Liu and colleagues developed a robust in cellulo plasmid DNA replication system in human cells for replicating plasmid-borne adeno-associated virus (AAV) Rep/Cap genes during recombinant AAV (rAAV) production. This new approach not only enables a 10- to 20-fold plasmid reduction to significantly lower manufacturing costs but also substantially enhances rAAV potency, titer, and purity.

arXiv:2609.09783v1 Announce Type: cross Abstract: Asynchronous reinforcement learning has become the standard way to scale training for language models, but the resulting policy lag biases the critic toward the stale behavior policy. Existing work on asynchronous LLM training corrects the actor and leaves this bias unaddressed, while the off-policy value correction of classical RL does not carry over to long-horizon agentic tasks, since a short correction horizon leaves the regression target free of the reward and a long one lets the product of importance ratios drift exponentially with the trajectory length. We propose BRACE, an anchored Bellman-residual correction for stale value models. BRACE bounds the correction horizon to a prefix of policy tokens and anchors a constant-weight Monte-Carlo tail beyond it, which separates policy correction from reward propagation. BRACE improves mean@1 on BrowseComp-Plus by $2.4\%$ over the strongest baseline, runs $2.46\times$ faster per step than synchronous training, and remains stable $50$ updates off-policy.

arXiv:2509.25084v3 Announce Type: replace-cross Abstract: Data-analytic agents are emerging as a key catalyst for automated scientific discovery and for the vision of Innovating AI. Current approaches, however, rely heavily on prompt engineering over proprietary models, while open-source models struggle to face diverse-format, large-scale data files and long-horizon, multi-step reasoning that real-world analytics demands. This paper introduces DataMind, a scalable data synthesis and agent training recipe designed to build generalist data-analytic agents. DataMind tackles three key challenges in building open-source data-analytic agents, including insufficient data resources, improper training strategy, and unstable code-based multi-turn rollout. Concretely, DataMind applies 1) a fine-grained task taxonomy and a recursive easy-to-hard task composition mechanism to increase the diversity and difficulty of synthesized queries; 2) a knowledge-augmented trajectory sampling strategy followed by model-based and rule-based filtering; 3) a dynamically adjustable training objective combining both SFT and RL losses; 4) a memory-frugal and stable code-based multi-turn rollout framework. Built on DataMind, we curate DataMind-12K, a high-quality trajectory set spanning diverse domains, task categories, and data file formats for data-analytic tasks. Trained on DataMind-12K, our DataMind-14B achieves state-of-the-art with an average score of 71.16% on multiple data analysis benchmarks, outperforming the strongest proprietary baselines DeepSeek-V3.1 and GPT-5. Our DataMind-7B also performs best among all open-source models with a score of 68.10%. We also incorporate some empirical insights gained from our exploratory trials into the analysis experiments, aiming to provide actionable insights about agentic training for the community. We will release DataMind-12K and DataMind-7B,14B for the community's future research.

arXiv:2603.00171v2 Announce Type: replace-cross Abstract: Multimodal Large Language Models (MLLMs) often struggle to accurately perceive fine-grained visual details, especially when targets are tiny or visually subtle. This challenge can be addressed through semantic-visual information fusion, which integrates global image context with fine-grained local evidence for multi-scale visual understanding. Recently, a paradigm termed "Thinking with Images" has emerged, enabling models to acquire high-resolution visual evidence by zooming or cropping image regions and fusing these local details with global context during reasoning. Although training-based approaches demonstrate the effectiveness of this capability, they require extensive computational resources and large-scale task-specific data. Consequently, lightweight training-free methods have been proposed as a practical alternative to incorporate local visual evidence during inference. However, existing training-free approaches still suffer from two key limitations. First, they indiscriminately extract and fuse local visual regions for all inputs regardless of necessity, introducing computational redundancy and perceptual noise. Second, they exhibit drift between semantic intent and visual attention, preventing accurate localization of user-focused regions. To address these challenges, we propose SvfEye, a training-free framework for adaptive visual-semantic fusion. SvfEye follows a two-stage pipeline with a confidence-based decision module to determine whether additional local visual information is needed, and a semantic-attention fusion module to identify informative local regions. Experiments show that SvfEye achieves substantial performance gains while obtaining an approximately 4.0x inference speedup over the state-of-the-art method ZoomEye.