Tootfinder

🇺🇦 #NowPlaying on KEXP's #VarietyMix
Anna von Hausswolff:
🎵 Struggle With the Beast
#AnnavonHausswolff
https://annavonhausswolff.bandcamp.com/track/struggle-with-the-beast-2
https://open.spotify.com/track/798vsh2RF4vx5UG0ez9QBn

Signs of Struggle: Spotting Cognitive Distortions across Language and Register
Abhishek Kuber, Enrico Liscio, Ruixuan Zhang, Caroline Figueroa, Pradeep K. Murukannaiah
https://arxiv.org/abs/2508.20771 …

Signs of Struggle: Spotting Cognitive Distortions across Language and Register
Rising mental health issues among youth have increased interest in automated approaches for detecting early signs of psychological distress in digital text. One key focus is the identification of cognitive distortions, irrational thought patterns that have a role in aggravating mental distress. Early detection of these distortions may enable timely, low-cost interventions. While prior work has focused on English clinical data, we present the first in-depth study of cross-lingual and cross-regis…

Packers-Cowboys: 'Sunday Night Football' ends in 40-40 tie as both defenses struggle https://www.nytimes.com/athletic/6671905/2025/09/29/packers-cowboys-snf-score-results/

Several US states have passed bills to ban or restrict AI mental health treatment, as experts say state laws lag behind the fast-moving AI therapy landscape (Devi Shastri/Associated Press)
https://apnews.com/article/ai-therapy-ban-illinois-therabo…

States struggle with AI therapy app rules amid mental health needs
As mental health chatbots driven by artificial intelligence proliferate, a small number of states are trying to regulate them. Laws in Illinois, Nevada and Utah are among the first in the nation to put limits on or ban therapy chatbots. But app creators, policymakers and mental health advocates say it is just a start and federal regulation would be ideal. App makers worry the patchwork of laws could stifle innovation needed due to a nationwide shortage of human therapists. They also note that m…

Lost in Transition: The Struggle of Women Returning to Software Engineering Research after Career Breaks
Shalini Chakraborty, Sebastian Baltes
https://arxiv.org/abs/2509.21533 h…

Lost in Transition: The Struggle of Women Returning to Software Engineering Research after Career Breaks
The IT industry provides supportive pathways such as returnship programs, coding boot camps, and buddy systems for women re-entering their job after a career break. Academia, however, offers limited opportunities to motivate women to return. We propose a diverse multicultural research project investigating the challenges faced by women with software engineering (SE) backgrounds re-entering academia or related research roles after a career break. Career disruptions due to pregnancy, immigration …

Portside : Can nonviolent struggle defeat a dictator? https://portside.org/2025-10-23/can-nonviolent-struggle-defeat-dictator-database-emphatically-says-yes

Can Nonviolent Struggle Defeat a Dictator? This Database Emphatically Says Yes
Can nonviolent struggle defeat a tyrant? There are many great resources that answer this question, but the one that’s been on my mind lately is the Global Nonviolent Action Database, or GNAD, built by the Peace Studies department at Swarthmore College. Freely accessible to the public, this database — which launched under my direction in 2011 — contains over 1,400 cases of nonviolent struggle from over a hundred countries, with more cases continually being added by student researchers.

Packers-Cowboys: 'Sunday Night Football' ends in 40-40 tie as both defenses struggle https://www.nytimes.com/athletic/6671905/2025/09/29/packers-cowboys-snf-score-results/

Established by the Black Guerrilla Family in San Quentin Prison in 1979,
Black August is an annual commemoration of the struggle for Black liberation
and a time to remember the freedom fighters who have passed
or who remain locked up in prison.
In 2025, as fascism rises in the US and around the globe,
what can the radical tradition of Black August teach us about keeping the fight for freedom alive in dark times?
In this on-the-ground edition of Rattling …

‘I believe in our people’s ability to find the light’: Celebrating Black August in dark times
“We don't want Black August to become like a new Black History Month, where [it’s] just about celebrations or parties. That's not the spirit of it. It's in resistance and how we can prepare ourselves to resist our own oppression.”

u aint know my pain u dont know my struggle

The financial AI bubble will burst, companies will go broke, investments will be lost, there will be a large consolidation phase, as with every wave of innovation. But there is not any signal that the actual use and application of GenAI will decline. We struggle with finding added value, using them in the right way, mitigating the risks, that is the main challenge.
#AI

I still have an SF YIMBY coffee mug from when I was a member, with the former slogan, "Legalize Housing." So much for that. Every SF YIMBY–endorsed member of the Board of Supervisors voted for this cruel RV ban with tacit YIMBY approval, criminalizing people's homes and banishing them from the city.
Can't speak for other #YIMBY movements around the world, but the SF version ha…

Tow trucks are coming for S.F.’s RVs. Their residents struggle for reprieve.
With San Francisco's RV parking ban starting on Nov. 1, dozens of RV dwellers say they were missed in a city-wide count of RVs parked on public streets.

New research has found that leading LLMs falter dramatically when exam questions are subtly reworded, raising doubts about whether these systems are truly capable of clinical reasoning.
https://www.computing.co.uk/news/2025/ai/t

Top AI models struggle with medical questions in real-world scenarios, study finds
AI systems that boast near-perfect scores on medical licensing exams may not be as clever as they seem.

Field hockey at the new Martha Dodson Field out past the East Hill Plaza -- Cornell Beat Vermont 3-1 in front of the largest crowd I've ever seen at a field hockey game. In the center are № 8 Ashley Plzak of the Big Red and № 15 Sophia Lefranc of the Catamounts, who won the US East Conference in 2024
#photo

FreqDebias: Towards Generalizable Deepfake Detection via Consistency-Driven Frequency Debiasing
Hossein Kashiani, Niloufar Alipour Talemi, Fatemeh Afghah
https://arxiv.org/abs/2509.22412

FreqDebias: Towards Generalizable Deepfake Detection via Consistency-Driven Frequency Debiasing
Deepfake detectors often struggle to generalize to novel forgery types due to biases learned from limited training data. In this paper, we identify a new type of model bias in the frequency domain, termed spectral bias, where detectors overly rely on specific frequency bands, restricting their ability to generalize across unseen forgeries. To address this, we propose FreqDebias, a frequency debiasing framework that mitigates spectral bias through two complementary strategies. First, we introduc…

ProRe: A Proactive Reward System for GUI Agents via Reasoner-Actor Collaboration
Gaole Dai, Shiqi Jiang, Ting Cao, Yuqing Yang, Yuanchun Li, Rui Tan, Mo Li, Lili Qiu
https://arxiv.org/abs/2509.21823

ProRe: A Proactive Reward System for GUI Agents via Reasoner-Actor Collaboration
Reward is critical to the evaluation and training of large language models (LLMs). However, existing rule-based or model-based reward methods struggle to generalize to GUI agents, where access to ground-truth trajectories or application databases is often unavailable, and static trajectory-based LLM-as-a-Judge approaches suffer from limited accuracy. To address these challenges, we propose ProRe, a proactive reward system that leverages a general-purpose reasoner and domain-specific evaluator a…

Thinking about #DiscoElysium succesors(?), including ones with heavy ethical concerns. A lot of them have gone for female protagonists: Good! There could have been a couple more leading women in DE. But the reveal trailers are mainly voiced by men... men berating or mocking the central character.
Longdue's Hopetown has a father figure voice over, berating the protagonist: "Who do you think you are? [...] You don't understand the forces at play" Infantalised, she doesn't get to respond.
ZA/UM's Zero Parades VO is also a man, initially flattering the spy protagonist "brilliant, relentless" ... then saying she came after him and "everything she touched, written into failure". Again, no chance for the female spy to tell her side of the story ... And there's an objectifying/threatening section about how her fair hair is like a dandelion. Tangeant: what's with the heavy/bad pseudo-German VO accent?
Do I bother mentioning DarkMath's XXX Nightshift? Yes, for once a woman's VO, but (as you'll have guessed from the name) a porn-like, male-gaze view of what a woman would say and think.
So, don't be fooled that a female protagonist means we're actually getting a woman's POV. It's just a cosmetic change. Dora Klindžić's https://bsky.app/profile/did:plc:rx3zlqzbjylvtyg6siikaouq experiences as a writer at ZA/UM show how women struggle to have a voice and stay employed in these types of studios. Many of the senior men she mentions are still at ZA/UM.

