Tootfinder

Bot or not? A short history of web bots and bot detection techniques
– from @…
🤖 https://sinja.io/blog/bot-or-not

A short history of web bots and bot detection techniques · OlegWock
Did you know your favorite website can detect when you're browsing it in public transport or when you scroll it in your bed? Moreover, this info sometimes helps them to fight bots.

[2025-06-27 Fri (UTC), 13 new articles found for cs.AI Artificial Intelligence]
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[2025-06-26 Thu (UTC), 1 new article found for cs.LO Logic in Computer Science]
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[2025-06-26 Thu (UTC), 33 new articles found for cs.CL Computation and Language]
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[2025-06-26 Thu (UTC), 2 new articles found for math.CT Category Theory]
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[2025-06-26 Thu (UTC), 4 new articles found for cs.PL Programming Languages]
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[2025-06-26 Thu (UTC), 28 new articles found for cs.RO Robotics]
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[2025-05-27 Tue (UTC), 1 new article found for cs.FL Formal Languages and Automata Theory]
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[2025-06-26 Thu (UTC), 24 new articles found for cs.CR Cryptography and Security]
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[2025-06-26 Thu (UTC), 9 new articles found for cs.SE Software Engineering]
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[2025-06-26 Thu (UTC), 8 new articles found for stat.ML Machine Learning]
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[2025-06-26 Thu (UTC), 10 new articles found for cs.HC Human-Computer Interaction]
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[2025-06-27 Fri (UTC), 1 new article found for cs.CY Computers and Society]
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[2025-06-26 Thu (UTC), 2 new articles found for math.LO Logic]
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[2025-06-26 Thu (UTC), 16 new articles found for math.CO Combinatorics]
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[2025-05-27 Tue (UTC), 1 new article found for q-fin.TR Trading and Market Microstructure]
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[2025-05-27 Tue (UTC), no new articles found for cs.OS Operating Systems]
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Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[5/5]:
- mSTEB: Massively Multilingual Evaluation of LLMs on Speech and Text Tasks
Beyene, Verma, Ma, Alabi, Schmidt, Nakatumba-Nabende, Adelani

[2025-06-27 Fri (UTC), 11 new articles found for cs.DC Distributed, Parallel, and Cluster Computing]
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[2025-06-26 Thu (UTC), 7 new articles found for math.NT Number Theory]
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Replaced article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[4/4]:
- Predictive Modeling, Pattern Recognition, and Spatiotemporal Representations of Plant Growth in S...
Mohamed Debbagh, Shangpeng Sun, Mark Lefsrud

[2025-06-27 Fri (UTC), 10 new articles found for math.AG Algebraic Geometry]
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[2025-05-26 Mon (UTC), 1 new article found for cs.CG Computational Geometry]
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[2025-06-26 Thu (UTC), 10 new articles found for eess.IV Image and Video Processing]
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[2025-06-26 Thu (UTC), 23 new articles found for hep-th High Energy Physics - Theory]
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[2025-06-26 Thu (UTC), 1 new article found for math.AT Algebraic Topology]
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[2025-05-26 Mon (UTC), 1 new article found for cs.DL Digital Libraries]
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[2025-06-26 Thu (UTC), 4 new articles found for cs.IT Information Theory]
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[2025-06-27 Fri (UTC), 2 new articles found for cs.LO Logic in Computer Science]
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[2025-06-27 Fri (UTC), no new articles found for math.CT Category Theory]
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[2025-05-26 Mon (UTC), 3 new articles found for cs.FL Formal Languages and Automata Theory]
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[2025-06-27 Fri (UTC), 11 new articles found for cs.SE Software Engineering]
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[2025-06-26 Thu (UTC), 5 new articles found for cs.CY Computers and Society]
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[2025-05-26 Mon (UTC), no new articles found for q-fin.TR Trading and Market Microstructure]
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[2025-05-26 Mon (UTC), no new articles found for cs.OS Operating Systems]
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Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[4/5]:
- WyckoffDiff -- A Generative Diffusion Model for Crystal Symmetry
Kelvinius, Andersson, Parackal, Qian, Armiento, Lindsten

[2025-06-27 Fri (UTC), 8 new articles found for math.NT Number Theory]
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Replaced article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[3/4]:
- ViStoryBench: Comprehensive Benchmark Suite for Story Visualization
Zhuang, Huang, Cheng, Wu, Hu, Liao, Huang, Wang, Liao, Cai, Xu, Zhang, Zeng, Yu, Zhang

This https://arxiv.org/abs/2505.02548 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Modal logic for reasoning about uncertainty and confusion
We consider a modal logic that can formalise statements about uncertainty and beliefs such as `I think that my wallet is in the drawer rather than elsewhere' or `I am confused whether my appointment is on Monday or Tuesday'. To do that, we expand Gödel modal logic $\mathbf{K}\mathsf{G}$ with the involutive negation $\sim$ defined as $v({\sim}ϕ,w)=1-v(ϕ,w)$. We provide semantics with the finite model property for our new logic that we call $\mathbf{K}\mathsf{G_{inv}}$ and show its equivalence…

