Tootfinder

Augment or Not? A Comparative Study of Pure and Augmented Large Language Model Recommenders
Wei-Hsiang Huang, Chen-Wei Ke, Wei-Ning Chiu, Yu-Xuan Su, Chun-Chun Yang, Chieh-Yuan Cheng, Yun-Nung Chen, Pu-Jen Cheng
https://arxiv.org/abs/2505.23053

Augment or Not? A Comparative Study of Pure and Augmented Large Language Model Recommenders
Large language models (LLMs) have introduced new paradigms for recommender systems by enabling richer semantic understanding and incorporating implicit world knowledge. In this study, we propose a systematic taxonomy that classifies existing approaches into two categories: (1) Pure LLM Recommenders, which rely solely on LLMs, and (2) Augmented LLM Recommenders, which integrate additional non-LLM techniques to enhance performance. This taxonomy provides a novel lens through which to examine the …

Enhancing Health Fact-Checking with LLM-Generated Synthetic Data
Jingze Zhang, Jiahe Qian, Yiliang Zhou, Yifan Peng
https://arxiv.org/abs/2508.20525 https://

Enhancing Health Fact-Checking with LLM-Generated Synthetic Data
Fact-checking for health-related content is challenging due to the limited availability of annotated training data. In this study, we propose a synthetic data generation pipeline that leverages large language models (LLMs) to augment training data for health-related fact checking. In this pipeline, we summarize source documents, decompose the summaries into atomic facts, and use an LLM to construct sentence-fact entailment tables. From the entailment relations in the table, we further generate …

Memory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learning
Sikuan Yan, Xiufeng Yang, Zuchao Huang, Ercong Nie, Zifeng Ding, Zonggen Li, Xiaowen Ma, Hinrich Sch\"utze, Volker Tresp, Yunpu Ma
https://arxiv.org/abs/2508.19828

Memory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learning
Large Language Models (LLMs) have demonstrated impressive capabilities across a wide range of NLP tasks, but they remain fundamentally stateless, constrained by limited context windows that hinder long-horizon reasoning. Recent efforts to address this limitation often augment LLMs with an external memory bank, yet most existing pipelines are static and heuristic-driven, lacking any learned mechanism for deciding what to store, update, or retrieve. We present Memory-R1, a reinforcement learning …

People Are Highly Cooperative with Large Language Models, Especially When Communication Is Possible or Following Human Interaction
Pawe{\l} Niszczota, Tomasz Grzegorczyk, Alexander Pastukhov
https://arxiv.org/abs/2507.18639

People Are Highly Cooperative with Large Language Models, Especially When Communication Is Possible or Following Human Interaction
Machines driven by large language models (LLMs) have the potential to augment humans across various tasks, a development with profound implications for business settings where effective communication, collaboration, and stakeholder trust are paramount. To explore how interacting with an LLM instead of a human might shift cooperative behavior in such settings, we used the Prisoner's Dilemma game -- a surrogate of several real-world managerial and economic scenarios. In Experiment 1 (N=100), part…

Understanding, Protecting, and Augmenting Human Cognition with Generative AI: A Synthesis of the CHI 2025 Tools for Thought Workshop
Lev Tankelevitch, Elena L. Glassman, Jessica He, Aniket Kittur, Mina Lee, Srishti Palani, Advait Sarkar, Gonzalo Ramos, Yvonne Rogers, Hari Subramonyam
https://arxiv.org/abs/2508.21036

Understanding, Protecting, and Augmenting Human Cognition with Generative AI: A Synthesis of the CHI 2025 Tools for Thought Workshop
Generative AI (GenAI) radically expands the scope and capability of automation for work, education, and everyday tasks, a transformation posing both risks and opportunities for human cognition. How will human cognition change, and what opportunities are there for GenAI to augment it? Which theories, metrics, and other tools are needed to address these questions? The CHI 2025 workshop on Tools for Thought aimed to bridge an emerging science of how the use of GenAI affects human thought, from met…

Injecting External Knowledge into the Reasoning Process Enhances Retrieval-Augmented Generation
Minghao Tang, Shiyu Ni, Jiafeng Guo, Keping Bi
https://arxiv.org/abs/2507.19333 h…