Nation's biggest law firms back off from challenging Trump policies (Washington Post)
https://www.washingtonpost.com/national-security/2025/10/26/smaller-law-firms-struggle-trump-administration-initiatives/
http://www.memeorandum.com/251026/p29#a251026p29

SafeSteer: Adaptive Subspace Steering for Efficient Jailbreak Defense in Vision-Language Models
Xiyu Zeng, Siyuan Liang, Liming Lu, Haotian Zhu, Enguang Liu, Jisheng Dang, Yongbin Zhou, Shuchao Pang
https://arxiv.org/abs/2509.21400

SafeSteer: Adaptive Subspace Steering for Efficient Jailbreak Defense in Vision-Language Models
As the capabilities of Vision Language Models (VLMs) continue to improve, they are increasingly targeted by jailbreak attacks. Existing defense methods face two major limitations: (1) they struggle to ensure safety without compromising the model's utility; and (2) many defense mechanisms significantly reduce the model's inference efficiency. To address these challenges, we propose SafeSteer, a lightweight, inference-time steering framework that effectively defends against diverse jailbreak atta…

A little fragment that I have read in a really supremacist, racist article in Walla.
I guess I'm one of those stupid Irish and Basques that for some reason identify the Palestinian struggle as yet another occupation struggle.
PS: I'm not linking to that piece of shit of an article. If you want to find it, go to Walla and search for the racist Nir Kipnis.

FlipWalker: Jacob's Ladder toy-inspired robot for locomotion across diverse, complex terrain
Diancheng Li, Nia Ralston, Bastiaan Hagen, Phoebe Tan, Matthew A. Robertson
https://arxiv.org/abs/2508.19380

FlipWalker: Jacob's Ladder toy-inspired robot for locomotion across diverse, complex terrain
This paper introduces FlipWalker, a novel underactuated robot locomotion system inspired by Jacob's Ladder illusion toy, designed to traverse challenging terrains where wheeled robots often struggle. Like the Jacob's Ladder toy, FlipWalker features two interconnected segments joined by flexible cables, enabling it to pivot and flip around singularities in a manner reminiscent of the toy's cascading motion. Actuation is provided by motor-driven legs within each segment that push off either the g…

What Makes AI Applications Acceptable or Unacceptable? A Predictive Moral Framework
Kimmo Eriksson, Simon Karlsson, Irina Vartanova, Pontus Strimling
https://arxiv.org/abs/2508.19317

What Makes AI Applications Acceptable or Unacceptable? A Predictive Moral Framework
As artificial intelligence rapidly transforms society, developers and policymakers struggle to anticipate which applications will face public moral resistance. We propose that these judgments are not idiosyncratic but systematic and predictable. In a large, preregistered study (N = 587, U.S. representative sample), we used a comprehensive taxonomy of 100 AI applications spanning personal and organizational contexts-including both functional uses and the moral treatment of AI itself. In particip…

EMG-UP: Unsupervised Personalization in Cross-User EMG Gesture Recognition
Nana Wang, Gen Li, Zhaoxin Fan, Suli Wang
https://arxiv.org/abs/2509.21589 https://

EMG-UP: Unsupervised Personalization in Cross-User EMG Gesture Recognition
Cross-user electromyography (EMG)-based gesture recognition represents a fundamental challenge in achieving scalable and personalized human-machine interaction within real-world applications. Despite extensive efforts, existing methodologies struggle to generalize effectively across users due to the intrinsic biological variability of EMG signals, resulting from anatomical heterogeneity and diverse task execution styles. To address this limitation, we introduce EMG-UP, a novel and effective fra…

Analysis: agencies have hired hundreds of thousands of "chatters" to impersonate 4M OnlyFans creators, as many Filipino chatters struggle with mental health (Michael Beltran/Nikkei Asia)
https://asia.nikkei.com/spotlight/soci

Filipino OnlyFans 'chatters' struggle with mental health, job security
Unrelenting, desensitizing work, lack of protections among biggest complaints

i like to imagine there's a version of this clip that perfectly loops, that he's locked in a sisyphean struggle against the matrix

Hey everyone, every Tuesday, my friend @… asks for your help to feed her family. She can’t do it today (they’re trying to stay alive during a genocide) so she asked if I could do it on her behalf.

They need $500 each week for their family of 6.

Every single dollar makes a real difference 
If you’re unable to donate, please consider sharing – it might r…

Help Sehwel family with their medical treatment
I'm Joe in the UK, I've been in touch with Aseel from the Sehwel family for over a year now and have been following their difficult struggle to afford medication, shelter and food in Gaza. I'm creating this Chuffed campaign for the family to help afford these things and hopefully help them get out of Gaza when the opportunity arises. Here's Aseel's writing:

On an optimization framework for damage localization in structures
Owais Saleem, Tim Suchan, Natalie Rauter, Kathrin Welker
https://arxiv.org/abs/2509.22492 https://

On an optimization framework for damage localization in structures
Efficient structural damage localization remains a challenge in structural health monitoring (SHM), particularly when the problem is coupled with uncertainty of conditions and complexity of structures. Traditional methods simply based on experimental data processing are often not sufficiently reliable, while complex models often struggle with computational inefficiency given the tremendous amount of model parameters. This paper focuses on closing the gap between data-driven SHM and physics-base…

Frustratingly Easy Zero-Day Audio DeepFake Detection via Retrieval Augmentation and Profile Matching
Xuechen Liu, Xin Wang, Junichi Yamagishi
https://arxiv.org/abs/2509.21728 ht…

Frustratingly Easy Zero-Day Audio DeepFake Detection via Retrieval Augmentation and Profile Matching
Modern audio deepfake detectors using foundation models and large training datasets have achieved promising detection performance. However, they struggle with zero-day attacks, where the audio samples are generated by novel synthesis methods that models have not seen from reigning training data. Conventional approaches against such attacks require fine-tuning the detectors, which can be problematic when prompt response is required. This study introduces a training-free framework for zero-day au…

QuantMind: A Context-Engineering Based Knowledge Framework for Quantitative Finance
Haoxue Wang, Keli Wen, Yuante Li, Qiancheng Qu, Xiangxu Mu, Xinjie Shen, Jiaqi Gao, Chenyang Chang, Chuhan Xie, San Yu Cheung, Zhuoyuan Hu, Xinyu Wang, Sirui Bi, Bi'an Du
https://arxiv.org/abs/2509.21507

QuantMind: A Context-Engineering Based Knowledge Framework for Quantitative Finance
Quantitative research increasingly relies on unstructured financial content such as filings, earnings calls, and research notes, yet existing LLM and RAG pipelines struggle with point-in-time correctness, evidence attribution, and integration into research workflows. To tackle this, We present QuantMind, an intelligent knowledge extraction and retrieval framework tailored to quantitative finance. QuantMind adopts a two-stage architecture: (i) a knowledge extraction stage that transforms heterog…

ADHD: I struggle with repetitive things
Autism: I struggle with new things
AuDHD: I struggle...

