Search: “models”

Reconsidering the energy efficiency of spiking neural networks

arXiv:2409.08290v4 Announce Type: replace-cross Abstract: Spiking Neural Networks (SNNs) promise higher energy efficiency over conventional Quantized Artificial Neural Networks (QNNs) due to their eve

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Reviving In-domain Fine-tuning Methods for Source-Free Cross-domain Few-shot Learning

DGX agent

arXiv:2605.11659v1 Announce Type: new Abstract: Cross-Domain Few-Shot Learning (CDFSL) aims to adapt large-scale pretrained models to specialized target domains with limited samples, yet the few-shot

model-releasesarxiv-cs-cv

13 May 2026

Robust LLM Unlearning Against Relearning Attacks: The Minor Components in Representations Matter

arXiv:2605.11685v1 Announce Type: new Abstract: Large language model (LLM) unlearning aims to remove specific data influences from pre-trained model without costly retraining, addressing privacy, copy

safetyarxiv-cs-cl

Robust Promptable Video Object Segmentation

arXiv:2605.12006v1 Announce Type: new Abstract: The performance of promptable video object segmentation (PVOS) models substantially degrades under input corruptions, which prevents PVOS deployment in

ROMER: Expert Replacement and Router Calibration for Robust MoE LLMs on Analog Compute-in-Memory Systems

model-releasesarxiv-cs-cl

arXiv:2605.11800v1 Announce Type: cross Abstract: Large language models (LLMs) with mixture-of-experts (MoE) architectures achieve remarkable scalability by sparsely activating a subset of experts per

Slicing and Dicing: Configuring Optimal Mixtures of Experts

model-releasesarxiv-cs-cl

arXiv:2605.11689v1 Announce Type: cross Abstract: Mixture-of-Experts (MoE) architectures have become standard in large language models, yet many of their core design choices - expert count, granularit

Support-Proximity Augmented Diffusion Estimation for Offline Black-Box Optimization

arXiv:2605.11246v1 Announce Type: new Abstract: Offline black-box optimization aims to discover novel designs with high property scores using only a static dataset, a task fundamentally challenged by

Hardware

To Err Is Human; To Annotate, SILICON? Toward Robust Reproducibility in LLM Annotation

arXiv:2412.14461v4 Announce Type: replace Abstract: Unstructured text data annotation is foundational to management research. LLMs offer a cost-effective and scalable alternative to human annotation,

hardwarearxiv-cs-cl

TokenRatio: Principled Token-Level Preference Optimization via Ratio Matching

arXiv:2605.12288v1 Announce Type: new Abstract: Direct Preference Optimization (DPO) is a widely used RL-free method for aligning language models from pairwise preferences, but it models preferences o

safetyarxiv-cs-cl

Towards Order Fairness: Mitigating LLMs Order Sensitivity through Dual Group Advantage Optimization

arXiv:2605.11974v1 Announce Type: new Abstract: Large Language Models (LLMs) suffer from order bias, where their performance is affected by the arrangement order of input elements. This unfairness lim

safetyarxiv-cs-lg

UHR-Micro: Diagnosing and Mitigating the Resolution Illusion in Earth Observation VLMs

arXiv:2605.12237v1 Announce Type: new Abstract: Vision-Language Models (VLMs) increasingly operate on ultra-high-resolution (UHR) Earth observation imagery, yet they remain vulnerable to a severe scal

AgentCollabBench: Diagnosing When Good Agents Make Bad Collaborators

arXiv:2605.08647v1 Announce Type: cross Abstract: Multi-agent systems achieve state-of-the-art outcomes through peer collaboration. However, when an agent in the pipeline silently drops a constraint,

AIPO: : Learning to Reason from Active Interaction

arXiv:2605.08401v1 Announce Type: cross Abstract: Recent advances in large language models (LLMs) have demonstrated remarkable reasoning capabilities, largely stimulated by Reinforcement Learning with

safetyarxiv-cs-ai

Aligning LLM Uncertainty with Human Disagreement in Subjectivity Analysis

arXiv:2605.10415v1 Announce Type: new Abstract: Large language models for subjectivity analysis are typically trained with aggregated labels, which compress variations in human judgment into a single

safetyarxiv-cs-cl

Artificial Intelligence in Number Theory: LLMs for Algorithm Generation and Ensemble Methods for Conjecture Verification

arXiv:2504.19451v3 Announce Type: cross Abstract: This paper presents two concrete applications of Artificial Intelligence to algorithmic and analytic number theory. Recent benchmarks of large languag

