Search: “tools”

Enhancing Trust in Large Language Models via Uncertainty-Calibrated Fine-Tuning

arXiv:2412.02904v2 Announce Type: replace Abstract: Large language models (LLMs) have revolutionized the field of natural language processing with their impressive reasoning and question-answering cap

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ESsEN: Training Compact Discriminative Vision-Language Transformers in a Low-Resource Setting

DGX agent

arXiv:2604.18452v1 Announce Type: cross Abstract: Vision-language modeling is rapidly increasing in popularity with an ever expanding list of available models. In most cases, these vision-language mod

model-releasesarxiv-cs-cl

21 Apr 2026

FLiP: Towards understanding and interpreting multimodal multilingual sentence embeddings

arXiv:2604.18109v1 Announce Type: new Abstract: This paper presents factorized linear projection (FLiP) models for understanding pretrained sentence embedding spaces. We train FLiP models to recover t

From Fallback to Frontline: When Can LLMs be Superior Annotators of Human Perspectives?

arXiv:2604.17968v1 Announce Type: cross Abstract: Although large language models (LLMs) are increasingly used as annotators at scale, they are typically treated as a pragmatic fallback rather than a f

FSEVAL: Feature Selection Evaluation Toolbox and Dashboard

arXiv:2604.18227v1 Announce Type: new Abstract: Feature selection is a fundamental machine learning and data mining task, involved with discriminating redundant features from informative ones. It is a

Graph neural network for colliding particles with an application to sea ice floe modeling

arXiv:2602.16213v2 Announce Type: replace-cross Abstract: This paper introduces a novel approach to sea ice modeling using Graph Neural Networks (GNNs), utilizing the natural graph structure of sea ic

tutorialsarxiv-cs-cv

HopWeaver: Cross-Document Synthesis of High-Quality and Authentic Multi-Hop Questions

arXiv:2505.15087v3 Announce Type: replace Abstract: Multi-Hop Question Answering (MHQA) is crucial for evaluating the model's capability to integrate information from diverse sources. However, creatin

How Much Data is Enough? The Zeta Law of Discoverability in Biomedical Data, featuring the enigmatic Riemann zeta function

arXiv:2604.17581v1 Announce Type: new Abstract: How much data is enough to make a scientific discovery? As biomedical datasets scale to millions of samples and AI models grow in capacity, progress inc

Hyperspectral Unmixing Hierarchies

arXiv:2604.16969v1 Announce Type: new Abstract: Unmixing reveals the spatial distribution and spectral details of different constituents, called endmembers, in a hyperspectral image. Because unmixing

researcharxiv-cs-cv

IYKYK (But AI Doesn't): Automated Content Moderation Does Not Capture Communities' Heterogeneous Attitudes Towards Reclaimed Language

arXiv:2604.16654v1 Announce Type: new Abstract: Reclaimed slur usage is a common and meaningful practice online for many marginalized communities. It serves as a source of solidarity, identity, and sh

safetyarxiv-cs-cl

Judge a Book by its Cover: Investigating Multi-Modal LLMs for Multi-Page Handwritten Document Transcription

model-releasesarxiv-cs-cv

arXiv:2502.20295v2 Announce Type: replace-cross Abstract: Handwriting text recognition (HTR) remains a challenging task. Existing approaches require fine-tuning on labeled data, which is impractical t

Applications

Large Language Models Are Bad Dice Players: LLMs Struggle to Generate Random Numbers from Statistical Distributions

arXiv:2601.05414v2 Announce Type: replace Abstract: As large language models (LLMs) transition from chat interfaces to integral components of stochastic pipelines and systems approaching general intel

applicationsarxiv-cs-cl

Local Ai

Learning to Seek Help: Dynamic Collaboration Between Small and Large Language Models

arXiv:2604.17827v1 Announce Type: new Abstract: Large language models (LLMs) offer strong capabilities but raise cost and privacy concerns, whereas small language models (SLMs) facilitate efficient an

local-aiarxiv-cs-cl

Linking Exteroception and Proprioception through Improved Contact Modeling for Soft Growing Robots

arXiv:2507.10694v2 Announce Type: replace Abstract: Passive deformation due to compliance is a commonly used benefit of soft robots, providing opportunities to achieve robust actuation with few active

researcharxiv-cs-ro

Agents

Live LTL Progress Tracking: Towards Task-Based Exploration

arXiv:2604.17106v1 Announce Type: new Abstract: Motivated by the challenge presented by non-Markovian objectives in reinforcement learning (RL), we present a novel framework to track and represent the

agentsarxiv-cs-lg

LVLMs and Humans Ground Differently in Referential Communication

arXiv:2601.19792v3 Announce Type: replace Abstract: For generative AI agents to partner effectively with human users, the ability to accurately predict human intent is critical. But this ability to co