Book description: "I can't breathe! 3 words ... significantly altered the political, social, and educational landscape of the consciousness of the USA and the world. Many have attributed this ... to the public murder of George Floyd at the hands of Officer Derek Chauvin while 3 other officers looked on. ... The constant struggle to elevate the lived experiences of Black people in the United States was front and center - for about two weeks."
The author of that book has now…

Reinforcement Learning Based Traffic Signal Design to Minimize Queue Lengths
Anirud Nandakumar, Chayan Banerjee, Lelitha Devi Vanajakshi
https://arxiv.org/abs/2509.21745 https:/…

Reinforcement Learning Based Traffic Signal Design to Minimize Queue Lengths
Efficient traffic signal control (TSC) is crucial for reducing congestion, travel delays, pollution, and for ensuring road safety. Traditional approaches, such as fixed signal control and actuated control, often struggle to handle dynamic traffic patterns. In this study, we propose a novel adaptive TSC framework that leverages Reinforcement Learning (RL), using the Proximal Policy Optimization (PPO) algorithm, to minimize total queue lengths across all signal phases. The challenge of efficientl…

Towards Automated EEG-Based Detection Using Deep Convolutional Autoencoders
Annika Stiehl, Nicolas Weeger, Christian Uhl, Dominic Bechtold, Nicole Ille, Stefan Gei{\ss}els\"oder
https://arxiv.org/abs/2508.20535

Towards Automated EEG-Based Detection Using Deep Convolutional Autoencoders
Epilepsy is one of the most common neurological disorders. This disease requires reliable and efficient seizure detection methods. Electroencephalography (EEG) is the gold standard for seizure monitoring, but its manual analysis is a time-consuming task that requires expert knowledge. In addition, there are no well-defined features that allow fully automated analysis. Existing deep learning-based approaches struggle to achieve high sensitivity while maintaining a low false alarm rate per hour (…

#CFP: I AM HERE TO FIGHT! An Anthology on the Struggle for Public Education
https://ift.tt/AgMtRGN
I Am Here to Fight!
via Input 4 RELCFP

#CFP: I AM HERE TO FIGHT! An Anthology on the Struggle for Public Education
I Am Here to Fight!

Just Lose, Baby: The Raiders’ Struggle With A Losing Culture https://raiderramble.com/2025/10/22/just-lose-baby-the-raiders-struggle-with-a-losing-culture/

Particle swarm optimization for online sparse streaming feature selection under uncertainty
Ruiyang Xu
https://arxiv.org/abs/2508.20123 https://arxiv.org/p…

Particle swarm optimization for online sparse streaming feature selection under uncertainty
In real-world applications involving high-dimensional streaming data, online streaming feature selection (OSFS) is widely adopted. Yet, practical deployments frequently face data incompleteness due to sensor failures or technical constraints. While online sparse streaming feature selection (OS2FS) mitigates this issue via latent factor analysis-based imputation, existing methods struggle with uncertain feature-label correlations, leading to inflexible models and degraded performance. To address…

Fourier Feature Networks for High-Fidelity Prediction of Perturbed Optical Fields
Joshua R. Jandrell, Mitchell A. Cox
https://arxiv.org/abs/2508.19751 https://

Fourier Feature Networks for High-Fidelity Prediction of Perturbed Optical Fields
Modelling the effects of perturbations on optical fields often requires learning highly oscillatory complex-valued functions. Standard multi-layer perceptrons (MLPs) struggle with this task due to an inherent spectral bias, preventing them from fitting high-frequency sinusoids. To overcome this, we incorporate Fourier features - a set of predefined sinusoids dependent on the perturbation - as an additional network input. This reframes the learning problem from approximating a complex function t…

TopoBind: Multi-Modal Prediction of Antibody-Antigen Binding Free Energy via Sequence Embeddings and Structural Topology
Ciyuan Yu, Hongzong Li, Jiahao Ma, Shiqin Tang, Ye-Fan Hu, Jian-Dong Huang
https://arxiv.org/abs/2508.19632

TopoBind: Multi-Modal Prediction of Antibody-Antigen Binding Free Energy via Sequence Embeddings and Structural Topology
Predicting the binding free energy between antibodies and antigens is a key challenge in structure-aware biomolecular modeling, with direct implications for antibody design. Most existing methods either rely solely on sequence embeddings or struggle to capture complex structural relationships, thus limiting predictive performance. In this work, we present a novel framework that integrates sequence-based representations from pre-trained protein language models (ESM-2) with a set of topological f…

More reflections on the philosophy of #degrowth from the European periphery, by Mladen Domazet.
Struggle for Justice, Freedom and Beauty as Denial of Despair  - Kronika
https://

#Wordle 1,591 3/6*
⬜🟨⬜⬜🟨 <1% of 227,507 (60)
🟨⬜🟨⬜🟨 3% of 162 (3)
🟩🟩🟩🟩🟩 5% of 22
WordleBot
Skill 92/99
Luck 73/99
You struggle to find a word that fits the clues and finally get one, but nah that won't be it, but maybe I'll get more good information...Really, that was it?!
Perfectly good word, just unexpected

DRMD: Deep Reinforcement Learning for Malware Detection under Concept Drift
Shae McFadden, Myles Foley, Mario D'Onghia, Chris Hicks, Vasilios Mavroudis, Nicola Paoletti, Fabio Pierazzi
https://arxiv.org/abs/2508.18839

DRMD: Deep Reinforcement Learning for Malware Detection under Concept Drift
Malware detection in real-world settings must deal with evolving threats, limited labeling budgets, and uncertain predictions. Traditional classifiers, without additional mechanisms, struggle to maintain performance under concept drift in malware domains, as their supervised learning formulation cannot optimize when to defer decisions to manual labeling and adaptation. Modern malware detection pipelines combine classifiers with monthly active learning (AL) and rejection mechanisms to mitigate t…

Towards stable AI systems for Evaluating Arabic Pronunciations
Hadi Zaatiti, Hatem Hajri, Osama Abdullah, Nader Masmoudi
https://arxiv.org/abs/2508.19587 https://

Towards stable AI systems for Evaluating Arabic Pronunciations
Modern Arabic ASR systems such as wav2vec 2.0 excel at word- and sentence-level transcription, yet struggle to classify isolated letters. In this study, we show that this phoneme-level task, crucial for language learning, speech therapy, and phonetic research, is challenging because isolated letters lack co-articulatory cues, provide no lexical context, and last only a few hundred milliseconds. Recogniser systems must therefore rely solely on variable acoustic cues, a difficulty heightened by A…

Analysis: agencies have hired hundreds of thousands of "chatters" to impersonate 4M OnlyFans creators, as many Filipino chatters struggle with mental health (Michael Beltran/Nikkei Asia)
https://asia.nikkei.com/spotlight/soci

Filipino OnlyFans 'chatters' struggle with mental health, job security
Unrelenting, desensitizing work, lack of protections among biggest complaints

Efficient Neuro-Symbolic Learning of Constraints and Objective
Marianne Defresne, Romain Gambardella, Sophie Barbe, Thomas Schiex
https://arxiv.org/abs/2508.20978 https://

Efficient Neuro-Symbolic Learning of Constraints and Objective
In the ongoing quest for hybridizing discrete reasoning with neural nets, there is an increasing interest in neural architectures that can learn how to solve discrete reasoning or optimization problems from natural inputs, a task that Large Language Models seem to struggle with. Objectives: We introduce a differentiable neuro-symbolic architecture and a loss function dedicated to learning how to solve NP-hard reasoning problems. Methods: Our new probabilistic loss allows for learning both t…

HyCoVAD: A Hybrid SSL-LLM Model for Complex Video Anomaly Detection
Mohammad Mahdi Hemmatyar, Mahdi Jafari, Mohammad Amin Yousefi, Mohammad Reza Nemati, Mobin Azadani, Hamid Reza Rastad, Amirmohammad Akbari
https://arxiv.org/abs/2509.22544

HyCoVAD: A Hybrid SSL-LLM Model for Complex Video Anomaly Detection
Video anomaly detection (VAD) is crucial for intelligent surveillance, but a significant challenge lies in identifying complex anomalies, which are events defined by intricate relationships and temporal dependencies among multiple entities rather than by isolated actions. While self-supervised learning (SSL) methods effectively model low-level spatiotemporal patterns, they often struggle to grasp the semantic meaning of these interactions. Conversely, large language models (LLMs) offer powerful…

Learning to Ball: Composing Policies for Long-Horizon Basketball Moves
Pei Xu, Zhen Wu, Ruocheng Wang, Vishnu Sarukkai, Kayvon Fatahalian, Ioannis Karamouzas, Victor Zordan, C. Karen Liu
https://arxiv.org/abs/2509.22442

Learning to Ball: Composing Policies for Long-Horizon Basketball Moves
Learning a control policy for a multi-phase, long-horizon task, such as basketball maneuvers, remains challenging for reinforcement learning approaches due to the need for seamless policy composition and transitions between skills. A long-horizon task typically consists of distinct subtasks with well-defined goals, separated by transitional subtasks with unclear goals but critical to the success of the entire task. Existing methods like the mixture of experts and skill chaining struggle with ta…

So my other big piece of the day is an inside look at the struggle for the future of the CVE program that just went live at CyberScoop.
Many thanks to Jay Jacobs of Empirical Security, Nick Leiserson of the Institute for Security and Technology, Mitchel Herckis of Wiz, Brian Fox of Sonatype, Peter Allor of the CVE Foundation, Ben Edwards of Bitsight and a few experts who go unnamed for their insight.

