Tootfinder

Interesting 👀 https://social.zarchbox.fr/notes/agclhhyre66bnqz3
Extension from @… :

[2025-09-17 Wed (UTC), 20 new articles found for cs.HC Human-Computer Interaction]
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OnlineHOI: Towards Online Human-Object Interaction Generation and Perception
Yihong Ji, Yunze Liu, Yiyao Zhuo, Weijiang Yu, Fei Ma, Joshua Huang, Fei Yu
https://arxiv.org/abs/2509.12250

OnlineHOI: Towards Online Human-Object Interaction Generation and Perception
The perception and generation of Human-Object Interaction (HOI) are crucial for fields such as robotics, AR/VR, and human behavior understanding. However, current approaches model this task in an offline setting, where information at each time step can be drawn from the entire interaction sequence. In contrast, in real-world scenarios, the information available at each time step comes only from the current moment and historical data, i.e., an online setting. We find that offline methods perform…

It looks like Nano Retina is back, or are they just trying to sell off their IP? No news since 2022 https://www.nano-retina.com retinal implant.
The clinical study https://

Replaced article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[5/5]:
- ICR: Iterative Clarification and Rewriting for Conversational Search
Zhiyu Cao, Peifeng Li, Qiaoming Zhu

Probabilistic Model Checking: Applications and Trends
Marta Kwiatkowska, Gethin Norman, David Parker
https://arxiv.org/abs/2509.12968 https://arxiv.org/pdf…

Probabilistic Model Checking: Applications and Trends
Probabilistic model checking is an approach to the formal modelling and analysis of stochastic systems. Over the past twenty five years, the number of different formalisms and techniques developed in this field has grown considerably, as has the range of problems to which it has been applied. In this paper, we identify the main application domains in which probabilistic model checking has proved valuable and discuss how these have evolved over time. We summarise the key strands of the underlyin…

[2025-09-17 Wed (UTC), no new articles found for math.CT Category Theory]
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Converting IEC 61131-3 LD into SFC Using Large Language Model: Dataset and Testing
Yimin Zhang, Mario de Sousa
https://arxiv.org/abs/2509.12593 https://arx…

Converting IEC 61131-3 LD into SFC Using Large Language Model: Dataset and Testing
In the domain of Programmable Logic Controller (PLC) programming, converting a Ladder Diagram (LD) into a Sequential Function Chart (SFC) is an inherently challenging problem, primarily due to the lack of domain-specific knowledge and the issue of state explosion in existing algorithms. However, the rapid development of Artificial Intelligence (AI) - especially Large Language Model (LLM) - offers a promising new approach. Despite this potential, data-driven approaches in this field have been …

On the Hardness of Order Finding and Equivalence Testing for ROABPs
C. Ramya, Pratik Shastri
https://arxiv.org/abs/2509.13238 https://arxiv.org/pdf/2509.13…

On the Hardness of Order Finding and Equivalence Testing for ROABPs
The complexity of representing a polynomial by a Read-Once Oblivious Algebraic Branching Program (ROABP) is highly dependent on the chosen variable ordering. Bhargava et al. prove that finding the optimal ordering is NP-hard, and provide some evidence (based on the Small Set Expansion hypothesis) that it is also hard to approximate the optimal ROABP width. In another work, Baraskar et al. show that it is NP-hard to test whether a polynomial is in the $\mathrm{GL}_n$ orbit of a polynomial of spa…

Secure Human Oversight of AI: Exploring the Attack Surface of Human Oversight
Jonas C. Ditz, Veronika Lazar, Elmar Lichtme{\ss}, Carola Plesch, Matthias Heck, Kevin Baum, Markus Langer
https://arxiv.org/abs/2509.12290

Secure Human Oversight of AI: Exploring the Attack Surface of Human Oversight
Human oversight of AI is promoted as a safeguard against risks such as inaccurate outputs, system malfunctions, or violations of fundamental rights, and is mandated in regulation like the European AI Act. Yet debates on human oversight have largely focused on its effectiveness, while overlooking a critical dimension: the security of human oversight. We argue that human oversight creates a new attack surface within the safety, security, and accountability architecture of AI operations. Drawing o…

Failure of Lichnerowicz-type result in parabolic geometries of real rank at least $3$
Toby Aldape
https://arxiv.org/abs/2509.12542 https://arxiv.org/pdf/25…

Failure of Lichnerowicz-type result in parabolic geometries of real rank at least $3$
Given a Yamaguchi nonrigid parabolic model geometry $(G,P)$ with $G$ simple of real rank at least $3$, we use techniques developed by Erickson to establish the existence of closed, nonflat, essential, regular, normal Cartan geometries modeled on $(G,P)$. Yamaguchi nonrigidity is a necessary condition for admitting nonflat, regular, normal examples. This rules out Lichnerowicz-type conjectures for these model geometries.

Evaluating Large Language Models for Functional and Maintainable Code in Industrial Settings: A Case Study at ASML
Yash Mundhra, Max Valk, Maliheh Izadi
https://arxiv.org/abs/2509.12395

Evaluating Large Language Models for Functional and Maintainable Code in Industrial Settings: A Case Study at ASML
Large language models have shown impressive performance in various domains, including code generation across diverse open-source domains. However, their applicability in proprietary industrial settings, where domain-specific constraints and code interdependencies are prevalent, remains largely unexplored. We present a case study conducted in collaboration with the leveling department at ASML to investigate the performance of LLMs in generating functional, maintainable code within a closed, high…

Uniqueness of purifications is equivalent to Haag duality
Lauritz van Luijk, Alexander Stottmeister, Henrik Wilming
https://arxiv.org/abs/2509.12911 https://

Uniqueness of purifications is equivalent to Haag duality
The uniqueness of purifications of quantum states on a system $A$ up to local unitary transformations on a purifying system $B$ is central to quantum information theory. We show that, if the two systems are modelled by commuting von Neumann algebras $M_A$ and $M_B$ on a Hilbert space $\mathcal H$, then uniqueness of purifications is equivalent to Haag duality $M_A = M_B'$. In particular, the uniqueness of purifications can fail in systems with infinitely many degrees of freedom -- even when $M_…

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The Homology Groups of Zero Divisor Graphs of Finite Commutative Rings
Fenglin Li
https://arxiv.org/abs/2510.14224 https://arxiv.org/pdf/2510.14224

Prompting the Professoriate: A Qualitative Study of Instructor Perspectives on LLMs in Data Science Education
Ana Elisa Lopez-Miranda, Tiffany Timbers, Rohan Alexander
https://arxiv.org/abs/2509.12283 …

