Tootfinder

Language-Instructed Reasoning for Group Activity Detection via Multimodal Large Language Model
Jihua Peng, Qianxiong Xu, Yichen Liu, Chenxi Liu, Cheng Long, Rui Zhao, Ziyue Li
https://arxiv.org/abs/2509.16054

Language-Instructed Reasoning for Group Activity Detection via Multimodal Large Language Model
Group activity detection (GAD) aims to simultaneously identify group members and categorize their collective activities within video sequences. Existing deep learning-based methods develop specialized architectures (e.g., transformer networks) to model the dynamics of individual roles and semantic dependencies between individuals and groups. However, they rely solely on implicit pattern recognition from visual features and struggle with contextual reasoning and explainability. In this work, we …

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 …

Synergizing Static Analysis with Large Language Models for Vulnerability Discovery and beyond
Vaibhav Agrawal, Kiarash Ahi
https://arxiv.org/abs/2509.15433 https://

Synergizing Static Analysis with Large Language Models for Vulnerability Discovery and beyond
This report examines the synergy between Large Language Models (LLMs) and Static Application Security Testing (SAST) to improve vulnerability discovery. Traditional SAST tools, while effective for proactive security, are limited by high false-positive rates and a lack of contextual understanding. Conversely, LLMs excel at code analysis and pattern recognition but can be prone to inconsistencies and hallucinations. By integrating these two technologies, a more intelligent and efficient system is…

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

Man I hate the new language for a task one doesn't want to do — "aversive”.
Primary reason is that it externalizes the problem. It's fixed mindset framing, that something _else_ has to change to make the situation better, that it's the task's fault for being aversive, instead of our own for not working on the aversion to the needed task.
We have a bad habit lately of attributing relational traits to the party in a relationship, rather than to the relationship itself and it weakens our thinking about things. Noticing this pattern really changes how you think, because you can start attributing things better and noticing the contexts — and contexts are often things that can be changed!

this should keep yous out of trouble for a while :luna_moth: 🎹
#liveCoding

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
…

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…

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…

Finally, what Xia & Lindell call a "separation problem" is, in our view, a feature of our approach and not a bug.
If, e.g., all languages in a family are polysynthetic (or none are), that’s not a statistical artefact – it’s the signal. The outcome is well associated with genealogy, showing that family membership captures someth genuinely informative about the process. When the model finds that family explains a large share of the variance, that's not a failure–it's evidence that phylogenetic structure dominates the pattern.
So while Xia & Lindell insist that "autocorrelation due to relationships and distance cannot be captured in family or regional-level analyses", we see that as an empirical question – and we treated it as one.
The real test is whether a mixed model that explicitly represents phylogeny and geography performs worse than their alternative, where the entire shared history of languages and environments is effectively collapsed into a single dimension (an eigenvector).
In other words: we model relationships – Xia & Lindell summarise them into one number per language.

Automating Code Generation for Semiconductor Equipment Control from Developer Utterances with LLMs
Youngkyoung Kim, Sanghyeok Park, Misoo Kim, Gangho Yoon, Eunseok Lee, Simon S. Woo
https://arxiv.org/abs/2509.13055

Automating Code Generation for Semiconductor Equipment Control from Developer Utterances with LLMs
Semiconductors form the backbone of modern electronics, with their manufacturing and testing relying on highly specialized equipment and domain-specific programming languages. Equipment languages such as the Algorithmic Pattern Generator (ALPG) are critical for precise hardware control but are challenging to program due to their low-level syntax and steep learning curve. While large language models (LLMs) have shown promise in generating high-level code from natural language, their effectivenes…

Talking with Oompa Loompas: A novel framework for evaluating linguistic acquisition of LLM agents
Sankalp Tattwadarshi Swain, Anshika Krishnatray, Dhruv Kumar, Jagat Sesh Challa
https://arxiv.org/abs/2509.07389

Talking with Oompa Loompas: A novel framework for evaluating linguistic acquisition of LLM agents
Existing evaluation studies on linguistic competence of large language models (LLM agents) have focused primarily on vocabulary learning, morphological rule induction, syntactic generalization, pragmatic inference, and cross-linguistic transfer. However, none assess whether LLM agents can acquire a language through pattern recognition and interactive feedback, a central feature of human language acquisition. We propose a novel experimental framework in which an LLM agent is evaluated on its abi…