WoW: Towards a World omniscient World model Through Embodied Interaction
Xiaowei Chi, Peidong Jia, Chun-Kai Fan, Xiaozhu Ju, Weishi Mi, Kevin Zhang, Zhiyuan Qin, Wanxin Tian, Kuangzhi Ge, Hao Li, Zezhong Qian, Anthony Chen, Qiang Zhou, Yueru Jia, Jiaming Liu, Yong Dai, Qingpo Wuwu, Chengyu Bai, Yu-Kai Wang, Ying Li, Lizhang Chen, Yong Bao, Zhiyuan Jiang, Jiacheng Zhu, Kai Tang, Ruichuan An, Yulin Luo, Qiuxuan Feng, Siyuan Zhou, Chi-min Chan, Chengkai Hou, Wei Xue, Sirui Han, Yike Guo, …

WoW: Towards a World omniscient World model Through Embodied Interaction
Humans develop an understanding of intuitive physics through active interaction with the world. This approach is in stark contrast to current video models, such as Sora, which rely on passive observation and therefore struggle with grasping physical causality. This observation leads to our central hypothesis: authentic physical intuition of the world model must be grounded in extensive, causally rich interactions with the real world. To test this hypothesis, we present WoW, a 14-billion-paramet…

Leveraging LLM Agents for Automated Video Game Testing
Chengjia Wang, Lanling Tang, Ming Yuan, Jiongchi Yu, Xiaofei Xie, Jiajun Bu
https://arxiv.org/abs/2509.22170 https://

Leveraging LLM Agents for Automated Video Game Testing
Testing MMORPGs (Massively Multiplayer Online Role-Playing Games) is a critical yet labor-intensive task in game development due to their complexity and frequent updating nature. Traditional automated game testing approaches struggle to achieve high state coverage and efficiency in these rich, open-ended environments, while existing LLM-based game-playing approaches are limited to shallow reasoning ability in understanding complex game state-action spaces and long-complex tasks. To address thes…

MixGAN: A Hybrid Semi-Supervised and Generative Approach for DDoS Detection in Cloud-Integrated IoT Networks
Tongxi Wu, Chenwei Xu, Jin Yang
https://arxiv.org/abs/2508.19273 htt…

MixGAN: A Hybrid Semi-Supervised and Generative Approach for DDoS Detection in Cloud-Integrated IoT Networks
The proliferation of cloud-integrated IoT systems has intensified exposure to Distributed Denial of Service (DDoS) attacks due to the expanded attack surface, heterogeneous device behaviors, and limited edge protection. However, DDoS detection in this context remains challenging because of complex traffic dynamics, severe class imbalance, and scarce labeled data. While recent methods have explored solutions to address class imbalance, many still struggle to generalize under limited supervision …

Rock legend Bobby Whitlock dies after hidden struggle
#TDIM #RIP
https://rollingout.com/2025/08/10/bobb

Rock legend Bobby Whitlock dies after hidden struggle
Bobby Whitlock, co-founder of Derek and the Dominos and collaborator on Eric Clapton's "Layla," died at 77 after brief cancer battle at his Texas home.

Understanding and Leveraging the Expert Specialization of Context Faithfulness in Mixture-of-Experts LLMs
Jun Bai, Minghao Tong, Yang Liu, Zixia Jia, Zilong Zheng
https://arxiv.org/abs/2508.19594

Understanding and Leveraging the Expert Specialization of Context Faithfulness in Mixture-of-Experts LLMs
Context faithfulness is essential for reliable reasoning in context-dependent scenarios. However, large language models often struggle to ground their outputs in the provided context, resulting in irrelevant responses. Inspired by the emergent expert specialization observed in mixture-of-experts architectures, this work investigates whether certain experts exhibit specialization in context utilization, offering a potential pathway toward targeted optimization for improved context faithfulness. …

🇺🇦 #NowPlaying on #KEXP's #DriveTime
Anna von Hausswolff:
🎵 Struggle With the Beast
#AnnavonHausswolff
https://annavonhausswolff.bandcamp.com/track/struggle-with-the-beast-2
https://open.spotify.com/track/798vsh2RF4vx5UG0ez9QBn
🎶 show playlist 👇
https://open.spotify.com/playlist/2KW4FxNKVGpVTgIJYolnNY
🎶 KEXP playlist 👇
https://open.spotify.com/playlist/6VNALrOa3gWbk794YuIrwg
Please 🔁 BOOST to share what you like
- your followers don't see if you ⭐ favourite a post

Neural Spline Operators for Risk Quantification in Stochastic Systems
Zhuoyuan Wang, Raffaele Romagnoli, Kamyar Azizzadenesheli, Yorie Nakahira
https://arxiv.org/abs/2508.20288 …

Neural Spline Operators for Risk Quantification in Stochastic Systems
Accurately quantifying long-term risk probabilities in diverse stochastic systems is essential for safety-critical control. However, existing sampling-based and partial differential equation (PDE)-based methods often struggle to handle complex varying dynamics. Physics-informed neural networks learn surrogate mappings for risk probabilities from varying system parameters of fixed and finite dimensions, yet can not account for functional variations in system dynamics. To address these challenges…

DS-STAR: Data Science Agent via Iterative Planning and Verification
Jaehyun Nam, Jinsung Yoon, Jiefeng Chen, Jinwoo Shin, Tomas Pfister
https://arxiv.org/abs/2509.21825 https://…

DS-STAR: Data Science Agent via Iterative Planning and Verification
Data science, which transforms raw data into actionable insights, is critical for data-driven decision-making. However, these tasks are often complex, involving steps for exploring multiple data sources and synthesizing findings to deliver insightful answers. While large language models (LLMs) show significant promise in automating this process, they often struggle with heterogeneous data formats and generate sub-optimal analysis plans, as verifying plan sufficiency is inherently difficult with…

B\'ezier Meets Diffusion: Robust Generation Across Domains for Medical Image Segmentation
Chen Li, Meilong Xu, Xiaoling Hu, Weimin Lyu, Chao Chen
https://arxiv.org/abs/2509.22476

Bézier Meets Diffusion: Robust Generation Across Domains for Medical Image Segmentation
Training robust learning algorithms across different medical imaging modalities is challenging due to the large domain gap. Unsupervised domain adaptation (UDA) mitigates this problem by using annotated images from the source domain and unlabeled images from the target domain to train the deep models. Existing approaches often rely on GAN-based style transfer, but these methods struggle to capture cross-domain mappings in regions with high variability. In this paper, we propose a unified framew…

DTC: Real-Time and Accurate Distributed Triangle Counting in Fully Dynamic Graph Streams
Wei Xuan, Yan Liang, Huawei Cao, Ning Lin, Xiaochun Ye, Dongrui Fan
https://arxiv.org/abs/2508.19057

DTC: Real-Time and Accurate Distributed Triangle Counting in Fully Dynamic Graph Streams
Triangle counting is a fundamental problem in graph mining, essential for analyzing graph streams with arbitrary edge orders. However, exact counting becomes impractical due to the massive size of real-world graph streams. To address this, approximate algorithms have been developed, but existing distributed streaming algorithms lack adaptability and struggle with edge deletions. In this article, we propose DTC, a novel family of single-pass distributed streaming algorithms for global and local …

Republicans struggle to explain shutdown of the House
“This shutdown strategy is old and Republicans need to have new tactics,”
Rep. Beth Van Duyne (R-Texas)
told House Republicans on a private call Tuesday afternoon,
as federal agencies passed their third week without funding.
Her critique highlighted the growing struggles that House Republicans face in their effort to try to force Democrats into opening up the federal government.