Injecting External Knowledge into the Reasoning Process Enhances Retrieval-Augmented Generation
Retrieval-augmented generation (RAG) has been widely adopted to augment large language models (LLMs) with external knowledge for knowledge-intensive tasks. However, its effectiveness is often undermined by the presence of noisy (i.e., low-quality) retrieved passages. Enhancing LLMs' robustness to such noise is critical for improving the reliability of RAG systems. Recent advances have equipped LLMs with strong reasoning and self-reflection capabilities, allowing them to identify and correct err…

LLM-Synth4KWS: Scalable Automatic Generation and Synthesis of Confusable Data for Custom Keyword Spotting
Pai Zhu, Quan Wang, Dhruuv Agarwal, Kurt Partridge
https://arxiv.org/abs/2505.22995

LLM-Synth4KWS: Scalable Automatic Generation and Synthesis of Confusable Data for Custom Keyword Spotting
Custom keyword spotting (KWS) allows detecting user-defined spoken keywords from streaming audio. This is achieved by comparing the embeddings from voice enrollments and input audio. State-of-the-art custom KWS models are typically trained contrastively using utterances whose keywords are randomly sampled from training dataset. These KWS models often struggle with confusing keywords, such as "blue" versus "glue". This paper introduces an effective way to augment the training with confusable utt…

Piecewise Linear Approximation in Learned Index Structures: Theoretical and Empirical Analysis
Jiayong Qin, Xianyu Zhu, Qiyu Liu, Guangyi Zhang, Zhigang Cai, Jianwei Liao, Sha Hu, Jingshu Peng, Yingxia Shao, Lei Chen
https://arxiv.org/abs/2506.20139

Piecewise Linear Approximation in Learned Index Structures: Theoretical and Empirical Analysis
A growing trend in the database and system communities is to augment conventional index structures, such as B+-trees, with machine learning (ML) models. Among these, error-bounded Piecewise Linear Approximation ($ε$-PLA) has emerged as a popular choice due to its simplicity and effectiveness. Despite its central role in many learned indexes, the design and analysis of $ε$-PLA fitting algorithms remain underexplored. In this paper, we revisit $ε$-PLA from both theoretical and empirical perspe…

Improving Multislice Electron Ptychography with a Generative Prior
Christian K. Belardi, Chia-Hao Lee, Yingheng Wang, Justin Lovelace, Kilian Q. Weinberger, David A. Muller, Carla P. Gomes
https://arxiv.org/abs/2507.17800

Improving Multislice Electron Ptychography with a Generative Prior
Multislice electron ptychography (MEP) is an inverse imaging technique that computationally reconstructs the highest-resolution images of atomic crystal structures from diffraction patterns. Available algorithms often solve this inverse problem iteratively but are both time consuming and produce suboptimal solutions due to their ill-posed nature. We develop MEP-Diffusion, a diffusion model trained on a large database of crystal structures specifically for MEP to augment existing iterative solve…

Application of Whisper in Clinical Practice: the Post-Stroke Speech Assessment during a Naming Task
Milena Davudova, Ziyuan Cai, Valentina Giunchiglia, Dragos C. Gruia, Giulia Sanguedolce, Adam Hampshire, Fatemeh Geranmayeh
https://arxiv.org/abs/2507.17326

Application of Whisper in Clinical Practice: the Post-Stroke Speech Assessment during a Naming Task
Detailed assessment of language impairment following stroke remains a cognitively complex and clinician-intensive task, limiting timely and scalable diagnosis. Automatic Speech Recognition (ASR) foundation models offer a promising pathway to augment human evaluation through intelligent systems, but their effectiveness in the context of speech and language impairment remains uncertain. In this study, we evaluate whether Whisper, a state-of-the-art ASR foundation model, can be applied to transcri…

In a saner environment, we’d be having a reasonable conversation about that: in what ways, if any, can a machine that repeats contextual patterns with no sense of meaning augment humans thinking through tricky things? In ways can it mislead? When, if ever, is it worth the tradeoffs? the resource costs? etc etc.
Right now, the off-the-charts money and hype make that reasonable conversation impossible except perhaps in hushed corners. (Please do not have that argument in my replies. I am tired.)
7/

Evaluating virtual-control-augmented trials for reproducing treatment effect from original RCTs
Alex Fernandes, Rapha\"el Porcher, Viet-Thi Tran, Fran\c{c}ois Petit
https://arxiv.org/abs/2507.16048

