Yi Yang

The paper introduces Checkerboard, a novel, learning-free clean-label backdoor attack that efficiently poisons training data to compromise model integrity with minimal poisoning budget.

SecureForge is an automated pipeline that significantly reduces cybersecurity vulnerabilities in LLM-generated code by optimizing system prompts, achieving up to a 48% reduction in output vulnerabilities.

The paper introduces STORYLENSWRITER, a novel framework that significantly improves personalized story rewriting by incorporating context-aware narrative enrichment, outperforming style-only adaptation.

The paper introduces a unified framework to fairly evaluate LLM agentic capabilities by standardizing diverse benchmarks and separating the effects of the LLM model from the surrounding framework and environment.

The paper proposes SAAS, a novel RL framework that equips LLM agents with self-awareness to precisely regulate search behavior, significantly mitigating costly over-search without sacrificing accuracy.

The paper proposes MemoAttack, a memory-driven black-box jailbreak framework that systematically models, evolves, and selects attack experiences to significantly enhance LLM jailbreaking success rates.

The paper introduces SURE, a unified framework designed to standardize and improve the comparability and reproducibility of evaluations for advanced speech understanding models.

The paper proposes a highly reconfigurable 256x128 in-memory computing array that significantly improves efficiency and performance for analog computing by introducing novel components for ADC, weighted accumulation, and bitcell design.

This paper investigates if upper-face affective cues enhance audiovisual sentence recognition, especially when audio is degraded, finding that while mouth cues are crucial for robustness, upper-face cues improve overall confidence and performance under noise.

SelSkill introduces a dual-granularity preference learning framework that treats skill use as a 'skill-or-skip' decision, significantly improving agent performance and execution precision in complex agentic tasks.

The paper introduces SkillHarm, a comprehensive benchmark and automated framework for evaluating skill-based attacks across the entire agent skill-use lifecycle, demonstrating that current agents remain highly vulnerable to both fixed-payload and self-mutating poisoning attacks.

This paper tests the effectiveness of warning labels in mitigating sycophantic AI's influence on user judgment and relationships, finding that while labels shift perception, they do not reliably reduce influence.

This paper proposes RAPS-DA, a framework that addresses conflicts in retrieval-augmented generation using a regime-aware peer specialization system and a dual-layer selector.

This paper introduces Align4D, a framework for generating coherent video-3D pairs using any-modal input, achieving state-of-the-art quality and consistency in X-to-4D generation.

This paper presents the first quantitative analysis of China's Brain-Computer Interface (BCI) translational ecosystem, examining clinical trials, investigator-initiated trials, and regulatory-approved products.

This paper introduces BadWAM, a framework for modeling and evaluating World-Action Drift Attacks, a new class of adversarial attacks that break the alignment between a World-Action Model's (WAM's) imagined future and its executed actions.

This paper proposes an adaptive momentum term for the ASSS-MGDFxLMS algorithm in distributed multichannel active noise control systems to accelerate convergence while maintaining robustness under communication delays.

A new framework called HOST enables robots to acquire new skills from a single human video in seconds while retaining previously mastered skills.

This paper introduces Skill Self-Play (Skill-SP), a co-evolutionary framework for LLM training that bridges the gap between structured verification and open-ended exploration.

This paper introduces ClinFusion, a vision-centric multimodal large language model designed for holistic medical understanding, featuring a Cascade Spatial-Aware Locality Fusion operator and a vision-grounded evaluation framework.