Ahead of the shutdown,…

Republicans struggle to explain shutdown of the House
Speaker Mike Johnson’s initial strategy to jam the Senate has not worked, but he refuses to change tactics.

A Lightweight Crowd Model for Robot Social Navigation
Maryam Kazemi Eskeri, Thomas Wiedemann, Ville Kyrki, Dominik Baumann, Tomasz Piotr Kucner
https://arxiv.org/abs/2508.19595 …

A Lightweight Crowd Model for Robot Social Navigation
Robots operating in human-populated environments must navigate safely and efficiently while minimizing social disruption. Achieving this requires estimating crowd movement to avoid congested areas in real-time. Traditional microscopic models struggle to scale in dense crowds due to high computational cost, while existing macroscopic crowd prediction models tend to be either overly simplistic or computationally intensive. In this work, we propose a lightweight, real-time macroscopic crowd predic…

TreeMind: Automatically Reproducing Android Bug Reports via LLM-empowered Monte Carlo Tree Search
Zhengyu Chen, Zhaoyi Meng, Wenxiang Zhao, Wansen Wang, Haoyang Zhao, Jiahao Zhan, Jie Cui, Hong Zhong
https://arxiv.org/abs/2509.22431

TreeMind: Automatically Reproducing Android Bug Reports via LLM-empowered Monte Carlo Tree Search
Automatically reproducing Android app crashes from textual bug reports is challenging, particularly when the reports are incomplete and the modern UI exhibits high combinatorial complexity. Existing approaches based on reinforcement learning or large language models (LLMs) exhibit limitations in such scenarios. They struggle to infer unobserved steps and reconstruct the underlying user action sequences to navigate the vast UI interaction space, primarily due to limited goal-directed reasoning a…

Pete Carroll says 'the competition is on' at quarterback as Raiders offense continues to struggle

https://www.cbssports.com/nfl/news/pete-ca

Predicting Drug-Drug Interactions Using Heterogeneous Graph Neural Networks: HGNN-DDI
Hongbo Liu, Siyi Li, Zheng Yu
https://arxiv.org/abs/2508.18766 https://

Predicting Drug-Drug Interactions Using Heterogeneous Graph Neural Networks: HGNN-DDI
Drug-drug interactions (DDIs) are a major concern in clinical practice, as they can lead to reduced therapeutic efficacy or severe adverse effects. Traditional computational approaches often struggle to capture the complex relationships among drugs, targets, and biological entities. In this work, we propose HGNN-DDI, a heterogeneous graph neural network model designed to predict potential DDIs by integrating multiple drug-related data sources. HGNN-DDI leverages graph representation learning to…

A Symbolic Adversarial Learning Framework for Evolving Fake News Generation and Detection
Chong Tian, Qirong Ho, Xiuying Chen
https://arxiv.org/abs/2508.19633 https://

A Symbolic Adversarial Learning Framework for Evolving Fake News Generation and Detection
Rapid LLM advancements heighten fake news risks by enabling the automatic generation of increasingly sophisticated misinformation. Previous detection methods, including fine-tuned small models or LLM-based detectors, often struggle with its dynamically evolving nature. In this work, we propose a novel framework called the Symbolic Adversarial Learning Framework (SALF), which implements an adversarial training paradigm by an agent symbolic learning optimization process, rather than relying on nu…

${C}^{3}$-GS: Learning Context-aware, Cross-dimension, Cross-scale Feature for Generalizable Gaussian Splatting
Yuxi Hu, Jun Zhang, Kuangyi Chen, Zhe Zhang, Friedrich Fraundorfer
https://arxiv.org/abs/2508.20754

${C}^{3}$-GS: Learning Context-aware, Cross-dimension, Cross-scale Feature for Generalizable Gaussian Splatting
Generalizable Gaussian Splatting aims to synthesize novel views for unseen scenes without per-scene optimization. In particular, recent advancements utilize feed-forward networks to predict per-pixel Gaussian parameters, enabling high-quality synthesis from sparse input views. However, existing approaches fall short in encoding discriminative, multi-view consistent features for Gaussian predictions, which struggle to construct accurate geometry with sparse views. To address this, we propose $\m…

Yield Sec: illegal gambling operators captured 71% of Europe's online betting and casino market in 2024, or €80.65B, as regulators struggle with crypto casinos (Navya Menon/Bloomberg)
https://www.bloomberg.com/news/articles/20

Beyond prior knowledge: The predictive role of knowledge-building in Tutor Learning
Tasmia Shahriar, Mia Ameen, Aditi Mallavarapu, Shiyan Jiang, Noboru Matsuda
https://arxiv.org/abs/2508.18545

Beyond prior knowledge: The predictive role of knowledge-building in Tutor Learning
When adopting the role of a teacher in learning-by-teaching environments, students often struggle to engage in knowledge-building activities, such as providing explanations and addressing misconceptions. Instead, they frequently default to knowledge-telling behaviors, where they simply dictate what they already know or what to do without deeper reflection, thereby limiting learning. Teachable agents, particularly those capable of posing persistent follow-up questions, have been shown to encoura…

VLA-Reasoner: Empowering Vision-Language-Action Models with Reasoning via Online Monte Carlo Tree Search
Wenkai Guo, Guanxing Lu, Haoyuan Deng, Zhenyu Wu, Yansong Tang, Ziwei Wang
https://arxiv.org/abs/2509.22643

VLA-Reasoner: Empowering Vision-Language-Action Models with Reasoning via Online Monte Carlo Tree Search
Vision-Language-Action models (VLAs) achieve strong performance in general robotic manipulation tasks by scaling imitation learning. However, existing VLAs are limited to predicting short-sighted next-action, which struggle with long-horizon trajectory tasks due to incremental deviations. To address this problem, we propose a plug-in framework named VLA-Reasoner that effectively empowers off-the-shelf VLAs with the capability of foreseeing future states via test-time scaling. Specifically, VLA-…

Boosting Skeleton-Driven SMT Solver Fuzzing by Leveraging LLM to Produce Formula Generators
Maolin Sun, Yibiao Yang, Yuming Zhou
https://arxiv.org/abs/2508.20340 https://…

Boosting Skeleton-Driven SMT Solver Fuzzing by Leveraging LLM to Produce Formula Generators
Satisfiability Modulo Theory (SMT) solvers are foundational to modern systems and programming languages research, providing the foundation for tasks like symbolic execution and automated verification. Because these solvers sit on the critical path, their correctness is essential, and high-quality test formulas are key to uncovering bugs. However, while prior testing techniques performed well on earlier solver versions, they struggle to keep pace with rapidly evolving features. Recent approaches…

How Can Input Reformulation Improve Tool Usage Accuracy in a Complex Dynamic Environment? A Study on $\tau$-bench
Venkatesh Mishra, Amir Saeidi, Satyam Raj, Mutsumi Nakamura, Jayanth Srinivasa, Gaowen Liu, Ali Payani, Chitta Baral
https://arxiv.org/abs/2508.20931