Agents

Matrix: Peer-to-Peer Multi-Agent Synthetic Data Generation Framework

arXiv:2511.21686v2 Announce Type: replace Abstract: Synthetic data has become increasingly important for training large language models, especially when real data is scarce, expensive, or privacy-sens

agentsarxiv-cs-cl

Hardware

Muscle-inspired magnetic actuators that push, pull, crawl, and grasp

arXiv:2604.18090v1 Announce Type: new Abstract: Functional magnetic composites capable of large deformation, load bearing, and multifunctional motion are essential for next-generation adaptive soft ro

hardwarearxiv-cs-ro

On the Convergence and Size Transferability of Continuous-depth Graph Neural Networks

arXiv:2510.03923v2 Announce Type: replace Abstract: Continuous-depth graph neural networks, also known as Graph Neural Differential Equations (GNDEs), combine the structural inductive bias of Graph Ne

safetyarxiv-cs-lg

On the Predictive Power of Representation Dispersion in Language Models

arXiv:2506.24106v2 Announce Type: replace Abstract: We show that a language model's ability to predict text is tightly linked to the breadth of its embedding space: models that spread their contextual

PAC-Bayes Bounds for Gibbs Posteriors via Singular Learning Theory

model-releasesarxiv-cs-lg

arXiv:2604.17219v1 Announce Type: cross Abstract: We derive explicit non-asymptotic PAC-Bayes generalization bounds for Gibbs posteriors, that is, data-dependent distributions over model parameters ob

Pearmut: Human Evaluation of Translation Made Trivial

arXiv:2601.02933v3 Announce Type: replace Abstract: Human evaluation is the gold standard for multilingual NLP, but is often skipped in practice and substituted with automatic metrics because it is no

PersonalHomeBench: Evaluating Agents in Personalized Smart Homes

arXiv:2604.16813v1 Announce Type: cross Abstract: Agentic AI systems are rapidly advancing toward real-world applications, yet their readiness in complex and personalized environments remains insuffic

PFDelta: A Benchmark Dataset for Power Flow under Load, Generation, and Topology Variations

model-releasesarxiv-cs-lg

arXiv:2510.22048v3 Announce Type: replace Abstract: Power flow (PF) calculations are the backbone of real-time grid operations, across workflows such as contingency analysis (where repeated PF evaluat

Applications

Physics-Informed Neural Networks for Biological 2D{+}t Reaction-Diffusion Systems

arXiv:2604.18548v1 Announce Type: new Abstract: Physics-informed neural networks (PINNs) provide a powerful framework for learning governing equations of dynamical systems from data. Biologically-info

applicationsarxiv-cs-lg

Hardware

PiERN: Token-Level Routing for Integrating High-Precision Computation and Reasoning

arXiv:2509.18169v3 Announce Type: replace-cross Abstract: Tasks on complex systems require high-precision numerical computation to support decisions, but current large language models (LLMs) cannot in

hardwarearxiv-cs-cl

PrinciplismQA: A Philosophy-Grounded Approach to Assessing LLM-Human Clinical Medical Ethics Alignment

arXiv:2508.05132v2 Announce Type: replace Abstract: As medical LLMs transition to clinical deployment, assessing their ethical reasoning capability becomes critical. While achieving high accuracy on k

safetyarxiv-cs-cl

Privacy Collapse: Benign Fine-Tuning Can Break Contextual Privacy in Language Models

arXiv:2601.15220v2 Announce Type: replace Abstract: We identify a novel phenomenon in language models: benign fine-tuning of frontier models can lead to privacy collapse. We find that diverse, subtle

safetyarxiv-cs-cl

PyEPO: A PyTorch-based End-to-End Predict-then-Optimize Library for Linear and Integer Programming

arXiv:2206.14234v3 Announce Type: replace-cross Abstract: In deterministic optimization, it is typically assumed that all problem parameters are fixed and known. In practice, however, some parameters

tutorialsarxiv-cs-lg

Local Ai

Q-DeepSight: Incentivizing Thinking with Images for Image Quality Assessment and Refinement

arXiv:2604.16858v1 Announce Type: new Abstract: Image Quality Assessment (IQA) models are increasingly deployed as perceptual critics to guide generative models and image restoration. This role demand

local-aiarxiv-cs-cv

QuickScope: Certifying Hard Questions in Dynamic LLM Benchmarks

arXiv:2604.17842v1 Announce Type: new Abstract: LLM benchmarks are increasingly dynamic: instead of containing a fixed set of questions, they define templates and parameters that can generate an effec

