A research project explores using natural-language tactical instructions from humans to guide autonomous AI agents in a football simulation. The system enables human coaches to issue high-level directives like 'press aggressively' or 'exploit the left side', which the AI agents then adapt to in real time within a dynamic, team-based environment.
Research Project: Injecting Natural-Language Tactical Intent into Multi-Agent Football Policies
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.
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.
Dementia-Agents: Multi-Modal Multi-Agent System for Dementia Staging
Dementia-Agents introduces a clinically aligned multi-agent framework for real-world dementia staging and phenotyping. It improves diagnostic performance over monolithic models and prior systems, while maintaining domain-level interpretability, using data from 1,066 patients across two cognitive neurology services.
AllenAI releases MolmoMotion vision models for future motion prediction
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.