Tootfinder

Video models are zero-shot learners and reasoners
Thadd\"aus Wiedemer, Yuxuan Li, Paul Vicol, Shixiang Shane Gu, Nick Matarese, Kevin Swersky, Been Kim, Priyank Jaini, Robert Geirhos
https://arxiv.org/abs/2509.20328

Video models are zero-shot learners and reasoners
The remarkable zero-shot capabilities of Large Language Models (LLMs) have propelled natural language processing from task-specific models to unified, generalist foundation models. This transformation emerged from simple primitives: large, generative models trained on web-scale data. Curiously, the same primitives apply to today's generative video models. Could video models be on a trajectory towards general-purpose vision understanding, much like LLMs developed general-purpose language underst…

Advances in Large Language Models for Medicine
Zhiyu Kan, Wensheng Gan, Zhenlian Qi, Philip S. Yu
https://arxiv.org/abs/2509.18690 https://arxiv.org/pdf/25…

Political Ideology Shifts in Large Language Models
Pietro Bernardelle, Stefano Civelli, Leon Fr\"ohling, Riccardo Lunardi, Kevin Roitero, Gianluca Demartini
https://arxiv.org/abs/2508.16013

Political Ideology Shifts in Large Language Models
Large language models (LLMs) are increasingly deployed in politically sensitive settings, raising concerns about their potential to encode, amplify, or be steered toward specific ideologies. We investigate how adopting synthetic personas influences ideological expression in LLMs across seven models (7B-70B+ parameters) from multiple families, using the Political Compass Test as a standardized probe. Our analysis reveals four consistent patterns: (i) larger models display broader and more polari…

VT-LVLM-AR: A Video-Temporal Large Vision-Language Model Adapter for Fine-Grained Action Recognition in Long-Term Videos
Kaining Li, Shuwei He, Zihan Xu
https://arxiv.org/abs/2508.15903

VT-LVLM-AR: A Video-Temporal Large Vision-Language Model Adapter for Fine-Grained Action Recognition in Long-Term Videos
Human action recognition in long-term videos, characterized by complex backgrounds and subtle action differences, poses significant challenges for traditional deep learning models due to computational overhead, difficulty in capturing long-range temporal dependencies, and limited semantic understanding. While Large Language Models (LLMs) and Large Vision-Language Models (LVLMs) have shown remarkable capabilities in multi-modal understanding and reasoning, their direct application to continuous …

RECALLED: An Unbounded Resource Consumption Attack on Large Vision-Language Models
Haoran Gao, Yuanhe Zhang, Zhenhong Zhou, Lei Jiang, Fanyu Meng, Yujia Xiao, Kun Wang, Yang Liu, Junlan Feng
https://arxiv.org/abs/2507.18053

RECALLED: An Unbounded Resource Consumption Attack on Large Vision-Language Models
Resource Consumption Attacks (RCAs) have emerged as a significant threat to the deployment of Large Language Models (LLMs). With the integration of vision modalities, additional attack vectors exacerbate the risk of RCAs in large vision-language models (LVLMs). However, existing red-teaming studies have largely overlooked visual inputs as a potential attack surface, resulting in insufficient mitigation strategies against RCAs in LVLMs. To address this gap, we propose RECALLED (\textbf{RE}source…

OpenNav: Open-World Navigation with Multimodal Large Language Models
Mingfeng Yuan, Letian Wang, Steven L. Waslander
https://arxiv.org/abs/2507.18033 https://

OpenNav: Open-World Navigation with Multimodal Large Language Models
Pre-trained large language models (LLMs) have demonstrated strong common-sense reasoning abilities, making them promising for robotic navigation and planning tasks. However, despite recent progress, bridging the gap between language descriptions and actual robot actions in the open-world, beyond merely invoking limited predefined motion primitives, remains an open challenge. In this work, we aim to enable robots to interpret and decompose complex language instructions, ultimately synthesizing a…

DIFFA: Large Language Diffusion Models Can Listen and Understand
Jiaming Zhou, Hongjie Chen, Shiwan Zhao, Jian Kang, Jie Li, Enzhi Wang, Yujie Guo, Haoqin Sun, Hui Wang, Aobo Kong, Yong Qin, Xuelong Li
https://arxiv.org/abs/2507.18452

DIFFA: Large Language Diffusion Models Can Listen and Understand
Recent advances in Large language models (LLMs) have shown remarkable capabilities across textual and multimodal domains. In parallel, diffusion-based language models have emerged as a promising alternative to the autoregressive paradigm, offering improved controllability, bidirectional context modeling, and robust generation. However, their application to the audio modality remains underexplored. In this work, we introduce \textbf{DIFFA}, the first diffusion-based Large Audio-Language Model de…

Reverse Engineering User Stories from Code using Large Language Models
Mohamed Ouf, Haoyu Li, Michael Zhang, Mariam Guizani
https://arxiv.org/abs/2509.19587 https://

Reverse Engineering User Stories from Code using Large Language Models
User stories are essential in agile development, yet often missing or outdated in legacy and poorly documented systems. We investigate whether large language models (LLMs) can automatically recover user stories directly from source code and how prompt design impacts output quality. Using 1,750 annotated C++ snippets of varying complexity, we evaluate five state-of-the-art LLMs across six prompting strategies. Results show that all models achieve, on average, an F1 score of 0.8 for code up to 20…

Zhu on AI/AGI: 'A sign of true intelligence... is the ability to reason towards a goal with minimal inputs ... a “small data, big task” approach, compared with the “big data, small task” approach employed by large language models like ChatGPT. AGI... is characterised by qualities such as resourcefulness in novel situations, social and physical intuition, and an understanding of cause and effect. Large language models... will never achieve this.'

Affective Computing and Emotional Data: Challenges and Implications in Privacy Regulations, The AI Act, and Ethics in Large Language Models
Nicola Fabiano
https://arxiv.org/abs/2509.20153

Affective Computing and Emotional Data: Challenges and Implications in Privacy Regulations, The AI Act, and Ethics in Large Language Models
This paper examines the integration of emotional intelligence into artificial intelligence systems, with a focus on affective computing and the growing capabilities of Large Language Models (LLMs), such as ChatGPT and Claude, to recognize and respond to human emotions. Drawing on interdisciplinary research that combines computer science, psychology, and neuroscience, the study analyzes foundational neural architectures - CNNs for processing facial expressions and RNNs for sequential data, such …

A Versatile Pathology Co-pilot via Reasoning Enhanced Multimodal Large Language Model
Zhe Xu, Ziyi Liu, Junlin Hou, Jiabo Ma, Cheng Jin, Yihui Wang, Zhixuan Chen, Zhengyu Zhang, Zhengrui Guo, Fengtao Zhou, Yingxue Xu, Xi Wang, Ronald Cheong Kin Chan, Li Liang, Hao Chen
https://arxiv.org/abs/2507.17303

A Versatile Pathology Co-pilot via Reasoning Enhanced Multimodal Large Language Model
Multimodal large language models (MLLMs) have emerged as powerful tools for computational pathology, offering unprecedented opportunities to integrate pathological images with language context for comprehensive diagnostic analysis. These models hold particular promise for automating complex tasks that traditionally require expert interpretation of pathologists. However, current MLLM approaches in pathology demonstrate significantly constrained reasoning capabilities, primarily due to their reli…

A Query-Aware Multi-Path Knowledge Graph Fusion Approach for Enhancing Retrieval-Augmented Generation in Large Language Models
Qikai Wei, Huansheng Ning, Chunlong Han, Jianguo Ding
https://arxiv.org/abs/2507.16826

A Query-Aware Multi-Path Knowledge Graph Fusion Approach for Enhancing Retrieval-Augmented Generation in Large Language Models
Retrieval Augmented Generation (RAG) has gradually emerged as a promising paradigm for enhancing the accuracy and factual consistency of content generated by large language models (LLMs). However, existing RAG studies primarily focus on retrieving isolated segments using similarity-based matching methods, while overlooking the intrinsic connections between them. This limitation hampers performance in RAG tasks. To address this, we propose QMKGF, a Query-Aware Multi-Path Knowledge Graph Fusion A…