Unveiling Causal Reasoning in Large Language Models: Reality or Mirage?
Haoang Chi, He Li, Wenjing Yang, Feng Liu, Long Lan, Xiaoguang Ren, Tongliang Liu, Bo Han
https://arxiv.org/abs/2506.21215

Unveiling Causal Reasoning in Large Language Models: Reality or Mirage?
Causal reasoning capability is critical in advancing large language models (LLMs) toward strong artificial intelligence. While versatile LLMs appear to have demonstrated capabilities in understanding contextual causality and providing responses that obey the laws of causality, it remains unclear whether they perform genuine causal reasoning akin to humans. However, current evidence indicates the contrary. Specifically, LLMs are only capable of performing shallow (level-1) causal reasoning, prim…

[2025-06-26 Thu (UTC), 11 new articles found for math.AG Algebraic Geometry]
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[2025-05-27 Tue (UTC), no new articles found for cs.CG Computational Geometry]
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[2025-05-27 Tue (UTC), 5 new articles found for cs.DL Digital Libraries]
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[2025-06-27 Fri (UTC), 4 new articles found for cs.IT Information Theory]
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AALC: Large Language Model Efficient Reasoning via Adaptive Accuracy-Length Control
Ruosen Li, Ziming Luo, Quan Zhang, Ruochen Li, Ben Zhou, Ali Payani, Xinya Du
https://arxiv.org/abs/2506.20160

AALC: Large Language Model Efficient Reasoning via Adaptive Accuracy-Length Control
Large reasoning models (LRMs) achieve impressive reasoning capabilities by generating lengthy chain-of-thoughts, but this "overthinking" incurs high latency and cost without commensurate accuracy gains. In this work, we introduce AALC, a lightweight, accuracy-aware length reward integrated into reinforcement learning that dynamically balances correctness and brevity during training. By incorporating validation accuracy into the reward and employing a smooth, dynamically scheduled length penalty…

This https://arxiv.org/abs/2406.14346 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Atomic Toposes with Co-Well-Founded Categories of Atoms
The atoms of the Schanuel topos can be described as the pairs $(n,G)$ where $n$ is a finite set and $G$ is a subgroup of $\operatorname{Aut}(n)$. We give a general criterion on an atomic site ensuring that the atoms of the topos of sheaves on that site can be described in a similar fashion. We deduce that these toposes are locally finitely presentable. By applying this to the Malitz-Gregory atomic topos, we obtain a counter-example to the conjecture that every locally finitely presentable topos…

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[3/5]:
- Flexible Infinite-Width Graph Convolutional Neural Networks
Ben Anson, Edward Milsom, Laurence Aitchison

Replaced article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[2/4]:
- Image Super-Resolution with Guarantees via Conformalized Generative Models
Eduardo Adame, Daniel Csillag, Guilherme Tegoni Goedert

This https://arxiv.org/abs/2505.01936 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Algebraic structures arising from the finite condensation on linear orders
The finite condensation $\sim_F$ is an equivalence relation defined on a linear order $L$ by $x \sim_F y$ if and only if the set of points lying between $x$ and $y$ is finite. We define an operation $\cdot_F$ on linear orders $L$ and $M$ by $L \cdot_F M = \operatorname{o.t.}\left((LM)/\!\sim_F\right)$; that is, $L \cdot_F M$ is the order type of the lexicographic product of $L$ and $M$ modulo the finite condensation. The infinite order types $L$ such that $L / \! \sim_F\, \cong 1$ are $ω, ω^*…

Beyond Reactive Safety: Risk-Aware LLM Alignment via Long-Horizon Simulation
Chenkai Sun, Denghui Zhang, ChengXiang Zhai, Heng Ji
https://arxiv.org/abs/2506.20949

Beyond Reactive Safety: Risk-Aware LLM Alignment via Long-Horizon Simulation
Given the growing influence of language model-based agents on high-stakes societal decisions, from public policy to healthcare, ensuring their beneficial impact requires understanding the far-reaching implications of their suggestions. We propose a proof-of-concept framework that projects how model-generated advice could propagate through societal systems on a macroscopic scale over time, enabling more robust alignment. To assess the long-term safety awareness of language models, we also introd…

Replaced article(s) found for cs.LO. https://arxiv.org/list/cs.LO/new
[1/1]:
- Modal Verification Patterns for Systems
Ismail Kuru, Colin S. Gordon

CCRS: A Zero-Shot LLM-as-a-Judge Framework for Comprehensive RAG Evaluation
Aashiq Muhamed
https://arxiv.org/abs/2506.20128 https://a…

CCRS: A Zero-Shot LLM-as-a-Judge Framework for Comprehensive RAG Evaluation
RAG systems enhance LLMs by incorporating external knowledge, which is crucial for domains that demand factual accuracy and up-to-date information. However, evaluating the multifaceted quality of RAG outputs, spanning aspects such as contextual coherence, query relevance, factual correctness, and informational completeness, poses significant challenges. Existing evaluation methods often rely on simple lexical overlap metrics, which are inadequate for capturing these nuances, or involve complex …

