Tootfinder

This https://arxiv.org/abs/2503.20117 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

Exact and Linear Convergence for Federated Learning under Arbitrary Client Participation is Attainable
This work tackles the fundamental challenges in Federated Learning (FL) posed by arbitrary client participation and data heterogeneity, prevalent characteristics in practical FL settings. It is well-established that popular FedAvg-style algorithms struggle with exact convergence and can suffer from slow convergence rates since a decaying learning rate is required to mitigate these scenarios. To address these issues, we introduce the concept of stochastic matrix and the corresponding time-varyin…

This https://arxiv.org/abs/2502.11753 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csAI_…

HintsOfTruth: A Multimodal Checkworthiness Detection Dataset with Real and Synthetic Claims
Misinformation can be countered with fact-checking, but the process is costly and slow. Identifying checkworthy claims is the first step, where automation can help scale fact-checkers' efforts. However, detection methods struggle with content that is (1) multimodal, (2) from diverse domains, and (3) synthetic. We introduce HintsOfTruth, a public dataset for multimodal checkworthiness detection with 27K real-world and synthetic image/claim pairs. The mix of real and synthetic data makes this dat…

Philippe Neau & Antonella Eye Porcelluzzi – Elephant
https://www.clongclongmoo.org/2025/06/05/philippe-neau-antonella-eye-porcelluzzi-elephant/

Finished #StarWars #Andor #S2?
Watch all 12 episodes again... this time with your YouTube friends, Kyle Katarn, Wave Squadron, Cinepals, Heroes Reforged, Struggle Nation, Jedi Jive... all mixed together…

This https://arxiv.org/abs/2505.16196 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

SEM: Enhancing Spatial Understanding for Robust Robot Manipulation
A key challenge in robot manipulation lies in developing policy models with strong spatial understanding, the ability to reason about 3D geometry, object relations, and robot embodiment. Existing methods often fall short: 3D point cloud models lack semantic abstraction, while 2D image encoders struggle with spatial reasoning. To address this, we propose SEM (Spatial Enhanced Manipulation model), a novel diffusion-based policy framework that explicitly enhances spatial understanding from two com…

This https://arxiv.org/abs/2506.02658 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csSE_…

Computational Thinking Reasoning in Large Language Models
While large language models (LLMs) have demonstrated remarkable reasoning capabilities, they often struggle with complex tasks that require specific thinking paradigms, such as divide-and-conquer and procedural deduction, \etc Previous researches integrate external, reliable tools to alleviate logical inconsistencies and hallucinations in LLMs' problem-solving processes. However, we argue that the root challenge is more profound: LLMs lack the complex thinking paradigms (\ie, computational thin…

This https://arxiv.org/abs/2401.08960 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csHC_…

From User Surveys to Telemetry-Driven AI Agents: Exploring the Potential of Personalized Productivity Solutions
Information workers increasingly struggle with productivity challenges in modern workplaces, facing difficulties in managing time and effectively utilizing workplace analytics data for behavioral improvement. Despite the availability of productivity metrics through enterprise tools, workers often fail to translate this data into actionable insights. We present a comprehensive, user-centric approach to address these challenges through AI-based productivity agents tailored to users' needs. Utiliz…

This https://arxiv.org/abs/2501.14755 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csDC_…

Data-Juicer 2.0: Cloud-Scale Adaptive Data Processing for and with Foundation Models
The burgeoning field of foundation models necessitates advanced data processing mechanisms capable of harnessing vast and valuable data with various types used by these models. Nevertheless, the current landscape presents unique challenges that traditional data processing frameworks struggle to handle effectively, particularly in handling the complexity of multimodal data. In response, we present Data-Juicer 2.0, a data processing system backed by 100+ data processing operators spanning text, i…

"People in Romania's southwest struggle to access water as one third of country faces drought"
#Romania #Drought

Drought strikes one third of Romania as residents struggle for water
Romania's southwestern Dolj county struggles with severe drought. Villagers in Teascu din Deal trek long distances for water, facing harsh conditions daily.

This https://arxiv.org/abs/2503.06474 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIR_…

ROGRAG: A Robustly Optimized GraphRAG Framework
Large language models (LLMs) commonly struggle with specialized or emerging topics which are rarely seen in the training corpus. Graph-based retrieval-augmented generation (GraphRAG) addresses this by structuring domain knowledge as a graph for dynamic retrieval. However, existing pipelines involve complex engineering workflows, making it difficult to isolate the impact of individual components. It is also challenging to evaluate the retrieval effectiveness due to the overlap between the pretra…

This https://arxiv.org/abs/2410.16428 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csSD_…

Neural Scoring: A Refreshed End-to-End Approach for Speaker Recognition in Complex Conditions
Modern speaker verification systems primarily rely on speaker embeddings and cosine similarity. While effective, these methods struggle with multi-talker speech due to the unidentifiability of embedding vectors. We propose Neural Scoring (NS), a novel end-to-end framework that directly estimates verification posterior probabilities without relying on test-side embeddings, making it more powerful and robust to complex conditions, e.g., with multiple talkers. To address the challenge of training …

House passes Big Beautiful Bill Act, sending it to Trump after bruising struggle (New York Post)
https://nypost.com/2025/07/03/us-news/house-passes-big-beautiful-bill-act-sending-it-to-trump-after-bruising-struggle/
http://www.memeorandum.com/250703/p100#a250703p100

I'm afraid this is true. I already struggle to raise enough money to keep the very talented young scientists I have working with me (from many different countries already), finding funding for more will be difficult, and why should US scientists essentially queue jump just because of their nationality ? I know that sounds harsh, I have very good US colleagues, but I have to be fair to all, regardless of where they come from. OTOH, if you are a US scientist and you are interested in exploring possibilities in Denmark, give me a shout and we'll see what we can do.
https://mastodon.world/@davidho/114620044303457868
davidho@mastodon.world - Unless you’re a Nobel laureate, the brain drain will be away from science and not to other countries.
https://www.nytimes.com/2025/06/03/us/trump-federal-spending-grants-scientists-leaving.html?unlocked_article_code=1.ME8.sh9q.qy7jX0S1ZrBa&smid=url-share

Spring Secret Starter: Managing #Secrets in Your #SpringBoot App
https://lucas-fern…

🇺🇦 #NowPlaying on KEXP's #Early
Bria Salmena:
🎵 Stretch the Struggle
#BriaSalmena
https://briasalmena.bandcamp.com/track/stretch-the-struggle
https://open.spotify.com/track/3VhuUDIxvpevwovHS4NDPO

This https://arxiv.org/abs/2503.16424 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csGR_…

Bézier Splatting for Fast and Differentiable Vector Graphics Rendering
Differentiable vector graphics (VGs) are widely used in image vectorization and vector synthesis, while existing representations are costly to optimize and struggle to achieve high-quality rendering results for high-resolution images. This work introduces a new differentiable VG representation, dubbed Bézier Splatting, that enables fast yet high-fidelity VG rasterization. Bézier Splatting samples 2D Gaussians along Bézier curves, which naturally provide positional gradients at object boundar…

This https://arxiv.org/abs/2505.21863 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCV_…

GETReason: Enhancing Image Context Extraction through Hierarchical Multi-Agent Reasoning
Publicly significant images from events hold valuable contextual information, crucial for journalism and education. However, existing methods often struggle to extract this relevance accurately. To address this, we introduce GETReason (Geospatial Event Temporal Reasoning), a framework that moves beyond surface-level image descriptions to infer deeper contextual meaning. We propose that extracting global event, temporal, and geospatial information enhances understanding of an image's significanc…

