arXiv AI

Beyond the Autoregressive Horizon: A Comprehensive Survey of Diffusion Models, World Modelling, and State Space Models for Code

arXiv:2606. 23690v1 Announce Type: cross Abstract: Autoregressive (AR) language models have driven significant progress in automated software engineering, enabling powerful code generation and assistance systems.

arXiv AI

Sep 15

Exploring the Potential of Diffusion Large Language Models in Code Generation

arXiv:2509.11252v3 Announce Type: replace-cross Abstract: LLMs have become the mainstream approaches to code generation. Existing LLMs mainly employ autoregressive generation, i.e. generating code to...

By Chengze Li, Yitong Zhang, Jia Li, Liyi Cai, Ge Li

llms diffusion benchmarks

arXiv AI

Jul 3

TokenScope: Token-Level Explainability and Interpretability for Code-Oriented Tasks in Large Language Models

arXiv:2607. 01235v1 Announce Type: cross Abstract: Understanding how Large Language Models (LLMs) make token-level decisions during code generation remains a major challenge for both researchers and practitioners.

By Amirreza Esmaeili, Fatemeh Fard

llms safety

arXiv AI

Jun 30

Code Reasoning for Software Engineering Tasks: A Survey and A Call to Action

arXiv:2506. 13932v3 Announce Type: replace-cross Abstract: The rise of large language models (LLMs) has led to dramatic improvements across a wide range of natural language tasks.

By Saurabh Pujar, Ira Ceka, Irene Manotas, Gail Kaiser, Baishakhi Ray, Shyam Ramji

llms agents benchmarks

arXiv Machine Learning

Jun 16

Exploring Extrinsic and Intrinsic Properties for Effective Reasoning with Code Interpreter

arXiv:2606. 16934v1 Announce Type: cross Abstract: Reasoning with a Code Interpreter (CI) has emerged as an effective paradigm for enhancing the reasoning capabilities of large language models (LLMs) through executable computation and iterative verification.

By Patomporn Payoungkhamdee, Napat Laosaengpha, Jenta Wonglertsakul, Pittawat Taveekitworachai, Pume Tuchinda, Panjapong Poobanchuen, Ekapol Chuangsuwanich, Can Udomcharoenchaikit, Samuel Cahyawijaya, Peerat Limkonchotiwat, Sarana Nutanong

llms reinforcement-learning fine-tuning benchmarks

arXiv AI

Jun 19

Diffusion Language Models: An Experimental Analysis

arXiv:2606. 19475v1 Announce Type: new Abstract: Large Language Models (LLMs) have revolutionized language modeling through autoregressive generation, enabling strong performance across a wide range of tasks.

By Thomas Bertolani, Davide Bucciarelli, Leonardo Zini, Marcella Cornia, Lorenzo Baraldi

llms diffusion benchmarks

arXiv AI

Jun 3

$R^2$-dLLM: Accelerating Diffusion Large Language Models via Spatio-Temporal Redundancy Reduction

arXiv:2604. 18995v2 Announce Type: replace-cross Abstract: Diffusion Large Language Models (dLLMs) have emerged as a promising alternative to autoregressive generation by enabling parallel token prediction.

By Zhenbang Du, Kejing Xia, Xinrui Zhong, Yonggan Fu, Nicolai Oswald, Binfei Ji, Brucek Khailany, Pavlo Molchanov, Yingyan Lin

llms diffusion fine-tuning efficiency

arXiv Computation and Language

Sep 16

Register Tokens for Bounded-State Reasoning in Diffusion Language Models

arXiv:2609.16372v1 Announce Type: new Abstract: Masked diffusion language models (dLLMs) generate text by iteratively denoising masked tokens with bidirectional attention. Extending reasoning across...

By Albert Ge, Chandan Singh, Yufan Zhuang, Xiaodong Liu, Jianfeng Gao, Frederic Sala

llms diffusion reinforcement-learning benchmarks

arXiv AI

Sep 2

Adaptive Critical Token-Aware Retrieval for Repository-Level Code Generation

arXiv:2609.01601v1 Announce Type: cross Abstract: The repository-level code generation task requires synthesizing code that satisfies task requirements while remaining consistent with the target repo...

By Kefeng Duan, Dewu Zheng, Yanlin Wang, Terry Yue Zhuo, Mingwei Liu, Jianxing Yu, Jiachi Chen, Ensheng Shi, Xilin Liu, Yuchi Ma, Zibin Zheng

llms rag benchmarks

arXiv AI

Jun 2

SimSD: Simple Speculative Decoding in Diffusion Language Models

arXiv:2606. 02544v1 Announce Type: cross Abstract: Diffusion large language models (dLLMs) have recently emerged as a promising alternative to autoregressive (AR) LLMs, offering faster inference through parallel or blockwise decoding.

By Junxia Cui, Haotian Ye, Runchu Tian, Hongcan Guo, Jinya Jiang, Haoru Li, Chaojie Ren, Yiming Huang, Kaijie Zhu, Zhongkai Yu, Kun Zhou, Jingbo Shang

llms diffusion efficiency benchmarks

arXiv AI

Sep 1

DIP: Dynamic In-Context Planner For Diffusion Language Models

arXiv:2601.03199v2 Announce Type: replace-cross Abstract: Diffusion language models (DLMs) have shown strong potential for general natural language tasks with in-context examples. Existing In-Context...

By Yang Li, Han Meng, Chenan Wang, Zhenyu Bi, Xuan Wang, Haipeng Chen

llms diffusion benchmarks

arXiv Computation and Language

Sep 23

BigO(Bench): Can LLMs Generate Code with Controlled Time and Space Complexity?

arXiv:2503.15242v3 Announce Type: replace Abstract: We introduce BigO(Bench), a novel coding benchmark designed to evaluate the capabilities of generative language models in understanding and generat...

By Pierre Chambon, Baptiste Roziere, Benoit Sagot, Gabriel Synnaeve

llms efficiency benchmarks

arXiv Machine Learning

Jun 10

CodeAlchemy: Synthetic Code Rewriting at Scale

arXiv:2606. 10087v1 Announce Type: cross Abstract: Pre-training on raw code teaches syntax but provides sparse signal for diverse real-world task formats.

By Ankit Gupta, Aditya Prasad, Rameswar Panda

llms benchmarks

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