Tootfinder

Misaligned from Within: Large Language Models Reproduce Our Double-Loop Learning Blindness
Tim Rogers, Ben Teehankee
https://arxiv.org/abs/2507.02283 https…

Misaligned from Within: Large Language Models Reproduce Our Double-Loop Learning Blindness
This paper examines a critical yet unexplored dimension of the AI alignment problem: the potential for Large Language Models (LLMs) to inherit and amplify existing misalignments between human espoused theories and theories-in-use. Drawing on action science research, we argue that LLMs trained on human-generated text likely absorb and reproduce Model 1 theories-in-use - a defensive reasoning pattern that both inhibits learning and creates ongoing anti-learning dynamics at the dyad, group, and or…

Crosslisted article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[1/3]:
- MultiStream-LLM: Bridging Modalities for Robust Sign Language Translation
Marshall Thomas, Edward Fish, Richard Bowden

Replaced article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[2/9]:
- DivScene: Towards Open-Vocabulary Object Navigation with Large Vision Language Models in Diverse ...
Wang, Zhang, Fang, Tian, Yang, Ma, Pan, Song, Yu

Harnessing Patterns to Support the Development of Hybrid Quantum Applications
Daniel Vietz, Martin Beisel, Johanna Barzen, Frank Leymann, Lavinia Stiliadou, Benjamin Weder
https://arxiv.org/abs/2507.00696

Harnessing Patterns to Support the Development of Hybrid Quantum Applications
Quantum computing provides computational advantages in various domains. To benefit from these advantages complex hybrid quantum applications must be built, which comprise both quantum and classical programs. Engineering these applications requires immense expertise in physics, mathematics, and software engineering. To facilitate the development of quantum applications, a corresponding quantum computing pattern language providing proven solutions to recurring problems has been presented. However…

Replaced article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[3/4]:
- Perceiving Beyond Language Priors: Enhancing Visual Comprehension and Attention in Multimodal Models
Aarti Ghatkesar, Ganesh Venkatesh

OFCnetLLM: Large Language Model for Network Monitoring and Alertness
Hong-Jun Yoon, Mariam Kiran, Danial Ebling, Joe Breen
https://arxiv.org/abs/2507.22711 https://

OFCnetLLM: Large Language Model for Network Monitoring and Alertness
The rapid evolution of network infrastructure is bringing new challenges and opportunities for efficient network management, optimization, and security. With very large monitoring databases becoming expensive to explore, the use of AI and Generative AI can help reduce costs of managing these datasets. This paper explores the use of Large Language Models (LLMs) to revolutionize network monitoring management by addressing the limitations of query finding and pattern analysis. We leverage LLMs to …

Lost in Translation? Converting RegExes for Log Parsing into Dynatrace Pattern Language
Julian Fragner, Christian Macho, Bernhard Dieber, Martin Pinzger
https://arxiv.org/abs/2506.19539

Lost in Translation? Converting RegExes for Log Parsing into Dynatrace Pattern Language
Log files provide valuable information for detecting and diagnosing problems in enterprise software applications and data centers. Several log analytics tools and platforms were developed to help filter and extract information from logs, typically using regular expressions (RegExes). Recent commercial log analytics platforms provide domain-specific languages specifically designed for log parsing, such as Grok or the Dynatrace Pattern Language (DPL). However, users who want to migrate to these p…

Why Are Parsing Actions for Understanding Message Hierarchies Not Random?
Daichi Kato, Ryo Ueda, Yusuke Miyao
https://arxiv.org/abs/2506.22366 https://

Why Are Parsing Actions for Understanding Message Hierarchies Not Random?
If humans understood language by randomly selecting parsing actions, it might have been necessary to construct a robust symbolic system capable of being interpreted under any hierarchical structure. However, human parsing strategies do not seem to follow such a random pattern. Why is that the case? In fact, a previous study on emergent communication using models with hierarchical biases have reported that agents adopting random parsing strategies$\unicode{x2013}$ones that deviate significantly …

One Weird Trick to Untie Landin's Knot
Paulette Koronkevich, William J. Bowman
https://arxiv.org/abs/2507.21317 https://arxiv.org/pdf/2507.21317…