This https://arxiv.org/abs/2401.06584 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Dagger categories and the complex numbers: Axioms for the category of finite-dimensional Hilbert spaces and linear contractions
We unravel a deep connection between limits of real numbers and limits in category theory. Using a new variant of the classical characterisation of the real numbers, we characterise the category of finite-dimensional Hilbert spaces and linear contractions in terms of simple category-theoretic structures and properties that do not refer to norms, continuity, or real numbers. This builds on Heunen, Kornell, and Van der Schaaf's easier characterisation of the category of all Hilbert spaces and lin…

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[2/5]:
- Supporting renewable energy planning and operation with data-driven high-resolution ensemble weat...
Wang, Chao, Yang, Nai, Ren, Deng, Chen, Liu, Wen, Xiao, Zhang, Wang, Guan, Pan

Replaced article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[1/4]:
- KD-DETR: Knowledge Distillation for Detection Transformer with Consistent Distillation Points Sam...
Yu Wang, Xin Li, Shengzhao Weng, Gang Zhang, Haixiao Yue, Haocheng Feng, Junyu Han, Errui Ding
…

Dynamic Context-Aware Prompt Recommendation for Domain-Specific AI Applications
Xinye Tang, Haijun Zhai, Chaitanya Belwal, Vineeth Thayanithi, Philip Baumann, Yogesh K Roy
https://arxiv.org/abs/2506.20815

Dynamic Context-Aware Prompt Recommendation for Domain-Specific AI Applications
LLM-powered applications are highly susceptible to the quality of user prompts, and crafting high-quality prompts can often be challenging especially for domain-specific applications. This paper presents a novel dynamic context-aware prompt recommendation system for domain-specific AI applications. Our solution combines contextual query analysis, retrieval-augmented knowledge grounding, hierarchical skill organization, and adaptive skill ranking to generate relevant and actionable prompt sugges…

Practical Exploration of Polyhedral Model Checking
Yuri Andriaccio, Vincenzo Ciancia, Diego Latella, Mieke Massink
https://arxiv.org/abs/2506.20176 https:/…

Practical Exploration of Polyhedral Model Checking
This work explores the potential of spatial model checking of polyhedral models on a number of selected examples. In computer graphics polyhedral models can be found in the form of triangular surface meshes of tetrahedral volume meshes which are abundant. Spatial model checking is used to analyse spatial properties of interest of such models expressed in a suitable spatial logic. The original contributions of this paper are twofold. First we illustrate how a polyhedral model can be enriched by …

Leveraging AI Graders for Missing Score Imputation to Achieve Accurate Ability Estimation in Constructed-Response Tests
Masaki Uto, Yuma Ito
https://arxiv.org/abs/2506.20119

Leveraging AI Graders for Missing Score Imputation to Achieve Accurate Ability Estimation in Constructed-Response Tests
Evaluating the abilities of learners is a fundamental objective in the field of education. In particular, there is an increasing need to assess higher-order abilities such as expressive skills and logical thinking. Constructed-response tests such as short-answer and essay-based questions have become widely used as a method to meet this demand. Although these tests are effective, they require substantial manual grading, making them both labor-intensive and costly. Item response theory (IRT) prov…

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[1/5]:
- Backpropagation Through Time For Networks With Long-Term Dependencies
George Bird, Maxim E. Polivoda

IPFormer: Visual 3D Panoptic Scene Completion with Context-Adaptive Instance Proposals
Markus Gross, Aya Fahmy, Danit Niwattananan, Dominik Muhle, Rui Song, Daniel Cremers, Henri Mee{\ss}
https://arxiv.org/abs/2506.20671

IPFormer: Visual 3D Panoptic Scene Completion with Context-Adaptive Instance Proposals
Semantic Scene Completion (SSC) has emerged as a pivotal approach for jointly learning scene geometry and semantics, enabling downstream applications such as navigation in mobile robotics. The recent generalization to Panoptic Scene Completion (PSC) advances the SSC domain by integrating instance-level information, thereby enhancing object-level sensitivity in scene understanding. While PSC was introduced using LiDAR modality, methods based on camera images remain largely unexplored. Moreover, …

MAGPIE: A dataset for Multi-AGent contextual PrIvacy Evaluation
Gurusha Juneja, Alon Albalak, Wenyue Hua, William Yang Wang
https://arxiv.org/abs/2506.20737

MAGPIE: A dataset for Multi-AGent contextual PrIvacy Evaluation
The proliferation of LLM-based agents has led to increasing deployment of inter-agent collaboration for tasks like scheduling, negotiation, resource allocation etc. In such systems, privacy is critical, as agents often access proprietary tools and domain-specific databases requiring strict confidentiality. This paper examines whether LLM-based agents demonstrate an understanding of contextual privacy. And, if instructed, do these systems preserve inference time user privacy in non-adversarial m…