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…

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…

Poseidon: A OneGraph Engine
Brad Bebee, \"Umit V. \c{C}ataly\"urek, Olaf Hartig, Ankesh Khandelwal, Simone Rondelli, Michael Schmidt, Lefteris Sidirourgos, Bryan Thompson
https://arxiv.org/abs/2510.11166

Poseidon: A OneGraph Engine
We present the Poseidon engine behind the Neptune Analytics graph database service. Customers interact with Poseidon using the declarative openCypher query language, which enables requests that seamlessly combine traditional querying paradigms (such as graph pattern matching, variable length paths, aggregation) with algorithm invocations and has been syntactically extended to facilitate OneGraph interoperability, such as the disambiguation between globally unique IRIs (as exposed via RDF) vs. l…

TIGeR: Tool-Integrated Geometric Reasoning in Vision-Language Models for Robotics
Yi Han, Cheng Chi, Enshen Zhou, Shanyu Rong, Jingkun An, Pengwei Wang, Zhongyuan Wang, Lu Sheng, Shanghang Zhang
https://arxiv.org/abs/2510.07181

TIGeR: Tool-Integrated Geometric Reasoning in Vision-Language Models for Robotics
Vision-Language Models (VLMs) have shown remarkable capabilities in spatial reasoning, yet they remain fundamentally limited to qualitative precision and lack the computational precision required for real-world robotics. Current approaches fail to leverage metric cues from depth sensors and camera calibration, instead reducing geometric problems to pattern recognition tasks that cannot deliver the centimeter-level accuracy essential for robotic manipulation. We present TIGeR (Tool-Integrated Ge…

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

Reasoning Pattern Matters: Learning to Reason without Human Rationales
Chaoxu Pang, Yixuan Cao, Ping Luo
https://arxiv.org/abs/2510.12643 https://arxiv.org…

Reasoning Pattern Matters: Learning to Reason without Human Rationales
Large Language Models (LLMs) have demonstrated remarkable reasoning capabilities under the widely adopted SFT+RLVR paradigm, which first performs Supervised Fine-Tuning (SFT) on human-annotated reasoning trajectories (rationales) to establish initial reasoning behaviors, then applies Reinforcement Learning with Verifiable Rewards (RLVR) to optimize the model using verifiable signals without golden rationales. However, annotating high-quality rationales for the SFT stage remains prohibitively ex…

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

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…

Architecting Resilient LLM Agents: A Guide to Secure Plan-then-Execute Implementations
Ron F. Del Rosario, Klaudia Krawiecka, Christian Schroeder de Witt
https://arxiv.org/abs/2509.08646

Architecting Resilient LLM Agents: A Guide to Secure Plan-then-Execute Implementations
As Large Language Model (LLM) agents become increasingly capable of automating complex, multi-step tasks, the need for robust, secure, and predictable architectural patterns is paramount. This paper provides a comprehensive guide to the ``Plan-then-Execute'' (P-t-E) pattern, an agentic design that separates strategic planning from tactical execution. We explore the foundational principles of P-t-E, detailing its core components - the Planner and the Executor - and its architectural advantages i…

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
[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

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,…

Understanding Economic Tradeoffs Between Human and AI Agents in Bargaining Games
Crystal Qian, Kehang Zhu, John Horton, Benjamin S. Manning, Vivian Tsai, James Wexler, Nithum Thain
https://arxiv.org/abs/2509.09071

Understanding Economic Tradeoffs Between Human and AI Agents in Bargaining Games
Coordination tasks traditionally performed by humans are increasingly being delegated to autonomous agents. As this pattern progresses, it becomes critical to evaluate not only these agents' performance but also the processes through which they negotiate in dynamic, multi-agent environments. Furthermore, different agents exhibit distinct advantages: traditional statistical agents, such as Bayesian models, may excel under well-specified conditions, whereas large language models (LLMs) can genera…

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 …

Replaced article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[4/4]:
- Evaluating the Robustness of Open-Source Vision-Language Models to Domain Shift in Object Captioning
Federico Tavella, Amber Drinkwater, Angelo Cangelosi

