1 citations · 1 across the 12 of their papers we have counts for
13 papers
Mitigating Bias in Large Vision-Language Models via Counterfactual Ensemble Decoding
Yisong Xiao, Aishan Liu, Yongxin Huang +6
Large Vision-Language Models (LVLMs) have achieved remarkable performance across a wide range of tasks; however, they often inherit social biases from their training data, resultin…
SemPIC: Learning Semantic Position-Independent KV Caches
Hui Xie, Peng Xiao, Yutong Deng +3
Long-context retrieval and agentic workloads repeatedly reuse the same documents under changing instructions, histories, and document orders. Prefix caching cannot exploit this reu…
HTMLCure: Turning Browser Experience into State Guided Repair for Interactive HTML
Jiajun Wu, Jian Yang, Tuney Zheng +4
LLMs can now produce full HTML pages, but many of those pages are only superficially correct: they render once, then fail under scroll, hover, click, resize, or gameplay. Evaluatio…
AgentVisor: Defending LLM Agents Against Prompt Injection via Semantic Virtualization
Zonghao Ying, Haozheng Wang, Jiangfan Liu +5
Large Language Model (LLM) agents are increasingly used to automate complex workflows, but integrating untrusted external data with privileged execution exposes them to severe secu…
InCoder-32B-Thinking: Industrial Code World Model for Thinking
Jian Yang, Wei Zhang, Jiajun Wu +22
Industrial software development across chip design, GPU optimization, and embedded systems lacks expert reasoning traces showing how engineers reason about hardware constraints and…
Evolving Deception: When Agents Evolve, Deception Wins
Zonghao Ying, Haowen Dai, Tianyuan Zhang +6
Self-evolving agents offer a promising path toward scalable autonomy. However, in this work, we show that in competitive environments, self-evolution can instead give rise to a ser…
M2G-Eval: Enhancing and Evaluating Multi-granularity Multilingual Code Generation
Fanglin Xu, Wei Zhang, Jian Yang +5
The rapid advancement of code large language models (LLMs) has sparked significant research interest in systematically evaluating their code generation capabilities, yet existing b…
Context as a Tool: Context Management for Long-Horizon SWE-Agents
Shukai Liu, Jian Yang, Bo Jiang +4
Agents based on large language models have recently shown strong potential on real-world software engineering (SWE) tasks that require long-horizon interaction with repository-scal…
RoboSafe: Safeguarding Embodied Agents via Executable Safety Logic
Le Wang, Zonghao Ying, Xiao Yang +7
Embodied agents powered by vision-language models (VLMs) are increasingly capable of executing complex real-world tasks, yet they remain vulnerable to hazardous instructions that m…
CodeSimpleQA: Scaling Factuality in Code Large Language Models
Jian Yang, Wei Zhang, Yizhi Li +8
Large language models (LLMs) have made significant strides in code generation, achieving impressive capabilities in synthesizing code snippets from natural language instructions. H…
UCoder: Unsupervised Code Generation by Internal Probing of Large Language Models
Jiajun Wu, Jian Yang, Wei Zhang +6
Large language models (LLMs) have demonstrated remarkable capabilities in code generation tasks. However, their effectiveness heavily relies on supervised training with extensive l…
Scaling Laws for Code: Every Programming Language Matters
Jian Yang, Shawn Guo, Lin Jing +8
Code large language models (Code LLMs) are powerful but costly to train, with scaling laws predicting performance from model size, data, and compute. However, different programming…
Behavior Backdoor for Deep Learning Models
Jiakai Wang, Pengfei Zhang, Renshuai Tao +5
The various post-processing methods for deep-learning-based models, such as quantification, pruning, and fine-tuning, play an increasingly important role in artificial intelligence…