Yiming Zhang

The paper proposes a novel Adversarial Attenuation Patch (AAP) method, which is a physically realizable and stealthy adversarial attack designed to degrade SAR target detection performance.

The paper proposes Fasco, a lightweight confidential container runtime utilizing ARM CCA to significantly reduce startup latency and resource overhead compared to existing microVM-based confidential container architectures.

The paper introduces ProvMind, a provenance-grounded reasoning framework that significantly improves materials synthesis process optimization by accurately predicting optimal synthesis routes under challenging, out-of-distribution conditions.

The paper introduces SkillBrew, a multi-objective framework that treats skill bank curation as a constrained optimization problem to build efficient and well-curated skill repositories for LLM agents.

The paper theoretically explains that optimizing LLMs solely on outcomes leads to brittle reasoning (Reward-Induced Manifold Collapse) by favoring low-complexity shortcuts, and proposes process-based supervision to fix this.

This paper presents the benefits of visual pretraining for foundation model intelligence, outperforming text-only pretraining on multiple backbones and benchmarks.

The paper proposes SAGA, a framework for schema-aware grounding in agentic text-to-SPARQL generation, which maintains a persistent type state, filters incompatible property candidates, and handles missing schema information permissively.