The paper introduces the Geometric Action Model (GAM), which leverages a pretrained geometric foundation model to enhance language-conditioned manipulation in 3D physical environments. GAM splits the foundation model into an observation encoding layer and a future prediction layer, enabling it to predict future tokens from language, proprioception, and action history before decoding them into actions. This 3D-aware approach significantly improves accuracy, robustness, efficiency, and speed over standard 2D vision-language-action models in both simulated and real-robot contact-rich tasks.
The paper proposes Data2Story, a multi-agent framework that automates data journalism by mimicking a virtual newsroom with distinct roles. It generates evidence-based news stories in multiple formats, such as text articles, interactive maps, and audio, each linked to data sources for verifiability. In evaluations against expert human journalists, Data2Story showed competitive performance, particularly excelling in transparency and auditability. Human journalists still outperform in editorial angle and creative design. The system is designed as a collaborative tool for journalists, not a replacement.
FastContext is a system that decouples repository exploration from code solving in LLM coding agents to reduce token waste from irrelevant snippets. It deploys specialized exploration models as a dedicated subagent, issuing parallel tool calls and delivering focused context via concise file paths and line ranges. The approach cuts token consumption by up to 60% and improves resolution rates by up to 5.5% relative to baseline agents.
APPO is a new agentic reinforcement learning method that improves multi-turn tool-use in large language model agents. It refines branching and credit assignment by focusing on fine-grained token-level decision points rather than coarse heuristic interaction units. The method selects branching locations using token uncertainty and policy-induced likelihood gains, leading to more precise exploration and better credit distribution across branched rollouts. Experiments across 13 benchmarks show APPO consistently boosts performance over existing agentic RL methods by approximately 4 points. The approach also ensures efficient tool-calls and maintains behavioral interpretability.
HarnessX is a platform that enables composable, adaptive, and evolvable agent runtime harnesses. It introduces compositional primitives and AEGIS, a trace-driven evolution engine that iteratively refines harness design using execution feedback. Traditional static, hand-crafted harnesses are replaced with a substitution algebra for dynamic adaptation. Evaluated across multiple benchmarks, HarnessX achieved an average performance improvement of +14.5% over conventional harnesses, highlighting the impact of runtime interface evolution alongside model scaling. The full codebase will be released in the future.
OmniDirector introduces a unified framework for camera motion cloning in video generation that uses grid motion videos to visually encode camera parameters, supporting diverse trajectories for multi-shot scenes. It trains on a large dataset of camera grid-video pairs, eliminating the need for cross-paired data. The framework integrates characters, actions, and cameras via multimodal diffusion transformers, providing director-level control. A hierarchical prompt expansion agent harmonizes different control signals to enhance camera motion and visual content descriptions. Extensive experiments demonstrate its superior performance and controllability over existing methods.