Deciding Robust Instances of an Escape Problem for Dynamical Systems in Euclidean Space
Eike Neumann
https://arxiv.org/abs/2506.21481 https://

Deciding Robust Instances of an Escape Problem for Dynamical Systems in Euclidean Space
We study the problem of deciding whether a point escapes a closed subset of $\mathbb{R}^d$ under the iteration of a continuous map $f \colon \mathbb{R}^d \to \mathbb{R}^d$ in the bit-model of real computation. We give a sound partial decision method for this problem which is complete in the sense that its halting set contains the halting set of all sound partial decision methods for the problem. Equivalently, our decision method terminates on all problem instances whose answer is robust under a…

A Multi-Pass Large Language Model Framework for Precise and Efficient Radiology Report Error Detection
Songsoo Kim, Seungtae Lee, See Young Lee, Joonho Kim, Keechan Kan, Dukyong Yoon
https://arxiv.org/abs/2506.20112

A Multi-Pass Large Language Model Framework for Precise and Efficient Radiology Report Error Detection
Background: The positive predictive value (PPV) of large language model (LLM)-based proofreading for radiology reports is limited due to the low error prevalence. Purpose: To assess whether a three-pass LLM framework enhances PPV and reduces operational costs compared with baseline approaches. Materials and Methods: A retrospective analysis was performed on 1,000 consecutive radiology reports (250 each: radiography, ultrasonography, CT, MRI) from the MIMIC-III database. Two external datasets (C…

MMSearch-R1: Incentivizing LMMs to Search
Jinming Wu, Zihao Deng, Wei Li, Yiding Liu, Bo You, Bo Li, Zejun Ma, Ziwei Liu
https://arxiv.org/abs/2506.20670 h…

MMSearch-R1: Incentivizing LMMs to Search
Robust deployment of large multimodal models (LMMs) in real-world scenarios requires access to external knowledge sources, given the complexity and dynamic nature of real-world information. Existing approaches such as retrieval-augmented generation (RAG) and prompt engineered search agents rely on rigid pipelines, often leading to inefficient or excessive search behaviors. We present MMSearch-R1, the first end-to-end reinforcement learning framework that enables LMMs to perform on-demand, multi…

The Singapore Consensus on Global AI Safety Research Priorities
Yoshua Bengio, Tegan Maharaj, Luke Ong, Stuart Russell, Dawn Song, Max Tegmark, Lan Xue, Ya-Qin Zhang, Stephen Casper, Wan Sie Lee, S\"oren Mindermann, Vanessa Wilfred, Vidhisha Balachandran, Fazl Barez, Michael Belinsky, Imane Bello, Malo Bourgon, Mark Brakel, Sim\'eon Campos, Duncan Cass-Beggs, Jiahao Chen, Rumman Chowdhury, Kuan Chua Seah, Jeff Clune, Juntao Dai, Agnes Delaborde, Nouha Dziri, Francisco Eiras, J…

The Singapore Consensus on Global AI Safety Research Priorities
Rapidly improving AI capabilities and autonomy hold significant promise of transformation, but are also driving vigorous debate on how to ensure that AI is safe, i.e., trustworthy, reliable, and secure. Building a trusted ecosystem is therefore essential -- it helps people embrace AI with confidence and gives maximal space for innovation while avoiding backlash. The "2025 Singapore Conference on AI (SCAI): International Scientific Exchange on AI Safety" aimed to support research in this space…

Pebble Games and Algebraic Proof Systems
Lisa-Marie Jaser, Jacobo Toran
https://arxiv.org/abs/2506.21149 https://arxiv.org/pdf/2506.2…

Pebble Games and Algebraic Proof Systems
Analyzing refutations of the well known 0pebbling formulas Peb$(G)$ we prove some new strong connections between pebble games and algebraic proof system, showing that there is a parallelism between the reversible, black and black-white pebbling games on one side, and the three algebraic proof systems Nullstellensatz, Monomial Calculus and Polynomial Calculus on the other side. In particular we prove that for any DAG $G$ with a single sink, if there is a Monomial Calculus refutation for Peb$(G)$…

ITFormer: Bridging Time Series and Natural Language for Multi-Modal QA with Large-Scale Multitask Dataset
Yilin Wang, Peixuan Lei, Jie Song, Yuzhe Hao, Tao Chen, Yuxuan Zhang, Lei Jia, Yuanxiang Li, Zhongyu Wei
https://arxiv.org/abs/2506.20093

ITFormer: Bridging Time Series and Natural Language for Multi-Modal QA with Large-Scale Multitask Dataset
Time-series data are critical in diverse applications, such as industrial monitoring, medical diagnostics, and climate research. However, effectively integrating these high-dimensional temporal signals with natural language for dynamic, interactive tasks remains a significant challenge. To address this, we introduce the Time-Series Question Answering (Time-Series QA) task and release EngineMT-QA, the first large-scale, multi-task, temporal-textual QA dataset designed to capture complex interact…