Leveraging Large Language Models to Detect Missed Peephole Optimizations
Zhenyang Xu, Hongxu Xu, Yongqiang Tian, Xintong Zhou, Chengnian Sun
https://arxiv.org/abs/2508.16125 htt…

Leveraging Large Language Models to Detect Missed Peephole Optimizations
By replacing small, suboptimal instruction sequences within programs with a more efficient equivalent, peephole optimization can not only directly optimize code size and performance, but also potentially enables further transformations in the subsequent optimization pipeline. Although peephole optimization is a critical class of compiler optimizations, discovering new and effective peephole optimizations is challenging as the instruction sets can be extremely complex and diverse. Previous metho…

Cooling Matters: Benchmarking Large Language Models and Vision-Language Models on Liquid-Cooled Versus Air-Cooled H100 GPU Systems
Imran Latif, Muhammad Ali Shafique, Hayat Ullah, Alex C. Newkirk, Xi Yu, Arslan Munir
https://arxiv.org/abs/2507.16781

Cooling Matters: Benchmarking Large Language Models and Vision-Language Models on Liquid-Cooled Versus Air-Cooled H100 GPU Systems
The unprecedented growth in artificial intelligence (AI) workloads, recently dominated by large language models (LLMs) and vision-language models (VLMs), has intensified power and cooling demands in data centers. This study benchmarks LLMs and VLMs on two HGX nodes, each with 8x NVIDIA H100 graphics processing units (GPUs), using liquid and air cooling. Leveraging GPU Burn, Weights and Biases, and IPMItool, we collect detailed thermal, power, and computation data. Results show that the liquid-c…

Hardwired-Neurons Language Processing Units as General-Purpose Cognitive Substrates
Yang Liu, Yi Chen, Yongwei Zhao, Yifan Hao, Zifu Zheng, Weihao Kong, Zhangmai Li, Dongchen Jiang, Ruiyang Xia, Zhihong Ma, Zisheng Liu, Zhaoyong Wan, Yunqi Lu, Ximing Liu, Hongrui Guo, Zhihao Yang, Zhe Wang, Tianrui Ma, Mo Zou, Rui Zhang, Ling Li, Xing Hu, Zidong Du, Zhiwei Xu, Qi Guo, Tianshi Chen, Yunji Chen

Hardwired-Neurons Language Processing Units as General-Purpose Cognitive Substrates
The rapid advancement of Large Language Models (LLMs) has established language as a core general-purpose cognitive substrate, driving the demand for specialized Language Processing Units (LPUs) tailored for LLM inference. To overcome the growing energy consumption of LLM inference systems, this paper proposes a Hardwired-Neurons Language Processing Unit (HNLPU), which physically hardwires LLM weight parameters into the computational fabric, achieving several orders of magnitude computational ef…

Congestion Control System Optimization with Large Language Models
Zhiyuan He, Aashish Gottipati, Lili Qiu, Yuqing Yang, Francis Y. Yan
https://arxiv.org/abs/2508.16074 https://

Congestion Control System Optimization with Large Language Models
Congestion control is a fundamental component of Internet infrastructure, and researchers have dedicated considerable effort to developing improved congestion control algorithms. However, despite extensive study, existing algorithms continue to exhibit suboptimal performance across diverse network environments. In this paper, we introduce a novel approach that automatically optimizes congestion control algorithms using large language models (LLMs). Our framework consists of a structured algorit…

Benchmarking Gaslighting Attacks Against Speech Large Language Models
Jinyang Wu, Bin Zhu, Xiandong Zou, Qiquan Zhang, Xu Fang, Pan Zhou
https://arxiv.org/abs/2509.19858 https:/…

Benchmarking Gaslighting Attacks Against Speech Large Language Models
As Speech Large Language Models (Speech LLMs) become increasingly integrated into voice-based applications, ensuring their robustness against manipulative or adversarial input becomes critical. Although prior work has studied adversarial attacks in text-based LLMs and vision-language models, the unique cognitive and perceptual challenges of speech-based interaction remain underexplored. In contrast, speech presents inherent ambiguity, continuity, and perceptual diversity, which make adversarial…

Reasoning-Driven Retrosynthesis Prediction with Large Language Models via Reinforcement Learning
Situo Zhang, Hanqi Li, Lu Chen, Zihan Zhao, Xuanze Lin, Zichen Zhu, Bo Chen, Xin Chen, Kai Yu
https://arxiv.org/abs/2507.17448

Reasoning-Driven Retrosynthesis Prediction with Large Language Models via Reinforcement Learning
Retrosynthesis planning, essential in organic synthesis and drug discovery, has greatly benefited from recent AI-driven advancements. Nevertheless, existing methods frequently face limitations in both applicability and explainability. Traditional graph-based and sequence-to-sequence models often lack generalized chemical knowledge, leading to predictions that are neither consistently accurate nor easily explainable. To address these challenges, we introduce RetroDFM-R, a reasoning-based large l…

Decoding Consumer Preferences Using Attention-Based Language Models
Joshua Foster, Fredrik Odegaard
https://arxiv.org/abs/2507.17564 https://arxiv.org/pdf/…

Decoding Consumer Preferences Using Attention-Based Language Models
This paper proposes a new demand estimation method using attention-based language models. An encoder-only language model is trained in a two-stage process to analyze the natural language descriptions of used cars from a large US-based online auction marketplace. The approach enables semi-nonparametrically estimation for the demand primitives of a structural model representing the private valuations and market size for each vehicle listing. In the first stage, the language model is fine-tuned to…

EchoBench: Benchmarking Sycophancy in Medical Large Vision-Language Models
Botai Yuan, Yutian Zhou, Yingjie Wang, Fushuo Huo, Yongcheng Jing, Li Shen, Ying Wei, Zhiqi Shen, Ziwei Liu, Tianwei Zhang, Jie Yang, Dacheng Tao
https://arxiv.org/abs/2509.20146

EchoBench: Benchmarking Sycophancy in Medical Large Vision-Language Models
Recent benchmarks for medical Large Vision-Language Models (LVLMs) emphasize leaderboard accuracy, overlooking reliability and safety. We study sycophancy -- models' tendency to uncritically echo user-provided information -- in high-stakes clinical settings. We introduce EchoBench, a benchmark to systematically evaluate sycophancy in medical LVLMs. It contains 2,122 images across 18 departments and 20 modalities with 90 prompts that simulate biased inputs from patients, medical students, and ph…

Data Efficient Adaptation in Large Language Models via Continuous Low-Rank Fine-Tuning
Xiao Han, Zimo Zhao, Wanyu Wang, Maolin Wang, Zitao Liu, Yi Chang, Xiangyu Zhao
https://arxiv.org/abs/2509.18942

Data Efficient Adaptation in Large Language Models via Continuous Low-Rank Fine-Tuning
Recent advancements in Large Language Models (LLMs) have emphasized the critical role of fine-tuning (FT) techniques in adapting LLMs to specific tasks, especially when retraining from scratch is computationally infeasible. Fine-tuning enables LLMs to leverage task- or domain-specific data, producing models that more effectively meet the requirements of targeted applications. However, con- ventional FT approaches often suffer from catastrophic forgetting and suboptimal data efficiency, limiting…

On the Evolution of Federated Post-Training Large Language Models: A Model Accessibility View
Tao Guo, Junxiao Wang, Fushuo Huo, Laizhong Cui, Song Guo, Jie Gui, Dacheng Tao
https://arxiv.org/abs/2508.16261

On the Evolution of Federated Post-Training Large Language Models: A Model Accessibility View
Federated Learning (FL) enables training models across decentralized data silos while preserving client data privacy. Recent research has explored efficient methods for post-training large language models (LLMs) within FL to address computational and communication challenges. While existing approaches often rely on access to LLMs' internal information, which is frequently restricted in real-world scenarios, an inference-only paradigm (black-box FedLLM) has emerged to address these limitations. …

Building and Measuring Trust between Large Language Models
Maarten Buyl, Yousra Fettach, Guillaume Bied, Tijl De Bie
https://arxiv.org/abs/2508.15858 https://

Building and Measuring Trust between Large Language Models
As large language models (LLMs) increasingly interact with each other, most notably in multi-agent setups, we may expect (and hope) that `trust' relationships develop between them, mirroring trust relationships between human colleagues, friends, or partners. Yet, though prior work has shown LLMs to be capable of identifying emotional connections and recognizing reciprocity in trust games, little remains known about (i) how different strategies to build trust compare, (ii) how such trust can be …

