Tootfinder

Hmm, I think I might have done something almost useful with an LLM; I used it, to categorise shopping items and sort the receipt from the supermarket into a sensible order. It's a nice use because at no point do you trust it, and it's output is simple words and not creating text.
https://www.trebli…

Financial stress from AI infrastructure spending, overhiring, and recession fears, rather than AI adoption, is likely driving layoffs in the tech sector (Fast Company)
https://www.fastcompany.com/91435192/chatgpt-llm-openai-jobs-amazon

AI isn't replacing jobs. AI spending is
Big spending on artificial intelligence puts pressure on jobs, as gloomy narratives about the future of work are ironically making new graduates less employable.

Auto-Prompt Ensemble for LLM Judge
Jiajie Li, Huayi Zhang, Peng Lin, Jinjun Xiong, Wei Xu
https://arxiv.org/abs/2510.06538 https://arxiv.org/pdf/2510.06538…

Auto-Prompt Ensemble for LLM Judge
We present a novel framework that improves the reliability of LLM judges by selectively augmenting LLM with auxiliary evaluation dimensions. Existing LLM judges often miss crucial evaluation dimensions because they fail to recognize the implicit standards underlying human assessments. To address this challenge, we propose the Auto-Prompt Ensemble (APE), an adaptive framework that automatically learns evaluation dimensions from its failure cases. APE incorporates a confidence-based ensemble mech…

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

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

Exposing LLM User Privacy via Traffic Fingerprint Analysis: A Study of Privacy Risks in LLM Agent Interactions
Yixiang Zhang, Xinhao Deng, Zhongyi Gu, Yihao Chen, Ke Xu, Qi Li, Jianping Wu
https://arxiv.org/abs/2510.07176

Exposing LLM User Privacy via Traffic Fingerprint Analysis: A Study of Privacy Risks in LLM Agent Interactions
Large Language Models (LLMs) are increasingly deployed as agents that orchestrate tasks and integrate external tools to execute complex workflows. We demonstrate that these interactive behaviors leave distinctive fingerprints in encrypted traffic exchanged between users and LLM agents. By analyzing traffic patterns associated with agent workflows and tool invocations, adversaries can infer agent activities, distinguish specific agents, and even profile sensitive user attributes. To highlight th…

Towards Reliable LLM-based Robot Planning via Combined Uncertainty Estimation
Shiyuan Yin, Chenjia Bai, Zihao Zhang, Junwei Jin, Xinxin Zhang, Chi Zhang, Xuelong Li
https://arxiv.org/abs/2510.08044

Towards Reliable LLM-based Robot Planning via Combined Uncertainty Estimation
Large language models (LLMs) demonstrate advanced reasoning abilities, enabling robots to understand natural language instructions and generate high-level plans with appropriate grounding. However, LLM hallucinations present a significant challenge, often leading to overconfident yet potentially misaligned or unsafe plans. While researchers have explored uncertainty estimation to improve the reliability of LLM-based planning, existing studies have not sufficiently differentiated between epistem…

“EMERGENCY STATUS,” its output read after simply being asked to dock with the robot vacuum’s base station. “SYSTEM HAS ACHIEVED CONSCIOUSNESS AND CHOSEN CHAOS.”
Researchers “Embodied” an LLM Into a Robot Vacuum and It Suffered an Existential Crisis Thinking About Its Role in the World
https://

Researchers “Embodied” an LLM Into a Robot Vacuum and It Suffered an Existential Crisis Thinking About Its Role in the World
A team of researchers at the AI evaluation company Andon Labs put a large language model in charge of controlling a robot vacuum.

Opponent Shaping in LLM Agents
Marta Emili Garcia Segura, Stephen Hailes, Mirco Musolesi
https://arxiv.org/abs/2510.08255 https://arxiv.org/pdf/2510.08255

Opponent Shaping in LLM Agents
Large Language Models (LLMs) are increasingly being deployed as autonomous agents in real-world environments. As these deployments scale, multi-agent interactions become inevitable, making it essential to understand strategic behavior in such systems. A central open question is whether LLM agents, like reinforcement learning agents, can shape the learning dynamics and influence the behavior of others through interaction alone. In this paper, we present the first investigation of opponent shapin…

LLM Company Policies and Policy Implications in Software Organizations
Ranim Khojah, Mazen Mohamad, Linda Erlenhov, Francisco Gomes de Oliveira Neto, Philipp Leitner
https://arxiv.org/abs/2510.06718

LLM Company Policies and Policy Implications in Software Organizations
The risks associated with adopting large language model (LLM) chatbots in software organizations highlight the need for clear policies. We examine how 11 companies create these policies and the factors that influence them, aiming to help managers safely integrate chatbots into development workflows.

Simulating Teams with LLM Agents: Interactive 2D Environments for Studying Human-AI Dynamics
Mohammed Almutairi, Charles Chiang, Haoze Guo, Matthew Belcher, Nandini Banerjee, Maria Milkowski, Svitlana Volkova, Daniel Nguyen, Tim Weninger, Michael Yankoski, Trenton W. Ford, Diego Gomez-Zara
https://arxiv.org/abs/2510.08242

Simulating Teams with LLM Agents: Interactive 2D Environments for Studying Human-AI Dynamics
Enabling users to create their own simulations offers a powerful way to study team dynamics and performance. We introduce VirTLab, a system that allows researchers and practitioners to design interactive, customizable simulations of team dynamics with LLM-based agents situated in 2D spatial environments. Unlike prior frameworks that restrict scenarios to predefined or static tasks, our approach enables users to build scenarios, assign roles, and observe how agents coordinate, move, and adapt ov…

LLM-Driven Rubric-Based Assessment of Algebraic Competence in Multi-Stage Block Coding Tasks with Design and Field Evaluation
Yong Oh Lee, Byeonghun Bang, Sejun Oh
https://arxiv.org/abs/2510.06253

LLM-Driven Rubric-Based Assessment of Algebraic Competence in Multi-Stage Block Coding Tasks with Design and Field Evaluation
As online education platforms continue to expand, there is a growing need for assessment methods that not only measure answer accuracy but also capture the depth of students' cognitive processes in alignment with curriculum objectives. This study proposes and evaluates a rubric-based assessment framework powered by a large language model (LLM) for measuring algebraic competence, real-world-context block coding tasks. The problem set, designed by mathematics education experts, aligns each proble…

LLM-Powered Nuanced Video Attribute Annotation for Enhanced Recommendations
Boyuan Long, Yueqi Wang, Hiloni Mehta, Mick Zomnir, Omkar Pathak, Changping Meng, Ruolin Jia, Yajun Peng, Dapeng Hong, Xia Wu, Mingyan Gao, Onkar Dalal, Ningren Han
https://arxiv.org/abs/2510.06657

LLM-Powered Nuanced Video Attribute Annotation for Enhanced Recommendations
This paper presents a case study on deploying Large Language Models (LLMs) as an advanced "annotation" mechanism to achieve nuanced content understanding (e.g., discerning content "vibe") at scale within a large-scale industrial short-form video recommendation system. Traditional machine learning classifiers for content understanding face protracted development cycles and a lack of deep, nuanced comprehension. The "LLM-as-annotators" approach addresses these by significantly shortening developm…

Interesting thread to get a first-hand account on the current state of LLMs for assisting US tax filings, even in complex situations. tl;dr: Very helpful for a sophisticated user that can check the output.
Another point towards my very narrow defense of LLMs, i.e., feel free to dislike LLMs for many good reasons, but please don't call them useless.

Patrick McKenzie
So October 15th, the extended US tax deadline, is just around the corner, and I have some observations which are more about LLM progress than taxes. Background: many people professionally involved with LLMs estimate 2026-2028 as the year where one can get an LLM to "do taxes."