Convergence and Divergence of Language Models under Different Random Seeds
Finlay Fehlauer (ETH Zurich), Kyle Mahowald (University of Texas at Austin), Tiago Pimentel (ETH Zurich)
https://arxiv.org/abs/2509.26643

Convergence and Divergence of Language Models under Different Random Seeds
In this paper, we investigate the convergence of language models (LMs) trained under different random seeds, measuring convergence as the expected per-token Kullback--Leibler (KL) divergence across seeds. By comparing LM convergence as a function of model size and training checkpoint, we identify a four-phase convergence pattern: (i) an initial uniform phase, (ii) a sharp-convergence phase, (iii) a sharp-divergence phase, and (iv) a slow-reconvergence phase. Further, we observe that larger mode…

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

Replaced article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[1/5]:
- Video-based Sign Language Recognition without Temporal Segmentation
Jie Huang, Wengang Zhou, Qilin Zhang, Houqiang Li, Weiping Li

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

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…

AI Playing Business Games: Benchmarking Large Language Models on Managerial Decision-Making in Dynamic Simulations
Berdymyrat Ovezmyradov
https://arxiv.org/abs/2509.26331 https:…

AI Playing Business Games: Benchmarking Large Language Models on Managerial Decision-Making in Dynamic Simulations
The rapid advancement of LLMs sparked significant interest in their potential to augment or automate managerial functions. One of the most recent trends in AI benchmarking is performance of Large Language Models (LLMs) over longer time horizons. While LLMs excel at tasks involving natural language and pattern recognition, their capabilities in multi-step, strategic business decision-making remain largely unexplored. Few studies demonstrated how results can be different from benchmarks in short-…

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
[5/5]:
- TIGeR: Tool-Integrated Geometric Reasoning in Vision-Language Models for Robotics
Han, Chi, Zhou, Rong, An, Wang, Wang, Sheng, Zhang

Replaced article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[3/7]:
- AutoDrive-QA: A Multiple-Choice Benchmark for Vision-Language Evaluation in Urban Autonomous Driving
Boshra Khalili, Andrew W. Smyth

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 …

Crosslisted article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[1/1]:
- Sample-efficient Integration of New Modalities into Large Language Models
Osman Batur \.Ince, Andr\'e F. T. Martins, Oisin Mac Aodha, Edoardo M. Ponti

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
[3/5]:
- RS-OOD: A Vision-Language Augmented Framework for Out-of-Distribution Detection in Remote Sensing
Chenhao Wang, Yingrui Ji, Yu Meng, Yunjian Zhang, Yao Zhu

Replaced article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[5/5]:
- HAMLET: Switch your Vision-Language-Action Model into a History-Aware Policy
Myungkyu Koo, Daewon Choi, Taeyoung Kim, Kyungmin Lee, Changyeon Kim, Younggyo Seo, Jinwoo Shin

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

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

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
[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
[2/7]:
- LFTR: Learning-Free Token Reduction for Multimodal Large Language Models
Zihui Zhao, Yingxin Li, Yang Li

Replaced article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[5/7]:
- CoFFT: Chain of Foresight-Focus Thought for Visual Language Models
Zhang, Dong, Zhang, Jia, Dang, Fernando, Liu, Shou

Crosslisted article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[1/2]:
- SGAligner : Cross-Modal Language-Aided 3D Scene Graph Alignment
Binod Singh, Sayan Deb Sarkar, Iro Armeni

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

Crosslisted article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[2/2]:
- CaTS-Bench: Can Language Models Describe Numeric Time Series?
Luca Zhou, Pratham Yashwante, Marshall Fisher, Alessio Sampieri, Zihao Zhou, Fabio Galasso, Rose Yu

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

Replaced article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[4/5]:
- Investigating Traffic Accident Detection Using Multimodal Large Language Models
Ilhan Skender, Kailin Tong, Selim Solmaz, Daniel Watzenig

Replaced article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[2/8]:
- SafeEraser: Enhancing Safety in Multimodal Large Language Models through Multimodal Machine Unlea...
Chen, Deng, Zheng, Yan, Liu, Wu, Jiang, Liu, Hu

Replaced article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[8/8]:
- The Better You Learn, The Smarter You Prune: Towards Efficient Vision-language-action Models via ...
Jiang, Jiang, Ma, Wen, Li, Zhan, Jia, Liu, Sun, Lang

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