Disentangled representations of microscopy images
Jacopo Dapueto, Vito Paolo Pastore, Nicoletta Noceti, Francesca Odone
https://arxiv.org/abs/2506.20649 ht…

Disentangled representations of microscopy images
Microscopy image analysis is fundamental for different applications, from diagnosis to synthetic engineering and environmental monitoring. Modern acquisition systems have granted the possibility to acquire an escalating amount of images, requiring a consequent development of a large collection of deep learning-based automatic image analysis methods. Although deep neural networks have demonstrated great performance in this field, interpretability, an essential requirement for microscopy image an…

Replaced article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[4/4]:
- Graph-Assisted Stitching for Offline Hierarchical Reinforcement Learning
Seungho Baek, Taegeon Park, Jongchan Park, Seungjun Oh, Yusung Kim

Bridging Compositional and Distributional Semantics: A Survey on Latent Semantic Geometry via AutoEncoder
Yingji Zhang, Danilo S. Carvalho, Andr\'e Freitas
https://arxiv.org/abs/2506.20083

Bridging Compositional and Distributional Semantics: A Survey on Latent Semantic Geometry via AutoEncoder
Integrating compositional and symbolic properties into current distributional semantic spaces can enhance the interpretability, controllability, compositionality, and generalisation capabilities of Transformer-based auto-regressive language models (LMs). In this survey, we offer a novel perspective on latent space geometry through the lens of compositional semantics, a direction we refer to as \textit{semantic representation learning}. This direction enables a bridge between symbolic and distri…

Joint attitude estimation and 3D neural reconstruction of non-cooperative space objects
Cl\'ement Forray, Pauline Delporte, Nicolas Delaygue, Florence Genin, Dawa Derksen
https://arxiv.org/abs/2506.20638

Joint attitude estimation and 3D neural reconstruction of non-cooperative space objects
Obtaining a better knowledge of the current state and behavior of objects orbiting Earth has proven to be essential for a range of applications such as active debris removal, in-orbit maintenance, or anomaly detection. 3D models represent a valuable source of information in the field of Space Situational Awareness (SSA). In this work, we leveraged Neural Radiance Fields (NeRF) to perform 3D reconstruction of non-cooperative space objects from simulated images. This scenario is challenging for N…

Replaced article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[3/4]:
- Protein Structure Tokenization: Benchmarking and New Recipe
Xinyu Yuan, Zichen Wang, Marcus Collins, Huzefa Rangwala

SACL: Understanding and Combating Textual Bias in Code Retrieval with Semantic-Augmented Reranking and Localization
Dhruv Gupta, Gayathri Ganesh Lakshmy, Yiqing Xie
https://arxiv.org/abs/2506.20081

SACL: Understanding and Combating Textual Bias in Code Retrieval with Semantic-Augmented Reranking and Localization
Retrieval-Augmented Code Generation (RACG) is a critical technique for enhancing code generation by retrieving relevant information. In this work, we conduct an in-depth analysis of code retrieval by systematically masking specific features while preserving code functionality. Our discoveries include: (1) although trained on code, current retrievers heavily rely on surface-level textual features (e.g., docstrings, identifier names), and (2) they exhibit a strong bias towards well-documented cod…

Shape2Animal: Creative Animal Generation from Natural Silhouettes
Quoc-Duy Tran, Anh-Tuan Vo, Dinh-Khoi Vo, Tam V. Nguyen, Minh-Triet Tran, Trung-Nghia Le
https://arxiv.org/abs/2506.20616

Shape2Animal: Creative Animal Generation from Natural Silhouettes
Humans possess a unique ability to perceive meaningful patterns in ambiguous stimuli, a cognitive phenomenon known as pareidolia. This paper introduces Shape2Animal framework to mimics this imaginative capacity by reinterpreting natural object silhouettes, such as clouds, stones, or flames, as plausible animal forms. Our automated framework first performs open-vocabulary segmentation to extract object silhouette and interprets semantically appropriate animal concepts using vision-language model…

Replaced article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[2/4]:
- ReconX: Reconstruct Any Scene from Sparse Views with Video Diffusion Model
Fangfu Liu, Wenqiang Sun, Hanyang Wang, Yikai Wang, Haowen Sun, Junliang Ye, Jun Zhang, Yueqi Duan

A Modular Multitask Reasoning Framework Integrating Spatio-temporal Models and LLMs
Kethmi Hirushini Hettige, Jiahao Ji, Cheng Long, Shili Xiang, Gao Cong, Jingyuan Wang
https://arxiv.org/abs/2506.20073

A Modular Multitask Reasoning Framework Integrating Spatio-temporal Models and LLMs
Spatio-temporal data mining plays a pivotal role in informed decision making across diverse domains. However, existing models are often restricted to narrow tasks, lacking the capacity for multi-task inference and complex long-form reasoning that require generation of in-depth, explanatory outputs. These limitations restrict their applicability to real-world, multi-faceted decision scenarios. In this work, we introduce STReason, a novel framework that integrates the reasoning strengths of large…