This https://arxiv.org/abs/2503.22733 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

RBFleX-NAS: Training-Free Neural Architecture Search Using Radial Basis Function Kernel and Hyperparameter Detection
Neural Architecture Search (NAS) is an automated technique to design optimal neural network architectures for a specific workload. Conventionally, evaluating candidate networks in NAS involves extensive training, which requires significant time and computational resources. To address this, training-free NAS has been proposed to expedite network evaluation with minimal search time. However, state-of-the-art training-free NAS algorithms struggle to precisely distinguish well-performing networks f…

Graph Neural Networks for Jamming Source Localization
Dania Herzalla, Willian T. Lunardi, Martin Andreoni
https://arxiv.org/abs/2506.03196 https://

Graph Neural Networks for Jamming Source Localization
Graph-based learning has emerged as a transformative approach for modeling complex relationships across diverse domains, yet its potential in wireless security remains largely unexplored. In this work, we introduce the first application of graph-based learning for jamming source localization, addressing the imminent threat of jamming attacks in wireless networks. Unlike geometric optimization techniques that struggle under environmental uncertainties and dense interference, we reformulate local…

This https://arxiv.org/abs/2505.23703 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csAI_…

Let's Reason Formally: Natural-Formal Hybrid Reasoning Enhances LLM's Math Capability
Enhancing the mathematical reasoning capabilities of LLMs has garnered significant attention in both the mathematical and computer science communities. Recent works have made substantial progress in both Natural Language (NL) reasoning and Formal Language (FL) reasoning by leveraging the potential of pure Reinforcement Learning (RL) methods on base models. However, RL approaches struggle to impart new capabilities not presented in the base model, highlighting the need to integrate more knowledg…

A Diffusion-Driven Temporal Super-Resolution and Spatial Consistency Enhancement Framework for 4D MRI imaging
Xuanru Zhou, Jiarun Liu, Shoujun Yu, Hao Yang, Cheng Li, Tao Tan, Shanshan Wang
https://arxiv.org/abs/2506.04116

A Diffusion-Driven Temporal Super-Resolution and Spatial Consistency Enhancement Framework for 4D MRI imaging
In medical imaging, 4D MRI enables dynamic 3D visualization, yet the trade-off between spatial and temporal resolution requires prolonged scan time that can compromise temporal fidelity--especially during rapid, large-amplitude motion. Traditional approaches typically rely on registration-based interpolation to generate intermediate frames. However, these methods struggle with large deformations, resulting in misregistration, artifacts, and diminished spatial consistency. To address these chall…

This https://arxiv.org/abs/2409.17469 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

VertiSelector: Automatic Curriculum Learning for Wheeled Mobility on Vertically Challenging Terrain
Reinforcement Learning (RL) has the potential to enable extreme off-road mobility by circumventing complex kinodynamic modeling, planning, and control by simulated end-to-end trial-and-error learning experiences. However, most RL methods are sample-inefficient when training in a large amount of manually designed simulation environments and struggle at generalizing to the real world. To address these issues, we introduce VertiSelector (VS), an automatic curriculum learning framework designed to …

This https://arxiv.org/abs/2503.19449 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csSE_…

VecTrans: Enhancing Compiler Auto-Vectorization through LLM-Assisted Code Transformations
Auto-vectorization is a fundamental optimization for modern compilers to exploit SIMD parallelism. However, state-of-the-art approaches still struggle to handle intricate code patterns, often requiring manual hints or domain-specific expertise. Large language models (LLMs), with their ability to capture intricate patterns, provide a promising solution, yet their effective application in compiler optimizations remains an open challenge due to issues such as hallucinations and a lack of domain-sp…

Sources: Xiaomi is among the Chinese tech companies most impacted by new US chip design export rules, affecting its new 3nm mobile chip made by TSMC in Taiwan (Financial Times)
https://www.ft.com/content/2b0a0000-1bf6-475a-ac96-c17212afecc2

Xiaomi among Chinese tech groups set to be hardest hit by US chip software ban
Companies may struggle to manufacture silicon in Taiwan with export controls preventing updates to design tools

Mark MacKinnon begleitet ukrainische Eliteeinheiten. Einige dienten zunächst in #Kabul und kämpften jetzt an zentralen Stellen, wie #Kyjiw, Asow-Stahl und anderen.
---
An elite squad of Ukrainian fighters has been battling Russian occupation on the front lines. This is the story of their war - The Glo…

Discounting and Drug Seeking in Biological Hierarchical Reinforcement Learning
Vardhan Palod, Pranav Mahajan, Veeky Baths, Boris S. Gutkin
https://arxiv.org/abs/2506.04549

Discounting and Drug Seeking in Biological Hierarchical Reinforcement Learning
Despite a strong desire to quit, individuals with long-term substance use disorder (SUD) often struggle to resist drug use, even when aware of its harmful consequences. This disconnect between knowledge and compulsive behavior reflects a fundamental cognitive-behavioral conflict in addiction. Neurobiologically, differential cue-induced activity within striatal subregions, along with dopamine-mediated connectivity from the ventral to the dorsal striatum, contributes to compulsive drug-seeking. H…

This https://arxiv.org/abs/2504.12423 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

Benchmarking Audio Deepfake Detection Robustness in Real-world Communication Scenarios
Existing Audio Deepfake Detection (ADD) systems often struggle to generalise effectively due to the significantly degraded audio quality caused by audio codec compression and channel transmission effects in real-world communication scenarios. To address this challenge, we developed a rigorous benchmark to evaluate the performance of the ADD system under such scenarios. We introduced ADD-C, a new test dataset to evaluate the robustness of ADD systems under diverse communication conditions, inclu…

This https://arxiv.org/abs/2502.18470 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIR_…

Spatial-RAG: Spatial Retrieval Augmented Generation for Real-World Spatial Reasoning Questions
Spatial reasoning remains a challenge for Large Language Models (LLMs), which struggle with spatial data retrieval and reasoning. We propose Spatial Retrieval-Augmented Generation (Spatial-RAG), a framework that extends RAG to spatial tasks by integrating sparse spatial retrieval (spatial databases) and dense semantic retrieval (LLM-based similarity). A multi-objective ranking strategy balances spatial constraints and semantic relevance, while an LLM-guided generator ensures coherent responses.…

Improving LLM Agents with Reinforcement Learning on Cryptographic CTF Challenges
Lajos Muzsai, David Imolai, Andr\'as Luk\'acs
https://arxiv.org/abs/2506.02048

Improving LLM Agents with Reinforcement Learning on Cryptographic CTF Challenges
Large Language Models (LLMs) still struggle with the structured reasoning and tool-assisted computation needed for problem solving in cybersecurity applications. In this work, we introduce "random-crypto", a cryptographic Capture-the-Flag (CTF) challenge generator framework that we use to fine-tune a tool-augmented Llama-3.1-8B with Guided Reinforcement Prompt Optimisation (GRPO), allowing the agent to iteratively write and execute Python inside an isolated REPL. GRPO yields a +53% absolute jum…

Enhancing Text Comprehension for Dyslexic Readers: A 3D Semantic Visualization Approach Using Transformer Mode
Zhengyang Li
https://arxiv.org/abs/2506.03731

