4 citations · 7 across the 12 of their papers we have counts for
12 papers
SkillForge: Compositional Skill Synthesis with Verification-in-the-Loop for Generating Formally Verified Dafny Programs
Yanming Liu, Xinyue Peng, Jiannan Cao +2
Generating formally verified programs from natural language remains challenging: existing approaches either produce code in a single pass without recourse when verification fails,…
PADD: Path-Aligned Decompression Distillation for Non-Router Teacher to Guide MoE Student Learning
Xinyue Peng, Yi Qian, Jiaojiao Lin +2
As large language models (LLMs) continue to scale, it becomes increasingly challenging to grow model capacity under fixed computation budgets. We propose Path-Aligned Decompression…
Dep-Search: Learning Dependency-Aware Reasoning Traces with Persistent Memory
Yanming Liu, Xinyue Peng, Zixuan Yan +7
Large Language Models (LLMs) have demonstrated remarkable capabilities in complex reasoning tasks, particularly when augmented with search mechanisms that enable systematic explora…
NC2C: Automated Convexification of Generic Non-Convex Optimization Problems
Xinyue Peng, Yanming Liu, Yihan Cang +4
Non-convex optimization problems are pervasive across mathematical programming, engineering design, and scientific computing, often posing intractable challenges for traditional so…
ToolGate: Contract-Grounded and Verified Tool Execution for LLMs
Yanming Liu, Xinyue Peng, Jiannan Cao +5
Large Language Models (LLMs) augmented with external tools have demonstrated remarkable capabilities in complex reasoning tasks. However, existing frameworks rely heavily on natura…
Bridging Context Gaps: Leveraging Coreference Resolution for Long Contextual Understanding
Yanming Liu, Xinyue Peng, Jiannan Cao +6
Large language models (LLMs) have shown remarkable capabilities in natural language processing; however, they still face difficulties when tasked with understanding lengthy context…
DP-MemArc: Differential Privacy Transfer Learning for Memory Efficient Language Models
Yanming Liu, Xinyue Peng, Yuwei Zhang +10
Large language models have repeatedly shown outstanding performance across diverse applications. However, deploying these models can inadvertently risk user privacy. The significan…
Tool-Planner: Task Planning with Clusters across Multiple Tools
Yanming Liu, Xinyue Peng, Jiannan Cao +6
Large language models (LLMs) have demonstrated exceptional reasoning capabilities, enabling them to solve various complex problems. Recently, this ability has been applied to the p…
CPA-Enhancer: Chain-of-Thought Prompted Adaptive Enhancer for Object Detection under Unknown Degradations
Yuwei Zhang, Yan Wu, Yanming Liu +1
Object detection methods under known single degradations have been extensively investigated. However, existing approaches require prior knowledge of the degradation type and train…
SPA: Towards A Computational Friendly Cloud-Base and On-Devices Collaboration Seq2seq Personalized Generation with Casual Inference
Yanming Liu, Xinyue Peng, Ningjing Sang +10
Large language models(LLMs) have shown its outperforming ability on various tasks and question answering. However, LLMs require substantial memory storage on low-resource devices.…
ERA-CoT: Improving Chain-of-Thought through Entity Relationship Analysis
Yanming Liu, Xinyue Peng, Tianyu Du +3
Large language models (LLMs) have achieved commendable accomplishments in various natural language processing tasks. However, LLMs still encounter significant challenges when deali…
RA-ISF: Learning to Answer and Understand from Retrieval Augmentation via Iterative Self-Feedback
Yanming Liu, Xinyue Peng, Xuhong Zhang +4
Large language models (LLMs) demonstrate exceptional performance in numerous tasks but still heavily rely on knowledge stored in their parameters. Moreover, updating this knowledge…