Evaluating virtual-control-augmented trials for reproducing treatment effect from original RCTs
This study investigates the use of virtual patient data to augment control arms in randomised controlled trials (RCTs). Using data from the IST and IST3 trials, we simulated RCTs in which the recruitment in the control arms would stop after a fraction of the initially planned sample size, and would be completed by virtual patients generated by CTGAN and TVAE, two AI algorithms trained on the recruited control patients. In IST, the absolute risk difference(ARD) on death or dependency at 14 days …

Weighted Assumption Based Argumentation to reason about ethical principles and actions
Paolo Baldi, Fabio Aurelio D'Asaro, Abeer Dyoub, Francesca Alessandra Lisi
https://arxiv.org/abs/2506.18056

Weighted Assumption Based Argumentation to reason about ethical principles and actions
We augment Assumption Based Argumentation (ABA for short) with weighted argumentation. In a nutshell, we assign weights to arguments and then derive the weight of attacks between ABA arguments. We illustrate our proposal through running examples in the field of ethical reasoning, and present an implementation based on Answer Set Programming.

Over the past few months, I've been building out a home server that was originally purely to run on-prem LLMs. Since I wanted it to run killer AI, I named it "Delores." Over the weekend, I received a Mac Mini in the mail that I got a good deal on and set it up to augment Dolores. I was trying to figure out what to name it and eventually went with "Teddy."
From my laptop, iPads, etc, I can remote into an Ubuntu VM, Windows VM, or (now) MacOS and do whatever I am looking to do.

Foreboding only intensified Monday,
when Trump applauded the idea of arresting California Gov. Gavin Newsom (D),
followed by the mobilization of hundreds of active-duty Marines to augment the Guard members already in Los Angeles.

In a city that has increasingly found itself in the crosshairs of the federal government, turmoil can feel personal.
Local leaders accuse the Trump administration of ginning up a crisis for political gain,
and Angelenos from across …

‘He’s waging a war on us’: As Trump escalates, Angelenos defend their city
Across this vast metropolis, residents denounce what they consider an attack on the very fabric of their city — on their neighbors, co-workers, family members.

SocialCredit
Thabassum Aslam, Anees Aslam
https://arxiv.org/abs/2506.12099 https://arxiv.org/pdf/2506.12099

SocialCredit+
SocialCredit+ is AI powered credit scoring system that leverages publicly available social media data to augment traditional credit evaluation. It uses a conversational banking assistant to gather user consent and fetch public profiles. Multimodal feature extractors analyze posts, bios, images, and friend networks to generate a rich behavioral profile. A specialized Sharia-compliance layer flags any non-halal indicators and prohibited financial behavior based on Islamic ethics. The platform emp…

Make Your AUV Adaptive: An Environment-Aware Reinforcement Learning Framework For Underwater Tasks
Yimian Ding, Jingzehua Xu, Guanwen Xie, Shuai Zhang, Yi Li
https://arxiv.org/abs/2506.15082

Make Your AUV Adaptive: An Environment-Aware Reinforcement Learning Framework For Underwater Tasks
This study presents a novel environment-aware reinforcement learning (RL) framework designed to augment the operational capabilities of autonomous underwater vehicles (AUVs) in underwater environments. Departing from traditional RL architectures, the proposed framework integrates an environment-aware network module that dynamically captures flow field data, effectively embedding this critical environmental information into the state space. This integration facilitates real-time environmental ad…

Augmenting the Generality and Performance of Large Language Models for Software Engineering
Fabian C. Pe\~na
https://arxiv.org/abs/2506.11548 https://

Augmenting the Generality and Performance of Large Language Models for Software Engineering
Large Language Models (LLMs) are revolutionizing software engineering (SE), with special emphasis on code generation and analysis. However, their applications to broader SE practices including conceptualization, design, and other non-code tasks, remain partially underexplored. This research aims to augment the generality and performance of LLMs for SE by (1) advancing the understanding of how LLMs with different characteristics perform on various non-code tasks, (2) evaluating them as sources o…

MGVQ: Could VQ-VAE Beat VAE? A Generalizable Tokenizer with Multi-group Quantization
Mingkai Jia, Wei Yin, Xiaotao Hu, Jiaxin Guo, Xiaoyang Guo, Qian Zhang, Xiao-Xiao Long, Ping Tan
https://arxiv.org/abs/2507.07997

