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AI NEWSresearch

WMLLM: Self-Evolving Optimization Agents via Predict-Then-Act World Modeling

This arXiv update presents WMLLM, a self-evolving optimization-agent framework that uses predict-then-act world modeling. The approach leverages large language models to forecast promising optimization directions before candidate generation, followed by agentic refinement, population-based search, and reinforcement learning to improve both the world model and optimization strategy. Experiments on black-box optimization, especially multi-objective molecular optimization, demonstrate improved sample efficiency and competitive final performance, achieving state-of-the-art results under limited evaluation budgets.

Why it matters: If the findings generalize, WMLLM could reduce evaluation costs in complex optimization problems. This suggests a path to more efficient design cycles in domains relying on expensive experiments or simulations.

Primary source: cs.LG updates on arXiv.orgOpen source ↗
Entities:black-box optimizationlarge language modelsmulti-objective molecular optimizationpredict-then-act world modelingreinforcement learningWMLLM
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GAPS: Dimension-Level Gates for Conditional Activation Steering

GAPS introduces dimension-level conditioning to selective activation steering, adding static separability and dynamic posterior gates to restrict interventions to neurons with reliable concept information. The method aims to reduce unnecessary steering by applying gates that decide which neurons to intervene on, and it plugs into existing conditional methods. Evaluations on toxicity mitigation and concept removal show improved or matching Pareto performance compared to token-level approaches.

Why it matters: By adding dimension-level selectivity, GAPS reduces unnecessary intervention, potentially lowering unintended alterations to benign behavior while preserving or enhancing safety gains. This approach can tighten the efficiency and effectiveness of activation steering under fixed capability budgets.

Primary source: cs.CL updates on arXiv.orgOpen source ↗
Entities:Dimension-Level Gates for Conditional Activation SteeringDSASGAPSGemma-3OneSeCQwen-3RealToxicityPrompts
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UI-Venus-2 Technical Report

UI-Venus-2 presents a general-purpose GUI agent designed to operate across mobile, web, and desktop environments via a unified closed-loop reasoning-action framework. It scales environments, tasks, and verification to improve real-world deployment prospects, and adds safety-aware mechanisms for controlled action execution. The work emphasizes an open-source foundation to advance generalizability, verifiability, and self-reflection in agents.

Why it matters: By broadening environments and refining verification, UI-Venus-2 moves toward more reliable real-world GUI automation. The safety and open-source emphasis support practical deployment and community-driven improvement.

Primary source: cs.AI updates on arXiv.orgOpen source ↗
Entities:UI-Venus-2
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Zero-Shot Respiratory Sound Classification through LLM-Augmented Audio-Text Alignment

A framework that aligns self-supervised respiratory encoders with medical terminology in a shared latent space to enable zero-shot inference. To compensate for limited paired data, a medical LLM generates structured reports from metadata, providing semantic anchors for contrastive learning. The approach combines a sigmoid-based contrastive loss with the encoder’s SSL objective and targeted negative sampling, achieving strong zero-shot performance across multiple tasks and datasets.

Why it matters: Demonstrates that structured semantic alignment via LLM-generated anchors can improve zero-shot clinical diagnostics, potentially reducing labeled data needs for medical audio tasks.

Primary source: cs.CL updates on arXiv.orgOpen source ↗
Entities:Zero-Shot Respiratory Sound Classification through LLM-Augmented Audio-Text Alignment
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