgent implications for future scaling.

Real-world hardware evaluation of VLAs on blind bin-to-bin picking, achieving max 64 picks/hour across hundreds of runs, with full videos/data exposing gaps in production-scale robotic manipulation reliability.