Automated Code Review Using Large Language Models with Symbolic Reasoning
Busra Icoz, Goksel Biricik
https://arxiv.org/abs/2507.18476 https://arxiv.org/pdf…

Automated Code Review Using Large Language Models with Symbolic Reasoning
Code review is one of the key processes in the software development lifecycle and is essential to maintain code quality. However, manual code review is subjective and time consuming. Given its rule-based nature, code review is well suited for automation. In recent years, significant efforts have been made to automate this process with the help of artificial intelligence. Recent developments in Large Language Models (LLMs) have also emerged as a promising tool in this area, but these models ofte…

Retrieval-Augmented Defense: Adaptive and Controllable Jailbreak Prevention for Large Language Models
Guangyu Yang, Jinghong Chen, Jingbiao Mei, Weizhe Lin, Bill Byrne
https://arxiv.org/abs/2508.16406 …

Retrieval-Augmented Defense: Adaptive and Controllable Jailbreak Prevention for Large Language Models
Large Language Models (LLMs) remain vulnerable to jailbreak attacks, which attempt to elicit harmful responses from LLMs. The evolving nature and diversity of these attacks pose many challenges for defense systems, including (1) adaptation to counter emerging attack strategies without costly retraining, and (2) control of the trade-off between safety and utility. To address these challenges, we propose Retrieval-Augmented Defense (RAD), a novel framework for jailbreak detection that incorporate…

TULIP: Adapting Open-Source Large Language Models for Underrepresented Languages and Specialized Financial Tasks
\.Irem Demirta\c{s}, Burak Payzun, Se\c{c}il Arslan
https://arxiv.org/abs/2508.16243

TULIP: Adapting Open-Source Large Language Models for Underrepresented Languages and Specialized Financial Tasks
Thanks to the growing popularity of large language models over the years, there is great potential for their applications in finance. Despite the exceptional performance of larger proprietary models, which are presented as black-box solutions through APIs, smaller models that can be hosted on-premise present opportunities for adaptability and privacy. Especially in cases where the management of sensitive information and application of domain knowledge is important, like finance, enhancing the c…

G\"odel Test: Can Large Language Models Solve Easy Conjectures?
Moran Feldman, Amin Karbasi
https://arxiv.org/abs/2509.18383 https://arxiv.org/pdf/250…

A Comprehensive Evaluation on Quantization Techniques for Large Language Models
Yutong Liu, Cairong Zhao, Guosheng Hu
https://arxiv.org/abs/2507.17417 http…

A Comprehensive Evaluation on Quantization Techniques for Large Language Models
For large language models (LLMs), post-training quantization (PTQ) can significantly reduce memory footprint and computational overhead. Model quantization is a rapidly evolving research field. Though many papers have reported breakthrough performance, they may not conduct experiments on the same ground since one quantization method usually contains multiple components. In addition, analyzing the theoretical connections among existing methods is crucial for in-depth understanding. To bridge the…

Unveiling Chain of Step Reasoning for Vision-Language Models with Fine-grained Rewards
Honghao Chen, Xingzhou Lou, Xiaokun Feng, Kaiqi Huang, Xinlong Wang
https://arxiv.org/abs/2509.19003

Unveiling Chain of Step Reasoning for Vision-Language Models with Fine-grained Rewards
Chain of thought reasoning has demonstrated remarkable success in large language models, yet its adaptation to vision-language reasoning remains an open challenge with unclear best practices. Existing attempts typically employ reasoning chains at a coarse-grained level, which struggles to perform fine-grained structured reasoning and, more importantly, are difficult to evaluate the reward and quality of intermediate reasoning. In this work, we delve into chain of step reasoning for vision-langu…

V-GameGym: Visual Game Generation for Code Large Language Models
Wei Zhang, Jack Yang, Renshuai Tao, Lingzheng Chai, Shawn Guo, Jiajun Wu, Xiaoming Chen, Ganqu Cui, Ning Ding, Xander Xu, Hu Wei, Bowen Zhou
https://arxiv.org/abs/2509.20136

V-GameGym: Visual Game Generation for Code Large Language Models
Code large language models have demonstrated remarkable capabilities in programming tasks, yet current benchmarks primarily focus on single modality rather than visual game development. Most existing code-related benchmarks evaluate syntax correctness and execution accuracy, overlooking critical game-specific metrics such as playability, visual aesthetics, and user engagement that are essential for real-world deployment. To address the gap between current LLM capabilities in algorithmic problem…

DiagR1: A Vision-Language Model Trained via Reinforcement Learning for Digestive Pathology Diagnosis
Minxi Ouyang, Lianghui Zhu, Yaqing Bao, Qiang Huang, Jingli Ouyang, Tian Guan, Xitong Ling, Jiawen Li, Song Duan, Wenbin Dai, Li Zheng, Xuemei Zhang, Yonghong He
https://arxiv.org/abs/2507.18433

DiagR1: A Vision-Language Model Trained via Reinforcement Learning for Digestive Pathology Diagnosis
Multimodal large models have shown great potential in automating pathology image analysis. However, current multimodal models for gastrointestinal pathology are constrained by both data quality and reasoning transparency: pervasive noise and incomplete annotations in public datasets predispose vision language models to factual hallucinations when generating diagnostic text, while the absence of explicit intermediate reasoning chains renders the outputs difficult to audit and thus less trustwort…

When Long Helps Short: How Context Length in Supervised Fine-tuning Affects Behavior of Large Language Models
Yingming Zheng, Hanqi Li, Kai Yu, Lu Chen
https://arxiv.org/abs/2509.18762

Reinforcement Learning Fine-Tunes a Sparse Subnetwork in Large Language Models
Andrii Balashov
https://arxiv.org/abs/2507.17107 https://

Reinforcement Learning Fine-Tunes a Sparse Subnetwork in Large Language Models
Reinforcement learning (RL) is a key post-pretraining step for aligning large language models (LLMs) with complex tasks and human preferences. While it is often assumed that RL fine-tuning requires updating most of a model's parameters, we challenge this assumption with a surprising finding: RL fine-tuning consistently modifies only a small subnetwork (typically 5-30% of weights), leaving most parameters unchanged. We call this phenomenon RL-induced parameter update sparsity. It arises naturall…

A Cost-Benefit Analysis of On-Premise Large Language Model Deployment: Breaking Even with Commercial LLM Services
Guanzhong Pan, Haibo Wang
https://arxiv.org/abs/2509.18101 http…

A Cost-Benefit Analysis of On-Premise Large Language Model Deployment: Breaking Even with Commercial LLM Services
Large language models (LLMs) are becoming increasingly widespread. Organizations that want to use AI for productivity now face an important decision. They can subscribe to commercial LLM services or deploy models on their own infrastructure. Cloud services from providers such as OpenAI, Anthropic, and Google are attractive because they provide easy access to state-of-the-art models and are easy to scale. However, concerns about data privacy, the difficulty of switching service providers, and lo…

Lang2Morph: Language-Driven Morphological Design of Robotic Hands
Yanyuan Qiao, Kieran Gilday, Yutong Xie, Josie Hughes
https://arxiv.org/abs/2509.18937 https://

LLM4MEA: Data-free Model Extraction Attacks on Sequential Recommenders via Large Language Models
Shilong Zhao, Fei Sun, Kaike Zhang, Shaoling Jing, Du Su, Zhichao Shi, Zhiyi Yin, Huawei Shen, Xueqi Cheng
https://arxiv.org/abs/2507.16969

LLM4MEA: Data-free Model Extraction Attacks on Sequential Recommenders via Large Language Models
Recent studies have demonstrated the vulnerability of sequential recommender systems to Model Extraction Attacks (MEAs). MEAs collect responses from recommender systems to replicate their functionality, enabling unauthorized deployments and posing critical privacy and security risks. Black-box attacks in prior MEAs are ineffective at exposing recommender system vulnerabilities due to random sampling in data selection, which leads to misaligned synthetic and real-world distributions. To overcome…

MizanQA: Benchmarking Large Language Models on Moroccan Legal Question Answering
Adil Bahaj, Mounir Ghogho
https://arxiv.org/abs/2508.16357 https://arxiv.o…