MGVQ: Could VQ-VAE Beat VAE? A Generalizable Tokenizer with Multi-group Quantization
Vector Quantized Variational Autoencoders (VQ-VAEs) are fundamental models that compress continuous visual data into discrete tokens. Existing methods have tried to improve the quantization strategy for better reconstruction quality, however, there still exists a large gap between VQ-VAEs and VAEs. To narrow this gap, we propose \NickName, a novel method to augment the representation capability of discrete codebooks, facilitating easier optimization for codebooks and minimizing information loss…

Using Language and Road Manuals to Inform Map Reconstruction for Autonomous Driving
Akshar Tumu, Henrik I. Christensen, Marcell Vazquez-Chanlatte, Chikao Tsuchiya, Dhaval Bhanderi
https://arxiv.org/abs/2506.10317

Using Language and Road Manuals to Inform Map Reconstruction for Autonomous Driving
Lane-topology prediction is a critical component of safe and reliable autonomous navigation. An accurate understanding of the road environment aids this task. We observe that this information often follows conventions encoded in natural language, through design codes that reflect the road structure and road names that capture the road functionality. We augment this information in a lightweight manner to SMERF, a map-prior-based online lane-topology prediction model, by combining structured road…

Frosty for partial synchrony
Stephen Buttolph, Andrew Lewis-Pye, Kevin Sekniqi
https://arxiv.org/abs/2506.09823 https://arxiv.org/pdf…

Frosty for partial synchrony
Snowman is the consensus protocol used by blockchains on Avalanche. Recent work has shown both how to augment Snowman with a `liveness' module called `Frosty' that protects against liveness attacks, and also how to modify Snowman so as to be consistent in partial synchrony. Since Frosty assumes (a strong form of) synchrony, the aim of this note is to show how to modify Frosty to deal with the partially synchronous version of Snowman.

Harnessing the Power of Reinforcement Learning for Language-Model-Based Information Retriever via Query-Document Co-Augmentation
Jingming Liu, Yumeng Li, Wei Shi, Yao-Xiang Ding, Hui Su, Kun Zhou
https://arxiv.org/abs/2506.18670

Harnessing the Power of Reinforcement Learning for Language-Model-Based Information Retriever via Query-Document Co-Augmentation
Recent studies have proposed leveraging Large Language Models (LLMs) as information retrievers through query rewriting. However, for challenging corpora, we argue that enhancing queries alone is insufficient for robust semantic matching; the LLM should also have sufficient understanding of the corpus by directly handling and augmenting the documents themselves. To this end, we present an LLM-based retriever empowered to augment both user queries and corpus documents, with its policy fully explo…

BioSonix: Can Physics-Based Sonification Perceptualize Tissue Deformations From Tool Interactions?
Veronica Ruozzi, Sasan Matinfar, Laura Sch\"utz, Benedikt Wiestler, Alberto Redaelli, Emiliano Votta, Nassir Navab
https://arxiv.org/abs/2508.14688

BioSonix: Can Physics-Based Sonification Perceptualize Tissue Deformations From Tool Interactions?
Perceptualizing tool interactions with deformable structures in surgical procedures remains challenging, as unimodal visualization techniques often fail to capture the complexity of these interactions due to constraints such as occlusion and limited depth perception. This paper presents a novel approach to augment tool navigation in mixed reality environments by providing auditory representations of tool-tissue dynamics, particularly for interactions with soft tissue. BioSonix, a physics-inform…

Decision algorithms for fragments of real analysis. III: A theory of differentiable functions with (semi-)open intervals
G. Buriola, D. Cantone, G. Cincotti, E. G. Omodeo, G. T. Spart\`a
https://arxiv.org/abs/2507.02742

Decision algorithms for fragments of real analysis. III: A theory of differentiable functions with (semi-)open intervals
This paper enriches preexisting satisfiability tests for unquantified languages, which in turn augment a fragment of Tarski's elementary algebra with unary real functions possessing a continuous first derivative. Two sorts of individual variables are available, one ranging over real numbers and the other one ranging over the functions of interest. Numerical terms are built from real variables through constructs designating the four basic arithmetic operations and through the function-applicat…