ReflexiCoder: Teaching Large Language Models to Self-Reflect on Generated Code and Self-Correct It via Reinforcement Learning

arXiv:2603.05863v2 Announce Type: replace Abstract: While Large Language Models (LLMs) have revolutionized code generation, standard ``System 1'' approaches that generate solutions in a single forward

SQL Query Engine: A Self-Healing LLM Pipeline for Natural Language to PostgreSQL Translation

arXiv:2604.16511v1 Announce Type: cross Abstract: We present SQL Query Engine, an open-source, self-hosted service that translates natural language questions into validated PostgreSQL queries through

StepPO: Step-Aligned Policy Optimization for Agentic Reinforcement Learning

arXiv:2604.18401v1 Announce Type: new Abstract: General agents have given rise to phenomenal applications such as OpenClaw and Claude Code. As these agent systems (a.k.a. Harnesses) strive for bolder

SynthFix: Adaptive Neuro-Symbolic Code Vulnerability Repair

arXiv:2604.17184v1 Announce Type: cross Abstract: Large Language Models (LLMs) show promise for automated code repair but often struggle with the complex semantic and structural correctness required.

tutorialsarxiv-cs-lg

The GDN-CC Dataset: Automatic Corpus Clarification for AI-enhanced Democratic Citizen Consultations

arXiv:2601.14944v3 Announce Type: replace Abstract: LLMs are ubiquitous in modern NLP, and while their applicability extends to texts produced for democratic activities such as online deliberations or

The impact of postediting on AI generative translation in Yemeni context: Translating literary prose by ChatGPT

arXiv:2604.16704v1 Announce Type: new Abstract: This study examines the role of artificial intelligence in translation, focusing on ChatGPT, specifically ChatGPT-4, and the extent to which human poste

Topology Structure Optimization of Reservoirs Using GLMY Homology

arXiv:2509.11612v3 Announce Type: replace Abstract: Reservoir is an efficient network for time series processing. It is well known that network structure is one of the determinants of its performance.

Towards a Foundation-Model Paradigm for Aerodynamic Prediction in Three-dimensional Design

arXiv:2604.18062v1 Announce Type: new Abstract: Accurate machine-learning models for aerodynamic prediction are essential for accelerating shape optimization, yet remain challenging to develop for com

tutorialsarxiv-cs-lg

Towards Reliable Testing of Machine Unlearning

arXiv:2604.16536v1 Announce Type: new Abstract: Machine learning components are now central to AI-infused software systems, from recommendations and code assistants to clinical decision support. As re

Agents

Towards Self-Improving Error Diagnosis in Multi-Agent Systems

arXiv:2604.17658v1 Announce Type: cross Abstract: Large Language Model (LLM)-based Multi-Agent Systems (MAS) enable complex problem-solving but introduce significant debugging challenges, characterize

agentsarxiv-cs-cl

Train Separately, Merge Together: Modular Post-Training with Mixture-of-Experts

arXiv:2604.18473v1 Announce Type: new Abstract: Extending a fully post-trained language model with new domain capabilities is fundamentally limited by monolithic training paradigms: retraining from sc

safetyarxiv-cs-lg

Understanding Counting Mechanisms in Large Language and Vision-Language Models

arXiv:2511.17699v2 Announce Type: replace Abstract: Counting is one of the fundamental abilities of large language models (LLMs) and large vision-language models (LVLMs). This paper examines how these

researcharxiv-cs-cv

Wasserstein-p Central Limit Theorem Rates: From Local Dependence to Markov Chains

arXiv:2601.08184v3 Announce Type: replace-cross Abstract: Non-asymptotic central limit theorem (CLT) rates play a central role in modern machine learning and operations research. In this paper, we stu

Why Training-Free Token Reduction Collapses: The Inherent Instability of Pairwise Scoring Signals

arXiv:2604.16745v1 Announce Type: cross Abstract: Training-free token reduction methods for Vision Transformers (ToMe, ToFu, PiToMe, and MCTF) employ different scoring mechanisms, yet they share a clo

researcharxiv-cs-cv

A PennyLane-Centric Dataset to Enhance LLM-based Quantum Code Generation using RAG

model-releasesarxiv-cs-ai

arXiv:2503.02497v4 Announce Type: replace-cross Abstract: Large Language Models (LLMs) offer powerful capabilities in code generation, natural language understanding, and domain-specific reasoning. Th

20 Apr 2026

Analyzing Chain of Thought (CoT) Approaches in Control Flow Code Deobfuscation Tasks

arXiv:2604.15390v1 Announce Type: cross Abstract: Code deobfuscation is the task of recovering a readable version of a program while preserving its original behavior. In practice, this often requires

tutorialsarxiv-cs-ai

20 Apr 2026

Anthropomorphism and Trust in Human-Large Language Model interactions