How Can Input Reformulation Improve Tool Usage Accuracy in a Complex Dynamic Environment? A Study on $τ$-bench
Recent advances in reasoning and planning capabilities of large language models (LLMs) have enabled their potential as autonomous agents capable of tool use in dynamic environments. However, in multi-turn conversational environments like $τ$-bench, these agents often struggle with consistent reasoning, adherence to domain-specific policies, and extracting correct information over a long horizon of tool-calls and conversation. To capture and mitigate these failures, we conduct a comprehensive m…

Hybrid Diffusion for Simultaneous Symbolic and Continuous Planning
Sigmund Hennum H{\o}eg, Aksel Vaaler, Chaoqi Liu, Olav Egeland, Yilun Du
https://arxiv.org/abs/2509.21983 http…

Hybrid Diffusion for Simultaneous Symbolic and Continuous Planning
Constructing robots to accomplish long-horizon tasks is a long-standing challenge within artificial intelligence. Approaches using generative methods, particularly Diffusion Models, have gained attention due to their ability to model continuous robotic trajectories for planning and control. However, we show that these models struggle with long-horizon tasks that involve complex decision-making and, in general, are prone to confusing different modes of behavior, leading to failure. To remedy thi…

Distance-informed Neural Processes
Aishwarya Venkataramanan, Joachim Denzler
https://arxiv.org/abs/2508.18903 https://arxiv.org/pdf/2508.18903

Distance-informed Neural Processes
We propose the Distance-informed Neural Process (DNP), a novel variant of Neural Processes that improves uncertainty estimation by combining global and distance-aware local latent structures. Standard Neural Processes (NPs) often rely on a global latent variable and struggle with uncertainty calibration and capturing local data dependencies. DNP addresses these limitations by introducing a global latent variable to model task-level variations and a local latent variable to capture input similar…

H-PRM: A Pluggable Hotword Pre-Retrieval Module for Various Speech Recognition Systems
Huangyu Dai, Lingtao Mao, Ben Chen, Zihan Wang, Zihan Liang, Ying Han, Chenyi Lei, Han Li
https://arxiv.org/abs/2508.18295

H-PRM: A Pluggable Hotword Pre-Retrieval Module for Various Speech Recognition Systems
Hotword customization is crucial in ASR to enhance the accuracy of domain-specific terms. It has been primarily driven by the advancements in traditional models and Audio large language models (LLMs). However, existing models often struggle with large-scale hotwords, as the recognition rate drops dramatically with the number of hotwords increasing. In this paper, we introduce a novel hotword customization system that utilizes a hotword pre-retrieval module (H-PRM) to identify the most relevant …

Adaptive Root Cause Localization for Microservice Systems with Multi-Agent Recursion-of-Thought
Lingzhe Zhang, Tong Jia, Kangjin Wang, Weijie Hong, Chiming Duan, Minghua He, Ying Li
https://arxiv.org/abs/2508.20370

Adaptive Root Cause Localization for Microservice Systems with Multi-Agent Recursion-of-Thought
As contemporary microservice systems become increasingly popular and complex-often comprising hundreds or even thousands of fine-grained, interdependent subsystems-they are facing more frequent failures. Ensuring system reliability thus demands accurate root cause localization. While traces and metrics have proven to be effective data sources for this task, existing methods either heavily rely on pre-defined schemas, which struggle to adapt to evolving operational contexts, or lack interpretabi…

StateX: Enhancing RNN Recall via Post-training State Expansion
Xingyu Shen, Yingfa Chen, Zhen Leng Thai, Xu Han, Zhiyuan Liu, Maosong Sun
https://arxiv.org/abs/2509.22630 https:…

StateX: Enhancing RNN Recall via Post-training State Expansion
While Transformer-based models have demonstrated remarkable language modeling performance, their high complexities result in high costs when processing long contexts. In contrast, recurrent neural networks (RNNs) such as linear attention and state space models have gained popularity due to their constant per-token complexities. However, these recurrent models struggle with tasks that require accurate recall of contextual information from long contexts, because all contextual information is comp…

Privacy-Preserving Federated Learning Framework for Risk-Based Adaptive Authentication
Yaser Baseri, Abdelhakim Senhaji Hafid, Dimitrios Makrakis, Hamidreza Fereidouni
https://arxiv.org/abs/2508.18453 …

Privacy-Preserving Federated Learning Framework for Risk-Based Adaptive Authentication
Balancing robust security with strong privacy guarantees is critical for Risk-Based Adaptive Authentication (RBA), particularly in decentralized settings. Federated Learning (FL) offers a promising solution by enabling collaborative risk assessment without centralizing user data. However, existing FL approaches struggle with Non-Independent and Identically Distributed (Non-IID) user features, resulting in biased, unstable, and poorly generalized global models. This paper introduces FL-RBA2, a n…

Enhancing XAI Interpretation through a Reverse Mapping from Insights to Visualizations
Aniket Nuthalapati, Nicholas Hinds, Brian Y. Lim, Qianwen Wang
https://arxiv.org/abs/2508.18640

Enhancing XAI Interpretation through a Reverse Mapping from Insights to Visualizations
As AI systems become increasingly integrated into high-stakes domains, enabling users to accurately interpret model behavior is critical. While AI explanations can be provided, users often struggle to reason effectively with these explanations, limiting their ability to validate or learn from AI decisions. To address this gap, we introduce Reverse Mapping, a novel approach that enhances visual explanations by incorporating user-derived insights back into the explanation workflow. Our system ext…

Learning on the Fly: Rapid Policy Adaptation via Differentiable Simulation
Jiahe Pan, Jiaxu Xing, Rudolf Reiter, Yifan Zhai, Elie Aljalbout, Davide Scaramuzza
https://arxiv.org/abs/2508.21065

Learning on the Fly: Rapid Policy Adaptation via Differentiable Simulation
Learning control policies in simulation enables rapid, safe, and cost-effective development of advanced robotic capabilities. However, transferring these policies to the real world remains difficult due to the sim-to-real gap, where unmodeled dynamics and environmental disturbances can degrade policy performance. Existing approaches, such as domain randomization and Real2Sim2Real pipelines, can improve policy robustness, but either struggle under out-of-distribution conditions or require costly…

HERMES: Human-to-Robot Embodied Learning from Multi-Source Motion Data for Mobile Dexterous Manipulation
Zhecheng Yuan, Tianming Wei, Langzhe Gu, Pu Hua, Tianhai Liang, Yuanpei Chen, Huazhe Xu
https://arxiv.org/abs/2508.20085

HERMES: Human-to-Robot Embodied Learning from Multi-Source Motion Data for Mobile Dexterous Manipulation
Leveraging human motion data to impart robots with versatile manipulation skills has emerged as a promising paradigm in robotic manipulation. Nevertheless, translating multi-source human hand motions into feasible robot behaviors remains challenging, particularly for robots equipped with multi-fingered dexterous hands characterized by complex, high-dimensional action spaces. Moreover, existing approaches often struggle to produce policies capable of adapting to diverse environmental conditions.…

OmniHuman-1.5: Instilling an Active Mind in Avatars via Cognitive Simulation
Jianwen Jiang, Weihong Zeng, Zerong Zheng, Jiaqi Yang, Chao Liang, Wang Liao, Han Liang, Yuan Zhang, Mingyuan Gao
https://arxiv.org/abs/2508.19209

OmniHuman-1.5: Instilling an Active Mind in Avatars via Cognitive Simulation
Existing video avatar models can produce fluid human animations, yet they struggle to move beyond mere physical likeness to capture a character's authentic essence. Their motions typically synchronize with low-level cues like audio rhythm, lacking a deeper semantic understanding of emotion, intent, or context. To bridge this gap, \textbf{we propose a framework designed to generate character animations that are not only physically plausible but also semantically coherent and expressive.} Our mod…

SteinerSQL: Graph-Guided Mathematical Reasoning for Text-to-SQL Generation
Xutao Mao, Tao Liu, Hongying Zan
https://arxiv.org/abs/2509.19623 https://arxiv.…