Geometric flow regularization in latent spaces for smooth dynamics with the efficient variations of curvature
Andrew Gracyk
https://arxiv.org/abs/2506.09679

Geometric flow regularization in latent spaces for smooth dynamics with the efficient variations of curvature
We design strategies in nonlinear geometric analysis to temper the effects of adversarial learning for sufficiently smooth data of numerical method-type dynamics in encoder-decoder methods, variational and deterministic, through the use of geometric flow regularization. We augment latent spaces with geometric flows to control structure. Our techniques rely on adaptations of curvature and Ricci flow. We invent new geometric flows or discover them neurally and non-parametrically. All of our flows…

How to Bridge the Sim-to-Real Gap in Digital Twin-Aided Telecommunication Networks
Clement Ruah, Houssem Sifaou, Osvaldo Simeone, Bashir M. Al-Hashimi
https://arxiv.org/abs/2507.07067

How to Bridge the Sim-to-Real Gap in Digital Twin-Aided Telecommunication Networks
Training effective artificial intelligence models for telecommunications is challenging due to the scarcity of deployment-specific data. Real data collection is expensive, and available datasets often fail to capture the unique operational conditions and contextual variability of the network environment. Digital twinning provides a potential solution to this problem, as simulators tailored to the current network deployment can generate site-specific data to augment the available training datase…

Revela: Dense Retriever Learning via Language Modeling
Fengyu Cai, Tong Chen, Xinran Zhao, Sihao Chen, Hongming Zhang, Sherry Tongshuang Wu, Iryna Gurevych, Heinz Koeppl
https://arxiv.org/abs/2506.16552

Revela: Dense Retriever Learning via Language Modeling
Dense retrievers play a vital role in accessing external and specialized knowledge to augment language models (LMs). Training dense retrievers typically requires annotated query-document pairs, which are costly and hard to obtain in specialized domains such as code-motivating growing interest in self-supervised retriever learning. Since LMs are trained to capture token-level dependencies through a self-supervised learning objective (i.e., next-token prediction), we can analogously cast retrieva…

Large Language Models as Visualization Agents for Immersive Binary Reverse Engineering
Dennis Brown, Samuel Mulder
https://arxiv.org/abs/2508.13413 https://

Large Language Models as Visualization Agents for Immersive Binary Reverse Engineering
Immersive virtual reality (VR) offers affordances that may reduce cognitive complexity in binary reverse engineering (RE), enabling embodied and external cognition to augment the RE process through enhancing memory, hypothesis testing, and visual organization. In prior work, we applied a cognitive systems engineering approach to identify an initial set of affordances and implemented a VR environment to support RE through spatial persistence and interactivity. In this work, we extend that platfo…

Learned LSM-trees: Two Approaches Using Learned Bloom Filters
Nicholas Fidalgo, Puyuan Ye
https://arxiv.org/abs/2508.00882 https://arxiv.org/pdf/2508.00882…

Learned LSM-trees: Two Approaches Using Learned Bloom Filters
Modern key-value stores rely heavily on Log-Structured Merge (LSM) trees for write optimization, but this design introduces significant read amplification. Auxiliary structures like Bloom filters help, but impose memory costs that scale with tree depth and dataset size. Recent advances in learned data structures suggest that machine learning models can augment or replace these components, trading handcrafted heuristics for data-adaptive behavior. In this work, we explore two approaches for inte…

Improving the average dilation of a metric graph by adding edges
Sariel Har-Peled, Eliot W. Robson
https://arxiv.org/abs/2506.04246 https://

Improving the average dilation of a metric graph by adding edges
For a graph $G$ spanning a metric space, the dilation of a pair of points is the ratio of their distance in the shortest path graph metric to their distance in the metric space. Given a graph $G$ and a budget $k$, a classic problem is to augment $G$ with $k$ additional edges to reduce the maximum dilation. In this note, we consider a variant of this problem where the goal is to reduce the average dilation for pairs of points in $G$. We provide an $O(k)$ approximation algorithm for this proble…

A Study of the Scale Invariant Signal to Distortion Ratio in Speech Separation with Noisy References
Simon Dahl Jepsen, Mads Gr{\ae}sb{\o}ll Christensen, Jesper Rindom Jensen
https://arxiv.org/abs/2508.14623

