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20232026
most citedA Comparative Study on Reasoning Patterns of OpenAI's o1 Model

12 citations · 51 across the 38 of their papers we have counts for

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14 papers · 1 filter

cs.SE2025★ 1 cited

From Code Foundation Models to Agents and Applications: A Comprehensive Survey and Practical Guide to Code Intelligence

Jian Yang, Xianglong Liu, Weifeng Lv +68

Large language models (LLMs) have fundamentally transformed automated software development by enabling direct translation of natural language descriptions into functional code, dri…

cs.CL2025

Seeing isn't Hearing: Benchmarking Vision Language Models at Interpreting Spectrograms

Tyler Loakman, Joseph James, Chenghua Lin

With the rise of Large Language Models (LLMs) and their vision-enabled counterparts (VLMs), numerous works have investigated their capabilities in tasks that fuse the modalities of…

cs.CL2025

COIG-Writer: A High-Quality Dataset for Chinese Creative Writing with Thought Processes

Yunwen Li, Shuangshuang Ying, Xingwei Qu +16

Large language models exhibit systematic deficiencies in creative writing, particularly in non-English contexts where training data is scarce and lacks process-level supervision. W…

cs.CL2025

Scaling Latent Reasoning via Looped Language Models

Rui-Jie Zhu, Zixuan Wang, Kai Hua +30

Modern LLMs are trained to "think" primarily via explicit text generation, such as chain-of-thought (CoT), which defers reasoning to post-training and under-leverages pre-training…

cs.CL2025

Beyond Correctness: Evaluating Subjective Writing Preferences Across Cultures

Shuangshuang Ying, Yunwen Li, Xingwei Qu +21

Current preference learning methods achieve high accuracy on standard benchmarks but exhibit significant performance degradation when objective quality signals are removed. We intr…

cs.LG2025

TreePO: Bridging the Gap of Policy Optimization and Efficacy and Inference Efficiency with Heuristic Tree-based Modeling

Yizhi Li, Qingshui Gu, Zhoufutu Wen +14

Recent advancements in aligning large language models via reinforcement learning have achieved remarkable gains in solving complex reasoning problems, but at the cost of expensive…

cs.CL2025

A Survey on Latent Reasoning

Rui-Jie Zhu, Tianhao Peng, Tianhao Cheng +30

Large Language Models (LLMs) have demonstrated impressive reasoning capabilities, especially when guided by explicit chain-of-thought (CoT) reasoning that verbalizes intermediate s…

cs.AI2025

First Return, Entropy-Eliciting Explore

Tianyu Zheng, Tianshun Xing, Qingshui Gu +10

Reinforcement Learning from Verifiable Rewards (RLVR) improves the reasoning abilities of Large Language Models (LLMs) but it struggles with unstable exploration. We propose FR3E (…

cs.CL2025

Re:Form -- Reducing Human Annotations in Scalable Formal Software Verification with RL in LLMs: A Preliminary Study on Dafny

Chuanhao Yan, Fengdi Che, Xuhan Huang +12

Existing informal language-based (e.g., human language) Large Language Models (LLMs) trained with Reinforcement Learning (RL) face a significant challenge: their verification proce…

cs.AI2025

Scaling Test-time Compute for LLM Agents

King Zhu, Hanhao Li, Siwei Wu +12

Scaling test time compute has shown remarkable success in improving the reasoning abilities of large language models (LLMs). In this work, we conduct the first systematic explorati…

cs.CL2025

Overview of the NLPCC 2025 Shared Task: Gender Bias Mitigation Challenge

Yizhi Li, Ge Zhang, Hanhua Hong +2

As natural language processing for gender bias becomes a significant interdisciplinary topic, the prevalent data-driven techniques, such as pre-trained language models, suffer from…

cs.IR2025

DocMMIR: A Framework for Document Multi-modal Information Retrieval

Zirui Li, Siwei Wu, Yizhi Li +3

The rapid advancement of unsupervised representation learning and large-scale pre-trained vision-language models has significantly improved cross-modal retrieval tasks. However, ex…

cs.CL2025★ 1 cited

LongEval: A Comprehensive Analysis of Long-Text Generation Through a Plan-based Paradigm

Siwei Wu, Yizhi Li, Xingwei Qu +7

Large Language Models (LLMs) have achieved remarkable success in various natural language processing tasks, yet their ability to generate long-form content remains poorly understoo…

cs.CL2025

Transforming Science with Large Language Models: A Survey on AI-assisted Scientific Discovery, Experimentation, Content Generation, and Evaluation

Steffen Eger, Yong Cao, Jennifer D'Souza +11

With the advent of large multimodal language models, science is now at a threshold of an AI-based technological transformation. An emerging ecosystem of models and tools aims to su…