AllenAI has released two MolmoMotion models that predict 3D point trajectories based on short video histories and natural-language instructions. One model uses a three-frame history, the other a one-frame history, enabling future motion forecasting for objects in 3D space.
AllenAI releases MolmoMotion vision models for future motion prediction
Gazer: Training-Free Semantic Correction for Autoregressive Visual Models
Gazer introduces a training-free framework that uses multimodal large language model feedback to correct semantic errors in real time during autoregressive visual model generation. By integrating reflective diagnosis and semantic correction stages, Gazer improves compositional accuracy and semantic alignment across multiple models without additional training.
Decoupling Declarative and Procedural Knowledge in Vision-Language-Action Models
w$^{2}$VLA introduces a modular approach that decouples declarative and procedural knowledge in Vision-Language-Action models. By restructuring information flow, it enables robust behavior cloning and unprecedented zero-shot skill transfer across unseen, dissimilar objects.
ARCO: Adaptive Rubric with Co-Evolution for Multi-Step LLM Agents
ARCO introduces a rubric framework that enables step-level credit assignment for multi-step LLM agents. It jointly updates a shared model with generation and scoring heads, allowing the rubric content and scoring function to co-evolve via on-policy data, improving performance and interpretability across benchmarks.
Decoupling Declarative and Procedural Knowledge in Vision-Language-Action Models
w$^{2}$VLA introduces a modular vision-language-action model that decouples declarative and procedural knowledge. By restructuring information flow, it enables robust behavior cloning and zero-shot skill transfer to novel, dissimilar objects.
Self-Evolution of Tool-Calling Agents via Divergence-Point Preference Learning
ToolGraph enhances multi-turn tool-using agents by integrating schema topology, transition weights, and history-aware controls. Training with DPO on 161 divergence-point preference pairs improves performance: ToolGraph+DPO achieves a 16.8% relative reward gain over baseline, especially in airline and retail tasks, with reward positivity emerging as the key diagnostic signal.