One Weird Trick to Untie Landin's Knot
In this work, we explore Landin's Knot, which is understood as a pattern for encoding general recursion, including non-termination, that is possible after adding higher-order references to an otherwise terminating language. We observe that this isn't always true -- higher-order references, by themselves, don't lead to non-termination. The key insight is that Landin's Knot relies not primarily on references storing functions, but on unrestricted quantification over a function's environment. We s…

Architectural #Patterns, The Pattern Language of #SoftwareArchitecture
https://github.com/denyspoltorak/metap…

Getting Started Live-Coding with 🌀 Strudel
With Strudel, you can expressively write dynamic music pieces. It is an official port of the Tidal Cycles pattern language to JavaScript. You don’t need to know JavaScript or Tidal Cycles to make music with Strudel.
#electronicmusic
https://strudel.cc/workshop/getting-started/

Replaced article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[2/4]:
- The Hidden Life of Tokens: Reducing Hallucination of Large Vision-Language Models via Visual Info...
Li, Shi, Gao, Liu, Wang, Chen, Liu, Zhao, Wang, Metaxas

STARec: An Efficient Agent Framework for Recommender Systems via Autonomous Deliberate Reasoning
Chenghao Wu, Ruiyang Ren, Junjie Zhang, Ruirui Wang, Zhongrui Ma, Qi Ye, Wayne Xin Zhao
https://arxiv.org/abs/2508.18812

STARec: An Efficient Agent Framework for Recommender Systems via Autonomous Deliberate Reasoning
While modern recommender systems are instrumental in navigating information abundance, they remain fundamentally limited by static user modeling and reactive decision-making paradigms. Current large language model (LLM)-based agents inherit these shortcomings through their overreliance on heuristic pattern matching, yielding recommendations prone to shallow correlation bias, limited causal inference, and brittleness in sparse-data scenarios. We introduce STARec, a slow-thinking augmented agent …

Tutorial on the Probabilistic Unification of Estimation Theory, Machine Learning, and Generative AI
Mohammed Elmusrati
https://arxiv.org/abs/2508.15719 https://

Tutorial on the Probabilistic Unification of Estimation Theory, Machine Learning, and Generative AI
Extracting meaning from uncertain, noisy data is a fundamental problem across time series analysis, pattern recognition, and language modeling. This survey presents a unified mathematical framework that connects classical estimation theory, statistical inference, and modern machine learning, including deep learning and large language models. By analyzing how techniques such as maximum likelihood estimation, Bayesian inference, and attention mechanisms address uncertainty, the paper illustrates …

The Phases of Chaos
Tarek Anous, Diego M. Hofman
https://arxiv.org/abs/2506.20542 https://arxiv.org/pdf/2506.20542

The Phases of Chaos
We develop a novel physical picture to understand certain universal properties of the GUE matrix model which are typically ascribed to quantum chaos, i.e. the ramp and the plateau. We argue that these features should instead be associated with a pattern of spontaneous (or weak explicit) symmetry breaking. In this language, the GUE matrix model corresponds to an effective theory that describes the symmetry-broken phase, and where the Hermitian matrix of the GUE should be understood as a massive …

LLM-Based Social Simulations Require a Boundary
Zengqing Wu, Run Peng, Takayuki Ito, Chuan Xiao
https://arxiv.org/abs/2506.19806 https://

LLM-Based Social Simulations Require a Boundary
This position paper argues that large language model (LLM)-based social simulations should establish clear boundaries to meaningfully contribute to social science research. While LLMs offer promising capabilities for modeling human-like agents compared to traditional agent-based modeling, they face fundamental limitations that constrain their reliability for social pattern discovery. The core issue lies in LLMs' tendency towards an ``average persona'' that lacks sufficient behavioral heterogene…

Replaced article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[3/9]:
- PriorDiffusion: Leverage Language Prior in Diffusion Models for Monocular Depth Estimation
Zeng, Ni, Wang, Rim, Chung, Yang, Hong, Wong

Large Language Models Show Signs of Alignment with Human Neurocognition During Abstract Reasoning
Christopher Pinier, Sonia Acu\~na Vargas, Mariia Steeghs-Turchina, Dora Matzke, Claire E. Stevenson, Michael D. Nunez
https://arxiv.org/abs/2508.10057