MizanQA: Benchmarking Large Language Models on Moroccan Legal Question Answering
The rapid advancement of large language models (LLMs) has significantly propelled progress in natural language processing (NLP). However, their effectiveness in specialized, low-resource domains-such as Arabic legal contexts-remains limited. This paper introduces MizanQA (pronounced Mizan, meaning "scale" in Arabic, a universal symbol of justice), a benchmark designed to evaluate LLMs on Moroccan legal question answering (QA) tasks, characterised by rich linguistic and legal complexity. The dat…

Assertion Messages with Large Language Models (LLMs) for Code
Ahmed Aljohani, Anamul Haque Mollah, Hyunsook Do
https://arxiv.org/abs/2509.19673 https://arx…

Assertion Messages with Large Language Models (LLMs) for Code
Assertion messages significantly enhance unit tests by clearly explaining the reasons behind test failures, yet they are frequently omitted by developers and automated test-generation tools. Despite recent advancements, Large Language Models (LLMs) have not been systematically evaluated for their ability to generate informative assertion messages. In this paper, we introduce an evaluation of four state-of-the-art Fill-in-the-Middle (FIM) LLMs - Qwen2.5-Coder-32B, Codestral-22B, CodeLlama-13B, a…

Scout: Leveraging Large Language Models for Rapid Digital Evidence Discovery
Shariq Murtuza
https://arxiv.org/abs/2507.18478 https://arxiv.org/pdf/2507.184…

Scout: Leveraging Large Language Models for Rapid Digital Evidence Discovery
Recent technological advancements and the prevalence of technology in day to day activities have caused a major increase in the likelihood of the involvement of digital evidence in more and more legal investigations. Consumer-grade hardware is growing more powerful, with expanding memory and storage sizes and enhanced processor capabilities. Forensics investigators often have to sift through gigabytes of data during an ongoing investigation making the process tedious. Memory forensics, disk ana…

Structuring GUI Elements through Vision Language Models: Towards Action Space Generation
Yi Xu, Yesheng Zhang, jiajia Liu, Jingdong Chen
https://arxiv.org/abs/2508.16271 https:/…

Structuring GUI Elements through Vision Language Models: Towards Action Space Generation
Multimodal large language models (MLLMs) have emerged as pivotal tools in enhancing human-computer interaction. In this paper we focus on the application of MLLMs in the field of graphical user interface (GUI) elements structuring, where they assist in processing user instructions based on screen contents. Despite the promise of MLLMs, their performance in precisely generating UI element coordinates, a critical aspect of GUI understanding, is hindered by the nature of next-token prediction trai…

SPADE: A Large Language Model Framework for Soil Moisture Pattern Recognition and Anomaly Detection in Precision Agriculture
Yeonju Lee, Rui Qi Chen, Joseph Oboamah, Po Nien Su, Wei-zhen Liang, Yeyin Shi, Lu Gan, Yongsheng Chen, Xin Qiao, Jing Li
https://arxiv.org/abs/2509.18123

SiLQ: Simple Large Language Model Quantization-Aware Training
Steven K. Esser, Jeffrey L. McKinstry, Deepika Bablani, Rathinakumar Appuswamy, Dharmendra S. Modha
https://arxiv.org/abs/2507.16933

SiLQ: Simple Large Language Model Quantization-Aware Training
Large language models can be quantized to reduce inference time latency, model size, and energy consumption, thereby delivering a better user experience at lower cost. A challenge exists to deliver quantized models with minimal loss of accuracy in reasonable time, and in particular to do so without requiring mechanisms incompatible with specialized inference accelerators. Here, we demonstrate a simple, end-to-end quantization-aware training approach that, with an increase in total model trainin…

Embedding Domain Knowledge for Large Language Models via Reinforcement Learning from Augmented Generation
Chaojun Nie, Jun Zhou, Guanxiang Wang, Shisong Wud, Zichen Wang
https://arxiv.org/abs/2509.20162

Embedding Domain Knowledge for Large Language Models via Reinforcement Learning from Augmented Generation
Large language models (LLMs) often exhibit limited performance on domain-specific tasks due to the natural disproportionate representation of specialized information in their training data and the static nature of these datasets. Knowledge scarcity and temporal lag create knowledge gaps for domain applications. While post-training on domain datasets can embed knowledge into models, existing approaches have some limitations. Continual Pre-Training (CPT) treats all tokens in domain documents with…

How Small is Enough? Empirical Evidence of Quantized Small Language Models for Automated Program Repair
Kazuki Kusama, Honglin Shu, Masanari Kondo, Yasutaka Kamei
https://arxiv.org/abs/2508.16499

How Small is Enough? Empirical Evidence of Quantized Small Language Models for Automated Program Repair
Background: Large language models (LLMs) have greatly improved the accuracy of automated program repair (APR) methods. However, LLMs are constrained by high computational resource requirements. Aims: We focus on small language models (SLMs), which perform well even with limited computational resources compared to LLMs. We aim to evaluate whether SLMs can achieve competitive performance in APR tasks. Method: We conducted experiments on the QuixBugs benchmark to compare the bug-fixing accuracy of…

LLM-as-classifier: Semi-Supervised, Iterative Framework for Hierarchical Text Classification using Large Language Models
Doohee You, Andy Parisi, Zach Vander Velden, Lara Dantas Inojosa
https://arxiv.org/abs/2508.16478

LLM-as-classifier: Semi-Supervised, Iterative Framework for Hierarchical Text Classification using Large Language Models
The advent of Large Language Models (LLMs) has provided unprecedented capabilities for analyzing unstructured text data. However, deploying these models as reliable, robust, and scalable classifiers in production environments presents significant methodological challenges. Standard fine-tuning approaches can be resource-intensive and often struggle with the dynamic nature of real-world data distributions, which is common in the industry. In this paper, we propose a comprehensive, semi-supervise…

Temporal-Aware User Behaviour Simulation with Large Language Models for Recommender Systems
Xinye Wanyan, Danula Hettiachchi, Chenglong Ma, Ziqi Xu, Jeffrey Chan
https://arxiv.org/abs/2509.16895

Temporal-Aware User Behaviour Simulation with Large Language Models for Recommender Systems
Large Language Models (LLMs) demonstrate human-like capabilities in language understanding, reasoning, and generation, driving interest in using LLM-based agents to simulate human feedback in recommender systems. However, most existing approaches rely on static user profiling, neglecting the temporal and dynamic nature of user interests. This limitation stems from a disconnect between language modelling and behaviour modelling, which constrains the capacity of agents to represent sequential pat…

ThinkFake: Reasoning in Multimodal Large Language Models for AI-Generated Image Detection
Tai-Ming Huang, Wei-Tung Lin, Kai-Lung Hua, Wen-Huang Cheng, Junichi Yamagishi, Jun-Cheng Chen
https://arxiv.org/abs/2509.19841

ThinkFake: Reasoning in Multimodal Large Language Models for AI-Generated Image Detection
The increasing realism of AI-generated images has raised serious concerns about misinformation and privacy violations, highlighting the urgent need for accurate and interpretable detection methods. While existing approaches have made progress, most rely on binary classification without explanations or depend heavily on supervised fine-tuning, resulting in limited generalization. In this paper, we propose ThinkFake, a novel reasoning-based and generalizable framework for AI-generated image detec…

Enhancing Requirement Traceability through Data Augmentation Using Large Language Models
Jianzhang Zhang, Jialong Zhou, Nan Niu, Chuang Liu
https://arxiv.org/abs/2509.20149 http…

Enhancing Requirement Traceability through Data Augmentation Using Large Language Models
Requirements traceability is crucial in software engineering to ensure consistency between requirements and code. However, existing automated traceability methods are constrained by the scarcity of training data and challenges in bridging the semantic gap between artifacts. This study aims to address the data scarcity problem in requirements traceability by employing large language models (LLMs) for data augmentation. We propose a novel approach that utilizes prompt-based techniques with LLMs t…

Evaluating the Safety and Skill Reasoning of Large Reasoning Models Under Compute Constraints
Adarsha Balaji, Le Chen, Rajeev Thakur, Franck Cappello, Sandeep Madireddy
https://arxiv.org/abs/2509.18382