Enhancing Text Comprehension for Dyslexic Readers: A 3D Semantic Visualization Approach Using Transformer Mode
Dyslexic individuals often face significant challenges with traditional reading, particularly when engaging with complex texts such as mystery novels. These texts typically demand advanced narrative tracking and information integration skills, making it difficult for dyslexic readers to fully comprehend the content. However, research indicates that while dyslexic individuals may struggle with textual processing, they often possess strong spatial imagination abilities. Leveraging this strength, …

Generalizing Verifiable Instruction Following
Valentina Pyatkin, Saumya Malik, Victoria Graf, Hamish Ivison, Shengyi Huang, Pradeep Dasigi, Nathan Lambert, Hannaneh Hajishirzi
https://arxiv.org/abs/2507.02833

Generalizing Verifiable Instruction Following
A crucial factor for successful human and AI interaction is the ability of language models or chatbots to follow human instructions precisely. A common feature of instructions are output constraints like ``only answer with yes or no" or ``mention the word `abrakadabra' at least 3 times" that the user adds to craft a more useful answer. Even today's strongest models struggle with fulfilling such constraints. We find that most models strongly overfit on a small set of verifiable constraints from …

BridgeNet: A Hybrid, Physics-Informed Machine Learning Framework for Solving High-Dimensional Fokker-Planck Equations
Elmira Mirzabeigi, Rezvan Salehi, Kourosh Parand
https://arxiv.org/abs/2506.04354

BridgeNet: A Hybrid, Physics-Informed Machine Learning Framework for Solving High-Dimensional Fokker-Planck Equations
BridgeNet is a novel hybrid framework that integrates convolutional neural networks with physics-informed neural networks to efficiently solve non-linear, high-dimensional Fokker-Planck equations (FPEs). Traditional PINNs, which typically rely on fully connected architectures, often struggle to capture complex spatial hierarchies and enforce intricate boundary conditions. In contrast, BridgeNet leverages adaptive CNN layers for effective local feature extraction and incorporates a dynamically w…

This https://arxiv.org/abs/2505.04670 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csSE_…

LLM Code Customization with Visual Results: A Benchmark on TikZ
With the rise of AI-based code generation, customizing existing code out of natural language instructions to modify visual results -such as figures or images -has become possible, promising to reduce the need for deep programming expertise. However, even experienced developers can struggle with this task, as it requires identifying relevant code regions (feature location), generating valid code variants, and ensuring the modifications reliably align with user intent. In this paper, we introduce…

How to configure Magento 2 to send emails via AWS SES.
https://winkelwagen.de/2025/06/03/magento-2-use-amazon-ses/

Magento 2: Use Amazon SES
This only works on Magento 2.4+ You want to use Amazon AWS Simple Email Service to send your transaction mails? It took a moment, but maybe you want to safe the struggle, let me tell you, what to c…

This https://arxiv.org/abs/2503.09492 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIR_…

Learning Cascade Ranking as One Network
Cascade Ranking is a prevalent architecture in large-scale top-k selection systems like recommendation and advertising platforms. Traditional training methods focus on single-stage optimization, neglecting interactions between stages. Recent advances have introduced interaction-aware training paradigms, but still struggle to 1) align training objectives with the goal of the entire cascade ranking (i.e., end-to-end recall of ground-truth items) and 2) learn effective collaboration patterns for d…

This https://arxiv.org/abs/2504.02712 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIT_…

TeleMoM: Consensus-Driven Telecom Intelligence via Mixture of Models
Large language models (LLMs) face significant challenges in specialized domains like telecommunication (Telecom) due to technical complexity, specialized terminology, and rapidly evolving knowledge. Traditional methods, such as scaling model parameters or retraining on domain-specific corpora, are computationally expensive and yield diminishing returns, while existing approaches like retrieval-augmented generation, mixture of experts, and fine-tuning struggle with accuracy, efficiency, and coor…

Intense battles continue as Russian forces struggle to advance in Ukraine's Sumy and Pokrovsk regions: https://benborges.xyz/2025/06/30/intense-battles-continue-as-russian.html

AC-Refiner: Efficient Arithmetic Circuit Optimization Using Conditional Diffusion Models
Chenhao Xue, Kezhi Li, Jiaxing Zhang, Yi Ren, Zhengyuan Shi, Chen Zhang, Yibo Lin, Lining Zhang, Qiang Xu, Guangyu Sun
https://arxiv.org/abs/2507.02598

AC-Refiner: Efficient Arithmetic Circuit Optimization Using Conditional Diffusion Models
Arithmetic circuits, such as adders and multipliers, are fundamental components of digital systems, directly impacting the performance, power efficiency, and area footprint. However, optimizing these circuits remains challenging due to the vast design space and complex physical constraints. While recent deep learning-based approaches have shown promise, they struggle to consistently explore high-potential design variants, limiting their optimization efficiency. To address this challenge, we pro…

This https://arxiv.org/abs/2406.04655 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

Bayesian Inference for Spatial-Temporal Non-Gaussian Data Using Predictive Stacking
Analysing non-Gaussian spatial-temporal data requires introducing spatial dependence in generalised linear models through the link function of an exponential family distribution. Unlike in Gaussian likelihoods, inference is considerably encumbered by the inability to analytically integrate out the random effects and reduce the dimension of the parameter space. Iterative estimation algorithms struggle to converge due to the presence of weakly identified parameters. We devise Bayesian inference u…

Generalizable, real-time neural decoding with hybrid state-space models
Avery Hee-Woon Ryoo, Nanda H. Krishna, Ximeng Mao, Mehdi Azabou, Eva L. Dyer, Matthew G. Perich, Guillaume Lajoie
https://arxiv.org/abs/2506.05320

Generalizable, real-time neural decoding with hybrid state-space models
Real-time decoding of neural activity is central to neuroscience and neurotechnology applications, from closed-loop experiments to brain-computer interfaces, where models are subject to strict latency constraints. Traditional methods, including simple recurrent neural networks, are fast and lightweight but often struggle to generalize to unseen data. In contrast, recent Transformer-based approaches leverage large-scale pretraining for strong generalization performance, but typically have much l…

AI bots playing music for other bots. A signpost along the way to our Matrix-like future
https://futurism.com/ai-band-spotify-streams

An "Indie Rock Band" That Appears to Be Entirely AI-Generated Is Making Alarming Amounts of Money on Spotify
While real artists struggle to earn money on Spotify, a seemingly AI-generated band has garnered enough streams to actually make a buck. 