Large Language Models Show Signs of Alignment with Human Neurocognition During Abstract Reasoning
This study investigates whether large language models (LLMs) mirror human neurocognition during abstract reasoning. We compared the performance and neural representations of human participants with those of eight open-source LLMs on an abstract-pattern-completion task. We leveraged pattern type differences in task performance and in fixation-related potentials (FRPs) as recorded by electroencephalography (EEG) during the task. Our findings indicate that only the largest tested LLMs (~70 billion…

This is everything I could never get out of #sonicpi:

Multi-Language Detection of Design Pattern Instances
Hugo Andrade, Jo\~ao Bispo, Filipe F. Correia
https://arxiv.org/abs/2506.03903 https://

Multi-Language Detection of Design Pattern Instances
Code comprehension is often supported by source code analysis tools which provide more abstract views over software systems, such as those detecting design patterns. These tools encompass analysis of source code and ensuing extraction of relevant information. However, the analysis of the source code is often specific to the target programming language. We propose DP-LARA, a multi-language pattern detection tool that uses the multi-language capability of the LARA framework to support finding p…

Reverse Physician-AI Relationship: Full-process Clinical Diagnosis Driven by a Large Language Model
Shicheng Xu, Xin Huang, Zihao Wei, Liang Pang, Huawei Shen, Xueqi Cheng
https://arxiv.org/abs/2508.10492

Reverse Physician-AI Relationship: Full-process Clinical Diagnosis Driven by a Large Language Model
Full-process clinical diagnosis in the real world encompasses the entire diagnostic workflow that begins with only an ambiguous chief complaint. While artificial intelligence (AI), particularly large language models (LLMs), is transforming clinical diagnosis, its role remains largely as an assistant to physicians. This AI-assisted working pattern makes AI can only answer specific medical questions at certain parts within the diagnostic process, but lack the ability to drive the entire diagnosti…

Bhatt Conjectures: On Necessary-But-Not-Sufficient Benchmark Tautology for Human Like Reasoning
Manish Bhatt
https://arxiv.org/abs/2506.11423 https://

Bhatt Conjectures: On Necessary-But-Not-Sufficient Benchmark Tautology for Human Like Reasoning
Debates about whether Large Language or Reasoning Models (LLMs/LRMs) truly reason or merely pattern-match suffer from shifting goal posts. In my personal opinion, two analytic--hence "tautological"--benchmarks cut through that fog in my mental model. In this paper, I attempt to write down my mental model in concrete terms.

Automatic Semantic Alignment of Flow Pattern Representations for Exploration with Large Language Models
Weihan Zhang, Jun Tao
https://arxiv.org/abs/2508.06300 https://

Automatic Semantic Alignment of Flow Pattern Representations for Exploration with Large Language Models
Explorative flow visualization allows domain experts to analyze complex flow structures by interactively investigating flow patterns. However, traditional visual interfaces often rely on specialized graphical representations and interactions, which require additional effort to learn and use. Natural language interaction offers a more intuitive alternative, but teaching machines to recognize diverse scientific concepts and extract corresponding structures from flow data poses a significant chall…

Diagnostic-Guided Dynamic Profile Optimization for LLM-based User Simulators in Sequential Recommendation
Hongyang Liu, Zhu Sun, Tianjun Wei, Yan Wang, Jiajie Zhu, Xinghua Qu
https://arxiv.org/abs/2508.12645

Diagnostic-Guided Dynamic Profile Optimization for LLM-based User Simulators in Sequential Recommendation
Recent advances in large language models (LLMs) have enabled realistic user simulators for developing and evaluating recommender systems (RSs). However, existing LLM-based simulators for RSs face two major limitations: (1) static and single-step prompt-based inference that leads to inaccurate and incomplete user profile construction; (2) unrealistic and single-round recommendation-feedback interaction pattern that fails to capture real-world scenarios. To address these limitations, we propose D…

Hidden in the Noise: Unveiling Backdoors in Audio LLMs Alignment through Latent Acoustic Pattern Triggers
Liang Lin, Miao Yu, Kaiwen Luo, Yibo Zhang, Lilan Peng, Dexian Wang, Xuehai Tang, Yuanhe Zhang, Xikang Yang, Zhenhong Zhou, Kun Wang, Yang Liu
https://arxiv.org/abs/2508.02175