SteinerSQL: Graph-Guided Mathematical Reasoning for Text-to-SQL Generation
Large Language Models (LLMs) struggle with complex Text-to-SQL queries that demand both sophisticated mathematical reasoning and intricate schema navigation. Existing methods often tackle these challenges in isolation, creating a fractured reasoning process that compromises logical and structural correctness. To resolve this, we introduce SteinerSQL, a framework that unifies these dual challenges into a single, graph-centric optimization problem. SteinerSQL operates in three stages: mathematica…

Towards Foundation Models for Zero-Shot Time Series Anomaly Detection: Leveraging Synthetic Data and Relative Context Discrepancy
Tian Lan, Hao Duong Le, Jinbo Li, Wenjun He, Meng Wang, Chenghao Liu, Chen Zhang
https://arxiv.org/abs/2509.21190

Towards Foundation Models for Zero-Shot Time Series Anomaly Detection: Leveraging Synthetic Data and Relative Context Discrepancy
Time series anomaly detection (TSAD) is a critical task, but developing models that generalize to unseen data in a zero-shot manner remains a major challenge. Prevailing foundation models for TSAD predominantly rely on reconstruction-based objectives, which suffer from a fundamental objective mismatch: they struggle to identify subtle anomalies while often misinterpreting complex normal patterns, leading to high rates of false negatives and positives. To overcome these limitations, we introduce…

Generative AI in Map-Making: A Technical Exploration and Its Implications for Cartographers
Claudio Affolter, Sidi Wu, Yizi Chen, Lorenz Hurni
https://arxiv.org/abs/2508.18959 h…

Generative AI in Map-Making: A Technical Exploration and Its Implications for Cartographers
Traditional map-making relies heavily on Geographic Information Systems (GIS), requiring domain expertise and being time-consuming, especially for repetitive tasks. Recent advances in generative AI (GenAI), particularly image diffusion models, offer new opportunities for automating and democratizing the map-making process. However, these models struggle with accurate map creation due to limited control over spatial composition and semantic layout. To address this, we integrate vector data to gu…

Interleaving Large Language Models for Compiler Testing
Yunbo Ni, Shaohua Li
https://arxiv.org/abs/2508.18955 https://arxiv.org/pdf/2508.18955

Interleaving Large Language Models for Compiler Testing
Testing compilers with AI models, especially large language models (LLMs), has shown great promise. However, current approaches struggle with two key problems: The generated programs for testing compilers are often too simple, and extensive testing with the LLMs is computationally expensive. In this paper, we propose a novel compiler testing framework that decouples the testing process into two distinct phases: an offline phase and an online phase. In the offline phase, we use LLMs to generate …

PEPS: Quantum-Inspired Reinforcement Learning for Coherent Reasoning Traces in LLMs
Venkat Margapuri, Garik Kazanjian, Naren Kosaraju
https://arxiv.org/abs/2509.20105 https://…

PEPS: Quantum-Inspired Reinforcement Learning for Coherent Reasoning Traces in LLMs
Large Language Models (LLMs) often struggle with maintaining coherent multi-step reasoning traces, particularly in tasks that require a structured logical flow. This work introduces a quantum-inspired approach to address the challenge by incorporating a fidelity-based reward derived from Projected Entangled Pair States (PEPS) into Proximal Policy Optimization. Unlike prior approaches that use direct supervision or contrastive objectives, the proposed method guides learning through structural co…

HiPlan: Hierarchical Planning for LLM-Based Agents with Adaptive Global-Local Guidance
Ziyue Li, Yuan Chang, Gaihong Yu, Xiaoqiu Le
https://arxiv.org/abs/2508.19076 https://

HiPlan: Hierarchical Planning for LLM-Based Agents with Adaptive Global-Local Guidance
Large language model (LLM)-based agents have demonstrated remarkable capabilities in decision-making tasks, but struggle significantly with complex, long-horizon planning scenarios. This arises from their lack of macroscopic guidance, causing disorientation and failures in complex tasks, as well as insufficient continuous oversight during execution, rendering them unresponsive to environmental changes and prone to deviations. To tackle these challenges, we introduce HiPlan, a hierarchical plann…

No Label Left Behind: A Unified Surface Defect Detection Model for all Supervision Regimes
Bla\v{z} Rolih, Matic Fu\v{c}ka, Danijel Sko\v{c}aj
https://arxiv.org/abs/2508.19060 h…

No Label Left Behind: A Unified Surface Defect Detection Model for all Supervision Regimes
Surface defect detection is a critical task across numerous industries, aimed at efficiently identifying and localising imperfections or irregularities on manufactured components. While numerous methods have been proposed, many fail to meet industrial demands for high performance, efficiency, and adaptability. Existing approaches are often constrained to specific supervision scenarios and struggle to adapt to the diverse data annotations encountered in real-world manufacturing processes, such a…

EvoMail: Self-Evolving Cognitive Agents for Adaptive Spam and Phishing Email Defense
Wei Huang, De-Tian Chu, Lin-Yuan Bai, Wei Kang, Hai-Tao Zhang, Bo Li, Zhi-Mo Han, Jing Ge, Hai-Feng Lin
https://arxiv.org/abs/2509.21129

EvoMail: Self-Evolving Cognitive Agents for Adaptive Spam and Phishing Email Defense
Modern email spam and phishing attacks have evolved far beyond keyword blacklists or simple heuristics. Adversaries now craft multi-modal campaigns that combine natural-language text with obfuscated URLs, forged headers, and malicious attachments, adapting their strategies within days to bypass filters. Traditional spam detection systems, which rely on static rules or single-modality models, struggle to integrate heterogeneous signals or to continuously adapt, leading to rapid performance degra…

LLM-based Contrastive Self-Supervised AMR Learning with Masked Graph Autoencoders for Fake News Detection
Shubham Gupta, Shraban Kumar Chatterjee, Suman Kundu
https://arxiv.org/abs/2508.18819

LLM-based Contrastive Self-Supervised AMR Learning with Masked Graph Autoencoders for Fake News Detection
The proliferation of misinformation in the digital age has led to significant societal challenges. Existing approaches often struggle with capturing long-range dependencies, complex semantic relations, and the social dynamics influencing news dissemination. Furthermore, these methods require extensive labelled datasets, making their deployment resource-intensive. In this study, we propose a novel self-supervised misinformation detection framework that integrates both complex semantic relations …

LatentGuard: Controllable Latent Steering for Robust Refusal of Attacks and Reliable Response Generation
Huizhen Shu, Xuying Li, Zhuo Li
https://arxiv.org/abs/2509.19839 https:/…

LatentGuard: Controllable Latent Steering for Robust Refusal of Attacks and Reliable Response Generation
Achieving robust safety alignment in large language models (LLMs) while preserving their utility remains a fundamental challenge. Existing approaches often struggle to balance comprehensive safety with fine-grained controllability at the representation level. We introduce LATENTGUARD, a novel three-stage framework that combines behavioral alignment with supervised latent space control for interpretable and precise safety steering. Our approach begins by fine-tuning an LLM on rationalized datase…

MemoryVLA: Perceptual-Cognitive Memory in Vision-Language-Action Models for Robotic Manipulation
Hao Shi, Bin Xie, Yingfei Liu, Lin Sun, Fengrong Liu, Tiancai Wang, Erjin Zhou, Haoqiang Fan, Xiangyu Zhang, Gao Huang
https://arxiv.org/abs/2508.19236

MemoryVLA: Perceptual-Cognitive Memory in Vision-Language-Action Models for Robotic Manipulation
Temporal context is essential for robotic manipulation because such tasks are inherently non-Markovian, yet mainstream VLA models typically overlook it and struggle with long-horizon, temporally dependent tasks. Cognitive science suggests that humans rely on working memory to buffer short-lived representations for immediate control, while the hippocampal system preserves verbatim episodic details and semantic gist of past experience for long-term memory. Inspired by these mechanisms, we propose…

Breaking the Trade-Off Between Faithfulness and Expressiveness for Large Language Models
Chenxu Yang, Qingyi Si, Zheng Lin
https://arxiv.org/abs/2508.18651 https://