Prompting the Professoriate: A Qualitative Study of Instructor Perspectives on LLMs in Data Science Education
Large Language Models (LLMs) have shifted in just a few years from novelty to ubiquity, raising fundamental questions for data science education. Tasks once used to teach coding, writing, and problem-solving can now be completed by LLMs, forcing educators to reconsider both pedagogy and assessment. To understand how instructors are adapting, we conducted semi-structured interviews with 42 instructors from 33 institutions in 10 countries in June and July 2025. Our qualitative analysis reveals a …

On the Diminished Sombor Index of Fixed-Order Molecular Graphs With Cyclomatic Number at Least 3
Abdulaziz Mutlaq Alotaibi, Abdulaziz M. Alanazi, Taher S. Hassan, Akbar Ali
https://arxiv.org/abs/2509.12294

On the Diminished Sombor Index of Fixed-Order Molecular Graphs With Cyclomatic Number at Least 3
For a graph $G$ with edge set $E$, let $d(u)$ denote the degree of a vertex $u$ in $G$. The diminished Sombor (DSO) index of $G$ is defined as $DSO(G)=\sum_{uv\in E}\sqrt{(d(u))^2+(d(v))^2}(d(u)+d(v))^{-1}$. The cyclomatic number of a graph is the smallest number of edges whose removal makes the graph acyclic. A connected graph of maximum degree at most $4$ is known as a molecular graph. The primary motivation of the present study comes from a conjecture concerning the minimum DSO index of fixe…

[2025-09-17 Wed (UTC), 3 new articles found for math.LO Logic]
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Balloon Animal Maps with Applications to the Cohomology of Hypertoric Hitchin Systems
Evan Sundbo
https://arxiv.org/abs/2509.12368 https://arxiv.org/pdf/25…

Balloon Animal Maps with Applications to the Cohomology of Hypertoric Hitchin Systems
In this article we introduce the notion of a balloon animal map between broken toric varieties and construct several long exact sequences in cohomology related to them. We give a new proof of the deletion-contraction relation on hypertoric Hitchin systems of Dansco-Mcbreen-Shende and present some refinements of it. The end result is a formula for the Poincaré polynomial of any hypertoric Hitchin system associated to a graph with first Betti number 2 along with a recipe to calculate the Poincar…

Enhancing Radiographic Disease Detection with MetaCheX, a Context-Aware Multimodal Model
Nathan He, Cody Chen
https://arxiv.org/abs/2509.12287 https://arxi…

Enhancing Radiographic Disease Detection with MetaCheX, a Context-Aware Multimodal Model
Existing deep learning models for chest radiology often neglect patient metadata, limiting diagnostic accuracy and fairness. To bridge this gap, we introduce MetaCheX, a novel multimodal framework that integrates chest X-ray images with structured patient metadata to replicate clinical decision-making. Our approach combines a convolutional neural network (CNN) backbone with metadata processed by a multilayer perceptron through a shared classifier. Evaluated on the CheXpert Plus dataset, MetaChe…

Humor in Pixels: Benchmarking Large Multimodal Models Understanding of Online Comics
Yuriel Ryan, Rui Yang Tan, Kenny Tsu Wei Choo, Roy Ka-Wei Lee
https://arxiv.org/abs/2509.12248

Humor in Pixels: Benchmarking Large Multimodal Models Understanding of Online Comics
Understanding humor is a core aspect of social intelligence, yet it remains a significant challenge for Large Multimodal Models (LMMs). We introduce PixelHumor, a benchmark dataset of 2,800 annotated multi-panel comics designed to evaluate LMMs' ability to interpret multimodal humor and recognize narrative sequences. Experiments with state-of-the-art LMMs reveal substantial gaps: for instance, top models achieve only 61% accuracy in panel sequencing, far below human performance. This underscore…

Replaced article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[4/5]:
- Evaluating the Robustness of Open-Source Vision-Language Models to Domain Shift in Object Captioning
Federico Tavella, Amber Drinkwater, Angelo Cangelosi

Determination of the fifth Busy Beaver value
The bbchallenge Collaboration, Justin Blanchard (Skelet), Daniel Briggs (Skelet), Konrad Deka (Skelet), Nathan Fenner (Skelet), Yannick Forster (Skelet), Georgi Georgiev (Skelet), Matthew L. House, Rachel Hunter, Iijil, Maja K\k{a}dzio{\l}ka, Pavel Kropitz, Shawn Ligocki, mxdys, Mateusz Na\'sciszewski, savask, Tristan St\'erin, Chris Xu, Jason Yuen, Th\'eo Zimmermann

Determination of the fifth Busy Beaver value
We prove that $S(5) = 47,176,870$ using the Coq proof assistant. The Busy Beaver value $S(n)$ is the maximum number of steps that an $n$-state 2-symbol Turing machine can perform from the all-zero tape before halting, and $S$ was historically introduced by Tibor Radó in 1962 as one of the simplest examples of an uncomputable function. The proof enumerates $181,385,789$ Turing machines with 5 states and, for each machine, decides whether it halts or not. Our result marks the first determination…

[2025-09-17 Wed (UTC), 6 new articles found for cs.PL Programming Languages]
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An elementary proof that linking problems are hard
Shannon Cheng, Anna Chlopecki, Saarah Nazar, Eric Samperton
https://arxiv.org/abs/2509.13120 https://arx…

An elementary proof that linking problems are hard
We give a new, elementary proof of what we believe is the simplest known example of a ``natural'' problem in computational 3-dimensional topology that is $\mathsf{NP}$-hard -- namely, the \emph{Trivial Sublink Problem}: given a diagram $L$ of a link in $S^3$ and a positive integer $k$, decide if $L$ contains a $k$ component sublink that is trivial. This problem was previously shown to be $\mathsf{NP}$-hard in independent works of Koenig-Tsvietkova and de Mesmay-Rieck-Sedgwick-Tancer, both of wh…

Towards Trustworthy Agentic IoEV: AI Agents for Explainable Cyberthreat Mitigation and State Analytics
Meryem Malak Dif, Mouhamed Amine Bouchiha, Abdelaziz Amara Korba, Yacine Ghamri-Doudane
https://arxiv.org/abs/2509.12233

Towards Trustworthy Agentic IoEV: AI Agents for Explainable Cyberthreat Mitigation and State Analytics
The Internet of Electric Vehicles (IoEV) envisions a tightly coupled ecosystem of electric vehicles (EVs), charging infrastructure, and grid services, yet it remains vulnerable to cyberattacks, unreliable battery-state predictions, and opaque decision processes that erode trust and performance. To address these challenges, we introduce a novel Agentic Artificial Intelligence (AAI) framework tailored for IoEV, where specialized agents collaborate to deliver autonomous threat mitigation, robust a…