ASACK : Adaptive Safe Active Continual Koopman Learning for Uncertain Systems with Contractive Guarantees

arXiv:2605.09659v1 Announce Type: new Abstract: Koopman operator theory provides a powerful framework for representing nonlinear dynamics through a linear operator acting on lifted observables, enabli

safetyarxiv-cs-ro

Auditing Data Membership in Reinforcement Learning With Verifiable Rewards

arXiv:2511.14045v2 Announce Type: replace-cross Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) has become a core training stage in recent large language models (LLMs). Its reliance on

safetyarxiv-cs-ai

Benchmarking Compositional Generalisation for Machine Learning Interatomic Potentials

arXiv:2605.08988v1 Announce Type: cross Abstract: Machine Learning Interatomic Potentials play a fundamental role in computational chemistry and materials science, enabling applications from molecular

Benchmarking Transformer and xLSTM for Time-Series Forecasting of Heat Consumption

arXiv:2605.09722v1 Announce Type: new Abstract: Obtaining an accurate short-term forecasting for heat demand is an essential part of operating district heating networks cost-efficient and reliable. He

Beyond the All-in-One Agent: Benchmarking Role-Specialized Multi-Agent Collaboration in Enterprise Workflows

arXiv:2605.08761v1 Announce Type: cross Abstract: Large language model (LLM) agents are increasingly expected to operate in enterprise environments, where work is distributed across specialized roles,

Beyond the False Trade-off: Adaptive EWC for Stealthy and Generalizable T2I Backdoors

arXiv:2605.08280v1 Announce Type: cross Abstract: Preserving model fidelity is essential for stealthy text-to-image (T2I) backdoor attacks. Existing methods such as Learning without Forgetting (LwF) r

Beyond the Singular: Revealing the Value of Multiple Generations in Benchmark Evaluation

arXiv:2502.08943v4 Announce Type: replace-cross Abstract: Large language models (LLMs) have demonstrated significant utility in real-world applications, exhibiting impressive capabilities in natural l

CERSA: Cumulative Energy-Retaining Subspace Adaptation for Memory-Efficient Fine-Tuning

arXiv:2605.08174v1 Announce Type: cross Abstract: To mitigate the memory constraints associated with fine-tuning large pre-trained models, existing parameter-efficient fine-tuning (PEFT) methods, such

Concordia: Self-Improving Synthetic Tables for Federated LLMs

arXiv:2605.09855v1 Announce Type: new Abstract: Federated learning (FL) enables training large language models (LLMs) without sharing raw data, but adapting LLMs under strict data isolation and non-II

ConQuR: Corner Aligned Activation Quantization via Optimized Rotations for LLMs

arXiv:2605.10793v1 Announce Type: new Abstract: Large language models (LLMs) are costly to deploy due to their large memory footprint and high inference cost. Weight-activation quantization can reduce

Containment Verification: AI Safety Guarantees Independent of Alignment

arXiv:2605.09045v1 Announce Type: new Abstract: Agentic frameworks are the software layer through which AI agents act in the world. Existing safety methods intervene on the model and therefore remain

safetyarxiv-cs-ai

Continuous Latent Contexts Enable Efficient Online Learning in Transformers

arXiv:2605.09867v1 Announce Type: cross Abstract: Large language models (LLMs) exhibit a strong capacity for in-context learning: Given labeled examples, they can generate good predictions without par

DeepLevy: Learning Heavy-Tailed Uncertainty in Highly Volatile Time Series

arXiv:2605.10364v1 Announce Type: new Abstract: Modeling uncertainty in heavy-tailed time series remains a critical challenge for deep probabilistic forecasting models, which often struggle to capture

researcharxiv-cs-lg

DiagnosticIQ: A Benchmark for LLM-Based Industrial Maintenance Action Recommendation from Symbolic Rules

arXiv:2605.08614v1 Announce Type: new Abstract: Monitoring complex industrial assets relies on engineer-authored symbolic rules that trigger based on sensor conditions and prompt technicians to perfor

Do Self-Evolving Agents Forget? Capability Degradation and Preservation in Lifelong LLM Agent Adaptation

arXiv:2605.09315v1 Announce Type: new Abstract: Recent advances in LLM agents enable systems that autonomously refine workflows, accumulate reusable skills, self-train their underlying models, and mai

Domain-Adaptive Arrhythmia Classification Using a Hybrid Transformer on Wearable Heart Signals

arXiv:2605.08199v1 Announce Type: cross Abstract: Cardiovascular disease remains the leading cause of death globally, underscoring the need for effective, accessible monitoring solutions, particularly

researcharxiv-cs-lg

Drift is a Sampling Error: SNR-Aware Power Distributions for Long-Horizon Robotic Planning

model-releasesarxiv-cs-ro

arXiv:2605.09537v1 Announce Type: new Abstract: Despite rapid progress in Vision-Language-Action (VLA) models for robotic control, instruction drift remains a persistent failure mode in long-horizon t

