AI research, made legible.

Magpie is a real-time renderer that uses foundation models to generate game visuals by processing white-box frames as a continuous denoising condition.

The researchers introduced SWE-Prime, a method that selects a small, high-quality subset of training trajectories to improve software engineering agent performance.

The paper introduces a layered composition framework for semantic data processing that integrates online learning components to reduce the cost of LLM-based query execution.

WikiSkill improves AI agent performance by consolidating execution traces into a structured, persistent wiki that informs future skill development.

CritICL improves large language model performance by providing targeted, failure-aware critique examples in the prompt to steer reasoning away from common pitfalls.

The PILOT harness allows AI agents to improve their performance in real time by employing a supervisor that provides live feedback and distills successful strategies during task execution.

The paper introduces UrbanGround, a sandbox environment using real-world 3D mapping data to evaluate how well MLLM agents navigate complex urban settings.

The ACE framework establishes a formal structure for evaluating and improving the data generated to train autonomous AI agents.

The paper introduces PAWBench, a new evaluation framework that measures how well video generation models predict the physical outcomes of various scenarios.

TTPO improves the reasoning accuracy of language models during test time by using label-free policy optimization that bypasses the need for manual ground-truth data.