[2025-09-17 Wed (UTC), 6 new articles found for math.DG Differential Geometry]
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[2025-09-17 Wed (UTC), 17 new articles found for cs.SE Software Engineering]
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Dynamics of Quantum Analogs of Classical Impact Oscillators
Arnab Acharya, Titir Mukherjee, Deepshikha Singh, Soumitro Banerjee
https://arxiv.org/abs/2509.12835 https://

Dynamics of Quantum Analogs of Classical Impact Oscillators
This paper investigates the dynamics of quantum analogs of classical impact oscillators to explore how complex nonlinear behaviors manifest in quantum systems. While classical impact oscillators exhibit chaos and bifurcations, quantum systems, governed by linear equations, appear to forbid such dynamics. Through simulations of unforced, forced, and dissipative quantum oscillators, we uncover quasiperiodicity, strange nonchaotic dynamics, and even chaos in the presence of dissipation. Using entr…

Replaced article(s) found for cs.HC. https://arxiv.org/list/cs.HC/new
[1/1]:
- Image memorability predicts social media virality and externally-associated commenting
Shikang Peng, Wilma A. Bainbridge

[2025-10-17 Fri (UTC), 2 new articles found for math.AC Commutative Algebra]
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[2025-09-17 Wed (UTC), 8 new articles found for cs.CY Computers and Society]
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[2025-09-17 Wed (UTC), 17 new articles found for math.CO Combinatorics]
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Polynomials of small slice rank and strength
Cosimo Flavi, Fulvio Gesmundo, Alessandro Oneto, Emanuele Ventura
https://arxiv.org/abs/2509.12322 https://arx…

Polynomials of small slice rank and strength
This paper investigates defining equations for secant varieties of the variety of reducible polynomials, which geometrically encode the notions of strength and slice rank of homogeneous polynomials. We present three main results. First, we reinterpret Ruppert's classical equations for reducible ternary forms in the language of representation theory and we extend them to an arbitrary number of variables. Second, we construct new determinantal equations for polynomials of small strength based on …

Crosslisted article(s) found for math.LO. https://arxiv.org/list/math.LO/new
[1/1]:
- Determination of the fifth Busy Beaver value
The bbchallenge Collaboration, et al.

Modular, On-Site Solutions with Lightweight Anomaly Detection for Sustainable Nutrient Management in Agriculture
Abigail R. Cohen, Yuming Sun, Zhihao Qin, Harsh S. Muriki, Zihao Xiao, Yeonju Lee, Matthew Housley, Andrew F. Sharkey, Rhuanito S. Ferrarezi, Jing Li, Lu Gan, Yongsheng Chen
https://arxiv.org/abs/2509.12247

Modular, On-Site Solutions with Lightweight Anomaly Detection for Sustainable Nutrient Management in Agriculture
Efficient nutrient management is critical for crop growth and sustainable resource consumption (e.g., nitrogen, energy). Current approaches require lengthy analyses, preventing real-time optimization; similarly, imaging facilitates rapid phenotyping but can be computationally intensive, preventing deployment under resource constraints. This study proposes a flexible, tiered pipeline for anomaly detection and status estimation (fresh weight, dry mass, and tissue nutrients), including a comprehen…

Replaced article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[3/5]:
- TokenSkip: Controllable Chain-of-Thought Compression in LLMs
Heming Xia, Chak Tou Leong, Wenjie Wang, Yongqi Li, Wenjie Li

[2025-09-17 Wed (UTC), 8 new articles found for cs.LO Logic in Computer Science]
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[2025-09-17 Wed (UTC), 2 new articles found for cs.CC Computational Complexity]
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Crosslisted article(s) found for cs.HC. https://arxiv.org/list/cs.HC/new
[1/1]:
- Secure Human Oversight of AI: Exploring the Attack Surface of Human Oversight
Ditz, Lazar, Lichtme{\ss}, Plesch, Heck, Baum, Langer

Replaced article(s) found for math.AC. https://arxiv.org/list/math.AC/new
[1/1]:
- A Study of S-Primary Decompositions
Tushar Singh, Ajim Uddin Ansari, Shiv Datt Kumar

Funding AI for Good: A Call for Meaningful Engagement
Hongjin Lin, Anna Kawakami, Catherine D'Ignazio, Kenneth Holstein, Krzysztof Gajos
https://arxiv.org/abs/2509.12455 htt…

Funding AI for Good: A Call for Meaningful Engagement
Artificial Intelligence for Social Good (AI4SG) is a growing area exploring AI's potential to address social issues like public health. Yet prior work has shown limited evidence of its tangible benefits for intended communities, and projects frequently face inadequate community engagement and sustainability challenges. Funding agendas play a crucial role in framing AI4SG initiatives and shaping their approaches. Through a qualitative analysis of 35 funding documents -- representing about $410 m…

[2025-09-17 Wed (UTC), 8 new articles found for math.AG Algebraic Geometry]
toXiv_bot_toot

RU-Net for Automatic Characterization of TRISO Fuel Cross Sections
Lu Cai, Fei Xu, Min Xian, Yalei Tang, Shoukun Sun, John Stempien
https://arxiv.org/abs/2509.12244 https://

RU-Net for Automatic Characterization of TRISO Fuel Cross Sections
During irradiation, phenomena such as kernel swelling and buffer densification may impact the performance of tristructural isotropic (TRISO) particle fuel. Post-irradiation microscopy is often used to identify these irradiation-induced morphologic changes. However, each fuel compact generally contains thousands of TRISO particles. Manually performing the work to get statistical information on these phenomena is cumbersome and subjective. To reduce the subjectivity inherent in that process and t…

Replaced article(s) found for math.CO. https://arxiv.org/list/math.CO/new
[1/1]:
- Positivity among P-partition generating functions
Nathan R. T. Lesnevich, Peter R. W. McNamara

Replaced article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[2/5]:
- EMOE: A Framework for Out-of-distribution Uncertainty Based Rejection via Model-Agnostic Expansiv...
Yunni Qu, James Wellnitz, Dzung Dinh, Bhargav Vaduri, Alexander Tropsha, Junier Oliva

Crosslisted article(s) found for cs.LO. https://arxiv.org/list/cs.LO/new
[1/1]:
- Large Language Models Imitate Logical Reasoning, but at what Cost?
Lachlan McGinness, Peter Baumgartner
…

Investigating Seamless Transitions Between Immersive Computational Notebooks and Embodied Data Interactions
Sungwon In, Eric Krokos, Kirsten Whitley, Chris North, Yalong Yang
https://arxiv.org/abs/2509.13295

Investigating Seamless Transitions Between Immersive Computational Notebooks and Embodied Data Interactions
A growing interest in Immersive Analytics (IA) has led to the extension of computational notebooks (e.g., Jupyter Notebook) into an immersive environment to enhance analytical workflows. However, existing solutions rely on the WIMP (windows, icons, menus, pointer) metaphor, which remains impractical for complex data exploration. Although embodied interaction offers a more intuitive alternative, immersive computational notebooks and embodied data exploration systems are implemented as standalone…