A Study of the Scale Invariant Signal to Distortion Ratio in Speech Separation with Noisy References
This paper examines the implications of using the Scale-Invariant Signal-to-Distortion Ratio (SI-SDR) as both evaluation and training objective in supervised speech separation, when the training references contain noise, as is the case with the de facto benchmark WSJ0-2Mix. A derivation of the SI-SDR with noisy references reveals that noise limits the achievable SI-SDR, or leads to undesired noise in the separated outputs. To address this, a method is proposed to enhance references and augment …

An Investigation into Maintenance Support for Neural Networks
Fatema Tuz Zohra, Brittany Johnson
https://arxiv.org/abs/2507.05245 https://

An Investigation into Maintenance Support for Neural Networks
As the potential for neural networks to augment our daily lives grows, ensuring their quality through effective testing, debugging, and maintenance is essential. This is especially the case as we acknowledge the prospects of negative impacts from these technologies. Traditional software engineering methods, such as testing and debugging, have proven effective in maintaining software quality; however, they reveal significant research and practice gaps in maintaining neural networks. In particula…

Crosslisted article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[3/9]:
- $\Delta$-AttnMask: Attention-Guided Masked Hidden States for Efficient Data Selection and Augment...
Jucheng Hu, Suorong Yang, Dongzhan Zhou

Sculptor: Empowering LLMs with Cognitive Agency via Active Context Management
Mo Li, L. H. Xu, Qitai Tan, Ting Cao, Yunxin Liu
https://arxiv.org/abs/2508.04664 https://

Sculptor: Empowering LLMs with Cognitive Agency via Active Context Management
Large Language Models (LLMs) suffer from significant performance degradation when processing long contexts due to proactive interference, where irrelevant information in earlier parts of the context disrupts reasoning and memory recall. While most research focuses on external memory systems to augment LLMs' capabilities, we propose a complementary approach: empowering LLMs with Active Context Management (ACM) tools to actively sculpt their internal working memory. We introduce Sculptor, a frame…

DermaCon-IN: A Multi-concept Annotated Dermatological Image Dataset of Indian Skin Disorders for Clinical AI Research
Shanawaj S Madarkar, Mahajabeen Madarkar, Madhumitha V, Teli Prakash, Konda Reddy Mopuri, Vinaykumar MV, KVL Sathwika, Adarsh Kasturi, Gandla Dilip Raj, PVN Supranitha, Harsh Udai
https://arxiv.org/abs/2506.06099…

DermaCon-IN: A Multi-concept Annotated Dermatological Image Dataset of Indian Skin Disorders for Clinical AI Research
Artificial intelligence is poised to augment dermatological care by enabling scalable image-based diagnostics. Yet, the development of robust and equitable models remains hindered by datasets that fail to capture the clinical and demographic complexity of real-world practice. This complexity stems from region-specific disease distributions, wide variation in skin tones, and the underrepresentation of outpatient scenarios from non-Western populations. We introduce DermaCon-IN, a prospectively cu…

This https://arxiv.org/abs/2505.19746 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCV_…

Improving Heart Rejection Detection in XPCI Images Using Synthetic Data Augmentation
Accurate identification of acute cellular rejection (ACR) in endomyocardial biopsies is essential for effective management of heart transplant patients. However, the rarity of high-grade rejection cases (3R) presents a significant challenge for training robust deep learning models. This work addresses the class imbalance problem by leveraging synthetic data generation using StyleGAN to augment the limited number of real 3R images. Prior to GAN training, histogram equalization was applied to sta…

CaloHadronic: a diffusion model for the generation of hadronic showers
Thorsten Buss, Frank Gaede, Gregor Kasieczka, Anatolii Korol, Katja Kr\"uger, Peter McKeown, Martina Mozzanica
https://arxiv.org/abs/2506.21720

CaloHadronic: a diffusion model for the generation of hadronic showers
Simulating showers of particles in highly-granular calorimeters is a key frontier in the application of machine learning to particle physics. Achieving high accuracy and speed with generative machine learning models can enable them to augment traditional simulations and alleviate a major computing constraint. Recent developments have shown how diffusion based generative shower simulation approaches that do not rely on a fixed structure, but instead generate geometry-independent point clouds, ar…

This https://arxiv.org/abs/2505.06750 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLO_…

