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.

Comprehensive evaluation of 89 policies showing optimal co-training practices mixing real robot data with sim/egocentric human videos to boost diversity and performance in large robotics foundation models.

Novel memory system preserving dynamic object identity and motion continuity across occlusions in video world models, addressing frozen/vanishing issues for improved predictive physics in embodied AI.

Massive dataset enabling dynamic world models with explicit states and actions, supporting predictive modeling for cross-embodiment robotic control.

New benchmark assessing world models on interaction tasks, pushing predictive physics and video modeling towards robotics applications with action-conditioned evaluation.

Unified MLLM framework with Sparse 3D VQ-VAE (70% token reduction) for part-level mesh decomposition and kinematic chain prediction, enabling physics-based robotic simulation from monolithic assets.