AECBench: A Hierarchical Benchmark for Knowledge Evaluation of Large Language Models in the AEC Field
Chen Liang, Zhaoqi Huang, Haofen Wang, Fu Chai, Chunying Yu, Huanhuan Wei, Zhengjie Liu, Yanpeng Li, Hongjun Wang, Ruifeng Luo, Xianzhong Zhao
https://arxiv.org/abs/2509.18776

Beyond the Leaderboard: Understanding Performance Disparities in Large Language Models via Model Diffing
Sabri Boughorbel, Fahim Dalvi, Nadir Durrani, Majd Hawasly
https://arxiv.org/abs/2509.18792

Citrus-V: Advancing Medical Foundation Models with Unified Medical Image Grounding for Clinical Reasoning
Guoxin Wang, Jun Zhao, Xinyi Liu, Yanbo Liu, Xuyang Cao, Chao Li, Zhuoyun Liu, Qintian Sun, Fangru Zhou, Haoqiang Xing, Zhenhong Yang
https://arxiv.org/abs/2509.19090

CompLeak: Deep Learning Model Compression Exacerbates Privacy Leakage
Na Li, Yansong Gao, Hongsheng Hu, Boyu Kuang, Anmin Fu
https://arxiv.org/abs/2507.16872 https://

CompLeak: Deep Learning Model Compression Exacerbates Privacy Leakage
Model compression is crucial for minimizing memory storage and accelerating inference in deep learning (DL) models, including recent foundation models like large language models (LLMs). Users can access different compressed model versions according to their resources and budget. However, while existing compression operations primarily focus on optimizing the trade-off between resource efficiency and model performance, the privacy risks introduced by compression remain overlooked and insufficien…

Seed&Steer: Guiding Large Language Models with Compilable Prefix and Branch Signals for Unit Test Generation
Shuaiyu Zhou, Zhengran Zeng, Xiaoling Zhou, Rui Xie, Shikun Zhang, Wei Ye
https://arxiv.org/abs/2507.17271

Seed&Steer: Guiding Large Language Models with Compilable Prefix and Branch Signals for Unit Test Generation
Unit tests play a vital role in the software development lifecycle. Recent advances in Large Language Model (LLM)-based approaches have significantly improved automated test generation, garnering attention from both academia and industry. We revisit LLM-based unit test generation from a novel perspective by decoupling prefix generation and assertion generation. To characterize their respective challenges, we define Initialization Complexity and adopt Cyclomatic Complexity to measure the difficu…

Retrieval Enhanced Feedback via In-context Neural Error-book
Jongyeop Hyun, Bumsoo Kim
https://arxiv.org/abs/2508.16313 https://arxiv.org/pdf/2508.16313

Retrieval Enhanced Feedback via In-context Neural Error-book
Recent advancements in Large Language Models (LLMs) have significantly improved reasoning capabilities, with in-context learning (ICL) emerging as a key technique for adaptation without retraining. While previous works have focused on leveraging correct examples, recent research highlights the importance of learning from errors to enhance performance. However, existing methods lack a structured framework for analyzing and mitigating errors, particularly in Multimodal Large Language Models (MLLM…

Memory in Large Language Models: Mechanisms, Evaluation and Evolution
Dianxing Zhang, Wendong Li, Kani Song, Jiaye Lu, Gang Li, Liuchun Yang, Sheng Li
https://arxiv.org/abs/2509.18868

Memory in Large Language Models: Mechanisms, Evaluation and Evolution
Under a unified operational definition, we define LLM memory as a persistent state written during pretraining, finetuning, or inference that can later be addressed and that stably influences outputs. We propose a four-part taxonomy (parametric, contextual, external, procedural/episodic) and a memory quadruple (location, persistence, write/access path, controllability). We link mechanism, evaluation, and governance via the chain write -> read -> inhibit/update. To avoid distorted comparisons acr…

Steering Multimodal Large Language Models Decoding for Context-Aware Safety
Zheyuan Liu, Zhangchen Xu, Guangyao Dou, Xiangchi Yuan, Zhaoxuan Tan, Radha Poovendran, Meng Jiang
https://arxiv.org/abs/2509.19212

Steering Multimodal Large Language Models Decoding for Context-Aware Safety
Multimodal Large Language Models (MLLMs) are increasingly deployed in real-world applications, yet their ability to make context-aware safety decisions remains limited. Existing methods often fail to balance oversensitivity (unjustified refusals of benign queries) and undersensitivity (missed detection of visually grounded risks), leaving a persistent gap in safety alignment. To address this issue, we introduce Safety-aware Contrastive Decoding (SafeCoDe), a lightweight and model-agnostic decod…

Understanding the Supply Chain and Risks of Large Language Model Applications
Yujie Ma, Lili Quan, Xiaofei Xie, Qiang Hu, Jiongchi Yu, Yao Zhang, Sen Chen
https://arxiv.org/abs/2507.18105

Understanding the Supply Chain and Risks of Large Language Model Applications
The rise of Large Language Models (LLMs) has led to the widespread deployment of LLM-based systems across diverse domains. As these systems proliferate, understanding the risks associated with their complex supply chains is increasingly important. LLM-based systems are not standalone as they rely on interconnected supply chains involving pretrained models, third-party libraries, datasets, and infrastructure. Yet, most risk assessments narrowly focus on model or data level, overlooking broader s…

CEQuest: Benchmarking Large Language Models for Construction Estimation
Yanzhao Wu, Lufan Wang, Rui Liu
https://arxiv.org/abs/2508.16081 https://arxiv.org/…

CEQuest: Benchmarking Large Language Models for Construction Estimation
Large Language Models (LLMs) have demonstrated remarkable capabilities across a wide range of general-domain tasks. However, their effectiveness in specialized fields, such as construction, remains underexplored. In this paper, we introduce CEQuest, a novel benchmark dataset specifically designed to evaluate the performance of LLMs in answering construction-related questions, particularly in the areas of construction drawing interpretation and estimation. We conduct comprehensive experiments us…

SilentStriker:Toward Stealthy Bit-Flip Attacks on Large Language Models
Haotian Xu, Qingsong Peng, Jie Shi, Huadi Zheng, Yu Li, Cheng Zhuo
https://arxiv.org/abs/2509.17371 https…

SilentStriker:Toward Stealthy Bit-Flip Attacks on Large Language Models
The rapid adoption of large language models (LLMs) in critical domains has spurred extensive research into their security issues. While input manipulation attacks (e.g., prompt injection) have been well studied, Bit-Flip Attacks (BFAs) -- which exploit hardware vulnerabilities to corrupt model parameters and cause severe performance degradation -- have received far less attention. Existing BFA methods suffer from key limitations: they fail to balance performance degradation and output naturalne…

From latent factors to language: a user study on LLM-generated explanations for an inherently interpretable matrix-based recommender system
Maxime Manderlier, Fabian Lecron, Olivier Vu Thanh, Nicolas Gillis
https://arxiv.org/abs/2509.18980

Ethical Considerations of Large Language Models in Game Playing
Qingquan Zhang, Yuchen Li, Bo Yuan, Julian Togelius, Georgios N. Yannakakis, Jialin Liu
https://arxiv.org/abs/2508.16065

Ethical Considerations of Large Language Models in Game Playing
Large language models (LLMs) have demonstrated tremendous potential in game playing, while little attention has been paid to their ethical implications in those contexts. This work investigates and analyses the ethical considerations of applying LLMs in game playing, using Werewolf, also known as Mafia, as a case study. Gender bias, which affects game fairness and player experience, has been observed from the behaviour of LLMs. Some roles, such as the Guard and Werewolf, are more sensitive than…

Beyond Language Barriers: Multi-Agent Coordination for Multi-Language Code Generation
Micheline B\'en\'edicte Moumoula, Serge Lionel Nikiema, Alb\'erick Euraste Djire, Abdoul Kader Kabore, Jacques Klein, Tegawend\'e F. Bissyande
https://arxiv.org/abs/2509.19918