Artificial Intelligence in Breast Cancer Care: Transforming Preoperative Planning and Patient Education with 3D Reconstruction
Mustafa Khanbhai, Giulia Di Nardo, Jun Ma, Vivienne Freitas, Caterina Masino, Ali Dolatabadi, Zhaoxun "Lorenz" Liu, Wey Leong, Wagner H. Souza, Amin Madani
https://arxiv.org/abs/2509.12242

Artificial Intelligence in Breast Cancer Care: Transforming Preoperative Planning and Patient Education with 3D Reconstruction
Effective preoperative planning requires accurate algorithms for segmenting anatomical structures across diverse datasets, but traditional models struggle with generalization. This study presents a novel machine learning methodology to improve algorithm generalization for 3D anatomical reconstruction beyond breast cancer applications. We processed 120 retrospective breast MRIs (January 2018-June 2023) through three phases: anonymization and manual segmentation of T1-weighted and dynamic contras…

Replaced article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[1/5]:
- Concurrent Linguistic Error Detection (CLED): a New Methodology for Error Detection in Large Lang...
Jinhua Zhu, Javier Conde, Zhen Gao, Pedro Reviriego, Shanshan Liu, Fabrizio Lombardi

Crosslisted article(s) found for math.AC. https://arxiv.org/list/math.AC/new
[1/1]:
- Efficient generation of projective modules: a motivic view
Aravind Asok, Morgan Opie, Brian Shin, Tariq Syed

Prompt Commons: Collective Prompting as Governance for Urban AI
Rashid Mushkani
https://arxiv.org/abs/2509.12415 https://arxiv.org/pdf/2509.12415

Prompt Commons: Collective Prompting as Governance for Urban AI
Large Language Models (LLMs) are entering urban governance, yet their outputs are highly sensitive to prompts that carry value judgments. We propose Prompt Commons - a versioned, community-maintained repository of prompts with governance metadata, licensing, and moderation - to steer model behaviour toward pluralism. Using a Montreal dataset (443 human prompts; 3,317 after augmentation), we pilot three governance states (open, curated, veto-enabled). On a contested policy benchmark, a single-au…

[2025-09-17 Wed (UTC), 108 new articles found for cs.CV Computer Vision and Pattern Recognition]
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Crosslisted article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[7/7]:
- ResidualViT for Efficient Temporally Dense Video Encoding
Mattia Soldan, Fabian Caba Heilbron, Bernard Ghanem, Josef Sivic, Bryan Russell

Towards an Embodied Composition Framework for Organizing Immersive Computational Notebooks
Sungwon In, Eric Krokos, Kirsten Whitley, Chris North, Yalong Yang
https://arxiv.org/abs/2509.13291

Towards an Embodied Composition Framework for Organizing Immersive Computational Notebooks
As immersive technologies evolve, immersive computational notebooks offer new opportunities for interacting with code, data, and outputs. However, scaling these environments remains a challenge, particularly when analysts manually arrange large numbers of cells to maintain both execution logic and visual coherence. To address this, we introduce an embodied composition framework, facilitating organizational processes in the context of immersive computational notebooks. To evaluate the effectiven…

Crosslisted article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[6/7]:
- Validating Solidity Code Defects using Symbolic and Concrete Execution powered by Large Language ...
\c{S}tefan-Claudiu Susan, Andrei Arusoaie, Dorel Lucanu

Evolution of Programmers' Trust in Generative AI Programming Assistants
Anshul Shah, Thomas Rexin, Elena Tomson, Leo Porter, William G. Griswold, Adalbert Gerald Soosai Raj
https://arxiv.org/abs/2509.13253

Evolution of Programmers' Trust in Generative AI Programming Assistants
Motivation. Trust in generative AI programming assistants is a vital attitude that impacts how programmers use those programming assistants. Programmers that are over-trusting may be too reliant on their tools, leading to incorrect or vulnerable code; programmers that are under-trusting may avoid using tools that can improve their productivity and well-being. Methods. Since trust is a dynamic attitude that may change over time, this study aims to understand programmers' evolution of trust aft…

Crosslisted article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[5/7]:
- A Pressure-Based Diffusion Model for Influence Maximization on Social Networks
Curt Stutsman, Eliot W. Robson, Abhishek K. Umrawal

Textarium: Entangling Annotation, Abstraction and Argument
Philipp Proff, Marian D\"ork
https://arxiv.org/abs/2509.13191 https://arxiv.org/pdf/2509.13…

Textarium: Entangling Annotation, Abstraction and Argument
We present a web-based environment that connects annotation, abstraction, and argumentation during the interpretation of text. As a visual interface for scholarly reading and writing, Textarium combines human analysis with lightweight computational processing to bridge close and distant reading practices. Readers can highlight text, group keywords into concepts, and embed these observations as anchors in essays. The interface renders these interpretive actions as parameterized visualization sta…

Crosslisted article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[4/7]:
- Beyond Artificial Misalignment: Detecting and Grounding Semantic-Coordinated Multimodal Manipulat...
Jinjie Shen, Yaxiong Wang, Lechao Cheng, Nan Pu, Zhun Zhong

Patient Perspectives on Telemonitoring during Colorectal Cancer Surgery Prehabilitation
Irina Bianca Serban (Eindhoven University of Technology), Dimitra Dritsa (Eindhoven University of Technology), David ten Cate (M\'axima Medical Centre Veldhoven), Loes Janssen (M\'axima Medical Centre Veldhoven), Margot Heijmans (M\'axima Medical Centre Veldhoven), Sara Colombo (Delft University of Technology), Aarnout Brombacher (Eindhoven University of Technology), Steven Houben (Eindh…

Patient Perspectives on Telemonitoring during Colorectal Cancer Surgery Prehabilitation
Multimodal prehabilitation for colorectal cancer (CRC) surgery aims to optimize patient fitness and reduce postoperative complications. While telemonitoring's clinical value in supporting decision-making is recognized, patient perspectives on its use in prehabilitation remain underexplored, particularly compared to its related clinical context, rehabilitation. To address this gap, we conducted interviews with five patients who completed a four-week CRC prehabilitation program incorporating cont…

Crosslisted article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[3/7]:
- Understanding Prompt Management in GitHub Repositories: A Call for Best Practices
Hao Li, Hicham Masri, Filipe R. Cogo, Abdul Ali Bangash, Bram Adams, Ahmed E. Hassan

More than Meets the Eye: Understanding the Effect of Individual Objects on Perceived Visual Privacy
Mete Harun Akcay, Siddharth Prakash Rao, Alexandros Bakas, Buse Gul Atli
https://arxiv.org/abs/2509.13051