Video Perception Models for 3D Scene Synthesis
Rui Huang, Guangyao Zhai, Zuria Bauer, Marc Pollefeys, Federico Tombari, Leonidas Guibas, Gao Huang, Francis Engelmann
https://arxiv.org/abs/2506.20601

Video Perception Models for 3D Scene Synthesis
Traditionally, 3D scene synthesis requires expert knowledge and significant manual effort. Automating this process could greatly benefit fields such as architectural design, robotics simulation, virtual reality, and gaming. Recent approaches to 3D scene synthesis often rely on the commonsense reasoning of large language models (LLMs) or strong visual priors of modern image generation models. However, current LLMs demonstrate limited 3D spatial reasoning ability, which restricts their ability to…

Replaced article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[1/4]:
- Towards Better Benchmark Datasets for Inductive Knowledge Graph Completion
Harry Shomer, Jay Revolinsky, Jiliang Tang

Doc2Agent: Scalable Generation of Tool-Using Agents from API Documentation
Xinyi Ni, Haonan Jian, Qiuyang Wang, Vedanshi Chetan Shah, Pengyu Hong
https://arxiv.org/abs/2506.19998 …

Doc2Agent: Scalable Generation of Tool-Using Agents from API Documentation
REST APIs play important roles in enriching the action space of web agents, yet most API-based agents rely on curated and uniform toolsets that do not reflect the complexity of real-world APIs. Building tool-using agents for arbitrary domains remains a major challenge, as it requires reading unstructured API documentation, testing APIs and inferring correct parameters. We propose Doc2Agent, a scalable pipeline to build agents that can call Python-based tools generated from API documentation. Do…

SFNet: Fusion of Spatial and Frequency-Domain Features for Remote Sensing Image Forgery Detection
Ji Qi, Xinchang Zhang, Dingqi Ye, Yongjia Ruan, Xin Guo, Shaowen Wang, Haifeng Li
https://arxiv.org/abs/2506.20599

SFNet: Fusion of Spatial and Frequency-Domain Features for Remote Sensing Image Forgery Detection
The rapid advancement of generative artificial intelligence is producing fake remote sensing imagery (RSI) that is increasingly difficult to detect, potentially leading to erroneous intelligence, fake news, and even conspiracy theories. Existing forgery detection methods typically rely on single visual features to capture predefined artifacts, such as spatial-domain cues to detect forged objects like roads or buildings in RSI, or frequency-domain features to identify artifacts from up-sampling …

TableMoE: Neuro-Symbolic Routing for Structured Expert Reasoning in Multimodal Table Understanding
Junwen Zhang, Pu Chen, Yin Zhang
https://arxiv.org/abs/2506.21393

TableMoE: Neuro-Symbolic Routing for Structured Expert Reasoning in Multimodal Table Understanding
Multimodal understanding of tables in real-world contexts is challenging due to the complexity of structure, symbolic density, and visual degradation (blur, skew, watermarking, incomplete structures or fonts, multi-span or hierarchically nested layouts). Existing multimodal large language models (MLLMs) struggle with such WildStruct conditions, resulting in limited performance and poor generalization. To address these challenges, we propose TableMoE, a neuro-symbolic Mixture-of-Connector-Expert…

Inference Scaled GraphRAG: Improving Multi Hop Question Answering on Knowledge Graphs
Travis Thompson, Seung-Hwan Lim, Paul Liu, Ruoying He, Dongkuan Xu
https://arxiv.org/abs/2506.19967

Inference Scaled GraphRAG: Improving Multi Hop Question Answering on Knowledge Graphs
Large Language Models (LLMs) have achieved impressive capabilities in language understanding and generation, yet they continue to underperform on knowledge-intensive reasoning tasks due to limited access to structured context and multi-hop information. Retrieval-Augmented Generation (RAG) partially mitigates this by grounding generation in retrieved context, but conventional RAG and GraphRAG methods often fail to capture relational structure across nodes in knowledge graphs. We introduce Infere…

WonderFree: Enhancing Novel View Quality and Cross-View Consistency for 3D Scene Exploration
Chaojun Ni, Jie Li, Haoyun Li, Hengyu Liu, Xiaofeng Wang, Zheng Zhu, Guosheng Zhao, Boyuan Wang, Chenxin Li, Guan Huang, Wenjun Mei
https://arxiv.org/abs/2506.20590

WonderFree: Enhancing Novel View Quality and Cross-View Consistency for 3D Scene Exploration
Interactive 3D scene generation from a single image has gained significant attention due to its potential to create immersive virtual worlds. However, a key challenge in current 3D generation methods is the limited explorability, which cannot render high-quality images during larger maneuvers beyond the original viewpoint, particularly when attempting to move forward into unseen areas. To address this challenge, we propose WonderFree, the first model that enables users to interactively generate…

Active Inference AI Systems for Scientific Discovery
Karthik Duraisamy
https://arxiv.org/abs/2506.21329 https://arxiv.org/pdf/2506.21…

