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20242026
most citedIntegrating Large Language Models into Recommendation via Mutual Augmentation and Adaptive Aggregation

5 citations · 5 across the 3 of their papers we have counts for

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14 papers · 1 filter

cs.CL2026

From Solver to Tutor: Evaluating the Pedagogical Intelligence of LLMs with KMP-Bench

Weikang Shi, Houxing Ren, Junting Pan +8

Large Language Models (LLMs) show significant potential in AI mathematical tutoring, yet current evaluations often rely on simplistic metrics or narrow pedagogical scenarios, faili…

cs.CL2025

LM-Searcher: Cross-domain Neural Architecture Search with LLMs via Unified Numerical Encoding

Yuxuan Hu, Jihao Liu, Ke Wang +7

Recent progress in Large Language Models (LLMs) has opened new avenues for solving complex optimization problems, including Neural Architecture Search (NAS). However, existing LLM-…

cs.CL2025

Alignment with Fill-In-the-Middle for Enhancing Code Generation

Houxing Ren, Zimu Lu, Weikang Shi +7

The code generation capabilities of Large Language Models (LLMs) have advanced applications like tool invocation and problem-solving. However, improving performance in code-related…

cs.CL2025

WebGen-Bench: Evaluating LLMs on Generating Interactive and Functional Websites from Scratch

Zimu Lu, Yunqiao Yang, Houxing Ren +7

LLM-based agents have demonstrated great potential in generating and managing code within complex codebases. In this paper, we introduce WebGen-Bench, a novel benchmark designed to…

cs.CL2025

Probability-Consistent Preference Optimization for Enhanced LLM Reasoning

Yunqiao Yang, Houxing Ren, Zimu Lu +6

Recent advances in preference optimization have demonstrated significant potential for improving mathematical reasoning capabilities in large language models (LLMs). While current…

cs.CL2025

ReflectionCoder: Learning from Reflection Sequence for Enhanced One-off Code Generation

Houxing Ren, Mingjie Zhan, Zhongyuan Wu +3

Code generation plays a crucial role in various tasks, such as code auto-completion and mathematical reasoning. Previous work has proposed numerous methods to enhance code generati…