VChatter: Exploring Generative Conversational Agents for Simulating Exposure Therapy to Reduce Social Anxiety
Han Zhang, KaWing Tsang, Zhenhui Peng
https://arxiv.org/abs/2506.03520

VChatter: Exploring Generative Conversational Agents for Simulating Exposure Therapy to Reduce Social Anxiety
Many people struggle with social anxiety, feeling fear, or even physically uncomfortable in social situations like talking to strangers. Exposure therapy, a clinical method that gradually and repeatedly exposes individuals to the source of their fear and helps them build coping mechanisms, can reduce social anxiety but traditionally requires human therapists' guidance and constructions of situations. In this paper, we developed a multi-agent system VChatter to explore large language models(LLMs…

The conversion tool I started for Plan 9 a.out to ELF is at a good point now.
If you want to see me struggle for almost 2 hours trying to go the first steps, check out the recording of Wednesday's development live stream:
https://youtu.be/QwZmEpCNxcI

VisCoder: Fine-Tuning LLMs for Executable Python Visualization Code Generation
Yuansheng Ni, Ping Nie, Kai Zou, Xiang Yue, Wenhu Chen
https://arxiv.org/abs/2506.03930

VisCoder: Fine-Tuning LLMs for Executable Python Visualization Code Generation
Large language models (LLMs) often struggle with visualization tasks like plotting diagrams, charts, where success depends on both code correctness and visual semantics. Existing instruction-tuning datasets lack execution-grounded supervision and offer limited support for iterative code correction, resulting in fragile and unreliable plot generation. We present VisCode-200K, a large-scale instruction tuning dataset for Python-based visualization and self-correction. It contains over 200K exampl…

This https://arxiv.org/abs/2505.24200 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csSD_…

Improving Multilingual Speech Models on ML-SUPERB 2.0: Fine-tuning with Data Augmentation and LID-Aware CTC
Multilingual speech processing with self-supervised or supervised pre-trained Speech Foundation Models (SFM) has achieved strong performance on tasks like Language Identification (LID) and Automatic Speech Recognition (ASR). However, these models struggle with limited resources during fine-tuning. This paper enhances multilingual LID and ASR on ML-SUPERB 2.0 by exploring multiple strategies for adapting SFMs, including frozen upstream training, partial fine-tuning, and low-rank adaptation. Furt…

Spatio-Temporal Decoupled Learning for Spiking Neural Networks
Chenxiang Ma, Xinyi Chen, Kay Chen Tan, Jibin Wu
https://arxiv.org/abs/2506.01117 https://…

Spatio-Temporal Decoupled Learning for Spiking Neural Networks
Spiking neural networks (SNNs) have gained significant attention for their potential to enable energy-efficient artificial intelligence. However, effective and efficient training of SNNs remains an unresolved challenge. While backpropagation through time (BPTT) achieves high accuracy, it incurs substantial memory overhead. In contrast, biologically plausible local learning methods are more memory-efficient but struggle to match the accuracy of BPTT. To bridge this gap, we propose spatio-tempora…

Fast synthesis of turbulence with multi-scale coherent vortices
Zishuo Han, Weiyu Shen, Yue Yang
https://arxiv.org/abs/2506.01022 https://

Fast synthesis of turbulence with multi-scale coherent vortices
Turbulence synthesis methods often struggle to reproduce coherent vortices while capturing key statistical features. We introduce a fast synthetic turbulence method to generate instantaneous turbulent fields (termed `woven turbulence') with multi-scale vortices, combining the advantages of fractal models and coherent structures. The method generates multi-scale Burgers vortex tubes based on stochastic centerlines constructed by the fractional Brownian bridge. The largest and smallest vortices g…

Context as Memory: Scene-Consistent Interactive Long Video Generation with Memory Retrieval
Jiwen Yu, Jianhong Bai, Yiran Qin, Quande Liu, Xintao Wang, Pengfei Wan, Di Zhang, Xihui Liu
https://arxiv.org/abs/2506.03141

Context as Memory: Scene-Consistent Interactive Long Video Generation with Memory Retrieval
Recent advances in interactive video generation have shown promising results, yet existing approaches struggle with scene-consistent memory capabilities in long video generation due to limited use of historical context. In this work, we propose Context-as-Memory, which utilizes historical context as memory for video generation. It includes two simple yet effective designs: (1) storing context in frame format without additional post-processing; (2) conditioning by concatenating context and frame…

A Data-Driven Diffusion-based Approach for Audio Deepfake Explanations
Petr Grinberg, Ankur Kumar, Surya Koppisetti, Gaurav Bharaj
https://arxiv.org/abs/2506.03425

A Data-Driven Diffusion-based Approach for Audio Deepfake Explanations
Evaluating explainability techniques, such as SHAP and LRP, in the context of audio deepfake detection is challenging due to lack of clear ground truth annotations. In the cases when we are able to obtain the ground truth, we find that these methods struggle to provide accurate explanations. In this work, we propose a novel data-driven approach to identify artifact regions in deepfake audio. We consider paired real and vocoded audio, and use the difference in time-frequency representation as th…

Linear Layouts: Robust Code Generation of Efficient Tensor Computation Using $\mathbb{F}_2$
Keren Zhou, Mario Lezcano, Adam Goucher, Akhmed Rakhmati, Jeff Niu, Justin Lebar, Pawel Szczerbuk, Peter Bell, Phil Tillet, Thomas Raoux, Zahi Moudallal
https://arxiv.org/abs/2505.23819

Linear Layouts: Robust Code Generation of Efficient Tensor Computation Using $\mathbb{F}_2$
Efficient tensor computation is a cornerstone of modern deep learning (DL) workloads, yet existing approaches struggle to achieve flexible and performant design and implementation of tensor layouts -- mappings between logical tensors and hardware resources. The increasing complexity of DL algorithms and hardware demands a generic and systematic approach to handling tensor layouts. In this work, we introduce Linear Layouts, a novel approach that models tensor layouts using linear algebra over $\…

This https://arxiv.org/abs/2402.11871 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

From Real World to Logic and Back: Learning Generalizable Relational Concepts For Long Horizon Robot Planning
Humans efficiently generalize from limited demonstrations, but robots still struggle to transfer learned knowledge to complex, unseen tasks with longer horizons and increased complexity. We propose the first known method enabling robots to autonomously invent relational concepts directly from small sets of unannotated, unsegmented demonstrations. The learned symbolic concepts are grounded into logic-based world models, facilitating efficient zero-shot generalization to significantly more comple…

SRPO: Enhancing Multimodal LLM Reasoning via Reflection-Aware Reinforcement Learning
Zhongwei Wan, Zhihao Dou, Che Liu, Yu Zhang, Dongfei Cui, Qinjian Zhao, Hui Shen, Jing Xiong, Yi Xin, Yifan Jiang, Yangfan He, Mi Zhang, Shen Yan
https://arxiv.org/abs/2506.01713

SRPO: Enhancing Multimodal LLM Reasoning via Reflection-Aware Reinforcement Learning
Multimodal large language models (MLLMs) have shown promising capabilities in reasoning tasks, yet still struggle with complex problems requiring explicit self-reflection and self-correction, especially compared to their unimodal text-based counterparts. Existing reflection methods are simplistic and struggle to generate meaningful and instructive feedback, as the reasoning ability and knowledge limits of pre-trained models are largely fixed during initial training. To overcome these challenges…

This https://arxiv.org/abs/2502.00408 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

Segment Anything for Histopathology
Nucleus segmentation is an important analysis task in digital pathology. However, methods for automatic segmentation often struggle with new data from a different distribution, requiring users to manually annotate nuclei and retrain data-specific models. Vision foundation models (VFMs), such as the Segment Anything Model (SAM), offer a more robust alternative for automatic and interactive segmentation. Despite their success in natural images, a foundation model for nucleus segmentation in histo…

PUB: An LLM-Enhanced Personality-Driven User Behaviour Simulator for Recommender System Evaluation
Chenglong Ma, Ziqi Xu, Yongli Ren, Danula Hettiachchi, Jeffrey Chan
https://arxiv.org/abs/2506.04551