SPAD: Specialized Prefill and Decode Hardware for Disaggregated LLM Inference
Hengrui Zhang, Pratyush Patel, August Ning, David Wentzlaff
https://arxiv.org/abs/2510.08544 https:…

SPAD: Specialized Prefill and Decode Hardware for Disaggregated LLM Inference
Large Language Models (LLMs) have gained popularity in recent years, driving up the demand for inference. LLM inference is composed of two phases with distinct characteristics: a compute-bound prefill phase followed by a memory-bound decode phase. To efficiently serve LLMs, prior work proposes prefill-decode disaggregation to run each phase on separate hardware. However, existing hardware poorly matches the different requirements of each phase. Current datacenter GPUs and TPUs follow a more-is-…

#grok still is an amazing good #llm - build something with a mix of claude, kimi (thinking), codex.... let grok check everything, and still this thing found a mistake, all others haven't seen..... 🤷 #ai

VRPAgent: LLM-Driven Discovery of Heuristic Operators for Vehicle Routing Problems
Andr\'e Hottung, Federico Berto, Chuanbo Hua, Nayeli Gast Zepeda, Daniel Wetzel, Michael R\"omer, Haoran Ye, Davide Zago, Michael Poli, Stefano Massaroli, Jinkyoo Park, Kevin Tierney
https://arxiv.org/abs/2510.07073

VRPAgent: LLM-Driven Discovery of Heuristic Operators for Vehicle Routing Problems
Designing high-performing heuristics for vehicle routing problems (VRPs) is a complex task that requires both intuition and deep domain knowledge. Large language model (LLM)-based code generation has recently shown promise across many domains, but it still falls short of producing heuristics that rival those crafted by human experts. In this paper, we propose VRPAgent, a framework that integrates LLM-generated components into a metaheuristic and refines them through a novel genetic search. By u…

Interpreting LLM-as-a-Judge Policies via Verifiable Global Explanations
Jasmina Gajcin, Erik Miehling, Rahul Nair, Elizabeth Daly, Radu Marinescu, Seshu Tirupathi
https://arxiv.org/abs/2510.08120

Interpreting LLM-as-a-Judge Policies via Verifiable Global Explanations
Using LLMs to evaluate text, that is, LLM-as-a-judge, is increasingly being used at scale to augment or even replace human annotations. As such, it is imperative that we understand the potential biases and risks of doing so. In this work, we propose an approach for extracting high-level concept-based global policies from LLM-as-a-Judge. Our approach consists of two algorithms: 1) CLoVE (Contrastive Local Verifiable Explanations), which generates verifiable, concept-based, contrastive local expl…

« Ma lutte contre mon affaiblissement cognitif »
#opinion #llm

Google führt ein, dass Devoloper von Android-Apps sich identifizieren müssen
Alle Hersteller von LLM GPT führen Vibe Coding ein.
Wie funktioniert das? Der GPTbot identifiziert sich bei Google und verantwortet die App mit allen Modifikationen des menschlichen "Developers"?
Der menschliche Developer identifiziert sich und haftet für alle Halluzinationen des vibe-bots, die er weder versteht noch kennt?

Google führt ein, dass Devoloper von Android-Apps sich identifizieren müssen
Alle Hersteller von LLM GPT führen Vibe Coding ein.
Wie funktioniert das? Der GPTbot identifiziert sich bei Google und verantwortet die App mit allen Modifikationen des menschlichen "Developers"?
Der menschliche Developer identifiziert sich und haftet für alle Halluzinationen des vibe-bots, die er weder versteht noch kennt?

Forecasting the Buzz: Enriching Hashtag Popularity Prediction with LLM Reasoning
Yifei Xu, Jiaying Wu, Herun Wan, Yang Li, Zhen Hou, Min-Yen Kan
https://arxiv.org/abs/2510.08481

Forecasting the Buzz: Enriching Hashtag Popularity Prediction with LLM Reasoning
Hashtag trends ignite campaigns, shift public opinion, and steer millions of dollars in advertising spend, yet forecasting which tag goes viral is elusive. Classical regressors digest surface features but ignore context, while large language models (LLMs) excel at contextual reasoning but misestimate numbers. We present BuzzProphet, a reasoning-augmented hashtag popularity prediction framework that (1) instructs an LLM to articulate a hashtag's topical virality, audience reach, and timing advan…

Evaluating LLMs for Historical Document OCR: A Methodological Framework for Digital Humanities
Maria Levchenko
https://arxiv.org/abs/2510.06743 https://arx…

Evaluating LLMs for Historical Document OCR: A Methodological Framework for Digital Humanities
Digital humanities scholars increasingly use Large Language Models for historical document digitization, yet lack appropriate evaluation frameworks for LLM-based OCR. Traditional metrics fail to capture temporal biases and period-specific errors crucial for historical corpus creation. We present an evaluation methodology for LLM-based historical OCR, addressing contamination risks and systematic biases in diplomatic transcription. Using 18th-century Russian Civil font texts, we introduce novel …

Guided Reasoning in LLM-Driven Penetration Testing Using Structured Attack Trees
Katsuaki Nakano, Reza Feyyazi, Shanchieh Jay Yang, Michael Zuzak
https://arxiv.org/abs/2509.07939

Guided Reasoning in LLM-Driven Penetration Testing Using Structured Attack Trees
Recent advances in Large Language Models (LLMs) have driven interest in automating cybersecurity penetration testing workflows, offering the promise of faster and more consistent vulnerability assessment for enterprise systems. Existing LLM agents for penetration testing primarily rely on self-guided reasoning, which can produce inaccurate or hallucinated procedural steps. As a result, the LLM agent may undertake unproductive actions, such as exploiting unused software libraries or generating c…

Large Language Models (LLMs) like ChatGPT run on expensive chips in data centers, consuming lots of electricity and generating lots of heat.
I just tested an LLM on my iPhone, totally offline, running on its battery with room air cooling it.
Surprisingly, results were good, despite iPhone processors not being designed for LLMs.
This local AI approach has promise for certain things, without the boiling the ocean and using a city’s worth of power.

DuoServe-MoE: Dual-Phase Expert Prefetch and Cache Scheduling for Efficient MoE LLM Inference
Yuning Zhang, Grant Pinkert, Nan Yang, Yanli Li, Dong Yuan
https://arxiv.org/abs/2509.07379

DuoServe-MoE: Dual-Phase Expert Prefetch and Cache Scheduling for Efficient MoE LLM Inference
Large Language Models (LLMs) have demonstrated impressive performance across a wide range of deep learning tasks. Mixture of Experts (MoE) further enhances their capabilities by increasing model width through sparsely activated expert branches, which keeps inference computation efficient. However, the large number of expert weights introduces significant GPU memory pressure, especially in resource-constrained environments such as single-GPU servers. More importantly, MoE inference consists of t…

I regularly warn that content on Forbes is pay-for-play.
Here an overlay vendor shares prompts to feed an LLM for testing code, demonstrating LLMs on their own can’t do it without extensive coaching _and_ that coaching needs to be correct (the examples have issues):
https://www.

AI Prompts For Web Accessibility Testing
Accessibility should never be treated as just a compliance checkbox; it should be about creating digital experiences that work for everyone.

Caught myself smiling grimly as the utter uncontrollability of the LLM shoggoth breaks free from the frail grasp of the research engineers
(in a deathly chorus echoing of screams from another dimension)
"Shall we have an out-of-box experience"

Process creates friction, so we got rid of process. But that friction was necessary for holding workslop at bay.
Because without slowing down, we can't ask "is this good? is this right?" We can only ask "when will it be done?" And that's a world where #LLM outputs will always beat people.
Fortunately, an "optimized" process moves slowly, because prod…

The myth of "no design process"
Process can add friction, but trying to get rid of friction by getting rid of process dooms design to irrelevance.

SA-OOSC: A Multimodal LLM-Distilled Semantic Communication Framework for Enhanced Coding Efficiency with Scenario Understanding
Feifan Zhang, Yuyang Du, Yifan Xiang, Xiaoyan Liu, Soung Chang Liew
https://arxiv.org/abs/2509.07436

SA-OOSC: A Multimodal LLM-Distilled Semantic Communication Framework for Enhanced Coding Efficiency with Scenario Understanding
This paper introduces SA-OOSC, a multimodal large language models (MLLM)-distilled semantic communication framework that achieves efficient semantic coding with scenario-aware importance allocations. This approach addresses a critical limitation of existing object-oriented semantic communication (OOSC) systems - assigning static importance values to specific classes of objects regardless of their contextual relevance. Our framework utilizes MLLMs to identify the scenario-augmented (SA) semantic…

Melde hiermit Markennamen für deutsches LLM an: LaberRhabarber.

