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新论文 · v1arXiv:2611.03523

DistDebug-Bench: Can LLM Agents Diagnose Bugs in Distributed Systems?

Yuxuan Jiang, Chenglin Liu, John Rose, Mason Miller, Ruthesh Thavamani, Juntao Wu, Yinwei Huang, Jiahao Xu 等 9 位作者

原文摘要

LLM agents show strong potential in software engineering tasks such as competitive programming and code synthesis. Existing benchmarks, however, largely focus on small-scale settings such as self-contained Python modules, leaving distributed systems, an essential class of production software, under-examined. We introduce DistDebug-Bench, a benchmark for evaluating agents' ability to diagnose complex-system failures at the code level. It curates 120 high-quality real-world bugs from 11 widely used distributed systems, spanning 5 programming languages and 10 failure patterns. Through an extensive evaluation of state-of-the-art LLMs and agents, we identify substantial gaps between current agents and real-world debugging tasks. A recurring gap is tunnel vision due to a lack of explicit reasoning structure and nondeterminism from inconsistent tool-use scope. The benchmark and evaluator are available at https://github.com/OrderLab/distdebug-bench, with agent traces and a leaderboard at https://distdebug.com.

cs.SE · cs.DC · cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2411.14493

From Statistical Methods to Pre-Trained Models; A Survey on Automatic Speech Recognition for Resource Scarce Urdu Language

Muhammad Sharif, Zeeshan Abbas, Jiangyan Yi, Chenglin Liu

原文摘要

Automatic Speech Recognition (ASR) technology has witnessed significant advancements in recent years, revolutionizing human-computer interactions. While major languages have benefited from these developments, lesser-resourced languages like Urdu face unique challenges. This paper provides an extensive exploration of the dynamic landscape of ASR research, focusing particularly on the resource-constrained Urdu language, which is widely spoken across South Asian nations. It outlines current research trends, technological advancements, and potential directions for future studies in Urdu ASR, aiming to pave the way for forthcoming researchers interested in this domain. By leveraging contemporary technologies, analyzing existing datasets, and evaluating effective algorithms and tools, the paper seeks to shed light on the unique challenges and opportunities associated with Urdu language processing and its integration into the broader field of speech research.

cs.CL · cs.SD · eess.AS

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arXiv ↗PDF ↗
首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.1351ACL Anthology · ACL 2026

Interleaved Latent Visual Reasoning with Selective Perceptual Modeling

Shuai Dong, Siyuan Wang The Chinese University of Hong Kong, Xingyu Liu, Chenglin Li, Haowen Hou ETHZ - ETH Zurich and Shanghai Jiao Tong University, Zhongyu Wei Fudan University

原文摘要

Interleaved reasoning paradigms enhance Multimodal Large Language Models (MLLMs) with visual feedback but are hindered by the prohibitive computational cost of re-encoding pixel-dense images. A promising alternative, latent visual reasoning, circumvents this bottleneck yet faces limitations: methods either fail to capture intermediate state evolution due to single-step, non-interleaved structures, or sacrifice precise perceptual modeling by over-compressing features. We introduce Interleaved Latent Visual Reasoning (ILVR), a framework that unifies dynamic state evolution with precise perceptual modeling. ILVR interleaves textual generation with latent visual representations that act as specific, evolving cues for subsequent reasoning. Specifically, we employ a self-supervision strategy where a momentum teacher model selectively distills relevant features from ground-truth intermediate images into sparse supervision targets. This adaptive selection mechanism guides the model to autonomously generate context-aware visual signals. Extensive experiments on multimodal reasoning benchmarks demonstrate that ILVR outperforms existing approaches, effectively bridging the gap between fine-grained perception and sequential multimodal reasoning.

cs.CL · cs.CV

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.341ACL Anthology · ACL 2026

VideoPro: Adaptive Program Reasoning for Long Video Understanding

Chenglin Li, Feng Han, Yikun Wang Fudan University and Shanghai Innovation Institute, Ruilin Li Wuhan University and Shanghai Innovation Institute, Shuai Dong, Haowen Hou ETHZ - ETH Zurich and Shanghai Jiao Tong University, Haitao Li, Qianglong Chen 等 12 位作者

原文摘要

Understanding long videos remains challenging due to the sparsity of visual evidence relevant to a given query. Prior work has explored program-based visual grounding, typically relying on executable programs generated by auxiliary large language models. However, when scaling to long videos, existing approaches face several critical limitations: (1) frame-centric vision modules are often insufficient for long video processing; (2) naively applying program-based reasoning to all queries incurs considerable computational overhead; and (3) errors arising from low-confidence predictions and imperfect program execution are difficult to recover from. To address these challenges, we propose VideoPro, a unified framework that enables VideoLLMs to adaptively reason over long videos and refine their predictions through executable programs. VideoPro first performs adaptive reasoning, dynamically determining whether a query can be resolved directly by the native VideoLLM or requires explicit multi-step program reasoning. For complex queries, the model decomposes the task into executable programs that invoke specialized vision modules for precise temporal and semantic grounding. To further improve robustness, VideoPro incorporates a self-refinement mechanism that leverages execution feedback and confidence signals to correct erroneous executions and refine low-confidence reasoning programs. By tightly integrating adaptive reasoning with self-refinement, VideoPro consistently outperforms prior methods across multiple long-video understanding benchmarks, yielding an average 6.7% improvement for Qwen3-VL-8B.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表