Beyond Language Barriers: Multi-Agent Coordination for Multi-Language Code Generation
Producing high-quality code across multiple programming languages is increasingly important as today's software systems are built on heterogeneous stacks. Large language models (LLMs) have advanced the state of automated programming, yet their proficiency varies sharply between languages, especially those with limited training data such as Rust, Perl, OCaml, and Erlang. Many current solutions including language-specific fine-tuning, multi-agent orchestration, transfer learning, and intermediate…

iFinder: Structured Zero-Shot Vision-Based LLM Grounding for Dash-Cam Video Reasoning
Manyi Yao, Bingbing Zhuang, Sparsh Garg, Amit Roy-Chowdhury, Christian Shelton, Manmohan Chandraker, Abhishek Aich
https://arxiv.org/abs/2509.19552

iFinder: Structured Zero-Shot Vision-Based LLM Grounding for Dash-Cam Video Reasoning
Grounding large language models (LLMs) in domain-specific tasks like post-hoc dash-cam driving video analysis is challenging due to their general-purpose training and lack of structured inductive biases. As vision is often the sole modality available for such analysis (i.e., no LiDAR, GPS, etc.), existing video-based vision-language models (V-VLMs) struggle with spatial reasoning, causal inference, and explainability of events in the input video. To this end, we introduce iFinder, a structured …

The Moral Gap of Large Language Models
Maciej Skorski, Alina Landowska
https://arxiv.org/abs/2507.18523 https://arxiv.org/pdf/2507.18523

The Moral Gap of Large Language Models
Moral foundation detection is crucial for analyzing social discourse and developing ethically-aligned AI systems. While large language models excel across diverse tasks, their performance on specialized moral reasoning remains unclear. This study provides the first comprehensive comparison between state-of-the-art LLMs and fine-tuned transformers across Twitter and Reddit datasets using ROC, PR, and DET curve analysis. Results reveal substantial performance gaps, with LLMs exhibiting high f…

Understanding Post-Training Structural Changes in Large Language Models
Xinyu He, Xianghui Cao
https://arxiv.org/abs/2509.17866 https://arxiv.org/pdf/2509.…

Understanding Post-Training Structural Changes in Large Language Models
Post-training fundamentally alters the behavior of large language models (LLMs), yet its impact on the internal parameter space remains poorly understood. In this work, we conduct a systematic singular value decomposition (SVD) analysis of principal linear layers in pretrained LLMs, focusing on two widely adopted post-training methods: instruction tuning and long-chain-of-thought (Long-CoT) distillation. Our analysis reveals two consistent and unexpected structural changes:(1) a near-uniform ge…

LLM-GUARD: Large Language Model-Based Detection and Repair of Bugs and Security Vulnerabilities in C and Python
Akshay Mhatre, Noujoud Nader, Patrick Diehl, Deepti Gupta
https://arxiv.org/abs/2508.16419

LLM-GUARD: Large Language Model-Based Detection and Repair of Bugs and Security Vulnerabilities in C++ and Python
Large Language Models (LLMs) such as ChatGPT-4, Claude 3, and LLaMA 4 are increasingly embedded in software/application development, supporting tasks from code generation to debugging. Yet, their real-world effectiveness in detecting diverse software bugs, particularly complex, security-relevant vulnerabilities, remains underexplored. This study presents a systematic, empirical evaluation of these three leading LLMs using a benchmark of foundational programming errors, classic security flaws, a…

Global-Recent Semantic Reasoning on Dynamic Text-Attributed Graphs with Large Language Models
Yunan Wang, Jianxin Li, Ziwei Zhang
https://arxiv.org/abs/2509.18742 https://

Global-Recent Semantic Reasoning on Dynamic Text-Attributed Graphs with Large Language Models
Dynamic Text-Attribute Graphs (DyTAGs), characterized by time-evolving graph interactions and associated text attributes, are prevalent in real-world applications. Existing methods, such as Graph Neural Networks (GNNs) and Large Language Models (LLMs), mostly focus on static TAGs. Extending these existing methods to DyTAGs is challenging as they largely neglect the recent-global temporal semantics: the recent semantic dependencies among interaction texts and the global semantic evolution of nod…

BetterCheck: Towards Safeguarding VLMs for Automotive Perception Systems
Malsha Ashani Mahawatta Dona, Beatriz Cabrero-Daniel, Yinan Yu, Christian Berger
https://arxiv.org/abs/2507.17722

BetterCheck: Towards Safeguarding VLMs for Automotive Perception Systems
Large language models (LLMs) are growingly extended to process multimodal data such as text and video simultaneously. Their remarkable performance in understanding what is shown in images is surpassing specialized neural networks (NNs) such as Yolo that is supporting only a well-formed but very limited vocabulary, ie., objects that they are able to detect. When being non-restricted, LLMs and in particular state-of-the-art vision language models (VLMs) show impressive performance to describe eve…

BadReasoner: Planting Tunable Overthinking Backdoors into Large Reasoning Models for Fun or Profit
Biao Yi, Zekun Fei, Jianing Geng, Tong Li, Lihai Nie, Zheli Liu, Yiming Li
https://arxiv.org/abs/2507.18305

BadReasoner: Planting Tunable Overthinking Backdoors into Large Reasoning Models for Fun or Profit
Large reasoning models (LRMs) have emerged as a significant advancement in artificial intelligence, representing a specialized class of large language models (LLMs) designed to tackle complex reasoning tasks. The defining characteristic of LRMs lies in their extensive chain-of-thought (CoT) reasoning capabilities. In this paper, we identify a previously unexplored attack vector against LRMs, which we term "overthinking backdoors". We advance this concept by proposing a novel tunable backdoor, w…

DeepThink3D: Enhancing Large Language Models with Programmatic Reasoning in Complex 3D Situated Reasoning Tasks
Jiayi Song, Rui Wan, Lipeng Ma, Weidong Yang, Qingyuan Zhou, Yixuan Li, Ben Fei
https://arxiv.org/abs/2508.15548

DeepThink3D: Enhancing Large Language Models with Programmatic Reasoning in Complex 3D Situated Reasoning Tasks
This work enhances the ability of large language models (LLMs) to perform complex reasoning in 3D scenes. Recent work has addressed the 3D situated reasoning task by invoking tool usage through large language models. Large language models call tools via APIs and integrate the generated programs through a chain of thought to solve problems based on the program results. However, due to the simplicity of the questions in the dataset, the generated program reasoning chains are relatively short. To …

NoCode-bench: A Benchmark for Evaluating Natural Language-Driven Feature Addition
Le Deng, Zhonghao Jiang, Jialun Cao, Michael Pradel, Zhongxin Liu
https://arxiv.org/abs/2507.18130

NoCode-bench: A Benchmark for Evaluating Natural Language-Driven Feature Addition
Natural language-driven no-code development allows users to specify software functionality using natural language (NL) instead of editing source code, promising increased productivity and democratized development. Large language models (LLMs) show potential in enabling this paradigm. In this context, software documentation acts as an NL specification for functionality. This work introduces NoCode-bench, a benchmark designed to evaluate LLMs on real-world NL-driven feature addition tasks, consis…

Boardwalk: Towards a Framework for Creating Board Games with LLMs
\'Alvaro Guglielmin Becker, Gabriel Bauer de Oliveira, Lana Bertoldo Rossato, Anderson Rocha Tavares
https://arxiv.org/abs/2508.16447

Boardwalk: Towards a Framework for Creating Board Games with LLMs
Implementing board games in code can be a time-consuming task. However, Large Language Models (LLMs) have been proven effective at generating code for domain-specific tasks with simple contextual information. We aim to investigate whether LLMs can implement digital versions of board games from rules described in natural language. This would be a step towards an LLM-assisted framework for quick board game code generation. We expect to determine the main challenges for LLMs to implement the board…

Improving Large Language Models Function Calling and Interpretability via Guided-Structured Templates
Hy Dang, Tianyi Liu, Zhuofeng Wu, Jingfeng Yang, Haoming Jiang, Tao Yang, Pei Chen, Zhengyang Wang, Helen Wang, Huasheng Li, Bing Yin, Meng Jiang
https://arxiv.org/abs/2509.18076

Improving Large Language Models Function Calling and Interpretability via Guided-Structured Templates
Large language models (LLMs) have demonstrated strong reasoning and tool-use capabilities, yet they often fail in real-world tool-interactions due to incorrect parameterization, poor tool selection, or misinterpretation of user intent. These issues often stem from an incomplete understanding of user goals and inadequate comprehension of tool documentation. While Chain-of-Thought (CoT) prompting has proven effective for enhancing reasoning in general contexts, our analysis reveals that free-form…