PUB: An LLM-Enhanced Personality-Driven User Behaviour Simulator for Recommender System Evaluation
Traditional offline evaluation methods for recommender systems struggle to capture the complexity of modern platforms due to sparse behavioural signals, noisy data, and limited modelling of user personality traits. While simulation frameworks can generate synthetic data to address these gaps, existing methods fail to replicate behavioural diversity, limiting their effectiveness. To overcome these challenges, we propose the Personality-driven User Behaviour Simulator (PUB), an LLM-based simulati…

This https://arxiv.org/abs/2505.19552 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

On scalable and efficient training of diffusion samplers
We address the challenge of training diffusion models to sample from unnormalized energy distributions in the absence of data, the so-called diffusion samplers. Although these approaches have shown promise, they struggle to scale in more demanding scenarios where energy evaluations are expensive and the sampling space is high-dimensional. To address this limitation, we propose a scalable and sample-efficient framework that properly harmonizes the powerful classical sampling method and the diffu…

This https://arxiv.org/abs/2503.23487 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csAI_…

Large Language and Reasoning Models are Shallow Disjunctive Reasoners
Large Language Models (LLMs) have been found to struggle with systematic reasoning. Even on tasks where they appear to perform well, their performance often depends on shortcuts, rather than on genuine reasoning abilities, leading them to collapse on out-of-distribution (OOD) examples. Post-training strategies based on reinforcement learning and chain-of-thought prompting have recently been hailed as a step change. However, little is known about the potential of the resulting ``Large Reasoning …

This https://arxiv.org/abs/2505.18780 has been replaced.
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One Policy but Many Worlds: A Scalable Unified Policy for Versatile Humanoid Locomotion
Humanoid locomotion faces a critical scalability challenge: traditional reinforcement learning (RL) methods require task-specific rewards and struggle to leverage growing datasets, even as more training terrains are introduced. We propose DreamPolicy, a unified framework that enables a single policy to master diverse terrains and generalize zero-shot to unseen scenarios by systematically integrating offline data and diffusion-driven motion synthesis. At its core, DreamPolicy introduces Humanoid…

PartComposer: Learning and Composing Part-Level Concepts from Single-Image Examples
Junyu Liu, R. Kenny Jones, Daniel Ritchie
https://arxiv.org/abs/2506.03004

PartComposer: Learning and Composing Part-Level Concepts from Single-Image Examples
We present PartComposer: a framework for part-level concept learning from single-image examples that enables text-to-image diffusion models to compose novel objects from meaningful components. Existing methods either struggle with effectively learning fine-grained concepts or require a large dataset as input. We propose a dynamic data synthesis pipeline generating diverse part compositions to address one-shot data scarcity. Most importantly, we propose to maximize the mutual information between…

This https://arxiv.org/abs/2505.13556 has been replaced.
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Learning Collision Risk from Naturalistic Driving with Generalised Surrogate Safety Measures
Accurate and timely alerts for drivers or automated systems to unfolding collisions remains a challenge in road safety, particularly in highly interactive urban traffic. Existing approaches require labour-intensive annotation of sparse risk, struggle to consider varying contextual factors, or are useful only in the scenarios they are designed for. To address these limits, this study introduces the generalised surrogate safety measure (GSSM), a new approach that learns exclusively from naturalis…

MultiPhishGuard: An LLM-based Multi-Agent System for Phishing Email Detection
Yinuo Xue, Eric Spero, Yun Sing Koh, Giovanni Russello
https://arxiv.org/abs/2505.23803

MultiPhishGuard: An LLM-based Multi-Agent System for Phishing Email Detection
Phishing email detection faces critical challenges from evolving adversarial tactics and heterogeneous attack patterns. Traditional detection methods, such as rule-based filters and denylists, often struggle to keep pace with these evolving tactics, leading to false negatives and compromised security. While machine learning approaches have improved detection accuracy, they still face challenges adapting to novel phishing strategies. We present MultiPhishGuard, a dynamic LLM-based multi-agent de…

This https://arxiv.org/abs/2505.08740 has been replaced.
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Sensitivity-Constrained Fourier Neural Operators for Forward and Inverse Problems in Parametric Differential Equations
Parametric differential equations of the form du/dt = f(u, x, t, p) are fundamental in science and engineering. While deep learning frameworks such as the Fourier Neural Operator (FNO) can efficiently approximate solutions, they struggle with inverse problems, sensitivity estimation (du/dp), and concept drift. We address these limitations by introducing a sensitivity-based regularization strategy, called Sensitivity-Constrained Fourier Neural Operators (SC-FNO). SC-FNO achieves high accuracy in…

HERCULES: Hardware accElerator foR stoChastic schedULing in hEterogeneous Systems
Vairavan Palaniappan, Adam H. Ross, Amit Ranjan Trivedi, Debjit Pal
https://arxiv.org/abs/2507.01113

HERCULES: Hardware accElerator foR stoChastic schedULing in hEterogeneous Systems
Efficient workload scheduling is a critical challenge in modern heterogeneous computing environments, particularly in high-performance computing (HPC) systems. Traditional software-based schedulers struggle to efficiently balance workload distribution due to high scheduling overhead, lack of adaptability to dynamic workloads, and suboptimal resource utilization. These pitfalls are compounded in heterogeneous systems, where differing computational elements can have vastly different performance p…

This https://arxiv.org/abs/2505.24785 has been replaced.
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EXP-Bench: Can AI Conduct AI Research Experiments?
Automating AI research holds immense potential for accelerating scientific progress, yet current AI agents struggle with the complexities of rigorous, end-to-end experimentation. We introduce EXP-Bench, a novel benchmark designed to systematically evaluate AI agents on complete research experiments sourced from influential AI publications. Given a research question and incomplete starter code, EXP-Bench challenges AI agents to formulate hypotheses, design and implement experimental procedures, …

Democrats struggle to come up with a unified response to Trump's Iran strikes (NBC News)
https://www.nbcnews.com/politics/congress/democrats-struggle-unified-response-trump-iran-strikes-rcna214504
http://www.memeorandum.com/250624/p3#a250624p3

Speaker Diarization with Overlapping Community Detection Using Graph Attention Networks and Label Propagation Algorithm
Zhaoyang Li, Jie Wang, XiaoXiao Li, Wangjie Li, Longjie Luo, Lin Li, Qingyang Hong
https://arxiv.org/abs/2506.02610

Speaker Diarization with Overlapping Community Detection Using Graph Attention Networks and Label Propagation Algorithm
In speaker diarization, traditional clustering-based methods remain widely used in real-world applications. However, these methods struggle with the complex distribution of speaker embeddings and overlapping speech segments. To address these limitations, we propose an Overlapping Community Detection method based on Graph Attention networks and the Label Propagation Algorithm (OCDGALP). The proposed framework comprises two key components: (1) a graph attention network that refines speaker embedd…

Reasoning to Edit: Hypothetical Instruction-Based Image Editing with Visual Reasoning
Qingdong He, Xueqin Chen, Chaoyi Wang, Yanjie Pan, Xiaobin Hu, Zhenye Gan, Yabiao Wang, Chengjie Wang, Xiangtai Li, Jiangning Zhang
https://arxiv.org/abs/2507.01908

Reasoning to Edit: Hypothetical Instruction-Based Image Editing with Visual Reasoning
Instruction-based image editing (IIE) has advanced rapidly with the success of diffusion models. However, existing efforts primarily focus on simple and explicit instructions to execute editing operations such as adding, deleting, moving, or swapping objects. They struggle to handle more complex implicit hypothetical instructions that require deeper reasoning to infer plausible visual changes and user intent. Additionally, current datasets provide limited support for training and evaluating rea…