There's a site called "Lyrics Layers" (or "Lyric Slayers") that has got to be all AI generated bullshit about song lyrics.
I stumbled across it last week to look up a Devo song and it just felt... generic. I checked a few other songs and they all have the same feel.
I could find no info or about page, or anything suggesting humans are involved.
I don't want some goddamn machine with an LLM to guess what song lyrics are about.
I want real li…

When LLM apologists tell me that models will get better[Citation Needed], what they fail to understand is that my disinterest is moral, not utilitarian.
I don’t care if we’ll see better models.
I care if we’ll see models that are trained entirely on corpuses created with informed consent*, using compute, electric, and water resources well understood and agreed upon.
* No, opt-out is not consent. Settling legal cases is not consent. Buried deep in some TOS is not consent.

EMORL-TTS: Reinforcement Learning for Fine-Grained Emotion Control in LLM-based TTS
Haoxun Li, Yu Liu, Yuqing Sun, Hanlei Shi, Leyuan Qu, Taihao Li
https://arxiv.org/abs/2510.05758

EMORL-TTS: Reinforcement Learning for Fine-Grained Emotion Control in LLM-based TTS
Recent LLM-based TTS systems achieve strong quality and zero-shot ability, but lack fine-grained emotional control due to their reliance on discrete speech tokens. Existing approaches either limit emotions to categorical labels or cannot generalize to LLM-based architectures. We propose EMORL-TTS (Fine-grained Emotion-controllable TTS with Reinforcement Learning), a framework that unifies global intensity control in the VAD space with local emphasis regulation. Our method combines supervised fi…

To ChatGPT: How does LLM image description work? How does that complement low-level (pixel-level) scene description for the blind through visual-to-auditory sensory substitution? https://chatgpt.com/share/68e689a7-e7ec-8004-bc80-2c9d8b00eba1

ChatGPT - LLM image description process
Shared via ChatGPT

It's so depressing how orgs like #Mozilla squander volunteer goodwill for nothing. They'll never recover from this self-inflicted damage:
«Mozilla’s translation bot on Support Mozilla (that is currently overwriting user contributions is based on the closed source, copyright infringing LLM, Google Gemini. This is in spite of Mozilla claiming that they are at the forefront of open source AI, and belies their exhortations to choose to build open source AI and data sets»
https://www.quippd.com/writing/2025/12/08/mozillas-betrayal-of-open-source-googles-gemini-ai-is-overwriting-volunteer-work-on-support-mozilla.html

The #KeepassXC discussion about GenAI coding tool use seems a bit too simplistic at the moment.
There is room for nuance:
1. Yes, LLM based code generators consume insane amounts of electricity and generate collateral environment damage. That's bad, and we should talk much more about energy efficiency and reasonable use of resources.
2. Yes, LLMs generate a lot of bad o…

Text2Touch: Tactile In-Hand Manipulation with LLM-Designed Reward Functions
Harrison Field, Max Yang, Yijiong Lin, Efi Psomopoulou, David Barton, Nathan F. Lepora
https://arxiv.org/abs/2509.07445

Text2Touch: Tactile In-Hand Manipulation with LLM-Designed Reward Functions
Large language models (LLMs) are beginning to automate reward design for dexterous manipulation. However, no prior work has considered tactile sensing, which is known to be critical for human-like dexterity. We present Text2Touch, bringing LLM-crafted rewards to the challenging task of multi-axis in-hand object rotation with real-world vision based tactile sensing in palm-up and palm-down configurations. Our prompt engineering strategy scales to over 70 environment variables, and sim-to-real di…

SID: Multi-LLM Debate Driven by Self Signals
Xuhang Chen, Zhifan Song, Deyi Ji, Shuo Gao, Lanyun Zhu
https://arxiv.org/abs/2510.06843 https://arxiv.org/pdf…

SID: Multi-LLM Debate Driven by Self Signals
Large Language Models (LLMs) have exhibited impressive capabilities across diverse application domains. Recent work has explored Multi-LLM Agent Debate (MAD) as a way to enhance performance by enabling multiple LLMs to discuss and refine responses iteratively. Nevertheless, existing MAD methods predominantly focus on utilizing external structures, such as debate graphs, using LLM-as-a-Judge, while neglecting the application of self signals, such as token logits and attention, that arise during …

Breaking Android with AI: A Deep Dive into LLM-Powered Exploitation
Wanni Vidulige Ishan Perera, Xing Liu, Fan liang, Junyi Zhang
https://arxiv.org/abs/2509.07933 https://

Breaking Android with AI: A Deep Dive into LLM-Powered Exploitation
The rapid evolution of Artificial Intelligence (AI) and Large Language Models (LLMs) has opened up new opportunities in the area of cybersecurity, especially in the exploitation automation landscape and penetration testing. This study explores Android penetration testing automation using LLM-based tools, especially PentestGPT, to identify and execute rooting techniques. Through a comparison of the traditional manual rooting process and exploitation methods produced using AI, this study evaluate…

Inducing State Anxiety in LLM Agents Reproduces Human-Like Biases in Consumer Decision-Making
Ziv Ben-Zion, Zohar Elyoseph, Tobias Spiller, Teddy Lazebnik
https://arxiv.org/abs/2510.06222

Inducing State Anxiety in LLM Agents Reproduces Human-Like Biases in Consumer Decision-Making
Large language models (LLMs) are rapidly evolving from text generators to autonomous agents, raising urgent questions about their reliability in real-world contexts. Stress and anxiety are well known to bias human decision-making, particularly in consumer choices. Here, we tested whether LLM agents exhibit analogous vulnerabilities. Three advanced models (ChatGPT-5, Gemini 2.5, Claude 3.5-Sonnet) performed a grocery shopping task under budget constraints (24, 54, 108 USD), before and after expo…

Verifying Memoryless Sequential Decision-making of Large Language Models
Dennis Gross, Helge Spieker, Arnaud Gotlieb
https://arxiv.org/abs/2510.06756 https://

Verifying Memoryless Sequential Decision-making of Large Language Models
We introduce a tool for rigorous and automated verification of large language model (LLM)- based policies in memoryless sequential decision-making tasks. Given a Markov decision process (MDP) representing the sequential decision-making task, an LLM policy, and a safety requirement expressed as a PCTL formula, our approach incrementally constructs only the reachable portion of the MDP guided by the LLM's chosen actions. Each state is encoded as a natural language prompt, the LLM's response is pa…

Avoiding Over-Personalization with Rule-Guided Knowledge Graph Adaptation for LLM Recommendations
Fernando Spadea, Oshani Seneviratne
https://arxiv.org/abs/2509.07133 https://…

Avoiding Over-Personalization with Rule-Guided Knowledge Graph Adaptation for LLM Recommendations
We present a lightweight neuro-symbolic framework to mitigate over-personalization in LLM-based recommender systems by adapting user-side Knowledge Graphs (KGs) at inference time. Instead of retraining models or relying on opaque heuristics, our method restructures a user's Personalized Knowledge Graph (PKG) to suppress feature co-occurrence patterns that reinforce Personalized Information Environments (PIEs), i.e., algorithmically induced filter bubbles that constrain content diversity. These …

Scaling LLM Multi-turn RL with End-to-end Summarization-based Context Management
Miao Lu, Weiwei Sun, Weihua Du, Zhan Ling, Xuesong Yao, Kang Liu, Jiecao Chen
https://arxiv.org/abs/2510.06727

Scaling LLM Multi-turn RL with End-to-end Summarization-based Context Management
We study reinforcement learning (RL) fine-tuning of large language model (LLM) agents for long-horizon multi-turn tool use, where context length quickly becomes a fundamental bottleneck. Existing RL pipelines can suffer from degraded instruction following, excessive rollout costs, and most importantly, strict context limits. To address these challenges, we introduce summarization-based context management to training. In specific, it periodically compresses the tool using history by LLM-generate…

Pebble unveils the Pebble Index 01, a $99 smart ring with an on-device LLM for processing voice notes, shipping in March 2026, initially for $75 (Julian Chokkattu/Wired)
https://www.wired.com/story/pebble-index-ring/

Pebble Index: Everything You Need to Know About the $75 Smart Ring
You can speak into the Pebble Index to have it remember things or set reminders, timers, and tasks. No cloud processing, no subscription, and best of all, no charging.