Multi-Hierarchical Feature Detection for Large Language Model Generated Text
Luyan Zhang, Xinyu Xie
https://arxiv.org/abs/2509.18862 https://arxiv.org/pdf/…

Pointing to a Llama and Call it a Camel: On the Sycophancy of Multimodal Large Language Models
Renjie Pi, Kehao Miao, Li Peihang, Runtao Liu, Jiahui Gao, Jipeng Zhang, Xiaofang Zhou
https://arxiv.org/abs/2509.16149

Pointing to a Llama and Call it a Camel: On the Sycophancy of Multimodal Large Language Models
Multimodal large language models (MLLMs) have demonstrated extraordinary capabilities in conducting conversations based on image inputs. However, we observe that MLLMs exhibit a pronounced form of visual sycophantic behavior. While similar behavior has also been noted in text-based large language models (LLMs), it becomes significantly more prominent when MLLMs process image inputs. We refer to this phenomenon as the "sycophantic modality gap." To better understand this issue, we further analyz…

Towards Recommending Usability Improvements with Multimodal Large Language Models
Sebastian Lubos, Alexander Felfernig, Gerhard Leitner, Julian Schwazer
https://arxiv.org/abs/2508.16165

Towards Recommending Usability Improvements with Multimodal Large Language Models
Usability describes a set of essential quality attributes of user interfaces (UI) that influence human-computer interaction. Common evaluation methods, such as usability testing and inspection, are effective but resource-intensive and require expert involvement. This makes them less accessible for smaller organizations. Recent advances in multimodal LLMs offer promising opportunities to automate usability evaluation processes partly by analyzing textual, visual, and structural aspects of softwa…

EngiBench: A Benchmark for Evaluating Large Language Models on Engineering Problem Solving
Xiyuan Zhou, Xinlei Wang, Yirui He, Yang Wu, Ruixi Zou, Yuheng Cheng, Yulu Xie, Wenxuan Liu, Huan Zhao, Yan Xu, Jinjin Gu, Junhua Zhao
https://arxiv.org/abs/2509.17677

EngiBench: A Benchmark for Evaluating Large Language Models on Engineering Problem Solving
Large language models (LLMs) have shown strong performance on mathematical reasoning under well-posed conditions. However, real-world engineering problems require more than mathematical symbolic computation -- they need to deal with uncertainty, context, and open-ended scenarios. Existing benchmarks fail to capture these complexities. We introduce EngiBench, a hierarchical benchmark designed to evaluate LLMs on solving engineering problems. It spans three levels of increasing difficulty (founda…

RAGSR: Regional Attention Guided Diffusion for Image Super-Resolution
Haodong He, Yancheng Bai, Rui Lan, Xu Duan, Lei Sun, Xiangxiang Chu, Gui-Song Xia
https://arxiv.org/abs/2508.16158

RAGSR: Regional Attention Guided Diffusion for Image Super-Resolution
The rich textual information of large vision-language models (VLMs) combined with the powerful generative prior of pre-trained text-to-image (T2I) diffusion models has achieved impressive performance in single-image super-resolution (SISR). However, existing methods still face significant challenges in generating clear and accurate regional details, particularly in scenarios involving multiple objects. This challenge primarily stems from a lack of fine-grained regional descriptions and the mode…

LOCOFY Large Design Models -- Design to code conversion solution
Sohaib Muhammad, Ashwati Vipin, Karan Shetti, Honey Mittal
https://arxiv.org/abs/2507.16208

LOCOFY Large Design Models -- Design to code conversion solution
Despite rapid advances in Large Language Models and Multimodal Large Language Models (LLMs), numerous challenges related to interpretability, scalability, resource requirements and repeatability remain, related to their application in the design-to-code space. To address this, we introduce the Large Design Models (LDMs) paradigm specifically trained on designs and webpages to enable seamless conversion from design-to-code. We have developed a training and inference pipeline by incorporating dat…

Extractive Fact Decomposition for Interpretable Natural Language Inference in one Forward Pass
Nicholas Popovi\v{c}, Michael F\"arber
https://arxiv.org/abs/2509.18901 https…

Distilled Large Language Model in Confidential Computing Environment for System-on-Chip Design
Dong Ben, Hui Feng, Qian Wang
https://arxiv.org/abs/2507.16226

Distilled Large Language Model in Confidential Computing Environment for System-on-Chip Design
Large Language Models (LLMs) are increasingly used in circuit design tasks and have typically undergone multiple rounds of training. Both the trained models and their associated training data are considered confidential intellectual property (IP) and must be protected from exposure. Confidential Computing offers a promising solution to protect data and models through Trusted Execution Environments (TEEs). However, existing TEE implementations are not designed to support the resource-intensive n…

From Text to Talk: Audio-Language Model Needs Non-Autoregressive Joint Training
Tianqiao Liu, Xueyi Li, Hao Wang, Haoxuan Li, Zhichao Chen, Weiqi Luo, Zitao Liu
https://arxiv.org/abs/2509.20072

From Text to Talk: Audio-Language Model Needs Non-Autoregressive Joint Training
Recent advances in large language models have attracted significant interest in extending their capabilities to multimodal scenarios, particularly for speech-in speech-out conversational systems. However, existing multimodal models handling interleaved audio and text, such as MOSHI require complex multi stage training pipelines, incurring substantial computational costs. Moreover, these models uniformly apply autoregressive generation to both text and audio tokens, overlooking a fundamental asy…

MedOmni-45{\deg}: A Safety-Performance Benchmark for Reasoning-Oriented LLMs in Medicine
Kaiyuan Ji, Yijin Guo, Zicheng Zhang, Xiangyang Zhu, Yuan Tian, Ning Liu, Guangtao Zhai
https://arxiv.org/abs/2508.16213

MedOmni-45°: A Safety-Performance Benchmark for Reasoning-Oriented LLMs in Medicine
With the increasing use of large language models (LLMs) in medical decision-support, it is essential to evaluate not only their final answers but also the reliability of their reasoning. Two key risks are Chain-of-Thought (CoT) faithfulness -- whether reasoning aligns with responses and medical facts -- and sycophancy, where models follow misleading cues over correctness. Existing benchmarks often collapse such vulnerabilities into single accuracy scores. To address this, we introduce MedOmni-4…

Not All Features Deserve Attention: Graph-Guided Dependency Learning for Tabular Data Generation with Language Models
Zheyu Zhang, Shuo Yang, Bardh Prenkaj, Gjergji Kasneci
https://arxiv.org/abs/2507.18504

Not All Features Deserve Attention: Graph-Guided Dependency Learning for Tabular Data Generation with Language Models
Large Language Models (LLMs) have shown strong potential for tabular data generation by modeling textualized feature-value pairs. However, tabular data inherently exhibits sparse feature-level dependencies, where many feature interactions are structurally insignificant. This creates a fundamental mismatch as LLMs' self-attention mechanism inevitably distributes focus across all pairs, diluting attention on critical relationships, particularly in datasets with complex dependencies or semanticall…

TERAG: Token-Efficient Graph-Based Retrieval-Augmented Generation
Qiao Xiao, Hong Ting Tsang, Jiaxin Bai
https://arxiv.org/abs/2509.18667 https://arxiv.org…

CHURRO: Making History Readable with an Open-Weight Large Vision-Language Model for High-Accuracy, Low-Cost Historical Text Recognition
Sina J. Semnani, Han Zhang, Xinyan He, Merve Tekg\"urler, Monica S. Lam
https://arxiv.org/abs/2509.19768

CHURRO: Making History Readable with an Open-Weight Large Vision-Language Model for High-Accuracy, Low-Cost Historical Text Recognition
Accurate text recognition for historical documents can greatly advance the study and preservation of cultural heritage. Existing vision-language models (VLMs), however, are designed for modern, standardized texts and are not equipped to read the diverse languages and scripts, irregular layouts, and frequent degradation found in historical materials. This paper presents CHURRO, a 3B-parameter open-weight VLM specialized for historical text recognition. The model is trained on CHURRO-DS, the la…