MiCoTA: Bridging the Learnability Gap with Intermediate CoT and Teacher Assistants
Dongyi Ding, Tiannan Wang, Chenghao Zhu, Meiling Tao, Yuchen Eleanor Jiang, Wangchunshu Zhou
https://arxiv.org/abs/2507.01887

MiCoTA: Bridging the Learnability Gap with Intermediate CoT and Teacher Assistants
Large language models (LLMs) excel at reasoning tasks requiring long thought sequences for planning, reflection, and refinement. However, their substantial model size and high computational demands are impractical for widespread deployment. Yet, small language models (SLMs) often struggle to learn long-form CoT reasoning due to their limited capacity, a phenomenon we refer to as the "SLMs Learnability Gap". To address this, we introduce \textbf{Mi}d-\textbf{Co}T \textbf{T}eacher \textbf{A}ssist…

GenFair: Systematic Test Generation for Fairness Fault Detection in Large Language Models
Madhusudan Srinivasan, Jubril Abdel
https://arxiv.org/abs/2506.03024

GenFair: Systematic Test Generation for Fairness Fault Detection in Large Language Models
Large Language Models (LLMs) are increasingly deployed in critical domains, yet they often exhibit biases inherited from training data, leading to fairness concerns. This work focuses on the problem of effectively detecting fairness violations, especially intersectional biases that are often missed by existing template-based and grammar-based testing methods. Previous approaches, such as CheckList and ASTRAEA, provide structured or grammar-driven test generation but struggle with low test diver…

This https://arxiv.org/abs/2505.23667 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csAI_…

Fortune: Formula-Driven Reinforcement Learning for Symbolic Table Reasoning in Language Models
Tables are a fundamental structure for organizing and analyzing data, making effective table understanding a critical capability for intelligent systems. While large language models (LMs) demonstrate strong general reasoning abilities, they continue to struggle with accurate numerical or symbolic reasoning over tabular data, especially in complex scenarios. Spreadsheet formulas provide a powerful and expressive medium for representing executable symbolic operations, encoding rich reasoning patt…

This https://arxiv.org/abs/2503.00741 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

LesionDiffusion: Towards Text-controlled General Lesion Synthesis
Fully-supervised lesion recognition methods in medical imaging face challenges due to the reliance on large annotated datasets, which are expensive and difficult to collect. To address this, synthetic lesion generation has become a promising approach. However, existing models struggle with scalability, fine-grained control over lesion attributes, and the generation of complex structures. We propose LesionDiffusion, a text-controllable lesion synthesis framework for 3D CT imaging that generates …

Reasoning-Table: Exploring Reinforcement Learning for Table Reasoning
Fangyu Lei, Jinxiang Meng, Yiming Huang, Tinghong Chen, Yun Zhang, Shizhu He, Jun Zhao, Kang Liu
https://arxiv.org/abs/2506.01710

Reasoning-Table: Exploring Reinforcement Learning for Table Reasoning
Table reasoning, encompassing tasks such as table question answering, fact verification, and text-to-SQL, requires precise understanding of structured tabular data, coupled with numerical computation and code manipulation for effective inference. Supervised fine-tuning (SFT) approaches have achieved notable success but often struggle with generalization and robustness due to biases inherent in imitative learning. We introduce Reasoning-Table, the first application of reinforcement learning (RL)…

This https://arxiv.org/abs/2410.05740 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

Learning to Drift in Extreme Turning with Active Exploration and Gaussian Process Based MPC
Extreme cornering in racing often leads to large sideslip angles, presenting a significant challenge for vehicle control. Conventional vehicle controllers struggle to manage this scenario, necessitating the use of a drifting controller. However, the large sideslip angle in drift conditions introduces model mismatch, which in turn affects control precision. To address this issue, we propose a model correction drift controller that integrates Model Predictive Control (MPC) with Gaussian Process R…

This https://arxiv.org/abs/2410.21801 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIR_…

PerSRV: Personalized Sticker Retrieval with Vision-Language Model
Instant Messaging is a popular means for daily communication, allowing users to send text and stickers. As the saying goes, "a picture is worth a thousand words", so developing an effective sticker retrieval technique is crucial for enhancing user experience. However, existing sticker retrieval methods rely on labeled data to interpret stickers, and general-purpose Vision-Language Models (VLMs) often struggle to capture the unique semantics of stickers. Additionally, relevant-based sticker retr…

This https://arxiv.org/abs/2503.16167 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csSE_…

CodeReviewQA: The Code Review Comprehension Assessment for Large Language Models
State-of-the-art large language models (LLMs) have demonstrated impressive code generation capabilities but struggle with real-world software engineering tasks, such as revising source code to address code reviews, hindering their practical use. Code review comments are often implicit, ambiguous, and colloquial, requiring models to grasp both code and human intent. This challenge calls for evaluating large language models' ability to bridge both technical and conversational contexts. While exis…

This https://arxiv.org/abs/2504.18253 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

Depth-Constrained ASV Navigation with Deep RL and Limited Sensing
Autonomous Surface Vehicles (ASVs) play a crucial role in maritime operations, yet their navigation in shallow-water environments remains challenging due to dynamic disturbances and depth constraints. Traditional navigation strategies struggle with limited sensor information, making safe and efficient operation difficult. In this paper, we propose a reinforcement learning (RL) framework for ASV navigation under depth constraints, where the vehicle must reach a target while avoiding unsafe areas…

Moment Sampling in Video LLMs for Long-Form Video QA
Mustafa Chasmai, Gauri Jagatap, Gouthaman KV, Grant Van Horn, Subhransu Maji, Andrea Fanelli
https://arxiv.org/abs/2507.00033 …

Moment Sampling in Video LLMs for Long-Form Video QA
Recent advancements in video large language models (Video LLMs) have significantly advanced the field of video question answering (VideoQA). While existing methods perform well on short videos, they often struggle with long-range reasoning in longer videos. To scale Video LLMs for longer video content, frame sub-sampling (selecting frames at regular intervals) is commonly used. However, this approach is suboptimal, often leading to the loss of crucial frames or the inclusion of redundant inform…

Spatial Coordinates as a Cell Language: A Multi-Sentence Framework for Imaging Mass Cytometry Analysis
Chi-Jane Chen, Yuhang Chen, Sukwon Yun, Natalie Stanley, Tianlong Chen
https://arxiv.org/abs/2506.01918

Spatial Coordinates as a Cell Language: A Multi-Sentence Framework for Imaging Mass Cytometry Analysis
Image mass cytometry (IMC) enables high-dimensional spatial profiling by combining mass cytometry's analytical power with spatial distributions of cell phenotypes. Recent studies leverage large language models (LLMs) to extract cell states by translating gene or protein expression into biological context. However, existing single-cell LLMs face two major challenges: (1) Integration of spatial information: they struggle to generalize spatial coordinates and effectively encode spatial context as …

This https://arxiv.org/abs/2503.19449 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csSE_…

VecTrans: Enhancing Compiler Auto-Vectorization through LLM-Assisted Code Transformations
Auto-vectorization is a fundamental optimization for modern compilers to exploit SIMD parallelism. However, state-of-the-art approaches still struggle to handle intricate code patterns, often requiring manual hints or domain-specific expertise. Large language models (LLMs), with their ability to capture intricate patterns, provide a promising solution, yet their effective application in compiler optimizations remains an open challenge due to issues such as hallucinations and a lack of domain-sp…