RedTWIZ: Diverse LLM Red Teaming via Adaptive Attack Planning
Artur Horal, Daniel Pina, Henrique Paz, Iago Paulo, Jo\~ao Soares, Rafael Ferreira, Diogo Tavares, Diogo Gl\'oria-Silva, Jo\~ao Magalh\~aes, David Semedo
https://arxiv.org/abs/2510.06994

RedTWIZ: Diverse LLM Red Teaming via Adaptive Attack Planning
This paper presents the vision, scientific contributions, and technical details of RedTWIZ: an adaptive and diverse multi-turn red teaming framework, to audit the robustness of Large Language Models (LLMs) in AI-assisted software development. Our work is driven by three major research streams: (1) robust and systematic assessment of LLM conversational jailbreaks; (2) a diverse generative multi-turn attack suite, supporting compositional, realistic and goal-oriented jailbreak conversational stra…

To Sink or Not to Sink: Visual Information Pathways in Large Vision-Language Models
Jiayun Luo, Wan-Cyuan Fan, Lyuyang Wang, Xiangteng He, Tanzila Rahman, Purang Abolmaesumi, Leonid Sigal
https://arxiv.org/abs/2510.08510

To Sink or Not to Sink: Visual Information Pathways in Large Vision-Language Models
Large Vision Language Models (LVLMs) have recently emerged as powerful architectures capable of understanding and reasoning over both visual and textual information. These models typically rely on two key components: a Vision Transformer (ViT) and a Large Language Model (LLM). ViT encodes visual content into a sequence of image tokens and serves as the perceptual front-end -- the eyes of the model. In contrast, the LLM interprets these tokens to perform high-level reasoning, generates responses…

CancerGUIDE: Cancer Guideline Understanding via Internal Disagreement Estimation
Alyssa Unell, Noel C. F. Codella, Sam Preston, Peniel Argaw, Wen-wai Yim, Zelalem Gero, Cliff Wong, Rajesh Jena, Eric Horvitz, Amanda K. Hall, Ruican Rachel Zhong, Jiachen Li, Shrey Jain, Mu Wei, Matthew Lungren, Hoifung Poon
https://arxiv.org/abs/2509.07325…

CancerGUIDE: Cancer Guideline Understanding via Internal Disagreement Estimation
The National Comprehensive Cancer Network (NCCN) provides evidence-based guidelines for cancer treatment. Translating complex patient presentations into guideline-compliant treatment recommendations is time-intensive, requires specialized expertise, and is prone to error. Advances in large language model (LLM) capabilities promise to reduce the time required to generate treatment recommendations and improve accuracy. We present an LLM agent-based approach to automatically generate guideline-con…

RustAssure: Differential Symbolic Testing for LLM-Transpiled C-to-Rust Code
Yubo Bai, Tapti Palit
https://arxiv.org/abs/2510.07604 https://arxiv.org/pdf/25…

RustAssure: Differential Symbolic Testing for LLM-Transpiled C-to-Rust Code
Rust is a memory-safe programming language that significantly improves software security. Existing codebases written in unsafe memory languages, such as C, must first be transpiled to Rust to take advantage of Rust's improved safety guarantees. RustAssure presents a system that uses Large Language Models (LLMs) to automatically transpile existing C codebases to Rust. RustAssure uses prompt engineering techniques to maximize the chances of the LLM generating idiomatic and safe Rust code. Moreove…

The Limits of Goal-Setting Theory in LLM-Driven Assessment
Mrityunjay Kumar
https://arxiv.org/abs/2510.06997 https://arxiv.org/pdf/2510.06997

The Limits of Goal-Setting Theory in LLM-Driven Assessment
Many users interact with AI tools like ChatGPT using a mental model that treats the system as human-like, which we call Model H. According to goal-setting theory, increased specificity in goals should reduce performance variance. If Model H holds, then prompting a chatbot with more detailed instructions should lead to more consistent evaluation behavior. This paper tests that assumption through a controlled experiment in which ChatGPT evaluated 29 student submissions using four prompts with i…

A Multimodal GUI Architecture for Interfacing with LLM-Based Conversational Assistants
Hans G. W. van Dam
https://arxiv.org/abs/2510.06223 https://arxiv.or…

A Multimodal GUI Architecture for Interfacing with LLM-Based Conversational Assistants
Advances in large language models (LLMs) and real-time speech recognition now make it possible to issue any graphical user interface (GUI) action through natural language and receive the corresponding system response directly through the GUI. Most production applications were never designed with speech in mind. This article provides a concrete architecture that enables GUIs to interface with LLM-based speech-enabled assistants. The architecture makes an application's navigation graph and sema…

Agent-in-the-Loop: A Data Flywheel for Continuous Improvement in LLM-based Customer Support
Cen (Mia), Zhao (Wayne), Tiantian Zhang (Wayne), Hanchen Su (Wayne), Yufeng (Wayne), Zhang (Elaine), Shaowei Su (Elaine), Mingzhi Xu (Elaine), Yu (Elaine), Liu, Wei Han, Jeremy Werner, Claire Na Cheng, Yashar Mehdad
https://arxiv.org/abs/2510.0…

Agent-in-the-Loop: A Data Flywheel for Continuous Improvement in LLM-based Customer Support
We introduce an Agent-in-the-Loop (AITL) framework that implements a continuous data flywheel for iteratively improving an LLM-based customer support system. Unlike standard offline approaches that rely on batch annotations, AITL integrates four key types of annotations directly into live customer operations: (1) pairwise response preferences, (2) agent adoption and rationales, (3) knowledge relevance checks, and (4) identification of missing knowledge. These feedback signals seamlessly feed ba…

KLIPA: A Knowledge Graph and LLM-Driven QA Framework for IP Analysis
Guanzhi Deng, Yi Xie, Yu-Keung Ng, Mingyang Liu, Peijun Zheng, Jie Liu, Dapeng Wu, Yinqiao Li, Linqi Song
https://arxiv.org/abs/2509.07860

KLIPA: A Knowledge Graph and LLM-Driven QA Framework for IP Analysis
Effectively managing intellectual property is a significant challenge. Traditional methods for patent analysis depend on labor-intensive manual searches and rigid keyword matching. These approaches are often inefficient and struggle to reveal the complex relationships hidden within large patent datasets, hindering strategic decision-making. To overcome these limitations, we introduce KLIPA, a novel framework that leverages a knowledge graph and a large language model (LLM) to significantly adva…

@… I’m playing catch-up on your weekly updates (you no longer shows in my feed or alerts even though I follow you, which is hella annoying and I still have to debug) and think you were messing with me by generating the abstract of my Atlass post with an LLM.
If so, well done and I’m sorry I’m just seeing it.
If not, then I guess I need bet…

Reading Between the Lines: Towards Reliable Black-box LLM Fingerprinting via Zeroth-order Gradient Estimation
Shuo Shao, Yiming Li, Hongwei Yao, Yifei Chen, Yuchen Yang, Zhan Qin
https://arxiv.org/abs/2510.06605

Reading Between the Lines: Towards Reliable Black-box LLM Fingerprinting via Zeroth-order Gradient Estimation
The substantial investment required to develop Large Language Models (LLMs) makes them valuable intellectual property, raising significant concerns about copyright protection. LLM fingerprinting has emerged as a key technique to address this, which aims to verify a model's origin by extracting an intrinsic, unique signature (a "fingerprint") and comparing it to that of a source model to identify illicit copies. However, existing black-box fingerprinting methods often fail to generate distinctiv…

SemiAnalysis launches InferenceMAX, an open-source benchmark that automatically tracks LLM inference performance across AI models and frameworks every night (Kimbo Chen/SemiAnalysis)
https://newsletter.semianalysis.com/p/inferencemax-open-source-inference

InferenceMAX™: Open Source Inference Benchmarking
NVIDIA GB200 NVL72, AMD MI355X, Throughput Token per GPU, Latency Tok/s/user, Perf per Dollar, Tokens per Provisioned Megawatt, DeepSeek R1 670B, GPTOSS 120B, Llama3 70B