Active Inference AI Systems for Scientific Discovery
The rapid evolution of artificial intelligence has led to expectations of transformative scientific discovery, yet current systems remain fundamentally limited by their operational architectures, brittle reasoning mechanisms, and their separation from experimental reality. Building on earlier work, we contend that progress in AI-driven science now depends on closing three fundamental gaps -- the abstraction gap, the reasoning gap, and the reality gap -- rather than on model size/data/test time …

TRIM: A Self-Supervised Video Summarization Framework Maximizing Temporal Relative Information and Representativeness
Pritam Mishra, Coloma Ballester, Dimosthenis Karatzas
https://arxiv.org/abs/2506.20588

TRIM: A Self-Supervised Video Summarization Framework Maximizing Temporal Relative Information and Representativeness
The increasing ubiquity of video content and the corresponding demand for efficient access to meaningful information have elevated video summarization and video highlights as a vital research area. However, many state-of-the-art methods depend heavily either on supervised annotations or on attention-based models, which are computationally expensive and brittle in the face of distribution shifts that hinder cross-domain applicability across datasets. We introduce a pioneering self-supervised vid…

IXAII: An Interactive Explainable Artificial Intelligence Interface for Decision Support Systems
Pauline Speckmann, Mario Nadj, Christian Janiesch
https://arxiv.org/abs/2506.21310

IXAII: An Interactive Explainable Artificial Intelligence Interface for Decision Support Systems
Although several post-hoc methods for explainable AI have been developed, most are static and neglect the user perspective, limiting their effectiveness for the target audience. In response, we developed the interactive explainable intelligent system called IXAII that offers explanations from four explainable AI methods: LIME, SHAP, Anchors, and DiCE. Our prototype provides tailored views for five user groups and gives users agency over the explanations' content and their format. We evaluated I…

Learning-Based Distance Estimation for 360{\deg} Single-Sensor Setups
Yitong Quan, Benjamin Kiefer, Martin Messmer, Andreas Zell
https://arxiv.org/abs/2506.20586

Learning-Based Distance Estimation for 360° Single-Sensor Setups
Accurate distance estimation is a fundamental challenge in robotic perception, particularly in omnidirectional imaging, where traditional geometric methods struggle with lens distortions and environmental variability. In this work, we propose a neural network-based approach for monocular distance estimation using a single 360° fisheye lens camera. Unlike classical trigonometric techniques that rely on precise lens calibration, our method directly learns and infers the distance of objects from …

World-aware Planning Narratives Enhance Large Vision-Language Model Planner
Junhao Shi, Zhaoye Fei, Siyin Wang, Qipeng Guo, Jingjing Gong, Xipeng QIu
https://arxiv.org/abs/2506.21230

World-aware Planning Narratives Enhance Large Vision-Language Model Planner
Large Vision-Language Models (LVLMs) show promise for embodied planning tasks but struggle with complex scenarios involving unfamiliar environments and multi-step goals. Current approaches rely on environment-agnostic imitation learning that disconnects instructions from environmental contexts, causing models to struggle with context-sensitive instructions and rely on supplementary cues rather than visual reasoning during long-horizon interactions. In this work, we propose World-Aware Planning …

Dense Video Captioning using Graph-based Sentence Summarization
Zhiwang Zhang, Dong Xu, Wanli Ouyang, Luping Zhou
https://arxiv.org/abs/2506.20583 https://…

Dense Video Captioning using Graph-based Sentence Summarization
Recently, dense video captioning has made attractive progress in detecting and captioning all events in a long untrimmed video. Despite promising results were achieved, most existing methods do not sufficiently explore the scene evolution within an event temporal proposal for captioning, and therefore perform less satisfactorily when the scenes and objects change over a relatively long proposal. To address this problem, we propose a graph-based partition-and-summarization (GPaS) framework for d…

Replaced article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[4/4]:
- TaxaDiffusion: Progressively Trained Diffusion Model for Fine-Grained Species Generation
Amin Karimi Monsefi, Mridul Khurana, Rajiv Ramnath, Anuj Karpatne, Wei-Lun Chao, Cheng Zhang

Causal Representation Learning with Observational Grouping for CXR Classification
Rajat Rasal, Avinash Kori, Ben Glocker
https://arxiv.org/abs/2506.20582 h…

Causal Representation Learning with Observational Grouping for CXR Classification
Identifiable causal representation learning seeks to uncover the true causal relationships underlying a data generation process. In medical imaging, this presents opportunities to improve the generalisability and robustness of task-specific latent features. This work introduces the concept of grouping observations to learn identifiable representations for disease classification in chest X-rays via an end-to-end framework. Our experiments demonstrate that these causal representations improve gen…

Replaced article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[3/4]:
- DisCoPatch: Taming Adversarially-driven Batch Statistics for Improved Out-of-Distribution Detection
Caetano, Viviers, Zavala-Mondrag\'on, de With, van der Sommen