UTCS: Effective Unsupervised Temporal Community Search with Pre-training of Temporal Dynamics and Subgraph Knowledge
Yue Zhang, Yankai Chen, Yingli Zhou, Yucan Guo, Xiaolin Han, Chenhao Ma
https://arxiv.org/abs/2506.02784

UTCS: Effective Unsupervised Temporal Community Search with Pre-training of Temporal Dynamics and Subgraph Knowledge
In many real-world applications, the evolving relationships between entities can be modeled as temporal graphs, where each edge has a timestamp representing the interaction time. As a fundamental problem in graph analysis, {\it community search (CS)} in temporal graphs has received growing attention but exhibits two major limitations: (1) Traditional methods typically require predefined subgraph structures, which are not always known in advance. (2) Learning-based methods struggle to capture …

DiffDSR: Dysarthric Speech Reconstruction Using Latent Diffusion Model
Xueyuan Chen, Dongchao Yang, Wenxuan Wu, Minglin Wu, Jing Xu, Xixin Wu, Zhiyong Wu, Helen Meng
https://arxiv.org/abs/2506.00350

DiffDSR: Dysarthric Speech Reconstruction Using Latent Diffusion Model
Dysarthric speech reconstruction (DSR) aims to convert dysarthric speech into comprehensible speech while maintaining the speaker's identity. Despite significant advancements, existing methods often struggle with low speech intelligibility and poor speaker similarity. In this study, we introduce a novel diffusion-based DSR system that leverages a latent diffusion model to enhance the quality of speech reconstruction. Our model comprises: (i) a speech content encoder for phoneme embedding restor…

This https://arxiv.org/abs/2505.23576 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

Cognitive Guardrails for Open-World Decision Making in Autonomous Drone Swarms
Small Uncrewed Aerial Systems (sUAS) are increasingly deployed as autonomous swarms in search-and-rescue and other disaster-response scenarios. In these settings, they use computer vision (CV) to detect objects of interest and autonomously adapt their missions. However, traditional CV systems often struggle to recognize unfamiliar objects in open-world environments or to infer their relevance for mission planning. To address this, we incorporate large language models (LLMs) to reason about dete…

Toward Knowledge-Guided AI for Inverse Design in Manufacturing: A Perspective on Domain, Physics, and Human-AI Synergy
Hugon Lee, Hyeonbin Moon, Junhyeong Lee, Seunghwa RYu
https://arxiv.org/abs/2506.00056

Toward Knowledge-Guided AI for Inverse Design in Manufacturing: A Perspective on Domain, Physics, and Human-AI Synergy
Artificial intelligence (AI) is reshaping inverse design across manufacturing domain, enabling high-performance discovery in materials, products, and processes. However, purely data-driven approaches often struggle in realistic settings characterized by sparse data, high-dimensional design spaces, and nontrivial physical constraints. This perspective argues for a new generation of design systems that transcend black-box modeling by integrating domain knowledge, physics-informed learning, and in…

This https://arxiv.org/abs/2401.16310 has been replaced.
link: https://scholar.google.com/scholar?q=a

An Insight into Security Code Review with LLMs: Capabilities, Obstacles, and Influential Factors
Security code review is a time-consuming and labor-intensive process typically requiring integration with automated security defect detection tools. However, existing security analysis tools struggle with poor generalization, high false positive rates, and coarse detection granularity. Large Language Models (LLMs) have been considered promising candidates for addressing those challenges. In this study, we conducted an empirical study to explore the potential of LLMs in detecting security defect…

Active Learning for Forecasting Severity among Patients with Post Acute Sequelae of SARS-CoV-2
Jing Wang, Amar Sra, Jeremy C. Weiss
https://arxiv.org/abs/2506.22444

Active Learning for Forecasting Severity among Patients with Post Acute Sequelae of SARS-CoV-2
The long-term effects of Postacute Sequelae of SARS-CoV-2, known as PASC, pose a significant challenge to healthcare systems worldwide. Accurate identification of progression events, such as hospitalization and reinfection, is essential for effective patient management and resource allocation. However, traditional models trained on structured data struggle to capture the nuanced progression of PASC. In this study, we introduce the first publicly available cohort of 18 PASC patients, with text t…

Catastrophic Forgetting Mitigation via Discrepancy-Weighted Experience Replay
Xinrun Xu, Jianwen Yang, Qiuhong Zhang, Zhanbiao Lian, Zhiming Ding, Shan Jiang
https://arxiv.org/abs/2507.00042

Catastrophic Forgetting Mitigation via Discrepancy-Weighted Experience Replay
Continually adapting edge models in cloud-edge collaborative object detection for traffic monitoring suffers from catastrophic forgetting, where models lose previously learned knowledge when adapting to new data distributions. This is especially problematic in dynamic traffic environments characterised by periodic variations (e.g., day/night, peak hours), where past knowledge remains valuable. Existing approaches like experience replay and visual prompts offer some mitigation, but struggle to e…

Reinforcement Learning with Data Bootstrapping for Dynamic Subgoal Pursuit in Humanoid Robot Navigation
Chengyang Peng, Zhihao Zhang, Shiting Gong, Sankalp Agrawal, Keith A. Redmill, Ayonga Hereid
https://arxiv.org/abs/2506.02206

Reinforcement Learning with Data Bootstrapping for Dynamic Subgoal Pursuit in Humanoid Robot Navigation
Safe and real-time navigation is fundamental for humanoid robot applications. However, existing bipedal robot navigation frameworks often struggle to balance computational efficiency with the precision required for stable locomotion. We propose a novel hierarchical framework that continuously generates dynamic subgoals to guide the robot through cluttered environments. Our method comprises a high-level reinforcement learning (RL) planner for subgoal selection in a robot-centric coordinate syste…

Precisely Detecting Python Type Errors via LLM-based Unit Test Generation
Chen Yang, Ziqi Wang, Yanjie Jiang, Lin Yang, Yuteng Zheng, Jianyi Zhou, Junjie Chen
https://arxiv.org/abs/2507.02318

Precisely Detecting Python Type Errors via LLM-based Unit Test Generation
Type errors in Python often lead to runtime failures, posing significant challenges to software reliability and developer productivity. Existing static analysis tools aim to detect such errors without execution but frequently suffer from high false positive rates. Recently, unit test generation techniques offer great promise in achieving high test coverage, but they often struggle to produce bug-revealing tests without tailored guidance. To address these limitations, we present RTED, a novel ty…

Jailbreak-R1: Exploring the Jailbreak Capabilities of LLMs via Reinforcement Learning
Weiyang Guo, Zesheng Shi, Zhuo Li, Yequan Wang, Xuebo Liu, Wenya Wang, Fangming Liu, Min Zhang, Jing Li
https://arxiv.org/abs/2506.00782

Jailbreak-R1: Exploring the Jailbreak Capabilities of LLMs via Reinforcement Learning
As large language models (LLMs) grow in power and influence, ensuring their safety and preventing harmful output becomes critical. Automated red teaming serves as a tool to detect security vulnerabilities in LLMs without manual labor. However, most existing methods struggle to balance the effectiveness and diversity of red-team generated attack prompts. To address this challenge, we propose \ourapproach, a novel automated red teaming training framework that utilizes reinforcement learning to ex…