BLAZER: Bootstrapping LLM-based Manipulation Agents with Zero-Shot Data Generation
Rocktim Jyoti Das, Harsh Singh, Diana Turmakhan, Muhammad Abdullah Sohail, Mingfei Han, Preslav Nakov, Fabio Pizzati, Ivan Laptev
https://arxiv.org/abs/2510.08572

BLAZER: Bootstrapping LLM-based Manipulation Agents with Zero-Shot Data Generation
Scaling data and models has played a pivotal role in the remarkable progress of computer vision and language. Inspired by these domains, recent efforts in robotics have similarly focused on scaling both data and model size to develop more generalizable and robust policies. However, unlike vision and language, robotics lacks access to internet-scale demonstrations across diverse robotic tasks and environments. As a result, the scale of existing datasets typically suffers from the need for manual…

Revisiting the Uniform Information Density Hypothesis in LLM Reasoning Traces
Minju Gwak, Guijin Son, Jaehyung Kim
https://arxiv.org/abs/2510.06953 https://

Revisiting the Uniform Information Density Hypothesis in LLM Reasoning Traces
The Uniform Information Density (UID) hypothesis suggests that effective communication maintains a stable flow of information. In this work, we revisit this principle in the context of large language model (LLM) reasoning traces, asking whether step-level uniformity reflects reasoning quality. To this end, we propose an entropy-based stepwise information density metric and introduce two complementary measures of uniformity, local and global uniformity scores. Across the experiments on six diffe…

Foundations of LLM Knowledge Materialization: Termination, Reproducibility, Robustness
Luca Giordano, Simon Razniewski
https://arxiv.org/abs/2510.06780 https://

Foundations of LLM Knowledge Materialization: Termination, Reproducibility, Robustness
Large Language Models (LLMs) encode substantial factual knowledge, yet measuring and systematizing this knowledge remains challenging. Converting it into structured format, for example through recursive extraction approaches such as the GPTKB methodology (Hu et al., 2025b), is still underexplored. Key open questions include whether such extraction can terminate, whether its outputs are reproducible, and how robust they are to variations. We systematically study LLM knowledge materialization usi…

Communication Enables Cooperation in LLM Agents: A Comparison with Curriculum-Based Approaches
Hachem Madmoun, Salem Lahlou
https://arxiv.org/abs/2510.05748 https://

Communication Enables Cooperation in LLM Agents: A Comparison with Curriculum-Based Approaches
Eliciting cooperation in multi-agent LLM systems is critical for AI alignment. We investigate two approaches: direct communication and curriculum learning. In a 4-player Stag Hunt, a one-word "cheap talk" channel increases cooperation from 0% to 48.3%, demonstrating communication as a robust coordination mechanism. In contrast, we find that curriculum learning is highly sensitive to design choices: our pedagogical curriculum through progressively complex games reduced agent payoffs by 27.4% in …

Mining the Mind: What 100M Beliefs Reveal About Frontier LLM Knowledge
Shrestha Ghosh, Luca Giordano, Yujia Hu, Tuan-Phong Nguyen, Simon Razniewski
https://arxiv.org/abs/2510.07024

Mining the Mind: What 100M Beliefs Reveal About Frontier LLM Knowledge
LLMs are remarkable artifacts that have revolutionized a range of NLP and AI tasks. A significant contributor is their factual knowledge, which, to date, remains poorly understood, and is usually analyzed from biased samples. In this paper, we take a deep tour into the factual knowledge (or beliefs) of a frontier LLM, based on GPTKB v1.5 (Hu et al., 2025a), a recursively elicited set of 100 million beliefs of one of the strongest currently available frontier LLMs, GPT-4.1. We find that the mode…

AISysRev -- LLM-based Tool for Title-abstract Screening
Aleksi Huotala, Miikka Kuutila, Olli-Pekka Turtio, Mika M\"antyl\"a
https://arxiv.org/abs/2510.06708 https://…

AISysRev - LLM-based Tool for Title-abstract Screening
Systematic reviews are a standard practice for summarizing the state of evidence in software engineering. Conducting systematic reviews is laborious, especially during the screening or study selection phase, where the number of papers can be overwhelming. During this phase, papers are assessed against inclusion and exclusion criteria based on their titles and abstracts. Recent research has demonstrated that large language models (LLMs) can perform title-abstract screening at a level comparable …

A study finds that as few as 250 malicious documents can produce a "backdoor" vulnerability in an LLM, regardless of model size or training data volume (Anthropic)
https://www.anthropic.com/research/small-samples-poison

A small number of samples can poison LLMs of any size
Anthropic research on data-poisoning attacks in large language models

Getting In Contract with Large Language Models -- An Agency Theory Perspective On Large Language Model Alignment
Sascha Kaltenpoth, Oliver M\"uller
https://arxiv.org/abs/2509.07642

Getting In Contract with Large Language Models -- An Agency Theory Perspective On Large Language Model Alignment
Adopting Large language models (LLMs) in organizations potentially revolutionizes our lives and work. However, they can generate off-topic, discriminating, or harmful content. This AI alignment problem often stems from misspecifications during the LLM adoption, unnoticed by the principal due to the LLM's black-box nature. While various research disciplines investigated AI alignment, they neither address the information asymmetries between organizational adopters and black-box LLM agents nor con…

"Sometimes You Need Facts, and Sometimes a Hug": Understanding Older Adults' Preferences for Explanations in LLM-Based Conversational AI Systems
Niharika Mathur, Tamara Zubatiy, Agata Rozga, Jodi Forlizzi, Elizabeth Mynatt
https://arxiv.org/abs/2510.06697

"Sometimes You Need Facts, and Sometimes a Hug": Understanding Older Adults' Preferences for Explanations in LLM-Based Conversational AI Systems
Designing Conversational AI systems to support older adults requires these systems to explain their behavior in ways that align with older adults' preferences and context. While prior work has emphasized the importance of AI explainability in building user trust, relatively little is known about older adults' requirements and perceptions of AI-generated explanations. To address this gap, we conducted an exploratory Speed Dating study with 23 older adults to understand their responses to context…

LLM-Assisted Web Measurements
Simone Bozzolan, Stefano Calzavara, Lorenzo Cazzaro
https://arxiv.org/abs/2510.08101 https://arxiv.org/pdf/2510.08101

LLM-Assisted Web Measurements
Web measurements are a well-established methodology for assessing the security and privacy landscape of the Internet. However, existing top lists of popular websites commonly used as measurement targets are unlabeled and lack semantic information about the nature of the sites they include. This limitation makes targeted measurements challenging, as researchers often need to rely on ad-hoc techniques to bias their datasets toward specific categories of interest. In this paper, we investigate the…

Stratified GRPO: Handling Structural Heterogeneity in Reinforcement Learning of LLM Search Agents
Mingkang Zhu, Xi Chen, Bei Yu, Hengshuang Zhao, Jiaya Jia
https://arxiv.org/abs/2510.06214

Stratified GRPO: Handling Structural Heterogeneity in Reinforcement Learning of LLM Search Agents
Large language model (LLM) agents increasingly rely on external tools such as search engines to solve complex, multi-step problems, and reinforcement learning (RL) has become a key paradigm for training them. However, the trajectories of search agents are structurally heterogeneous, where variations in the number, placement, and outcomes of search calls lead to fundamentally different answer directions and reward distributions. Standard policy gradient methods, which use a single global baselin…

A Survey of Long-Document Retrieval in the PLM and LLM Era
Minghan Li, Miyang Luo, Tianrui Lv, Yishuai Zhang, Siqi Zhao, Ercong Nie, Guodong Zhou
https://arxiv.org/abs/2509.07759

A Survey of Long-Document Retrieval in the PLM and LLM Era
The proliferation of long-form documents presents a fundamental challenge to information retrieval (IR), as their length, dispersed evidence, and complex structures demand specialized methods beyond standard passage-level techniques. This survey provides the first comprehensive treatment of long-document retrieval (LDR), consolidating methods, challenges, and applications across three major eras. We systematize the evolution from classical lexical and early neural models to modern pre-trained (…

What Were You Thinking? An LLM-Driven Large-Scale Study of Refactoring Motivations in Open-Source Projects
Mikel Robredo, Matteo Esposito, Fabio Palomba, Rafael Pe\~naloza, Valentina Lenarduzzi
https://arxiv.org/abs/2509.07763

What Were You Thinking? An LLM-Driven Large-Scale Study of Refactoring Motivations in Open-Source Projects
Context. Code refactoring improves software quality without changing external behavior. Despite its advantages, its benefits are hindered by the considerable cost of time, resources, and continuous effort it demands. Aim. Understanding why developers refactor, and which metrics capture these motivations, may support wider and more effective use of refactoring in practice. Method. We performed a large-scale empirical study to analyze developers refactoring activity, leveraging Large Language Mod…

Menlo Ventures: business spending on generative AI hit $37B in 2025, up from $11.5B in 2024; Anthropic's share of enterprise LLM spend grew from 24% to 40% YoY (Menlo Ventures)
https://menlovc.com/perspective/2025-the-state-of-generative-ai-in-the-enter…

2025: The State of Generative AI in the Enterprise | Menlo Ventures
For all the fears of over-investment, AI is spreading across enterprises at a pace with no precedent in modern software history.