Exploring Large Language Models for Analyzing and Improving Method Names in Scientific Code
Gunnar Larsen, Carol Wong, Anthony Peruma
https://arxiv.org/abs/2507.16439

Exploring Large Language Models for Analyzing and Improving Method Names in Scientific Code
Research scientists increasingly rely on implementing software to support their research. While previous research has examined the impact of identifier names on program comprehension in traditional programming environments, limited work has explored this area in scientific software, especially regarding the quality of method names in the code. The recent advances in Large Language Models (LLMs) present new opportunities for automating code analysis tasks, such as identifier name appraisals and …

Restoring Rhythm: Punctuation Restoration Using Transformer Models for Bangla, a Low-Resource Language
Md Obyedullahil Mamun, Md Adyelullahil Mamun, Arif Ahmad, Md. Imran Hossain Emu
https://arxiv.org/abs/2507.18448

Restoring Rhythm: Punctuation Restoration Using Transformer Models for Bangla, a Low-Resource Language
Punctuation restoration enhances the readability of text and is critical for post-processing tasks in Automatic Speech Recognition (ASR), especially for low-resource languages like Bangla. In this study, we explore the application of transformer-based models, specifically XLM-RoBERTa-large, to automatically restore punctuation in unpunctuated Bangla text. We focus on predicting four punctuation marks: period, comma, question mark, and exclamation mark across diverse text domains. To address the…

Cetvel: A Unified Benchmark for Evaluating Language Understanding, Generation and Cultural Capacity of LLMs for Turkish
Yakup Abrek Er, Ilker Kesen, G\"ozde G\"ul \c{S}ahin, Aykut Erdem
https://arxiv.org/abs/2508.16431

Cetvel: A Unified Benchmark for Evaluating Language Understanding, Generation and Cultural Capacity of LLMs for Turkish
We introduce Cetvel, a comprehensive benchmark designed to evaluate large language models (LLMs) in Turkish. Existing Turkish benchmarks often lack either task diversity or culturally relevant content, or both. Cetvel addresses these gaps by combining a broad range of both discriminative and generative tasks ensuring content that reflects the linguistic and cultural richness of Turkish language. Cetvel covers 23 tasks grouped into seven categories, including tasks such as grammatical error corr…

D-REX: A Benchmark for Detecting Deceptive Reasoning in Large Language Models
Satyapriya Krishna, Andy Zou, Rahul Gupta, Eliot Krzysztof Jones, Nick Winter, Dan Hendrycks, J. Zico Kolter, Matt Fredrikson, Spyros Matsoukas
https://arxiv.org/abs/2509.17938

D-REX: A Benchmark for Detecting Deceptive Reasoning in Large Language Models
The safety and alignment of Large Language Models (LLMs) are critical for their responsible deployment. Current evaluation methods predominantly focus on identifying and preventing overtly harmful outputs. However, they often fail to address a more insidious failure mode: models that produce benign-appearing outputs while operating on malicious or deceptive internal reasoning. This vulnerability, often triggered by sophisticated system prompt injections, allows models to bypass conventional saf…

HAMSA: Hijacking Aligned Compact Models via Stealthy Automation
Alexey Krylov, Iskander Vagizov, Dmitrii Korzh, Maryam Douiba, Azidine Guezzaz, Vladimir Kokh, Sergey D. Erokhin, Elena V. Tutubalina, Oleg Y. Rogov
https://arxiv.org/abs/2508.16484

HAMSA: Hijacking Aligned Compact Models via Stealthy Automation
Large Language Models (LLMs), especially their compact efficiency-oriented variants, remain susceptible to jailbreak attacks that can elicit harmful outputs despite extensive alignment efforts. Existing adversarial prompt generation techniques often rely on manual engineering or rudimentary obfuscation, producing low-quality or incoherent text that is easily flagged by perplexity-based filters. We present an automated red-teaming framework that evolves semantically meaningful and stealthy jailb…

EnAnchored-X2X: English-Anchored Optimization for Many-to-Many Translation
Sen Yang, Yu Bao, Yu Lu, Jiajun Chen, Shujian Huang, Shanbo Cheng
https://arxiv.org/abs/2509.19770 htt…

EnAnchored-X2X: English-Anchored Optimization for Many-to-Many Translation
Large language models (LLMs) have demonstrated strong machine translation capabilities for English-centric language pairs but underperform in direct non-English (x2x) translation. This work addresses this limitation through a synthetic data generation framework that leverages models' established English-to-x (en2x) capabilities. By extending English parallel corpora into omnidirectional datasets and developing an English-referenced quality evaluation proxy, we enable effective collection of hig…

AraTable: Benchmarking LLMs' Reasoning and Understanding of Arabic Tabular Data
Rana Alshaikh, Israa Alghanmi, Shelan Jeawak
https://arxiv.org/abs/2507.18442 https://…

AraTable: Benchmarking LLMs' Reasoning and Understanding of Arabic Tabular Data
The cognitive and reasoning abilities of large language models (LLMs) have enabled remarkable progress in natural language processing. However, their performance in interpreting structured data, especially in tabular formats, remains limited. Although benchmarks for English tabular data are widely available, Arabic is still underrepresented because of the limited availability of public resources and its unique language features. To address this gap, we present AraTable, a novel and comprehensiv…

Variation in Verification: Understanding Verification Dynamics in Large Language Models
Yefan Zhou, Austin Xu, Yilun Zhou, Janvijay Singh, Jiang Gui, Shafiq Joty
https://arxiv.org/abs/2509.17995

Variation in Verification: Understanding Verification Dynamics in Large Language Models
Recent advances have shown that scaling test-time computation enables large language models (LLMs) to solve increasingly complex problems across diverse domains. One effective paradigm for test-time scaling (TTS) involves LLM generators producing multiple solution candidates, with LLM verifiers assessing the correctness of these candidates without reference answers. In this paper, we study generative verifiers, which perform verification by generating chain-of-thought (CoT) reasoning followed b…

Text Takes Over: A Study of Modality Bias in Multimodal Intent Detection
Ankan Mullick, Saransh Sharma, Abhik Jana, Pawan Goyal
https://arxiv.org/abs/2508.16122 https://

Text Takes Over: A Study of Modality Bias in Multimodal Intent Detection
The rise of multimodal data, integrating text, audio, and visuals, has created new opportunities for studying multimodal tasks such as intent detection. This work investigates the effectiveness of Large Language Models (LLMs) and non-LLMs, including text-only and multi-modal models, in the multimodal intent detection task. Our study reveals that Mistral-7B, a text-only LLM, outperforms most competitive multimodal models by approximately 9% on MIntRec-1 and 4% on MIntRec2.0 datasets. This perfor…

DeepMEL: A Multi-Agent Collaboration Framework for Multimodal Entity Linking
Fang Wang, Tianwei Yan, Zonghao Yang, Minghao Hu, Jun Zhang, Zhunchen Luo, Xiaoying Bai
https://arxiv.org/abs/2508.15876

DeepMEL: A Multi-Agent Collaboration Framework for Multimodal Entity Linking
Multimodal Entity Linking (MEL) aims to associate textual and visual mentions with entities in a multimodal knowledge graph. Despite its importance, current methods face challenges such as incomplete contextual information, coarse cross-modal fusion, and the difficulty of jointly large language models (LLMs) and large visual models (LVMs). To address these issues, we propose DeepMEL, a novel framework based on multi-agent collaborative reasoning, which achieves efficient alignment and disambigu…

Replaced article(s) found for cs.CL. https://arxiv.org/list/cs.CL/new
[4/5]:
- Macroeconomic Forecasting with Large Language Models
Andrea Carriero, Davide Pettenuzzo, Shubhranshu Shekhar

Crosslisted article(s) found for cs.CL. https://arxiv.org/list/cs.CL/new
[1/2]:
- Safe-SAIL: Towards a Fine-grained Safety Landscape of Large Language Models via Sparse Autoencode...
Jiaqi Weng, Han Zheng, Hanyu Zhang, Qinqin He, Jialing Tao, Hui Xue, Zhixuan Chu, Xiting Wang

Replaced article(s) found for cs.CL. https://arxiv.org/list/cs.CL/new
[2/5]:
- Benchmarking Critical Questions Generation: A Challenging Reasoning Task for Large Language Models
Banca Calvo Figueras, Rodrigo Agerri