Breaking the Trade-Off Between Faithfulness and Expressiveness for Large Language Models
Grounding responses in external knowledge represents an effective strategy for mitigating hallucinations in Large Language Models (LLMs). However, current LLMs struggle to seamlessly integrate knowledge while simultaneously maintaining faithfulness (or fidelity) and expressiveness, capabilities that humans naturally possess. This limitation results in outputs that either lack support from external knowledge, thereby compromising faithfulness, or appear overly verbose and unnatural, thus sacrifi…

Quantum-Classical Hybrid Framework for Zero-Day Time-Push GNSS Spoofing Detection
Abyad Enan, Mashrur Chowdhury, Sagar Dasgupta, Mizanur Rahman
https://arxiv.org/abs/2508.18085 …

Quantum-Classical Hybrid Framework for Zero-Day Time-Push GNSS Spoofing Detection
Global Navigation Satellite Systems (GNSS) are critical for Positioning, Navigation, and Timing (PNT) applications. However, GNSS are highly vulnerable to spoofing attacks, where adversaries transmit counterfeit signals to mislead receivers. Such attacks can lead to severe consequences, including misdirected navigation, compromised data integrity, and operational disruptions. Most existing spoofing detection methods depend on supervised learning techniques and struggle to detect novel, evolved,…

Enhancing Video-Based Robot Failure Detection Using Task Knowledge
Santosh Thoduka, Sebastian Houben, Juergen Gall, Paul G. Pl\"oger
https://arxiv.org/abs/2508.18705 https:…

Enhancing Video-Based Robot Failure Detection Using Task Knowledge
Robust robotic task execution hinges on the reliable detection of execution failures in order to trigger safe operation modes, recovery strategies, or task replanning. However, many failure detection methods struggle to provide meaningful performance when applied to a variety of real-world scenarios. In this paper, we propose a video-based failure detection approach that uses spatio-temporal knowledge in the form of the actions the robot performs and task-relevant objects within the field of vi…

EMMM, Explain Me My Model! Explainable Machine Generated Text Detection in Dialogues
Angela Yifei Yuan, Haoyi Li, Soyeon Caren Han, Christopher Leckie
https://arxiv.org/abs/2508.18715

EMMM, Explain Me My Model! Explainable Machine Generated Text Detection in Dialogues
The rapid adoption of large language models (LLMs) in customer service introduces new risks, as malicious actors can exploit them to conduct large-scale user impersonation through machine-generated text (MGT). Current MGT detection methods often struggle in online conversational settings, reducing the reliability and interpretability essential for trustworthy AI deployment. In customer service scenarios where operators are typically non-expert users, explanation become crucial for trustworthy M…

Graph RAG as Human Choice Model: Building a Data-Driven Mobility Agent with Preference Chain
Kai Hu, Parfait Atchade-Adelomou, Carlo Adornetto, Adrian Mora-Carrero, Luis Alonso-Pastor, Ariel Noyman, Yubo Liu, Kent Larson
https://arxiv.org/abs/2508.16172

Graph RAG as Human Choice Model: Building a Data-Driven Mobility Agent with Preference Chain
Understanding human behavior in urban environments is a crucial field within city sciences. However, collecting accurate behavioral data, particularly in newly developed areas, poses significant challenges. Recent advances in generative agents, powered by Large Language Models (LLMs), have shown promise in simulating human behaviors without relying on extensive datasets. Nevertheless, these methods often struggle with generating consistent, context-sensitive, and realistic behavioral outputs. T…

Does FLUX Already Know How to Perform Physically Plausible Image Composition?
Shilin Lu, Zhuming Lian, Zihan Zhou, Shaocong Zhang, Chen Zhao, Adams Wai-Kin Kong
https://arxiv.org/abs/2509.21278

Does FLUX Already Know How to Perform Physically Plausible Image Composition?
Image composition aims to seamlessly insert a user-specified object into a new scene, but existing models struggle with complex lighting (e.g., accurate shadows, water reflections) and diverse, high-resolution inputs. Modern text-to-image diffusion models (e.g., SD3.5, FLUX) already encode essential physical and resolution priors, yet lack a framework to unleash them without resorting to latent inversion, which often locks object poses into contextually inappropriate orientations, or brittle at…

Propose and Rectify: A Forensics-Driven MLLM Framework for Image Manipulation Localization
Keyang Zhang, Chenqi Kong, Hui Liu, Bo Ding, Xinghao Jiang, Haoliang Li
https://arxiv.org/abs/2508.17976

Propose and Rectify: A Forensics-Driven MLLM Framework for Image Manipulation Localization
The increasing sophistication of image manipulation techniques demands robust forensic solutions that can both reliably detect alterations and precisely localize tampered regions. Recent Multimodal Large Language Models (MLLMs) show promise by leveraging world knowledge and semantic understanding for context-aware detection, yet they struggle with perceiving subtle, low-level forensic artifacts crucial for accurate manipulation localization. This paper presents a novel Propose-Rectify framework…

ArgusCogito: Chain-of-Thought for Cross-Modal Synergy and Omnidirectional Reasoning in Camouflaged Object Segmentation
Jianwen Tan, Huiyao Zhang, Rui Xiong, Han Zhou, Hongfei Wang, Ye Li
https://arxiv.org/abs/2508.18050

ArgusCogito: Chain-of-Thought for Cross-Modal Synergy and Omnidirectional Reasoning in Camouflaged Object Segmentation
Camouflaged Object Segmentation (COS) poses a significant challenge due to the intrinsic high similarity between targets and backgrounds, demanding models capable of profound holistic understanding beyond superficial cues. Prevailing methods, often limited by shallow feature representation, inadequate reasoning mechanisms, and weak cross-modal integration, struggle to achieve this depth of cognition, resulting in prevalent issues like incomplete target separation and imprecise segmentation. Ins…

Learning to Summarize by Learning to Quiz: Adversarial Agentic Collaboration for Long Document Summarization
Weixuan Wang, Minghao Wu, Barry Haddow, Alexandra Birch
https://arxiv.org/abs/2509.20900

Learning to Summarize by Learning to Quiz: Adversarial Agentic Collaboration for Long Document Summarization
Long document summarization remains a significant challenge for current large language models (LLMs), as existing approaches commonly struggle with information loss, factual inconsistencies, and coherence issues when processing excessively long documents. We propose SummQ, a novel adversarial multi-agent framework that addresses these limitations through collaborative intelligence between specialized agents operating in two complementary domains: summarization and quizzing. Our approach employs…

Annotation-Free Open-Vocabulary Segmentation for Remote-Sensing Images
Kaiyu Li, Xiangyong Cao, Ruixun Liu, Shihong Wang, Zixuan Jiang, Zhi Wang, Deyu Meng
https://arxiv.org/abs/2508.18067

Annotation-Free Open-Vocabulary Segmentation for Remote-Sensing Images
Semantic segmentation of remote sensing (RS) images is pivotal for comprehensive Earth observation, but the demand for interpreting new object categories, coupled with the high expense of manual annotation, poses significant challenges. Although open-vocabulary semantic segmentation (OVSS) offers a promising solution, existing frameworks designed for natural images are insufficient for the unique complexities of RS data. They struggle with vast scale variations and fine-grained details, and the…

SGMem: Sentence Graph Memory for Long-Term Conversational Agents
Yaxiong Wu, Yongyue Zhang, Sheng Liang, Yong Liu
https://arxiv.org/abs/2509.21212 https://…

SGMem: Sentence Graph Memory for Long-Term Conversational Agents
Long-term conversational agents require effective memory management to handle dialogue histories that exceed the context window of large language models (LLMs). Existing methods based on fact extraction or summarization reduce redundancy but struggle to organize and retrieve relevant information across different granularities of dialogue and generated memory. We introduce SGMem (Sentence Graph Memory), which represents dialogue as sentence-level graphs within chunked units, capturing associatio…