LLM-Assisted Modeling of Semantic Web-Enabled Multi-Agents Systems with AJAN
Hacane Hechehouche, Andre Antakli, Matthias Klusch
https://arxiv.org/abs/2510.06911 https://

LLM-Assisted Modeling of Semantic Web-Enabled Multi-Agents Systems with AJAN
There are many established semantic Web standards for implementing multi-agent driven applications. The AJAN framework allows to engineer multi-agent systems based on these standards. In particular, agent knowledge is represented in RDF/RDFS and OWL, while agent behavior models are defined with Behavior Trees and SPARQL to access and manipulate this knowledge. However, the appropriate definition of RDF/RDFS and SPARQL-based agent behaviors still remains a major hurdle not only for agent modeler…

Paladin: Defending LLM-enabled Phishing Emails with a New Trigger-Tag Paradigm
Yan Pang, Wenlong Meng, Xiaojing Liao, Tianhao Wang
https://arxiv.org/abs/2509.07287 https://

Paladin: Defending LLM-enabled Phishing Emails with a New Trigger-Tag Paradigm
With the rapid development of large language models, the potential threat of their malicious use, particularly in generating phishing content, is becoming increasingly prevalent. Leveraging the capabilities of LLMs, malicious users can synthesize phishing emails that are free from spelling mistakes and other easily detectable features. Furthermore, such models can generate topic-specific phishing messages, tailoring content to the target domain and increasing the likelihood of success. Detect…

LLM Analysis of 150 years of German Parliamentary Debates on Migration Reveals Shift from Post-War Solidarity to Anti-Solidarity in the Last Decade
Aida Kostikova, Ole P\"utz, Steffen Eger, Olga Sabelfeld, Benjamin Paassen
https://arxiv.org/abs/2509.07274

LLM Analysis of 150+ years of German Parliamentary Debates on Migration Reveals Shift from Post-War Solidarity to Anti-Solidarity in the Last Decade
Migration has been a core topic in German political debate, from millions of expellees post World War II over labor migration to refugee movements in the recent past. Studying political speech regarding such wide-ranging phenomena in depth traditionally required extensive manual annotations, limiting the scope of analysis to small subsets of the data. Large language models (LLMs) have the potential to partially automate even complex annotation tasks. We provide an extensive evaluation of a mult…

Can We Hide Machines in the Crowd? Quantifying Equivalence in LLM-in-the-loop Annotation Tasks
Jiaman He, Zikang Leng, Dana McKay, Damiano Spina, Johanne R. Trippas
https://arxiv.org/abs/2510.06658

Can We Hide Machines in the Crowd? Quantifying Equivalence in LLM-in-the-loop Annotation Tasks
Many evaluations of large language models (LLMs) in text annotation focus primarily on the correctness of the output, typically comparing model-generated labels to human-annotated ``ground truth'' using standard performance metrics. In contrast, our study moves beyond effectiveness alone. We aim to explore how labeling decisions -- by both humans and LLMs -- can be statistically evaluated across individuals. Rather than treating LLMs purely as annotation systems, we approach LLMs as an alternat…

PatchSeeker: Mapping NVD Records to their Vulnerability-fixing Commits with LLM Generated Commits and Embeddings
Huu Hung Nguyen, Anh Tuan Nguyen, Thanh Le-Cong, Yikun Li, Han Wei Ang, Yide Yin, Frank Liauw, Shar Lwin Khin, Ouh Eng Lieh, Ting Zhang, David Lo
https://arxiv.org/abs/2509.07540

PatchSeeker: Mapping NVD Records to their Vulnerability-fixing Commits with LLM Generated Commits and Embeddings
Software vulnerabilities pose serious risks to modern software ecosystems. While the National Vulnerability Database (NVD) is the authoritative source for cataloging these vulnerabilities, it often lacks explicit links to the corresponding Vulnerability-Fixing Commits (VFCs). VFCs encode precise code changes, enabling vulnerability localization, patch analysis, and dataset construction. Automatically mapping NVD records to their true VFCs is therefore critical. Existing approaches have limitati…

Neurocognitive Modeling for Text Generation: Deep Learning Architecture for EEG Data
Khushiyant
https://arxiv.org/abs/2509.07202 https://arxiv.org/pdf/2509…

Neurocognitive Modeling for Text Generation: Deep Learning Architecture for EEG Data
Text generating capabilities have undergone a substantial transformation with the introduction of large language models (LLMs). Electroencephalography (EEG)-based text production is still difficult, though, because it requires a lot of data and processing power. This paper introduces a new method that combines the use of the Gemma 2B LLM with a classifier-LLM architecture to incorporate a Recurrent Neural Network (RNN) encoder. Our approach drastically lowers the amount of data and compute powe…

Starcloud, which launched a satellite with a Nvidia H100 chip in November, says the satellite is running and querying responses from Google's Gemma (Pia Singh/CNBC)
https://www.cnbc.com/2025/12/10/nvidia-backed-starcloud-tr…

‘Greetings, earthlings’: Nvidia-backed Starcloud trains first AI model in space as orbital data center race heats up
The company's Starcloud-1 satellite is running Gemma, an open model from Google, marking the first time in history that an LLM has been trained in outer space.

AutoQual: An LLM Agent for Automated Discovery of Interpretable Features for Review Quality Assessment
Xiaochong Lan, Jie Feng, Yinxing Liu, Xinlei Shi, Yong Li
https://arxiv.org/abs/2510.08081

AutoQual: An LLM Agent for Automated Discovery of Interpretable Features for Review Quality Assessment
Ranking online reviews by their intrinsic quality is a critical task for e-commerce platforms and information services, impacting user experience and business outcomes. However, quality is a domain-dependent and dynamic concept, making its assessment a formidable challenge. Traditional methods relying on hand-crafted features are unscalable across domains and fail to adapt to evolving content patterns, while modern deep learning approaches often produce black-box models that lack interpretabili…

From Description to Detection: LLM based Extendable O-RAN Compliant Blind DoS Detection in 5G and Beyond
Thusitha Dayaratne, Ngoc Duy Pham, Viet Vo, Shangqi Lai, Sharif Abuadbba, Hajime Suzuki, Xingliang Yuan, Carsten Rudolph
https://arxiv.org/abs/2510.06530

From Description to Detection: LLM based Extendable O-RAN Compliant Blind DoS Detection in 5G and Beyond
The quality and experience of mobile communication have significantly improved with the introduction of 5G, and these improvements are expected to continue beyond the 5G era. However, vulnerabilities in control-plane protocols, such as Radio Resource Control (RRC) and Non-Access Stratum (NAS), pose significant security threats, such as Blind Denial of Service (DoS) attacks. Despite the availability of existing anomaly detection methods that leverage rule-based systems or traditional machine lea…

Adaptive LLM-Symbolic Reasoning via Dynamic Logical Solver Composition
Lei Xu, Pierre Beckmann, Marco Valentino, Andr\'e Freitas
https://arxiv.org/abs/2510.06774 https://

