TL;DR: RoboArchitect automatically designs and continually improves a robotic agent around a frozen robot policy, without updating any weights. These agents develop their own control routines. Most converge on a dynamic split between the VLA policy and code-as-policy, and nearly all build a persistent memory across episodes. Average success rises substantially on unseen scenes and tasks. RoboArchitect also manages to evolve a unified agent across different robot platforms that calibrates itself to each robot.

TL;DR: We make memory management a trainable skill for LLM agents. AutoMem automates the learning of this skill end-to-end: automatically optimizing the agent scaffold (memory structure) and training a dedicated memory specialist from the agent's own traces (memory proficiency). This automated learning alone brings an open-weight Qwen2.5-32B to frontier-level performance on long-horizon tasks.

TL;DR: TVP is a new visual programming framework that builds reusable tools from its own problem-solving experience via two interconnected libraries: an Example Library that accumulates program solutions as experience, and a Tool Library that maintains functions abstracted from these programs. The dual-libraries enable the circular program-tool-program cycle: solving problems generates experience, experience guides tool creation, and newly created tools improve future problem-solving.

TL;DR: 3D-Generalist is a generative graphics framework for creating 3D environments. Key modules include: 1. a diffusion-based panoramic generator that renders environment structures; 2. a VLA trained via self-improving loop for code generation to refine the environments; and 3. another VLA for placing diverse unlabeled 3D assets. 3D-Generalist provides a controllable pipeline to scale up synthetic 3D environment data for embodied AI.

TL;DR: We release SocSci210, a dataset of 2.9 million responses from 400,491 participants across 210 social science experiments. Through finetuning, our Socrates models achieve substantially better alignment with human response distributions under varying experimental conditions. We show that finetuning on just a subset of conditions within a study enables robust generalization to unseen conditions, demonstrating the potential for accurate experimental hypothesis screening with limited sample data.

TL;DR: S-VCO is a novel finetuning method that enhances visual-centric capabilties of VLMs while preserving general performance. Key design is a symmetrical visual contrastive objective that optimizes over visual details while avoiding one-sided "preference" formulation. Across various VLM benchmark domains, S-VCO demonstrates most significant and consistent improvements, with especially strong gains on visually demanding tasks.

TL;DR: DiverseEvol is an efficient instruction-tuning method that allows the model itself to iteratively sample training subsets to improve its own performance, with a key selection principle of maintaining high diversity in the chosen subsets. Across three datasets and benchmarks, our models, trained on less than 4% of the original dataset, match or improve performance compared with finetuning on full data.

TL;DR: We release Qwen, a family of highly-capabale foundation LLMs and Chat-Models. QwenLMs achieve superior performance than baselines (e.g., LLaMA2) of similar sizes on a wide range of benchmarks that measure natural language understanding, reasoning, problem solving, etc. Qwen-72B also outperforms GPT-3.5 on 70% of all tasks.