More than Meets the Eye: Understanding the Effect of Individual Objects on Perceived Visual Privacy
User-generated content, such as photos, comprises the majority of online media content and drives engagement due to the human ability to process visual information quickly. Consequently, many online platforms are designed for sharing visual content, with billions of photos posted daily. However, photos often reveal more than they intended through visible and contextual cues, leading to privacy risks. Previous studies typically treat privacy as a property of the entire image, overlooking individ…

Crosslisted article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[2/7]:
- A Variational Physics-Informed Neural Network Framework Using Petrov-Galerkin Method for Solving ...
Vijay Kumar, Gautam Singh

Crosslisted article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[1/7]:
- Scalable RF Simulation in Generative 4D Worlds
Zhiwei Zheng, Dongyin Hu, Mingmin Zhao

Winds Through Time: Interactive Data Visualization and Physicalization for Paleoclimate Communication
David Hunter, Pablo Botin, Emily Snode-Brenneman, Amy Stevermer, Becca Hatheway, Dillon Amaya, Eddie Goldstein, Wayne A Seltzer, Mark D Gross, Kris Karnauskas, Daniel Leithinger, Ellen Yi-Luen Do
https://arxiv.org/abs/2509.13039

Winds Through Time: Interactive Data Visualization and Physicalization for Paleoclimate Communication
We describe a multidisciplinary collaboration to iteratively design an interactive exhibit for a public science center on paleoclimate, the study of past climates. We created a data physicalisation of mountains and ice sheets that can be tangibly manipulated by visitors to interact with a wind simulation visualisation that demonstrates how the climate of North America differed dramatically between now and the peak of the last ice age. We detail the system for interaction and visualisation plus …

Shapes of Cognition for Computational Cognitive Modeling
Marjorie McShane, Sergei Nirenburg, Sanjay Oruganti, Jesse English
https://arxiv.org/abs/2509.13288 https://

Shapes of Cognition for Computational Cognitive Modeling
Shapes of cognition is a new conceptual paradigm for the computational cognitive modeling of Language-Endowed Intelligent Agents (LEIAs). Shapes are remembered constellations of sensory, linguistic, conceptual, episodic, and procedural knowledge that allow agents to cut through the complexity of real life the same way as people do: by expecting things to be typical, recognizing patterns, acting by habit, reasoning by analogy, satisficing, and generally minimizing cognitive load to the degree si…

Gesture Evaluation in Virtual Reality
Axel Wiebe Werner, Jonas Beskow, Anna Deichler
https://arxiv.org/abs/2509.12816 https://arxiv.org/pdf/2509.12816

Gesture Evaluation in Virtual Reality
Gestures are central to human communication, enriching interactions through non-verbal expression. Virtual avatars increasingly use AI-generated gestures to enhance life-likeness, yet evaluations have largely been confined to 2D. Virtual Reality (VR) provides an immersive alternative that may affect how gestures are perceived. This paper presents a comparative evaluation of computer-generated gestures in VR and 2D, examining three models from the 2023 GENEA Challenge. Results show that gestures…

RepIt: Representing Isolated Targets to Steer Language Models
Vincent Siu, Nathan W. Henry, Nicholas Crispino, Yang Liu, Dawn Song, Chenguang Wang
https://arxiv.org/abs/2509.13281

RepIt: Representing Isolated Targets to Steer Language Models
While activation steering in large language models (LLMs) is a growing area of research, methods can often incur broader effects than desired. This motivates isolation of purer concept vectors to enable targeted interventions and understand LLM behavior at a more granular level. We present RepIt, a simple and data-efficient framework for isolating concept-specific representations. Across five frontier LLMs, RepIt enables precise interventions: it selectively suppresses refusal on targeted conce…

The Impact of Automation on Risk-Taking: The Role of Sense of Agency
Yang Chen, Zhijun Zhang
https://arxiv.org/abs/2509.12794 https://arxiv.org/pdf/2509.12…

The Impact of Automation on Risk-Taking: The Role of Sense of Agency
Automation significantly alters human behavior, particularly risk-taking. Previous researches have paid limited attention to the underlying characteristics of automation and their mechanisms of influence on risk-taking. This study investigated how automation affects risk-taking and examined the role of sense of agency therein. By quantifying sense of agency through subjective ratings, this research explored the impact of automation level and reliability level on risk-taking. The results of thre…

A Scenario-Driven Cognitive Approach to Next-Generation AI Memory
Linyue Cai, Yuyang Cheng, Xiaoding Shao, Huiming Wang, Yong Zhao, Wei Zhang, Kang Li
https://arxiv.org/abs/2509.13235

A Scenario-Driven Cognitive Approach to Next-Generation AI Memory
As artificial intelligence advances toward artificial general intelligence (AGI), the need for robust and human-like memory systems has become increasingly evident. Current memory architectures often suffer from limited adaptability, insufficient multimodal integration, and an inability to support continuous learning. To address these limitations, we propose a scenario-driven methodology that extracts essential functional requirements from representative cognitive scenarios, leading to a unifie…

PLUTO: A Public Value Assessment Tool
Laura Koesten, P\'eter Ferenc Gyarmati, Connor Hogan, Bernhard Jordan, Seliem El-Sayed, Barbara Prainsack, Torsten M\"oller
https://arxiv.org/abs/2509.12773

PLUTO: A Public Value Assessment Tool
We present PLUTO (Public VaLUe Assessment TOol), a framework for assessing the public value of specific instances of data use. Grounded in the concept of data solidarity, PLUTO aims to empower diverse stakeholders - including regulatory bodies, private enterprises, NGOs, and individuals - to critically engage with data projects through a structured assessment of the risks and benefits of data use, and by encouraging critical reflection. This paper discusses the theoretical foundation, developme…

Simulating Clinical AI Assistance using Multimodal LLMs: A Case Study in Diabetic Retinopathy
Nadim Barakat, William Lotter
https://arxiv.org/abs/2509.13234 https://

Simulating Clinical AI Assistance using Multimodal LLMs: A Case Study in Diabetic Retinopathy
Diabetic retinopathy (DR) is a leading cause of blindness worldwide, and AI systems can expand access to fundus photography screening. Current FDA-cleared systems primarily provide binary referral outputs, where this minimal output may limit clinical trust and utility. Yet, determining the most effective output format to enhance clinician-AI performance is an empirical challenge that is difficult to assess at scale. We evaluated multimodal large language models (MLLMs) for DR detection and thei…

