Tootfinder

Observing Without Doing: Pseudo-Apprenticeship Patterns in Student LLM Use
Jade Hak, Nathaniel Lam Johnson, Matin Amoozadeh, Amin Alipour, Souti Chattopadhyay
https://arxiv.org/abs/2510.04986

Observing Without Doing: Pseudo-Apprenticeship Patterns in Student LLM Use
Large Language Models (LLMs) such as ChatGPT have quickly become part of student programmers' toolkits, whether allowed by instructors or not. This paper examines how introductory programming (CS1) students integrate LLMs into their problem-solving processes. We conducted a mixed-methods study with 14 undergraduates completing three programming tasks while thinking aloud and permitted to access any resources they choose. The tasks varied in open-endedness and familiarity to the participants and…

A systematic comparison of Large Language Models for automated assignment assessment in programming education: Exploring the importance of architecture and vendor
Marcin Jukiewicz
https://arxiv.org/abs/2509.26483

A systematic comparison of Large Language Models for automated assignment assessment in programming education: Exploring the importance of architecture and vendor
This study presents the first large-scale, side-by-side comparison of contemporary Large Language Models (LLMs) in the automated grading of programming assignments. Drawing on over 6,000 student submissions collected across four years of an introductory programming course, we systematically analysed the distribution of grades, differences in mean scores and variability reflecting stricter or more lenient grading, and the consistency and clustering of grading patterns across models. Eighteen pub…

RS-OOD: A Vision-Language Augmented Framework for Out-of-Distribution Detection in Remote Sensing
Yingrui Ji, Jiansheng Chen, Jingbo Chen, Anzhi Yue, Chenhao Wang, Kai Li, Yao Zhu
https://arxiv.org/abs/2509.02273

RS-OOD: A Vision-Language Augmented Framework for Out-of-Distribution Detection in Remote Sensing
Out-of-distribution (OOD) detection represents a critical challenge in remote sensing applications, where reliable identification of novel or anomalous patterns is essential for autonomous monitoring, disaster response, and environmental assessment. Despite remarkable progress in OOD detection for natural images, existing methods and benchmarks remain poorly suited to remote sensing imagery due to data scarcity, complex multi-scale scene structures, and pronounced distribution shifts. To this e…

Are We SOLID Yet? An Empirical Study on Prompting LLMs to Detect Design Principle Violations
Fatih Pehlivan, Ar\c{c}in \"Ulk\"u Erg\"uzen, Sahand Moslemi Yengejeh, Mayasah Lami, Anil Koyuncu
https://arxiv.org/abs/2509.03093

Are We SOLID Yet? An Empirical Study on Prompting LLMs to Detect Design Principle Violations
Traditional static analysis methods struggle to detect semantic design flaws, such as violations of the SOLID principles, which require a strong understanding of object-oriented design patterns and principles. Existing solutions typically focus on individual SOLID principles or specific programming languages, leaving a gap in the ability to detect violations across all five principles in multi-language codebases. This paper presents a new approach: a methodology that leverages tailored prompt e…

Top-H Decoding: Adapting the Creativity and Coherence with Bounded Entropy in Text Generation
Erfan Baghaei Potraghloo, Seyedarmin Azizi, Souvik Kundu, Massoud Pedram
https://arxiv.org/abs/2509.02510

Top-H Decoding: Adapting the Creativity and Coherence with Bounded Entropy in Text Generation
Large language models (LLMs), despite their impressive performance across a wide range of tasks, often struggle to balance two competing objectives in open-ended text generation: fostering diversity and creativity while preserving logical coherence. Existing truncated sampling techniques, including temperature scaling, top-\$p\$ (nucleus) sampling, and min-\$p\$ sampling, aim to manage this trade-off. However, they exhibit limitations, particularly in the effective incorporation of the confiden…

Real Time FPGA Based Transformers & VLMs for Vision Tasks: SOTA Designs and Optimizations
Safa Mohammed Sali, Mahmoud Meribout, Ashiyana Abdul Majeed
https://arxiv.org/abs/2509.04162

Real Time FPGA Based Transformers & VLMs for Vision Tasks: SOTA Designs and Optimizations
Transformers and vision-language models (VLMs) have emerged as dominant architectures in computer vision and multimodal AI, offering state-of-the-art performance in tasks such as image classification, object detection, visual question answering, and caption generation. However, their high computational complexity, large memory footprints, and irregular data access patterns present significant challenges for deployment in latency- and power-constrained environments. Field-programmable gate array…

Pattern matching is a major feature in software development. While pattern matching applies in several locations, its current usage is limited to switch case blocks. I want to compare the power of pattern matching across a couple of programming languages I’m familiar with in this post.
https://blo…

Temporal-Aware Iterative Speech Model for Dementia Detection
Chukwuemeka Ugwu, Oluwafemi Oyeleke
https://arxiv.org/abs/2510.00030 https://arxiv.org/pdf/251…

Temporal-Aware Iterative Speech Model for Dementia Detection
Deep learning systems often struggle with processing long sequences, where computational complexity can become a bottleneck. Current methods for automated dementia detection using speech frequently rely on static, time-agnostic features or aggregated linguistic content, lacking the flexibility to model the subtle, progressive deterioration inherent in speech production. These approaches often miss the dynamic temporal patterns that are critical early indicators of cognitive decline. In this pap…

Confusion is the Final Barrier: Rethinking Jailbreak Evaluation and Investigating the Real Misuse Threat of LLMs
Yu Yan, Sheng Sun, Zhe Wang, Yijun Lin, Zenghao Duan, zhifei zheng, Min Liu, Zhiyi yin, Jianping Zhang
https://arxiv.org/abs/2508.16347

Confusion is the Final Barrier: Rethinking Jailbreak Evaluation and Investigating the Real Misuse Threat of LLMs
With the development of Large Language Models (LLMs), numerous efforts have revealed their vulnerabilities to jailbreak attacks. Although these studies have driven the progress in LLMs' safety alignment, it remains unclear whether LLMs have internalized authentic knowledge to deal with real-world crimes, or are merely forced to simulate toxic language patterns. This ambiguity raises concerns that jailbreak success is often attributable to a hallucination loop between jailbroken LLM and judger L…

SWE-Debate: Competitive Multi-Agent Debate for Software Issue Resolution
Han Li, Yuling Shi, Shaoxin Lin, Xiaodong Gu, Heng Lian, Xin Wang, Yantao Jia, Tao Huang, Qianxiang Wang
https://arxiv.org/abs/2507.23348

SWE-Debate: Competitive Multi-Agent Debate for Software Issue Resolution
Issue resolution has made remarkable progress thanks to the advanced reasoning capabilities of large language models (LLMs). Recently, agent-based frameworks such as SWE-agent have further advanced this progress by enabling autonomous, tool-using agents to tackle complex software engineering tasks. While existing agent-based issue resolution approaches are primarily based on agents' independent explorations, they often get stuck in local solutions and fail to identify issue patterns that span a…