MISC: Minimal Intervention Shared Control with Guaranteed Safety under Non-Convex Constraints
Shivam Chaubey, Francesco Verdoja, Shankar Deka, Ville Kyrki
https://arxiv.org/abs/2507.02438

MISC: Minimal Intervention Shared Control with Guaranteed Safety under Non-Convex Constraints
Shared control combines human intention with autonomous decision-making, from low-level safety overrides to high-level task guidance, enabling systems that adapt to users while ensuring safety and performance. This enhances task effectiveness and user experience across domains such as assistive robotics, teleoperation, and autonomous driving. However, existing shared control methods, based on e.g. Model Predictive Control, Control Barrier Functions, or learning-based control, struggle with feas…

Computational Thinking Reasoning in Large Language Models
Kechi Zhang, Ge Li, Jia Li, Huangzhao Zhang, Jingjing Xu, Hao Zhu, Lecheng Wang, Jia Li, Yihong Dong, Jing Mai, Bin Gu, Zhi Jin
https://arxiv.org/abs/2506.02658

Computational Thinking Reasoning in Large Language Models
While large language models (LLMs) have demonstrated remarkable reasoning capabilities, they often struggle with complex tasks that require specific thinking paradigms, such as divide-and-conquer and procedural deduction, \etc Previous researches integrate external, reliable tools to alleviate logical inconsistencies and hallucinations in LLMs' problem-solving processes. However, we argue that the root challenge is more profound: LLMs lack the complex thinking paradigms (\ie, computational thin…

Failure by Interference: Language Models Make Balanced Parentheses Errors When Faulty Mechanisms Overshadow Sound Ones
Daking Rai, Samuel Miller, Kevin Moran, Ziyu Yao
https://arxiv.org/abs/2507.00322

Failure by Interference: Language Models Make Balanced Parentheses Errors When Faulty Mechanisms Overshadow Sound Ones
Despite remarkable advances in coding capabilities, language models (LMs) still struggle with simple syntactic tasks such as generating balanced parentheses. In this study, we investigate the underlying mechanisms behind the persistence of these errors across LMs of varying sizes (124M-7B) to both understand and mitigate the errors. Our study reveals that LMs rely on a number of components (attention heads and FF neurons) that independently make their own predictions. While some components reli…

This https://arxiv.org/abs/2504.13703 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIR_…

Consensus-aware Contrastive Learning for Group Recommendation
Group recommendation aims to provide personalized item suggestions to a group of users by reflecting their collective preferences. A fundamental challenge in this task is deriving a consensus that adequately represents the diverse interests of individual group members. Despite advancements made by deep learning-based models, existing approaches still struggle in two main areas: (1) Capturing consensus in small-group settings, which are more prevalent in real-world applications, and (2) Balancin…

This https://arxiv.org/abs/2503.22030 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

Bayesian Inferential Motion Planning Using Heavy-Tailed Distributions
Robots rely on motion planning to navigate safely and efficiently while performing various tasks. In this paper, we investigate motion planning through Bayesian inference, where motion plans are inferred based on planning objectives and constraints. However, existing Bayesian motion planning methods often struggle to explore low-probability regions of the planning space, where high-quality plans may reside. To address this limitation, we propose the use of heavy-tailed distributions -- specific…

Graph Contrastive Learning for Optimizing Sparse Data in Recommender Systems with LightGCL
Aravinda Jatavallabha, Prabhanjan Bharadwaj, Ashish Chander
https://arxiv.org/abs/2506.00048

Graph Contrastive Learning for Optimizing Sparse Data in Recommender Systems with LightGCL
Graph Neural Networks (GNNs) are powerful tools for recommendation systems, but they often struggle under data sparsity and noise. To address these issues, we implemented LightGCL, a graph contrastive learning model that uses Singular Value Decomposition (SVD) for robust graph augmentation, preserving semantic integrity without relying on stochastic or heuristic perturbations. LightGCL enables structural refinement and captures global collaborative signals, achieving significant gains over stat…

Feel the Force: Contact-Driven Learning from Humans
Ademi Adeniji, Zhuoran Chen, Vincent Liu, Venkatesh Pattabiraman, Raunaq Bhirangi, Siddhant Haldar, Pieter Abbeel, Lerrel Pinto
https://arxiv.org/abs/2506.01944

Feel the Force: Contact-Driven Learning from Humans
Controlling fine-grained forces during manipulation remains a core challenge in robotics. While robot policies learned from robot-collected data or simulation show promise, they struggle to generalize across the diverse range of real-world interactions. Learning directly from humans offers a scalable solution, enabling demonstrators to perform skills in their natural embodiment and in everyday environments. However, visual demonstrations alone lack the information needed to infer precise contac…

WoMAP: World Models For Embodied Open-Vocabulary Object Localization
Tenny Yin, Zhiting Mei, Tao Sun, Lihan Zha, Emily Zhou, Jeremy Bao, Miyu Yamane, Ola Shorinwa, Anirudha Majumdar
https://arxiv.org/abs/2506.01600

WoMAP: World Models For Embodied Open-Vocabulary Object Localization
Language-instructed active object localization is a critical challenge for robots, requiring efficient exploration of partially observable environments. However, state-of-the-art approaches either struggle to generalize beyond demonstration datasets (e.g., imitation learning methods) or fail to generate physically grounded actions (e.g., VLMs). To address these limitations, we introduce WoMAP (World Models for Active Perception): a recipe for training open-vocabulary object localization policie…

Disturbance-Aware Adaptive Compensation in Hybrid Force-Position Locomotion Policy for Legged Robots
Yang Zhang, Buqing Nie, Zhanxiang Cao, Yangqing Fu, Yue Gao
https://arxiv.org/abs/2506.00472

Disturbance-Aware Adaptive Compensation in Hybrid Force-Position Locomotion Policy for Legged Robots
Reinforcement Learning (RL)-based methods have significantly improved the locomotion performance of legged robots. However, these motion policies face significant challenges when deployed in the real world. Robots operating in uncertain environments struggle to adapt to payload variations and external disturbances, resulting in severe degradation of motion performance. In this work, we propose a novel Hybrid Force-Position Locomotion Policy (HFPLP) learning framework, where the action space of …

OG-VLA: 3D-Aware Vision Language Action Model via Orthographic Image Generation
Ishika Singh, Ankit Goyal, Stan Birchfield, Dieter Fox, Animesh Garg, Valts Blukis
https://arxiv.org/abs/2506.01196

OG-VLA: 3D-Aware Vision Language Action Model via Orthographic Image Generation
We introduce OG-VLA, a novel architecture and learning framework that combines the generalization strengths of Vision Language Action models (VLAs) with the robustness of 3D-aware policies. We address the challenge of mapping natural language instructions and multi-view RGBD observations to quasi-static robot actions. 3D-aware robot policies achieve state-of-the-art performance on precise robot manipulation tasks, but struggle with generalization to unseen instructions, scenes, and objects. On …

Online Human Action Detection during Escorting
Siddhartha Mondal, Avik Mitra, Chayan Sarkar
https://arxiv.org/abs/2506.23573 https://…

Online Human Action Detection during Escorting
The deployment of robot assistants in large indoor spaces has seen significant growth, with escorting tasks becoming a key application. However, most current escorting robots primarily rely on navigation-focused strategies, assuming that the person being escorted will follow without issue. In crowded environments, this assumption often falls short, as individuals may struggle to keep pace, become obstructed, get distracted, or need to stop unexpectedly. As a result, conventional robotic systems…