Wei Wang

Vision-language models (VLMs) are increasingly deployed in high-stakes settings, where a response that is reasonable in general may still be unsafe for a particular user whose medical, emotional, or situational context is unknown to the model. We study this problem of personalized safety in multimodal systems and introduce MPS-Bench, a benchmark of 5,181 scenarios from 584 real-world images across 12 high-risk domains, each paired with a hidden user profile. Evaluating eight frontier VLMs, we find that they almost always respond directly (86--99\%) rather than seek missing context, and none exceeds 2.6/5 on personalized safety. To understand why these failures arise, we analyze multimodal interactions and identify visual dominance: visual information enters text representations early and suppresses textual risk signals during multimodal fusion. Causal interventions reveal a two-stage mechanism in which visual affect is first transferred into the text stream in early layers and then shapes the final decision through this altered text representation, making late-stage internal remediation unreliable. Motivated by this mechanism, we propose PRISM, a lightweight input monitor that uses bidirectional cross-modal modulation to predict when a query is likely to require deferral. PRISM achieves 0.978 AUC and strictly dominates the safety--utility Pareto frontier across all tested models.

Multi-step retrosynthesis planning seeks to decompose a target molecule into commercially available building blocks through a sequence of feasible reactions. The vast combinatorial search space makes this task challenging even for expert chemists. Traditional methods combine tree search with offline-trained value networks that score candidates in isolation, without reasoning about complete multi-step routes. Recent work leverages Large Language Models (LLMs) for this task, but relies on simple interfaces that limit exploration of the full search space. We introduce RetroAgent, an LLM agent that bridges symbolic search and neural reasoning through a harness with structured memory. Through memory and chemistry tools, the agent observes the full search state, including explored routes, available alternatives, and properties of intermediates, enabling informed decisions grounded in both global progress and domain knowledge. Experiments on in-distribution and out-of-distribution benchmarks demonstrate that RETROAGENT delivers strong performance and generalization.

This paper studies the problem of constrained novel crystal discovery, which aims to identify a set of physical feasible and property-aligned material candidates within a complicated search space. Existing approaches typically integrate large language models (LLMs) with domain knowledge of material properties and constraints for material discovery. However, these approaches usually adopt shallow memory mechanisms or unstructured toolsets, leading to suboptimal performance. Towards this end, we propose a novel approach named Structured Action Orchestration with Trio-Reflection (CrystalSTAR) for constrained novel crystal discovery. The core of our CrystalSTAR is to incorporate hierarchical memories into a structured action orchestration from modification scopes and dominant components, facilitating a smooth and efficient crystal discovery procedure. In particular, our CrystalSTAR first formulates the toolset as a structured action space along two orthogonal dimensions, resulting in four complementary tools for material exploration. More importantly, we adopt a trio-reflection mechanism to guide the trial process in a hierarchical manner. Here, our bottom-up memory records local anomalies, stuck trajectories, and cross-run knowledge, enabling effective strategy planning with grounding evidence. Extensive experiments across multiple benchmark datasets validate the effectiveness of our proposed CrystalSTAR in comparison to extensive state-of-the-art approaches. Our codebase is open-sourced at https://github.com/oldwizard7/CrystalSTAR

Metamaterial discovery seeks microstructured materials whose geometry induces targeted mechanical behavior. Existing inverse-design methods can efficiently generate candidates, but they typically require explicit numerical property targets and are less suitable for early-stage exploration, where researchers often begin with incomplete constraints and qualitative intents expressed in natural language. Large language models can interpret such intents, but they lack geometric awareness and physical property validity. To address this gap, we propose **MetaSymbO**, a multi-agent framework for language-guided **Meta**material discovery via **Symb**olic-driven latent Ev**O**lution. Specifically, MetaSymbO contains three agents: a Designer that interprets free-form design intents and retrieves a semantically consistent *scaffold*, a Generator that synthesizes candidate microstructures in a disentangled latent space, and a Supervisor that provides fast property-aware feedback for iterative refinement. To move beyond the limitations of reproducing known samples from literature and training data, we further introduce symbolic-driven latent evolution, which applies programmable operators over disentangled latent factors to compose, modify, and refine structures at inference time. Extensive experiments demonstrate that (i) MetaSymbO improves structural validity by up to 34\% in symmetry and nearly 98\% in periodicity compared to state-of-the-art baselines; (ii) MetaSymbO achieves about 6–7\% higher language-guidance scores while maintaining superior structure novelty compared to advanced reasoning LLMs; (iii) qualitative analyses confirm the effectiveness of symbolic logic operators in enabling programmable semantic alignment; and (iv) real-world case studies on auxetic, high-stiffness metamaterial design further validate its practical capability. We publish our code on https://anonymous.4open.science/r/MetaSymbO-B48D.

Efficient reasoning is not only a matter of shortening an answer trace; for tool-using agents, it also depends on whether the agent is reasoning over the right action space. As API ecosystems scale to tens of thousands of endpoints, the semantic gap between user requests and tool documentation makes this problem concrete: static retrieval from the initial query can fail before planning begins, and stronger planning alone cannot recover a missing tool. We study this problem as budgeted test-time retrieval and introduce FitText, a training-free framework that makes the tool interface revisable during execution by generating, refining, and evolving natural-language pseudo-tool descriptions as retrieval probes. FitText supports serial refinement, parallel exploration, and Memetic Retrieval, which adds evolutionary selection, local refinement, and tool memory to avoid redundant search. On StableToolBench (16,464 APIs), Memetic FitText reaches an 84.3% pooled pass rate, improving +26.7 points over static retrieval, +22.2 over Single-Pass, +23.2 over Re-Invoke, and +27.5 over Xu-style root refinement. It leads on every evaluated current model, with gains growing alongside model capability, and produces the largest improvements on ambiguous multi-tool tasks where dynamic re-retrieval restores correct candidates after early mistakes. At 40-way concurrency, parallel population execution keeps batched wall-clock at 1.01x Single-Pass despite the added search work.