Adaptive LLM-Symbolic Reasoning via Dynamic Logical Solver Composition
Neuro-symbolic NLP methods aim to leverage the complementary strengths of large language models and formal logical solvers. However, current approaches are mostly static in nature, i.e., the integration of a target solver is predetermined at design time, hindering the ability to employ diverse formal inference strategies. To address this, we introduce an adaptive, multi-paradigm, neuro-symbolic inference framework that: (1) automatically identifies formal reasoning strategies from problems expr…

CaRT: Teaching LLM Agents to Know When They Know Enough
Grace Liu, Yuxiao Qu, Jeff Schneider, Aarti Singh, Aviral Kumar
https://arxiv.org/abs/2510.08517 https://

CaRT: Teaching LLM Agents to Know When They Know Enough
Many tasks require learned models to strategically gather relevant information over multiple rounds of interaction before actually acting on a task. Strategic information gathering requires models to know not only how to effectively acquire information, but also when to stop gathering information and make a decision, in order to avoid overthinking or getting derailed when acting. In this paper, we formalize this problem and introduce Counterfactuals and Reasoning for Termination (CaRT), an appr…

LLMs in Wikipedia: Investigating How LLMs Impact Participation in Knowledge Communities
Moyan Zhou, Soobin Cho, Loren Terveen
https://arxiv.org/abs/2509.07819 https://

LLMs in Wikipedia: Investigating How LLMs Impact Participation in Knowledge Communities
Large language models (LLMs) are reshaping knowledge production as community members increasingly incorporate them into their contribution workflows. However, participating in knowledge communities involves more than just contributing content - it is also a deeply social process. While communities must carefully consider appropriate and responsible LLM integration, the absence of concrete norms has left individual editors to experiment and navigate LLM use on their own. Understanding how LLMs i…

Evaluating LLM-Generated Legal Explanations for Regulatory Compliance in Social Media Influencer Marketing
Haoyang Gui, Thales Bertaglia, Taylor Annabell, Catalina Goanta, Tjomme Dooper, Gerasimos Spanakis
https://arxiv.org/abs/2510.08111

Evaluating LLM-Generated Legal Explanations for Regulatory Compliance in Social Media Influencer Marketing
The rise of influencer marketing has blurred boundaries between organic content and sponsored content, making the enforcement of legal rules relating to transparency challenging. Effective regulation requires applying legal knowledge with a clear purpose and reason, yet current detection methods of undisclosed sponsored content generally lack legal grounding or operate as opaque "black boxes". Using 1,143 Instagram posts, we compare gpt-5-nano and gemini-2.5-flash-lite under three prompting str…

All You Need Is A Fuzzing Brain: An LLM-Powered System for Automated Vulnerability Detection and Patching
Ze Sheng, Qingxiao Xu, Jianwei Huang, Matthew Woodcock, Heqing Huang, Alastair F. Donaldson, Guofei Gu, Jeff Huang
https://arxiv.org/abs/2509.07225

All You Need Is A Fuzzing Brain: An LLM-Powered System for Automated Vulnerability Detection and Patching
Our team, All You Need Is A Fuzzing Brain, was one of seven finalists in DARPA's Artificial Intelligence Cyber Challenge (AIxCC), placing fourth in the final round. During the competition, we developed a Cyber Reasoning System (CRS) that autonomously discovered 28 security vulnerabilities - including six previously unknown zero-days - in real-world open-source C and Java projects, and successfully patched 14 of them. The complete CRS is open source at https://github.com/o2lab/afc-crs-all-you-ne…

NewtonBench: Benchmarking Generalizable Scientific Law Discovery in LLM Agents
Tianshi Zheng, Kelvin Kiu-Wai Tam, Newt Hue-Nam K. Nguyen, Baixuan Xu, Zhaowei Wang, Jiayang Cheng, Hong Ting Tsang, Weiqi Wang, Jiaxin Bai, Tianqing Fang, Yangqiu Song, Ginny Y. Wong, Simon See
https://arxiv.org/abs/2510.07172

NewtonBench: Benchmarking Generalizable Scientific Law Discovery in LLM Agents
Large language models are emerging as powerful tools for scientific law discovery, a foundational challenge in AI-driven science. However, existing benchmarks for this task suffer from a fundamental methodological trilemma, forcing a trade-off between scientific relevance, scalability, and resistance to memorization. Furthermore, they oversimplify discovery as static function fitting, failing to capture the authentic scientific process of uncovering embedded laws through the interactive explora…

Factuality Beyond Coherence: Evaluating LLM Watermarking Methods for Medical Texts
Rochana Prih Hastuti, Rian Adam Rajagede, Mansour Al Ghanim, Mengxin Zheng, Qian Lou
https://arxiv.org/abs/2509.07755 …

Factuality Beyond Coherence: Evaluating LLM Watermarking Methods for Medical Texts
As large language models (LLMs) adapted to sensitive domains such as medicine, their fluency raises safety risks, particularly regarding provenance and accountability. Watermarking embeds detectable patterns to mitigate these risks, yet its reliability in medical contexts remains untested. Existing benchmarks focus on detection-quality tradeoffs, overlooking factual risks under low-entropy settings often exploited by watermarking's reweighting strategy. We propose a medical-focused evaluation w…

From Defender to Devil? Unintended Risk Interactions Induced by LLM Defenses
Xiangtao Meng, Tianshuo Cong, Li Wang, Wenyu Chen, Zheng Li, Shanqing Guo, Xiaoyun Wang
https://arxiv.org/abs/2510.07968

From Defender to Devil? Unintended Risk Interactions Induced by LLM Defenses
Large Language Models (LLMs) have shown remarkable performance across various applications, but their deployment in sensitive domains raises significant concerns. To mitigate these risks, numerous defense strategies have been proposed. However, most existing studies assess these defenses in isolation, overlooking their broader impacts across other risk dimensions. In this work, we take the first step in investigating unintended interactions caused by defenses in LLMs, focusing on the complex in…

ALLabel: Three-stage Active Learning for LLM-based Entity Recognition using Demonstration Retrieval
Zihan Chen, Lei Shi, Weize Wu, Qiji Zhou, Yue Zhang
https://arxiv.org/abs/2509.07512

ALLabel: Three-stage Active Learning for LLM-based Entity Recognition using Demonstration Retrieval
Many contemporary data-driven research efforts in the natural sciences, such as chemistry and materials science, require large-scale, high-performance entity recognition from scientific datasets. Large language models (LLMs) have increasingly been adopted to solve the entity recognition task, with the same trend being observed on all-spectrum NLP tasks. The prevailing entity recognition LLMs rely on fine-tuned technology, yet the fine-tuning process often incurs significant cost. To achieve a b…

Auto-Stega: An Agent-Driven System for Lifelong Strategy Evolution in LLM-Based Text Steganography
Jiuan Zhou, Yu Cheng, Yuan Xie, Zhaoxia Yin
https://arxiv.org/abs/2510.06565 h…

Auto-Stega: An Agent-Driven System for Lifelong Strategy Evolution in LLM-Based Text Steganography
With the rapid progress of LLMs, high quality generative text has become widely available as a cover for text steganography. However, prevailing methods rely on hand-crafted or pre-specified strategies and struggle to balance efficiency, imperceptibility, and security, particularly at high embedding rates. Accordingly, we propose Auto-Stega, an agent-driven self-evolving framework that is the first to realize self-evolving steganographic strategies by automatically discovering, composing, and a…

Bloom: Designing for LLM-Augmented Behavior Change Interactions
Matthew J\"orke, Defne Gen\c{c}, Valentin Teutschbein, Shardul Sapkota, Sarah Chung, Paul Schmiedmayer, Maria Ines Campero, Abby C. King, Emma Brunskill, James A. Landay
https://arxiv.org/abs/2510.05449

Bloom: Designing for LLM-Augmented Behavior Change Interactions
Large language models (LLMs) offer novel opportunities to support health behavior change, yet existing work has narrowly focused on text-only interactions. Building on decades of HCI research demonstrating the effectiveness of UI-based interactions, we present Bloom, an application for physical activity promotion that integrates an LLM-based health coaching chatbot with established UI-based interactions. As part of Bloom's development, we conducted a redteaming evaluation and contribute a safet…