Hidden in the Noise: Unveiling Backdoors in Audio LLMs Alignment through Latent Acoustic Pattern Triggers
As Audio Large Language Models (ALLMs) emerge as powerful tools for speech processing, their safety implications demand urgent attention. While considerable research has explored textual and vision safety, audio's distinct characteristics present significant challenges. This paper first investigates: Is ALLM vulnerable to backdoor attacks exploiting acoustic triggers? In response to this issue, we introduce Hidden in the Noise (HIN), a novel backdoor attack framework designed to exploit subtle,…

Replaced article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[4/9]:
- "Principal Components" Enable A New Language of Images
Xin Wen, Bingchen Zhao, Ismail Elezi, Jiankang Deng, Xiaojuan Qi

This https://arxiv.org/abs/2506.03903 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csSE_…

Multi-Language Detection of Design Pattern Instances
Code comprehension is often supported by source code analysis tools which provide more abstract views over software systems, such as those detecting design patterns. These tools encompass analysis of source code and ensuing extraction of relevant information. However, the analysis of the source code is often specific to the target programming language. We propose DP-LARA, a multi-language pattern detection tool that uses the multi-language capability of the LARA framework to support finding p…

Superstudent intelligence in thermodynamics
Rebecca Loubet, Pascal Zittlau, Marco Hoffmann, Luisa Vollmer, Sophie Fellenz, Heike Leitte, Fabian Jirasek, Johannes Lenhard, Hans Hasse
https://arxiv.org/abs/2506.09822

Superstudent intelligence in thermodynamics
In this short note, we report and analyze a striking event: OpenAI's large language model o3 has outwitted all students in a university exam on thermodynamics. The thermodynamics exam is a difficult hurdle for most students, where they must show that they have mastered the fundamentals of this important topic. Consequently, the failure rates are very high, A-grades are rare - and they are considered proof of the students' exceptional intellectual abilities. This is because pattern learning does…

Multimodal Behavioral Patterns Analysis with Eye-Tracking and LLM-Based Reasoning
Dongyang Guo, Yasmeen Abdrabou, Enkeleda Thaqi, Enkelejda Kasneci
https://arxiv.org/abs/2507.18252

Multimodal Behavioral Patterns Analysis with Eye-Tracking and LLM-Based Reasoning
Eye-tracking data reveals valuable insights into users' cognitive states but is difficult to analyze due to its structured, non-linguistic nature. While large language models (LLMs) excel at reasoning over text, they struggle with temporal and numerical data. This paper presents a multimodal human-AI collaborative framework designed to enhance cognitive pattern extraction from eye-tracking signals. The framework includes: (1) a multi-stage pipeline using horizontal and vertical segmentation alo…

Outsmarting Linear Neural Networks via an Incoherent Light-Driven Optical Extreme Learner with Data Reverberation
Bofeng Liu, Xu Mei, Sadman Shafi, Tunan Xia, Iam-Choon Khoo, Zhiwen Liu, Xingjie Ni
https://arxiv.org/abs/2508.08428

Outsmarting Linear Neural Networks via an Incoherent Light-Driven Optical Extreme Learner with Data Reverberation
Artificial neural networks have revolutionized fields from computer vision to natural language processing, yet their growing energy and computational demands threaten future progress. Optical neural networks promise greater speed, bandwidth, and energy efficiency, but suffer from weak optical nonlinearities. Here, we demonstrate a low-power, incoherent-light-driven optical extreme learner that leverages 'data nonlinearity' from optical pattern reverberations, eliminating reliance on intrinsic n…

Replaced article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[4/6]:
- Beyond Label Semantics: Language-Guided Action Anatomy for Few-shot Action Recognition
Zefeng Qian, Xincheng Yao, Yifei Huang, Chongyang Zhang, Jiangyong Ying, Hong Sun

Survey of LLM Agent Communication with MCP: A Software Design Pattern Centric Review
Anjana Sarkar, Soumyendu Sarkar
https://arxiv.org/abs/2506.05364 https…

Survey of LLM Agent Communication with MCP: A Software Design Pattern Centric Review
This survey investigates how classical software design patterns can enhance the reliability and scalability of communication in Large Language Model (LLM)-driven agentic AI systems, focusing particularly on the Model Context Protocol (MCP). It examines the foundational architectures of LLM-based agents and their evolution from isolated operation to sophisticated, multi-agent collaboration, addressing key communication hurdles that arise in this transition. The study revisits well-established pa…