Participatory AI: A Scandinavian Approach to Human-Centered AI
Niklas Elmqvist, Eve Hoggan, Hans-J\"org Schulz, Marianne Graves Petersen, Peter Dalsgaard, Ira Assent, Olav W. Bertelsen, Akhil Arora, Kaj Gr{\o}nb{\ae}k, Susanne B{\o}dker, Clemens Nylandsted Klokmose, Rachel Charlotte Smith, Sebastian Hubenschmid, Christoph A. Johns, Gabriela Molina Le\'on, Anton Wolter, Johannes Ellemose, Vaishali Dhanoa, Simon Aagaard Enni, Mille Skovhus Lunding, Karl-Emil Kj{\ae}r Bilstrup, J…

Participatory AI: A Scandinavian Approach to Human-Centered AI
AI's transformative impact on work, education, and everyday life makes it as much a political artifact as a technological one. Current AI models are opaque, centralized, and overly generic. The algorithmic automation they provide threatens human agency and democratic values in both workplaces and daily life. To confront such challenges, we turn to Scandinavian Participatory Design (PD), which was devised in the 1970s to face a similar threat from mechanical automation. In the PD tradition, tech…

G-CSEA: A Graph-Based Conflict Set Extraction Algorithm for Identifying Infeasibility in Pseudo-Boolean Models
Kanishk Garg, Saranya D., Sanal Kumar, Saurabh Singh, Anupam Purwar
https://arxiv.org/abs/2509.13203

G-CSEA: A Graph-Based Conflict Set Extraction Algorithm for Identifying Infeasibility in Pseudo-Boolean Models
Workforce scheduling involves a variety of rule-based constraints-such as shift limits, staffing policies, working hour restrictions, and many similar scheduling rules-which can interact in conflicting ways, leading to infeasible models. Identifying the underlying causes of such infeasibility is critical for resolving scheduling issues and restoring feasibility. A common diagnostic approach is to compute Irreducible Infeasible Subsets (IISs): minimal sets of constraints that are jointly infeasi…

Agentic AI for Financial Crime Compliance
Henrik Axelsen, Valdemar Licht, Jan Damsgaard
https://arxiv.org/abs/2509.13137 https://arxiv.org/pdf/2509.13137…

Agentic AI for Financial Crime Compliance
The cost and complexity of financial crime compliance (FCC) continue to rise, often without measurable improvements in effectiveness. While AI offers potential, most solutions remain opaque and poorly aligned with regulatory expectations. This paper presents the design and deployment of an agentic AI system for FCC in digitally native financial platforms. Developed through an Action Design Research (ADR) process with a fintech firm and regulatory stakeholders, the system automates onboarding, m…

Harnessing the Power of AI in Qualitative Research: Role Assignment, Engagement, and User Perceptions of AI-Generated Follow-Up Questions in Semi-Structured Interviews
He Zhang, Yueyan Liu, Xin Guan, Jie Cai, John M. Carroll
https://arxiv.org/abs/2509.12709

Harnessing the Power of AI in Qualitative Research: Role Assignment, Engagement, and User Perceptions of AI-Generated Follow-Up Questions in Semi-Structured Interviews
Semi-structured interviews highly rely on the quality of follow-up questions, yet interviewers' knowledge and skills may limit their depth and potentially affect outcomes. While many studies have shown the usefulness of large language models (LLMs) for qualitative analysis, their possibility in the data collection process remains underexplored. We adopt an AI-driven "Wizard-of-Oz" setup to investigate how real-time LLM support in generating follow-up questions shapes semi-structured interviews.…

Reasoning with Preference Constraints: A Benchmark for Language Models in Many-to-One Matching Markets
Marylou Fauchard, Florian Carichon, Margarida Carvalho, Golnoosh Farnadi
https://arxiv.org/abs/2509.13131

Reasoning with Preference Constraints: A Benchmark for Language Models in Many-to-One Matching Markets
Recent advances in reasoning with large language models (LLMs) have demonstrated strong performance on complex mathematical tasks, including combinatorial optimization. Techniques such as Chain-of-Thought and In-Context Learning have further enhanced this capability, making LLMs both powerful and accessible tools for a wide range of users, including non-experts. However, applying LLMs to matching problems, which require reasoning under preferential and structural constraints, remains underexplo…

DoubleAgents: Exploring Mechanisms of Building Trust with Proactive AI
Tao Long, Xuanming Zhang, Sitong Wang, Zhou Yu, Lydia B Chilton
https://arxiv.org/abs/2509.12626 https://

DoubleAgents: Exploring Mechanisms of Building Trust with Proactive AI
Agentic workflows promise efficiency, but adoption hinges on whether people actually trust systems that act on their behalf. We present DoubleAgents, an agentic planning tool that embeds transparency and control through user intervention, value-reflecting policies, rich state visualizations, and uncertainty flagging for human coordination tasks. A built-in respondent simulation generates realistic scenarios, allowing users to rehearse, refine policies, and calibrate their reliance before live u…

A Visualized Framework for Event Cooperation with Generative Agents
Yuyang Tian, Shunqiang Mao, Wenchang Gao, Lanlan Qiu, Tianxing He
https://arxiv.org/abs/2509.13011 https://…

A Visualized Framework for Event Cooperation with Generative Agents
Large Language Models (LLMs) have revolutionized the simulation of agent societies, enabling autonomous planning, memory formation, and social interactions. However, existing frameworks often overlook systematic evaluations for event organization and lack visualized integration with physically grounded environments, limiting agents' ability to navigate spaces and interact with items realistically. We develop MiniAgentPro, a visualization platform featuring an intuitive map editor for customizin…

DPCheatSheet: Using Worked and Erroneous LLM-usage Examples to Scaffold Differential Privacy Implementation
Shao-Yu Chu, Yuhe Tian, Yu-Xiang Wang, Haojian Jin
https://arxiv.org/abs/2509.12590

DPCheatSheet: Using Worked and Erroneous LLM-usage Examples to Scaffold Differential Privacy Implementation
This paper explores how programmers without specialized expertise in differential privacy (DP) (i.e., novices) can leverage LLMs to implement DP programs with minimal training. We first conducted a need-finding study with 6 novices and 3 experts to understand how they utilize LLMs in DP implementation. While DP experts can implement correct DP analyses through a few prompts, novices struggle to articulate their requirements in prompts and lack the skills to verify the correctness of the generat…

Data-driven Methods of Extracting Text Structure and Information Transfer
Shinichi Honna, Taichi Murayama, Akira Matsui
https://arxiv.org/abs/2509.12999 https://

Data-driven Methods of Extracting Text Structure and Information Transfer
The Anna Karenina Principle (AKP) holds that success requires satisfying a small set of essential conditions, whereas failure takes diverse forms. We test AKP, its reverse, and two further patterns described as ordered and noisy across novels, online encyclopedias, research papers, and movies. Texts are represented as sequences of functional blocks, and convergence is assessed in transition order and position. Results show that structural principles vary by medium: novels follow reverse AKP in …