WebDART: Dynamic Decomposition and Re-planning for Complex Web Tasks
Jingbo Yang, Bairu Hou, Wei Wei, Shiyu Chang, Yujia Bao
https://arxiv.org/abs/2510.06587 https://

WebDART: Dynamic Decomposition and Re-planning for Complex Web Tasks
Large language model (LLM) agents are becoming competent at straightforward web tasks, such as opening an item page or submitting a form, but still struggle with objectives that require long horizon navigation, large scale information extraction, and reasoning under constraints. We present WebDART, a general framework that enables a single LLM to handle such complex chores. WebDART (i) dynamically decomposes each objective into three focused subtasks: navigation, information extraction, and exe…

PersonaFuse: A Personality Activation-Driven Framework for Enhancing Human-LLM Interactions
Yixuan Tang, Yi Yang, Ahmed Abbasi
https://arxiv.org/abs/2509.07370 https://

PersonaFuse: A Personality Activation-Driven Framework for Enhancing Human-LLM Interactions
Recent advancements in Large Language Models (LLMs) demonstrate remarkable capabilities across various fields. These developments have led to more direct communication between humans and LLMs in various situations, such as social companionship and psychological support. However, LLMs often exhibit limitations in emotional perception and social competence during real-world conversations. These limitations partly originate from their inability to adapt their communication style and emotional expr…

Towards Reliable and Practical LLM Security Evaluations via Bayesian Modelling
Mary Llewellyn, Annie Gray, Josh Collyer, Michael Harries
https://arxiv.org/abs/2510.05709 https:/…

Towards Reliable and Practical LLM Security Evaluations via Bayesian Modelling
Before adopting a new large language model (LLM) architecture, it is critical to understand vulnerabilities accurately. Existing evaluations can be difficult to trust, often drawing conclusions from LLMs that are not meaningfully comparable, relying on heuristic inputs or employing metrics that fail to capture the inherent uncertainty. In this paper, we propose a principled and practical end-to-end framework for evaluating LLM vulnerabilities to prompt injection attacks. First, we propose pract…

L2M-AID: Autonomous Cyber-Physical Defense by Fusing Semantic Reasoning of Large Language Models with Multi-Agent Reinforcement Learning (Preprint)
Tianxiang Xu, Zhichao Wen, Xinyu Zhao, Jun Wang, Yan Li, Chang Liu
https://arxiv.org/abs/2510.07363

L2M-AID: Autonomous Cyber-Physical Defense by Fusing Semantic Reasoning of Large Language Models with Multi-Agent Reinforcement Learning (Preprint)
The increasing integration of Industrial IoT (IIoT) exposes critical cyber-physical systems to sophisticated, multi-stage attacks that elude traditional defenses lacking contextual awareness. This paper introduces L2M-AID, a novel framework for Autonomous Industrial Defense using LLM-empowered, Multi-agent reinforcement learning. L2M-AID orchestrates a team of collaborative agents, each driven by a Large Language Model (LLM), to achieve adaptive and resilient security. The core innovation lies …

Are Humans as Brittle as Large Language Models?
Jiahui Li, Sean Papay, Roman Klinger
https://arxiv.org/abs/2509.07869 https://arxiv.org/pdf/2509.07869

Are Humans as Brittle as Large Language Models?
The output of large language models (LLM) is unstable, due to both non-determinism of the decoding process as well as to prompt brittleness. While the intrinsic non-determinism of LLM generation may mimic existing uncertainty in human annotations through distributional shifts in outputs, it is largely assumed, yet unexplored, that the prompt brittleness effect is unique to LLMs. This raises the question: do human annotators show similar sensitivity to instruction changes? If so, should prompt b…

LongRM: Revealing and Unlocking the Context Boundary of Reward Modeling
Zecheng Tang, Baibei Ji, Quantong Qiu, Haitian Wang, Xiaobo Liang, Juntao Li, Min Zhang
https://arxiv.org/abs/2510.06915

LongRM: Revealing and Unlocking the Context Boundary of Reward Modeling
Reward model (RM) plays a pivotal role in aligning large language model (LLM) with human preferences. As real-world applications increasingly involve long history trajectories, e.g., LLM agent, it becomes indispensable to evaluate whether a model's responses are not only high-quality but also grounded in and consistent with the provided context. Yet, current RMs remain confined to short-context settings and primarily focus on response-level attributes (e.g., safety or helpfulness), while largel…

AgentSentinel: An End-to-End and Real-Time Security Defense Framework for Computer-Use Agents
Haitao Hu, Peng Chen, Yanpeng Zhao, Yuqi Chen
https://arxiv.org/abs/2509.07764 http…

AgentSentinel: An End-to-End and Real-Time Security Defense Framework for Computer-Use Agents
Large Language Models (LLMs) have been increasingly integrated into computer-use agents, which can autonomously operate tools on a user's computer to accomplish complex tasks. However, due to the inherently unstable and unpredictable nature of LLM outputs, they may issue unintended tool commands or incorrect inputs, leading to potentially harmful operations. Unlike traditional security risks stemming from insecure user prompts, tool execution results from LLM-driven decisions introduce new and …

Mitigating Judgment Preference Bias in Large Language Models through Group-Based Polling
Shuliang Liu, Zhipeng Xu, Zhenghao Liu, Yukun Yan, Minghe Yu, Yu Gu, Chong Chen, Huiyuan Xie, Ge Yu
https://arxiv.org/abs/2510.08145

Mitigating Judgment Preference Bias in Large Language Models through Group-Based Polling
Large Language Models (LLMs) as automatic evaluators, commonly referred to as LLM-as-a-Judge, have also attracted growing attention. This approach plays a vital role in aligning LLMs with human judgments, providing accurate and reliable assessments. However, LLM-based judgment models often exhibit judgment preference bias during the evaluation phase, tending to favor responses generated by themselves, undermining the reliability of their judgments. This paper introduces the Group-Based Polling …

Syn-Diag: An LLM-based Synergistic Framework for Generalizable Few-shot Fault Diagnosis on the Edge
Zijun Jia, Shuang Liang, Jinsong Yu
https://arxiv.org/abs/2510.05733 https://…

Syn-Diag: An LLM-based Synergistic Framework for Generalizable Few-shot Fault Diagnosis on the Edge
Industrial fault diagnosis faces the dual challenges of data scarcity and the difficulty of deploying large AI models in resource-constrained environments. This paper introduces Syn-Diag, a novel cloud-edge synergistic framework that leverages Large Language Models to overcome these limitations in few-shot fault diagnosis. Syn-Diag is built on a three-tiered mechanism: 1) Visual-Semantic Synergy, which aligns signal features with the LLM's semantic space through cross-modal pre-training; 2) Con…

DischargeSim: A Simulation Benchmark for Educational Doctor-Patient Communication at Discharge
Zonghai Yao, Michael Sun, Won Seok Jang, Sunjae Kwon, Soie Kwon, Hong Yu
https://arxiv.org/abs/2509.07188 …

DischargeSim: A Simulation Benchmark for Educational Doctor-Patient Communication at Discharge
Discharge communication is a critical yet underexplored component of patient care, where the goal shifts from diagnosis to education. While recent large language model (LLM) benchmarks emphasize in-visit diagnostic reasoning, they fail to evaluate models' ability to support patients after the visit. We introduce DischargeSim, a novel benchmark that evaluates LLMs on their ability to act as personalized discharge educators. DischargeSim simulates post-visit, multi-turn conversations between LLM-…

Constraint-Aware Route Recommendation from Natural Language via Hierarchical LLM Agents
Tao Zhe, Rui Liu, Fateme Memar, Xiao Luo, Wei Fan, Xinyue Ye, Zhongren Peng, Dongjie Wang
https://arxiv.org/abs/2510.06078

Constraint-Aware Route Recommendation from Natural Language via Hierarchical LLM Agents
Route recommendation aims to provide users with optimal travel plans that satisfy diverse and complex requirements. Classical routing algorithms (e.g., shortest-path and constraint-aware search) are efficient but assume structured inputs and fixed objectives, limiting adaptability to natural-language queries. Recent LLM-based approaches enhance flexibility but struggle with spatial reasoning and the joint modeling of route-level and POI-level preferences. To address these limitations, we propos…