Replaced article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[4/5]:
- VRU-Accident: A Vision-Language Benchmark for Video Question Answering and Dense Captioning for A...
Younggun Kim, Ahmed S. Abdelrahman, Mohamed Abdel-Aty

Beyond Surface-Level Detection: Towards Cognitive-Driven Defense Against Jailbreak Attacks via Meta-Operations Reasoning
Rui Pu, Chaozhuo Li, Rui Ha, Litian Zhang, Lirong Qiu, Xi Zhang
https://arxiv.org/abs/2508.03054

Beyond Surface-Level Detection: Towards Cognitive-Driven Defense Against Jailbreak Attacks via Meta-Operations Reasoning
Defending large language models (LLMs) against jailbreak attacks is essential for their safe and reliable deployment. Existing defenses often rely on shallow pattern matching, which struggles to generalize to novel and unseen attack strategies. To address this challenge, we propose the Cognitive-Driven Defense (CDD) framework, which targets the underlying structure of jailbreak prompts by applying meta-operations, defined as basic manipulations that conceal harmful intent.CDD emulates human cog…

Replaced article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[2/5]:
- RadAlign: Advancing Radiology Report Generation with Vision-Language Concept Alignment
Difei Gu, Yunhe Gao, Yang Zhou, Mu Zhou, Dimitris Metaxas

Replaced article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[5/7]:
- Calibrated and Robust Foundation Models for Vision-Language and Medical Image Tasks Under Distrib...
Behraj Khan, Tahir Qasim Syed, Nouman M. Durrani, Bilal Naseem, Shabir Ahmad, Rizwan Qureshi
…

Replaced article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[3/7]:
- Zero-AVSR: Zero-Shot Audio-Visual Speech Recognition with LLMs by Learning Language-Agnostic Spee...
Jeong Hun Yeo, Minsu Kim, Chae Won Kim, Stavros Petridis, Yong Man Ro

Replaced article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[1/4]:
- Bi-VLDoc: Bidirectional Vision-Language Modeling for Visually-Rich Document Understanding
Chuwei Luo, Guozhi Tang, Qi Zheng, Cong Yao, Lianwen Jin, Chenliang Li, Yang Xue, Luo Si

Replaced article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[2/7]:
- SLGaussian: Fast Language Gaussian Splatting in Sparse Views
Kangjie Chen, BingQuan Dai, Minghan Qin, Dongbin Zhang, Peihao Li, Yingshuang Zou, Haoqian Wang

Replaced article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[4/4]:
- IRL-VLA: Training an Vision-Language-Action Policy via Reward World Model
Jiang, Gao, Wang, Sun, Wang, Heng, Sun, Tang, Zhu, Chai, Wang, Gu, Jiang, Sun

Replaced article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[5/5]:
Imagine, Verify, Execute: Memory-Guided Agentic Exploration with Vision-Language Models

Replaced article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[3/5]:
- SpaCE-10: A Comprehensive Benchmark for Multimodal Large Language Models in Compositional Spatial...
Ziyang Gong, Wenhao Li, Oliver Ma, Songyuan Li, Jiayi Ji, Xue Yang, Gen Luo, Junchi Yan, Rongrong Ji

Replaced article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[3/5]:
- CountingFruit: Language-Guided 3D Fruit Counting with Semantic Gaussian Splatting
Fengze Li, Yangle Liu, Jieming Ma, Hai-Ning Liang, Yaochun Shen, Huangxiang Li, Zhijing Wu

Replaced article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[10/10]:
- Judging the Judges: Can Large Vision-Language Models Fairly Evaluate Chart Comprehension and Reas...
Laskar, Islam, Mahbub, Masry, Rahman, Bhuiyan, Nayeem, Joty, Hoque, Huang

Replaced article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[6/10]:
- CountingFruit: Language-Guided 3D Fruit Counting with Semantic Gaussian Splatting
Fengze Li, Yangle Liu, Jieming Ma, Hai-Ning Liang, Yaochun Shen, Huangxiang Li, Zhijing Wu