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Scientists at NeurIPS, which drew a record 26,000 attendees this year, say key questions about how AI models work and how to measure them remain unresolved (Jared Perlo/NBC News)
https://www.nbcnews.com/tech/tech-news/ai-progress-surges-resea…

AI progress surges while researchers struggle to explain It
Academics, start-up founders and researchers representing industrial titans from around the globe descended on San Diego for the top gathering in AI.

Automated Program Repair of Uncompilable Student Code
Griffin Pitts, Aum Pandya, Darsh Rank, Tirth Bhatt, Muntasir Hoq, Bita Akram
https://arxiv.org/abs/2510.06187 https://

Automated Program Repair of Uncompilable Student Code
A significant portion of student programming submissions in CS1 learning environments are uncompilable, limiting their use in student modeling and downstream knowledge tracing. Traditional modeling pipelines often exclude these cases, discarding observations of student learning. This study investigates automated program repair as a strategy to recover uncompilable code while preserving students' structural intent for use in student modeling. Within this framework, we assess large language model…

MLE-Smith: Scaling MLE Tasks with Automated Multi-Agent Pipeline
Rushi Qiang, Yuchen Zhuang, Anikait Singh, Percy Liang, Chao Zhang, Sherry Yang, Bo Dai
https://arxiv.org/abs/2510.07307

MLE-Smith: Scaling MLE Tasks with Automated Multi-Agent Pipeline
While Language Models (LMs) have made significant progress in automating machine learning engineering (MLE), the acquisition of high-quality MLE training data is significantly constrained. Current MLE benchmarks suffer from low scalability and limited applicability because they rely on static, manually curated tasks, demanding extensive time and manual effort to produce. We introduce MLE-Smith, a fully automated multi-agent pipeline, to transform raw datasets into competition-style MLE challeng…

ConstraintLLM: A Neuro-Symbolic Framework for Industrial-Level Constraint Programming
Weichun Shi, Minghao Liu, Wanting Zhang, Langchen Shi, Fuqi Jia, Feifei Ma, Jian Zhang
https://arxiv.org/abs/2510.05774

ConstraintLLM: A Neuro-Symbolic Framework for Industrial-Level Constraint Programming
Constraint programming (CP) is a crucial technology for solving real-world constraint optimization problems (COPs), with the advantages of rich modeling semantics and high solving efficiency. Using large language models (LLMs) to generate formal modeling automatically for COPs is becoming a promising approach, which aims to build trustworthy neuro-symbolic AI with the help of symbolic solvers. However, CP has received less attention compared to works based on operations research (OR) models. We…

Automated Repeatable Adversary Threat Emulation with Effects Language (EL)
Suresh K. Damodaran, Paul D. Rowe
https://arxiv.org/abs/2510.06420 https://arxiv…

Automated Repeatable Adversary Threat Emulation with Effects Language (EL)
The emulation of multi-step attacks attributed to advanced persistent threats is valuable for training defenders and evaluating defense tools. In this paper, we discuss the numerous challenges and desired attributes associated with such automation. Additionally, we introduce the use of Effects Language (EL), a visual programming language with graph-based operational semantics, as a solution to address many of these challenges and requirements. We formally define the execution semantics of EL, a…

"Experts say the new policy, which ditches software that automatically captured text messages, opens ample room for both willful and unwitting noncompliance with federal records laws."
To Preserve Records, Homeland Security Now Relies on Officials to Take Screenshots - The New York Times
https://www.nytimes.com/2025/11/06/us/politics/homeland-security-foia.html

Adversarial Bug Reports as a Security Risk in Language Model-Based Automated Program Repair
Piotr Przymus (Nicolaus Copernicus University, Toru\'n, Poland), Andreas Happe (TU Wienm Vienna, Austria), J\"urgen Cito (TU Wienm Vienna, Austria)
https://arxiv.org/abs/2509.05372

Adversarial Bug Reports as a Security Risk in Language Model-Based Automated Program Repair
Large Language Model (LLM) - based Automated Program Repair (APR) systems are increasingly integrated into modern software development workflows, offering automated patches in response to natural language bug reports. However, this reliance on untrusted user input introduces a novel and underexplored attack surface. In this paper, we investigate the security risks posed by adversarial bug reports -- realistic-looking issue submissions crafted to mislead APR systems into producing insecure or ha…

Procrustes Problems on Random Matrices
Hajg Jasa, Ronny Bergmann, Christian K\"ummerle, Avanti Athreya, Zachary Lubberts
https://arxiv.org/abs/2510.05182 https://

Procrustes Problems on Random Matrices
Meaningful comparison between sets of observations often necessitates alignment or registration between them, and the resulting optimization problems range in complexity from those admitting simple closed-form solutions to those requiring advanced and novel techniques. We compare different Procrustes problems in which we align two sets of points after various perturbations by minimizing the norm of the difference between one matrix and an orthogonal transformation of the other. The minimization…

AUREXA-SE: Audio-Visual Unified Representation Exchange Architecture with Cross-Attention and Squeezeformer for Speech Enhancement
M. Sajid, Deepanshu Gupta, Yash Modi, Sanskriti Jain, Harshith Jai Surya Ganji, A. Rahaman, Harshvardhan Choudhary, Nasir Saleem, Amir Hussain, M. Tanveer
https://arxiv.org/abs/2510.05295

AUREXA-SE: Audio-Visual Unified Representation Exchange Architecture with Cross-Attention and Squeezeformer for Speech Enhancement
In this paper, we propose AUREXA-SE (Audio-Visual Unified Representation Exchange Architecture with Cross-Attention and Squeezeformer for Speech Enhancement), a progressive bimodal framework tailored for audio-visual speech enhancement (AVSE). AUREXA-SE jointly leverages raw audio waveforms and visual cues by employing a U-Net-based 1D convolutional encoder for audio and a Swin Transformer V2 for efficient and expressive visual feature extraction. Central to the architecture is a novel bidirect…

Revisiting Metric Reliability for Fine-grained Evaluation of Machine Translation and Summarization in Indian Languages
Amir Hossein Yari, Kalmit Kulkarni, Ahmad Raza Khan, Fajri Koto
https://arxiv.org/abs/2510.07061

Revisiting Metric Reliability for Fine-grained Evaluation of Machine Translation and Summarization in Indian Languages
While automatic metrics drive progress in Machine Translation (MT) and Text Summarization (TS), existing metrics have been developed and validated almost exclusively for English and other high-resource languages. This narrow focus leaves Indian languages, spoken by over 1.5 billion people, largely overlooked, casting doubt on the universality of current evaluation practices. To address this gap, we introduce ITEM, a large-scale benchmark that systematically evaluates the alignment of 26 automat…