DRNet: All-in-One Image Restoration via Prior-Guided Dynamic Reparameterization

arXiv:2605.08627v1 Announce Type: new Abstract: All-in-one image restoration aims to handle diverse degradations within a single model. However, existing methods often suffer from three key limitation

Tutorials

EAM: Enhancing Anything with Diffusion Transformers for Blind Super-Resolution

arXiv:2505.05209v4 Announce Type: replace Abstract: Utilizing pre-trained Text-to-Image (T2I) diffusion models to guide Blind Super-Resolution (BSR) has become a predominant approach in the field. Whi

tutorialsarxiv-cs-cv

Echo-LoRA: Parameter-Efficient Fine-Tuning via Cross-Layer Representation Injection

arXiv:2605.08177v1 Announce Type: cross Abstract: Parameter-efficient fine-tuning (PEFT) has become a practical route for adapting large language models to downstream tasks, with LoRA-style methods be

EchoAlign: Bridging Generative and Discriminative Learning under Noisy Labels

arXiv:2405.12969v3 Announce Type: replace Abstract: Noisy labels severely hinder the accuracy and generalization of machine learning models, especially when ambiguous instance features make reliable a

Applications

ERASE: Eliminating Redundant Visual Tokens via Adaptive Two-Stage Token Pruning

arXiv:2605.09982v1 Announce Type: new Abstract: Recent advancements in Vision-Language Models (VLMs) enable large language models (LLMs) to process high-resolution images, significantly improving real

applicationsarxiv-cs-cv

Explainable Knowledge Tracing via Probabilistic Embeddings and Pattern-based Reasoning

arXiv:2605.09369v1 Announce Type: new Abstract: Knowledge Tracing (KT) models students' knowledge states based on learning interactions to predict performance. While deep learning-based KT models have

researcharxiv-cs-ai

Fashion130K: An E-commerce Fashion Dataset for Outfit Generation with Unified Multi-modal Condition

arXiv:2605.10127v1 Announce Type: new Abstract: Recent research work on fashion outfit generation focuses on promoting visual consistency of garments by leveraging key information from reference image

Function-Space ADMM for Decentralized Federated Learning: A Control Theoretic Perspective

arXiv:2605.09356v1 Announce Type: new Abstract: Decentralized federated learning (FL) is a promising approach for training machine learning models on sensor networks, Internet of Things (IoT) devices,

Generative Actor-Critic with Soft Bridge Policies

arXiv:2605.08733v1 Announce Type: new Abstract: Expressive generative policies such as diffusion and flow models are appealing for MaxEnt online reinforcement learning because of their ability to mode

Geometry Guided Self-Consistency for Physical AI

arXiv:2605.08638v1 Announce Type: cross Abstract: State-of-the-art physical AI models generate a chunk of actions per inference through diffusion or flow matching, iteratively refining an initial nois

researcharxiv-cs-ai

GUARD: Guideline Upholding Test through Adaptive Role-play and Jailbreak Diagnostics for LLMs

arXiv:2508.20325v3 Announce Type: replace-cross Abstract: As Large Language Models (LLMs) become increasingly integral to various domains, their potential to generate harmful responses has prompted si

How Should LLMs Listen While Speaking? A Study of User-Stream Routing in Full-Duplex Spoken Dialogue

arXiv:2605.10199v1 Announce Type: new Abstract: Full-duplex spoken dialogue requires a model to keep listening while generating its own spoken response. This is challenging for large language models (

researcharxiv-cs-cl

K12-KGraph: A Curriculum-Aligned Knowledge Graph for Benchmarking and Training Educational LLMs

model-releasesarxiv-cs-cl

arXiv:2605.09635v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used in K-12 education, yet existing benchmarks such as C-Eval, CMMLU, GaokaoBench, and EduEval mainly eva

Kaczmarz Linear Attention

arXiv:2605.08587v1 Announce Type: cross Abstract: Long-context language modeling remains central to modern sequence modeling, but the quadratic cost of Transformer attention makes scaling computationa

researcharxiv-cs-ai

Agents

LE-PAVD: Learning-Enhanced Physics-Aware Vehicle Dynamics for High-Speed Autonomous Navigation

arXiv:2605.08489v1 Announce Type: new Abstract: Accurate modeling of nonlinear vehicle dynamics is essential for high-speed autonomous racing, where controllers operate at the handling limits. Model-b

agentsarxiv-cs-ro

LLaVA-UHD v4: What Makes Efficient Visual Encoding in MLLMs?

arXiv:2605.08985v1 Announce Type: new Abstract: Visual encoding constitutes a major computational bottleneck in Multimodal Large Language Models (MLLMs), especially for high-resolution image inputs. T