Conflect: Designing Reflective Thinking-Based Contextual Privacy Policy for Mobile Applications
Shuning Zhang, Sixing Tao, Eve He, Yuting Yang, Ying Ma, Ailei Wang, Xin Yi, Hewu Li
https://arxiv.org/abs/2509.12578

Conflect: Designing Reflective Thinking-Based Contextual Privacy Policy for Mobile Applications
Privacy policies are lengthy and complex, leading to user neglect. While contextual privacy policies (CPPs) present information at the point of risk, they may lack engagement and disrupt tasks. We propose Conflect, an interactive CPP for mobile apps, guided by a reflective thinking framework. Through three workshops with experienced designers and researchers, we constructed the design space of reflective thinking-based CPP design, and identified the disconnect between context and action as the …

Toward PDDL Planning Copilot
Yarin Benyamin, Argaman Mordoch, Shahaf S. Shperberg, Roni Stern
https://arxiv.org/abs/2509.12987 https://arxiv.org/pdf/2509.1…

Toward PDDL Planning Copilot
Large Language Models (LLMs) are increasingly being used as autonomous agents capable of performing complicated tasks. However, they lack the ability to perform reliable long-horizon planning on their own. This paper bridges this gap by introducing the Planning Copilot, a chatbot that integrates multiple planning tools and allows users to invoke them through instructions in natural language. The Planning Copilot leverages the Model Context Protocol (MCP), a recently developed standard for conne…

The Adaptation Paradox: Agency vs. Mimicry in Companion Chatbots
T. James Brandt, Cecilia Xi Wang
https://arxiv.org/abs/2509.12525 https://arxiv.org/pdf/25…

The Adaptation Paradox: Agency vs. Mimicry in Companion Chatbots
Generative AI powers a growing wave of companion chatbots, yet principles for fostering genuine connection remain unsettled. We test two routes: visible user authorship versus covert language-style mimicry. In a preregistered 3x2 experiment (N = 162), we manipulated user-controlled avatar generation (none, premade, user-generated) and Language Style Matching (LSM) (static vs. adaptive). Generating an avatar boosted rapport ($ω^2$ = .040, p = .013), whereas adaptive LSM underperformed static st…

Forget What's Sensitive, Remember What Matters: Token-Level Differential Privacy in Memory Sculpting for Continual Learning
Bihao Zhan, Jie Zhou, Junsong Li, Yutao Yang, Shilian Chen, Qianjun Pan, Xin Li, Wen Wu, Xingjiao Wu, Qin Chen, Hang Yan, Liang He
https://arxiv.org/abs/2509.12958

Forget What's Sensitive, Remember What Matters: Token-Level Differential Privacy in Memory Sculpting for Continual Learning
Continual Learning (CL) models, while adept at sequential knowledge acquisition, face significant and often overlooked privacy challenges due to accumulating diverse information. Traditional privacy methods, like a uniform Differential Privacy (DP) budget, indiscriminately protect all data, leading to substantial model utility degradation and hindering CL deployment in privacy-sensitive areas. To overcome this, we propose a privacy-enhanced continual learning (PeCL) framework that forgets what'…

Black-box Model Merging for Language-Model-as-a-Service with Massive Model Repositories
Shilian Chen, Jie Zhou, Tianyu Huai, Yujiang Lu, Junsong Li, Bihao Zhan, Qianjun Pan, Yutao Yang, Xin Li, Qin Chen, Hang Yan, Liang He
https://arxiv.org/abs/2509.12951

Black-box Model Merging for Language-Model-as-a-Service with Massive Model Repositories
Model merging refers to the process of integrating multiple distinct models into a unified model that preserves and combines the strengths and capabilities of the individual models. Most existing approaches rely on task vectors to combine models, typically under the assumption that model parameters are accessible. However, for extremely large language models (LLMs) such as GPT-4, which are often provided solely as black-box services through API interfaces (Language-Model-as-a-Service), model we…

Extended AI Interactions Shape Sycophancy and Perspective Mimesis
Shomik Jain, Charlotte Park, Matheus Mesquita Viana, Ashia Wilson, Dana Calacci
https://arxiv.org/abs/2509.12517

Extended AI Interactions Shape Sycophancy and Perspective Mimesis
We investigate whether long-context interactions between users and LLMs lead to AI mirroring behaviors. We focus on two forms of mirroring: (1) sycophancy -- the tendency of models to be overly agreeable with users, and (2) perspective mimesis -- the extent to which models reflect a user's perspective. Using two weeks of interaction context collected from 38 users, we compare model responses with and without long-context for two tasks: political explanations and personal advice. Our results dem…

The Anatomy of Alignment: Decomposing Preference Optimization by Steering Sparse Features
Jeremias Ferrao, Matthijs van der Lende, Ilija Lichkovski, Clement Neo
https://arxiv.org/abs/2509.12934

The Anatomy of Alignment: Decomposing Preference Optimization by Steering Sparse Features
Aligning large language models is critical for their usability and safety. However, the prevailing approach of Reinforcement Learning from Human Feedback (RLHF) induces diffuse, opaque parameter changes, making it difficult to discern what the model has internalized. Hence, we introduce Feature Steering with Reinforcement Learning (FSRL), a transparent alignment framework that trains a lightweight adapter to steer behavior by modulating interpretable features from a Sparse Autoencoder (SAE). Fi…

Beyond Gaze Overlap: Analyzing Joint Visual Attention Dynamics Using Egocentric Data
Kumushini Thennakoon, Yasasi Abeysinghe, Bhanuka Mahanama, Vikas Ashok, Sampath Jayarathna
https://arxiv.org/abs/2509.12419

Beyond Gaze Overlap: Analyzing Joint Visual Attention Dynamics Using Egocentric Data
Joint visual attention (JVA) provides informative cues on human behavior during social interactions. The ubiquity of egocentric eye-trackers and large-scale datasets on everyday interactions offer research opportunities in identifying JVA in multi-user environments. We propose a novel approach utilizing spatiotemporal tubes centered on attention rendered by individual gaze and detect JVA using deep-learning-based feature mapping. Our results reveal object-focused collaborative tasks to yield hi…

HLSMAC: A New StarCraft Multi-Agent Challenge for High-Level Strategic Decision-Making
Xingxing Hong, Yungong Wang, Dexin Jin, Ye Yuan, Ximing Huang, Zijian Wu, Wenxin Li
https://arxiv.org/abs/2509.12927