LPrL: An Asynchronous Linear Time Hyper Logic
We present a novel asynchronous hyper linear time temporal logic named LPrL (Linear Time Predicate Logic) and establish its basic theory. LPrL is a natural first order extension of LTL (Linear time temporal logic), in which the predicates specify the properties of and the relationships between traces (infinite sequences of actions) using Boolean combinations of LTL formulas. To augment the expressive power of the logic, we introduce a simple language of terms and add the equality predicate t = …

A Novel cVAE-Augmented Deep Learning Framework for Pan-Cancer RNA-Seq Classification
Vinil Polepalli
https://arxiv.org/abs/2508.02743 https://arxiv.org/pdf…

A Novel cVAE-Augmented Deep Learning Framework for Pan-Cancer RNA-Seq Classification
Pan-cancer classification using transcriptomic (RNA-Seq) data can inform tumor subtyping and therapy selection, but is challenging due to extremely high dimensionality and limited sample sizes. In this study, we propose a novel deep learning framework that uses a class-conditional variational autoencoder (cVAE) to augment training data for pan-cancer gene expression classification. Using 801 tumor RNA-Seq samples spanning 5 cancer types from The Cancer Genome Atlas (TCGA), we first perform feat…

StARS DCM: A Sleep Stage-Decoding Forehead EEG Patch for Real-time Modulation of Sleep Physiology
William G. Coon, Preston Peranich, Griffin Milsap
https://arxiv.org/abs/2506.03442

StARS DCM: A Sleep Stage-Decoding Forehead EEG Patch for Real-time Modulation of Sleep Physiology
The System to Augment Restorative Sleep (StARS) is a modular hardware/software platform designed for real-time sleep monitoring and intervention. Utilizing the compact DCM biosignal device, StARS captures electrophysiological signals (EEG, EMG, EOG) and synchronizes sensor data using the ezmsg real-time software framework. StARS supports interventions such as closed-loop auditory stimulation and dynamic thermal modulation guided by sleep-stage decoding via advanced neural network models and tra…

Mediator-Guided Multi-Agent Collaboration among Open-Source Models for Medical Decision-Making
Kaitao Chen, Mianxin Liu, Daoming Zong, Chaoyue Ding, Shaohao Rui, Yankai Jiang, Mu Zhou, Xiaosong Wang
https://arxiv.org/abs/2508.05996

Mediator-Guided Multi-Agent Collaboration among Open-Source Models for Medical Decision-Making
Complex medical decision-making involves cooperative workflows operated by different clinicians. Designing AI multi-agent systems can expedite and augment human-level clinical decision-making. Existing multi-agent researches primarily focus on language-only tasks, yet their extension to multimodal scenarios remains challenging. A blind combination of diverse vision-language models (VLMs) can amplify an erroneous outcome interpretation. VLMs in general are less capable in instruction following a…

Representation Quantization for Collaborative Filtering Augmentation
Yunze Luo, Yinjie Jiang, Gaode Chen, Jingchi Wang, Shicheng Wang, Ruina Sun, Jiang Yuezihan, Jun Zhang, Jian Liang, Han Li, Kun Gai, Kaigui Bian
https://arxiv.org/abs/2508.11194

Representation Quantization for Collaborative Filtering Augmentation
As the core algorithm in recommendation systems, collaborative filtering (CF) algorithms inevitably face the problem of data sparsity. Since CF captures similar users and items for recommendations, it is effective to augment the lacking user-user and item-item homogeneous linkages. However, existing methods are typically limited to connecting through overlapping interacted neighbors or through similar attributes and contents. These approaches are constrained by coarse-grained, sparse attributes…

On the Limits of Selective AI Prediction: A Case Study in Clinical Decision Making
Sarah Jabbour, David Fouhey, Nikola Banovic, Stephanie D. Shepard, Ella Kazerooni, Michael W. Sjoding, Jenna Wiens
https://arxiv.org/abs/2508.07617

On the Limits of Selective AI Prediction: A Case Study in Clinical Decision Making
AI has the potential to augment human decision making. However, even high-performing models can produce inaccurate predictions when deployed. These inaccuracies, combined with automation bias, where humans overrely on AI predictions, can result in worse decisions. Selective prediction, in which potentially unreliable model predictions are hidden from users, has been proposed as a solution. This approach assumes that when AI abstains and informs the user so, humans make decisions as they would w…

