Tootfinder

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

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…

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…

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…

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…

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…

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…

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 …

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…

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,…

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…

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

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…

This https://arxiv.org/abs/2505.19988 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csDB_…

Automatic Metadata Extraction for Text-to-SQL
Large Language Models (LLMs) have recently become sophisticated enough to automate many tasks ranging from pattern finding to writing assistance to code generation. In this paper, we examine text-to-SQL generation. We have observed from decades of experience that the most difficult part of query development lies in understanding the database contents. These experiences inform the direction of our research. Text-to-SQL benchmarks such as SPIDER and Bird contain extensive metadata that is gener…

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…

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 …

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…

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 …

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
[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

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

Benford's Curse: Tracing Digit Bias to Numerical Hallucination in LLMs
Jiandong Shao, Yao Lu, Jianfei Yang
https://arxiv.org/abs/2506.01734 https://

Benford's Curse: Tracing Digit Bias to Numerical Hallucination in LLMs
Large Language Models (LLMs) exhibit impressive performance on complex reasoning tasks, yet they frequently fail on basic numerical problems, producing incorrect outputs. Inspired by Benford's Law -- a statistical pattern where lower digits occur more frequently as leading digits -- we hypothesize that the long-tailed digit distributions in web-collected corpora may be learned by LLMs during pretraining, leading to biased numerical generation. To investigate the hypothesis, we first examine whe…

This https://arxiv.org/abs/2405.07671 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csFL_…

Constructing a BPE Tokenization DFA
Many natural language processing systems operate over tokenizations of text to address the open-vocabulary problem. In this paper, we give and analyze an algorithm for the efficient construction of deterministic finite automata (DFA) designed to operate directly on tokenizations produced by the popular byte pair encoding (BPE) technique. This makes it possible to apply many existing techniques and algorithms to the tokenized case, such as pattern matching, equivalence checking of tokenization d…

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.

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

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…

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

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…

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

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

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

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

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
[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
[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
[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
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