HLSMAC: A New StarCraft Multi-Agent Challenge for High-Level Strategic Decision-Making
Benchmarks are crucial for assessing multi-agent reinforcement learning (MARL) algorithms. While StarCraft II-related environments have driven significant advances in MARL, existing benchmarks like SMAC focus primarily on micromanagement, limiting comprehensive evaluation of high-level strategic intelligence. To address this, we introduce HLSMAC, a new cooperative MARL benchmark with 12 carefully designed StarCraft II scenarios based on classical stratagems from the Thirty-Six Stratagems. Each …

FlexMind: Scaffolding Flexible Ideation Workflows with AI in Creative Problem-Solving
Yaqing Yang, Vikram Mohanty, Nikolas Martelaro, Aniket Kittur, Yan-Ying Chen, Matthew K. Hong
https://arxiv.org/abs/2509.12408

FlexMind: Scaffolding Flexible Ideation Workflows with AI in Creative Problem-Solving
Divergent thinking in the ideation stage of creative problem-solving demands that individuals explore a broad design space. Yet this exploration rarely follows a neat, linear sequence; problem-solvers constantly shift among searching, creating, and evaluating ideas. Existing interfaces either impose rigid, step-by-step workflows or permit unguided free-form exploration. To strike a balance between flexibility and guidance for augmenting people's efficiency and creativity, we introduce a human-A…

Population Estimation using Deep Learning over Gandhinagar Urban Area
Jai Singla, Peal Jotania, Keivalya Pandya
https://arxiv.org/abs/2509.12926 https://ar…

Population Estimation using Deep Learning over Gandhinagar Urban Area
Population estimation is crucial for various applications, from resource allocation to urban planning. Traditional methods such as surveys and censuses are expensive, time-consuming and also heavily dependent on human resources, requiring significant manpower for data collection and processing. In this study a deep learning solution is proposed to estimate population using high resolution (0.3 m) satellite imagery, Digital Elevation Models (DEM) of 0.5m resolution and vector boundaries. Propose…

Data selves and identity theft in the age of AI
Tim Gorichanaz
https://arxiv.org/abs/2509.12383 https://arxiv.org/pdf/2509.12383

Data selves and identity theft in the age of AI
This chapter examines identity theft in the digital age, particularly in the context of emerging artificial intelligence (AI) technologies. It begins with a discussion of big data and selfhood, the concepts of data selves and data doubles, and the process of identification in the digital age. Next, the literature on online identity theft is reviewed, including its theoretical and empirical aspects. As is evident from that review, AI technologies have increased the speed and scale of identity cr…

Stochastic Streets: A Walk Through Random LLM Address Generation in four European Cities
Tairan Fu, David Campo-Nazareno, Javier Coronado-Bl\'azquez, Javier Conde, Pedro Reviriego, Fabrizio Lombardi
https://arxiv.org/abs/2509.12914

Stochastic Streets: A Walk Through Random LLM Address Generation in four European Cities
Large Language Models (LLMs) are capable of solving complex math problems or answer difficult questions on almost any topic, but can they generate random street addresses for European cities?

LTA-thinker: Latent Thought-Augmented Training Framework for Large Language Models on Complex Reasoning
Jiaqi Wang, Binquan Ji, Haibo Luo, Yiyang Qi, Ruiting Li, Huiyan Wang, Yuantao Han, Cangyi Yang, jiaxu Zhang, Feiliang Ren
https://arxiv.org/abs/2509.12875

LTA-thinker: Latent Thought-Augmented Training Framework for Large Language Models on Complex Reasoning
Complex Reasoning in Large Language Models can be dynamically optimized using Test-Time Scaling (TTS) to mitigate Overthinking. Methods such as Coconut, SoftCoT and its variant are effective in continuous latent space inference, the core bottleneck still lies in the efficient generation and utilization of high-quality Latent Thought. Drawing from the theory of SoftCoT++ that a larger variance in the generated Latent Thought distribution more closely approximates the golden truth distribution, w…

H$^2$R: Hierarchical Hindsight Reflection for Multi-Task LLM Agents
Shicheng Ye, Chao Yu, Kaiqiang Ke, Chengdong Xu, Yinqi Wei
https://arxiv.org/abs/2509.12810 https://

H$^2$R: Hierarchical Hindsight Reflection for Multi-Task LLM Agents
Large language model (LLM)-based agents have shown strong potential in multi-task scenarios, owing to their ability to transfer knowledge across diverse tasks. However, existing approaches often treat prior experiences and knowledge as monolithic units, leading to inefficient and coarse-grained knowledge transfer. In this work, we propose a novel hierarchical memory architecture that enables fine-grained knowledge transfer by decoupling high-level planning memory from low-level execution memory…

Zero-shot Graph Reasoning via Retrieval Augmented Framework with LLMs
Hanqing Li, Kiran Sheena Jyothi, Henry Liang, Sharika Mahadevan, Diego Klabjan
https://arxiv.org/abs/2509.12743

Zero-shot Graph Reasoning via Retrieval Augmented Framework with LLMs
We propose a new, training-free method, Graph Reasoning via Retrieval Augmented Framework (GRRAF), that harnesses retrieval-augmented generation (RAG) alongside the code-generation capabilities of large language models (LLMs) to address a wide range of graph reasoning tasks. In GRRAF, the target graph is stored in a graph database, and the LLM is prompted to generate executable code queries that retrieve the necessary information. This approach circumvents the limitations of existing methods th…

Large Language Models Imitate Logical Reasoning, but at what Cost?
Lachlan McGinness, Peter Baumgartner
https://arxiv.org/abs/2509.12645 https://arxiv.org/…

Large Language Models Imitate Logical Reasoning, but at what Cost?
We present a longitudinal study which evaluates the reasoning capability of frontier Large Language Models over an eighteen month period. We measured the accuracy of three leading models from December 2023, September 2024 and June 2025 on true or false questions from the PrOntoQA dataset and their faithfulness to reasoning strategies provided through in-context learning. The improvement in performance from 2023 to 2024 can be attributed to hidden Chain of Thought prompting. The introduction of …

Learn to Relax with Large Language Models: Solving Nonlinear Combinatorial Optimization Problems via Bidirectional Coevolution
Beidan Liu, Zhengqiu Zhu, Chen Gao, Yong Zhao, Wei Qi, Quanjun Yin
https://arxiv.org/abs/2509.12643

Learn to Relax with Large Language Models: Solving Nonlinear Combinatorial Optimization Problems via Bidirectional Coevolution
Nonlinear Combinatorial Optimization Problems (NCOPs) present a formidable computational hurdle in practice, as their nonconvex nature gives rise to multi-modal solution spaces that defy efficient optimization. Traditional constraint relaxation approaches rely heavily on expert-driven, iterative design processes that lack systematic automation and scalable adaptability. While recent Large Language Model (LLM)-based optimization methods show promise for autonomous problem-solving, they predomina…