Music Boomerang: Reusing Diffusion Models for Data Augmentation and Audio Manipulation
Alexander Fichtinger, Jan Schl\"uter, Gerhard Widmer
https://arxiv.org/abs/2507.04864

Music Boomerang: Reusing Diffusion Models for Data Augmentation and Audio Manipulation
Generative models of music audio are typically used to generate output based solely on a text prompt or melody. Boomerang sampling, recently proposed for the image domain, allows generating output close to an existing example, using any pretrained diffusion model. In this work, we explore its application in the audio domain as a tool for data augmentation or content manipulation. Specifically, implementing Boomerang sampling for Stable Audio Open, we augment training data for a state-of-the-art…

TongSearch-QR: Reinforced Query Reasoning for Retrieval
Xubo Qin, Jun Bai, Jiaqi Li, Zixia Jia, Zilong Zheng
https://arxiv.org/abs/2506.11603 https://

TongSearch-QR: Reinforced Query Reasoning for Retrieval
Traditional information retrieval (IR) methods excel at textual and semantic matching but struggle in reasoning-intensive retrieval tasks that require multi-hop inference or complex semantic understanding between queries and documents. One promising solution is to explicitly rewrite or augment queries using large language models (LLMs) to elicit reasoning-relevant content prior to retrieval. However, the widespread use of large-scale language models like GPT-4 or LLaMA3-70B remains impractical …

DeSTA2.5-Audio: Toward General-Purpose Large Audio Language Model with Self-Generated Cross-Modal Alignment
Ke-Han Lu, Zhehuai Chen, Szu-Wei Fu, Chao-Han Huck Yang, Sung-Feng Huang, Chih-Kai Yang, Chee-En Yu, Chun-Wei Chen, Wei-Chih Chen, Chien-yu Huang, Yi-Cheng Lin, Yu-Xiang Lin, Chi-An Fu, Chun-Yi Kuan, Wenze Ren, Xuanjun Chen, Wei-Ping Huang, En-Pei Hu, Tzu-Quan Lin, Yuan-Kuei Wu, Kuan-Po Huang, Hsiao-Ying Huang, Huang-Cheng Chou, Kai-Wei Chang, Cheng-Han Chiang, Boris Ginsburg, Yu…

DeSTA2.5-Audio: Toward General-Purpose Large Audio Language Model with Self-Generated Cross-Modal Alignment
We introduce DeSTA2.5-Audio, a general-purpose Large Audio Language Model (LALM) designed for robust auditory perception and instruction-following, without requiring task-specific audio instruction-tuning. Recent LALMs typically augment Large Language Models (LLMs) with auditory capabilities by training on large-scale, manually curated or LLM-synthesized audio-instruction datasets. However, these approaches have often suffered from the catastrophic forgetting of the LLM's original language abil…

Rethinking Contrastive Learning in Session-based Recommendation
Xiaokun Zhang, Bo Xu, Fenglong Ma, Zhizheng Wang, Liang Yang, Hongfei Lin
https://arxiv.org/abs/2506.05044

Rethinking Contrastive Learning in Session-based Recommendation
Session-based recommendation aims to predict intents of anonymous users based on limited behaviors. With the ability in alleviating data sparsity, contrastive learning is prevailing in the task. However, we spot that existing contrastive learning based methods still suffer from three obstacles: (1) they overlook item-level sparsity and primarily focus on session-level sparsity; (2) they typically augment sessions using item IDs like crop, mask and reorder, failing to ensure the semantic consist…

Lumename: Wearable Device for Hearing Impaired with Personalized ML-Based Auditory Detection and Haptic-Visual Alerts
Jeanelle Dao, Jadelynn Dao
https://arxiv.org/abs/2508.01576

Lumename: Wearable Device for Hearing Impaired with Personalized ML-Based Auditory Detection and Haptic-Visual Alerts
According to the World Health Organization, 430 million people experience disabling hearing loss. For them, recognizing spoken commands such as one's name is difficult. To address this issue, Lumename, a real-time smartwatch, utilizes on-device machine learning to detect a user-customized name before generating a haptic-visual alert. During training, to overcome the need for large datasets, Lumename uses novel audio modulation techniques to augment samples from one user and generate additional …