Show, Tell and Summarize: Dense Video Captioning Using Visual Cue Aided Sentence Summarization
Zhiwang Zhang, Dong Xu, Wanli Ouyang, Chuanqi Tan
https://arxiv.org/abs/2506.20567

Show, Tell and Summarize: Dense Video Captioning Using Visual Cue Aided Sentence Summarization
In this work, we propose a division-and-summarization (DaS) framework for dense video captioning. After partitioning each untrimmed long video as multiple event proposals, where each event proposal consists of a set of short video segments, we extract visual feature (e.g., C3D feature) from each segment and use the existing image/video captioning approach to generate one sentence description for this segment. Considering that the generated sentences contain rich semantic descriptions about the …

Replaced article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[2/4]:
- Is my Data in your AI Model? Membership Inference Test with Application to Face Images
DeAlcala, Morales, Fierrez, Mancera, Tolosana, Ortega-Garcia

AdvMIM: Adversarial Masked Image Modeling for Semi-Supervised Medical Image Segmentation
Lei Zhu, Jun Zhou, Rick Siow Mong Goh, Yong Liu
https://arxiv.org/abs/2506.20563

AdvMIM: Adversarial Masked Image Modeling for Semi-Supervised Medical Image Segmentation
Vision Transformer has recently gained tremendous popularity in medical image segmentation task due to its superior capability in capturing long-range dependencies. However, transformer requires a large amount of labeled data to be effective, which hinders its applicability in annotation scarce semi-supervised learning scenario where only limited labeled data is available. State-of-the-art semi-supervised learning methods propose combinatorial CNN-Transformer learning to cross teach a transform…

Replaced article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[1/4]:
- Review learning: Real world validation of privacy preserving continual learning across medical in...
Yoo, Choi, Yang, Kim, Choi, Lim, Lim, Joo, Kim, Park, Yoon, Kim

Lightweight Multi-Frame Integration for Robust YOLO Object Detection in Videos
Yitong Quan, Benjamin Kiefer, Martin Messmer, Andreas Zell
https://arxiv.org/abs/2506.20550

Lightweight Multi-Frame Integration for Robust YOLO Object Detection in Videos
Modern image-based object detection models, such as YOLOv7, primarily process individual frames independently, thus ignoring valuable temporal context naturally present in videos. Meanwhile, existing video-based detection methods often introduce complex temporal modules, significantly increasing model size and computational complexity. In practical applications such as surveillance and autonomous driving, transient challenges including motion blur, occlusions, and abrupt appearance changes can …

Pay Less Attention to Deceptive Artifacts: Robust Detection of Compressed Deepfakes on Online Social Networks
Manyi Li, Renshuai Tao, Yufan Liu, Chuangchuang Tan, Haotong Qin, Bing Li, Yunchao Wei, Yao Zhao
https://arxiv.org/abs/2506.20548

Pay Less Attention to Deceptive Artifacts: Robust Detection of Compressed Deepfakes on Online Social Networks
With the rapid advancement of deep learning, particularly through generative adversarial networks (GANs) and diffusion models (DMs), AI-generated images, or ``deepfakes", have become nearly indistinguishable from real ones. These images are widely shared across Online Social Networks (OSNs), raising concerns about their misuse. Existing deepfake detection methods overlook the ``block effects" introduced by compression in OSNs, which obscure deepfake artifacts, and primarily focus on raw images,…

AI-assisted radiographic analysis in detecting alveolar bone-loss severity and patterns
Chathura Wimalasiri, Piumal Rathnayake, Shamod Wijerathne, Sumudu Rasnayaka, Dhanushka Leuke Bandara, Roshan Ragel, Vajira Thambawita, Isuru Nawinne
https://arxiv.org/abs/2506.20522

AI-assisted radiographic analysis in detecting alveolar bone-loss severity and patterns
Periodontitis, a chronic inflammatory disease causing alveolar bone loss, significantly affects oral health and quality of life. Accurate assessment of bone loss severity and pattern is critical for diagnosis and treatment planning. In this study, we propose a novel AI-based deep learning framework to automatically detect and quantify alveolar bone loss and its patterns using intraoral periapical (IOPA) radiographs. Our method combines YOLOv8 for tooth detection with Keypoint R-CNN models to id…

A Deep Learning Approach to Identify Rock Bolts in Complex 3D Point Clouds of Underground Mines Captured Using Mobile Laser Scanners
Dibyayan Patra, Pasindu Ranasinghe, Bikram Banerjee, Simit Raval
https://arxiv.org/abs/2506.20464

A Deep Learning Approach to Identify Rock Bolts in Complex 3D Point Clouds of Underground Mines Captured Using Mobile Laser Scanners
Rock bolts are crucial components of the subterranean support systems in underground mines that provide adequate structural reinforcement to the rock mass to prevent unforeseen hazards like rockfalls. This makes frequent assessments of such bolts critical for maintaining rock mass stability and minimising risks in underground mining operations. Where manual surveying of rock bolts is challenging due to the low light conditions in the underground mines and the time-intensive nature of the proces…