Search: “models”

FediLoRA: Practical Federated Fine-Tuning of Foundation Models Under Missing-Modality Constraints

arXiv:2509.06984v3 Announce Type: replace-cross Abstract: Federated Learning with LoRA fine-tuning offers an efficient and privacy-aware solution for institutions to collaboratively leverage their lar

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Research

Metric-Guided Feature Fusion of Visual Foundation Models for Segmentation Tasks

DGX agent

arXiv:2605.16864v1 Announce Type: cross Abstract: Although large-scale visual foundation models (VFMs) achieve remarkable performance in semantic understanding, they still underperform in instance-awa

researcharxiv-cs-ai

19 May 2026

Hardware

NanoQuant: Efficient Sub-1-Bit Quantization of Large Language Models

arXiv:2602.06694v2 Announce Type: replace Abstract: Weight-only quantization has become a standard approach for efficiently serving large language models (LLMs). However, existing methods fail to effi

hardwarearxiv-cs-lg

OrbiSim: World Models as Differentiable Physics Engines for Embodied Intelligence

arXiv:2605.16395v1 Announce Type: cross Abstract: We present OrbiSim, a novel robotic simulation paradigm that redefines world models as a fully differentiable physics engine for embodied intelligence

safetyarxiv-cs-lg

Test-Time Hinting for Black-Box Vision-Language Models

arXiv:2605.16410v1 Announce Type: new Abstract: Test-time scaling (TTS) methods have proven highly effective for LLMs, yet their application to vision-language models (VLMs) remains relatively underex

Applications

Traces of Social Competence in Large Language Models

arXiv:2603.04161v2 Announce Type: replace Abstract: The False Belief Test (FBT) has been the main method for assessing Theory of Mind (ToM) and related socio-cognitive competencies. For Large Language

applicationsarxiv-cs-cl

Applications

Training data attribution in diffusion models via mirrored unlearning and noise-consistent skew

arXiv:2605.17938v1 Announce Type: cross Abstract: Training data attribution (TDA) should enable generative model interpretability and foster a variety of related downstream tasks. Nonetheless, current

applicationsarxiv-cs-ai

Vision Foundation Models as Generalist Tokenizers for Image Generation

arXiv:2605.18390v1 Announce Type: new Abstract: In this work, we explore the largely unexplored direction of building a generalist image tokenizer directly on top of a frozen vision foundation model (

WinQ: Accelerating Quantization-Aware Training of Language Models Around Saddle Points

arXiv:2605.17471v1 Announce Type: new Abstract: Quantization-aware training (QAT) is widely adopted to quantize language models by training full-precision weights using gradients from the quantized mo

researcharxiv-cs-lg

ASRU: Activation Steering Meets Reinforcement Unlearning for Multimodal Large Language Models

arXiv:2605.15687v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) may memorize sensitive cross-modal information during pretraining, making machine unlearning (MU) crucial. Ex

Can Large Language Models Imitate Human Speech for Clinical Assessment? LLM-Driven Data Augmentation for Cognitive Score Prediction

model-releasesarxiv-cs-cl

arXiv:2605.16077v1 Announce Type: new Abstract: Accurate assessment of cognitive decline from spontaneous speech remains challenging due to limited dataset size and class imbalance. In this work, we p

CG-MLLM: Captioning and Generating 3D content via Multi-modal Large Language Models

arXiv:2601.21798v2 Announce Type: replace Abstract: Large Language Models(LLMs) have revolutionized text generation and multimodal perception,but their capabilities in 3D content generation remain und

EgoExo-WM: Unlocking Exo Video for Ego World Models

arXiv:2605.15477v1 Announce Type: new Abstract: Egocentric world models present a promising direction for enabling agents to predict and plan, but their performance is constrained by the limited avail

safetyarxiv-cs-cv

Few-Shot Large Language Models for Actionable Triage Categorization of Online Patient Inquiries

model-releasesarxiv-cs-cl

arXiv:2605.15680v1 Announce Type: new Abstract: Online patient inquiries are often informal, incomplete, and written before professional assessment, yet they must still be routed to an appropriate lev

Few-Step Diffusion Language Models via Trajectory Self-Distillation

arXiv:2602.12262v3 Announce Type: replace Abstract: Diffusion large language models (DLLMs) have emerged as powerful generative models with the promise of fast text generation through parallel decodin

researcharxiv-cs-cl

Large Language Models Could Be Rote Learners

arXiv:2504.08300v5 Announce Type: replace-cross Abstract: Benchmark-based evaluation, e.g., multiple-choice questions (MCQs) and open-ended questions (OEQs), is widely used for evaluating Large Langua

Prompt Stability Scoring for Text Annotation with Large Language Models

arXiv:2407.02039v3 Announce Type: replace Abstract: Researchers are increasingly using language models (LMs) for text annotation. These approaches rely only on a prompt telling the model to return a g

researcharxiv-cs-cl

Retrieval-Augmented Large Language Models for Schema-Constrained Clinical Information Extraction

arXiv:2605.15467v1 Announce Type: cross Abstract: Conversational nurse-patient transcripts contain actionable observations, but converting these transcripts into structured representations at scale re

Sparse Autoencoders enable Robust and Interpretable Fine-tuning of CLIP models

arXiv:2605.15961v1 Announce Type: new Abstract: Large-scale pre-trained vision-language models like CLIP demonstrate remarkable zero-shot performance across diverse tasks. However, fine-tuning these m

Testing properties of trees in graphical models with covariance queries

arXiv:2605.15996v1 Announce Type: cross Abstract: We consider the problem of testing properties of graphs underlying high-dimensional graphical models. We adopt the model of covariance queries introdu

researcharxiv-cs-lg

💯. Way too much focus on language models.

💯. Way too much focus on language models. Fei-Fei Li warns that AI may be staring too hard at language models. The world is not just text on a screen. It is physical, visual, spatial, and always chang

safetygary-marcus--x

16 May 2026

Anti-Length Shift: Dynamic Outlier Truncation for Training Efficient Reasoning Models

arXiv:2601.03969v2 Announce Type: replace Abstract: Large reasoning models enhanced by reinforcement learning with verifiable rewards have achieved significant performance gains by extending their cha

BiTrajDiff: Bidirectional Trajectory Generation with Diffusion Models for Offline Reinforcement Learning

arXiv:2506.05762v5 Announce Type: replace Abstract: Recent advances in offline Reinforcement Learning (RL) have proven that effective policy learning can benefit from imposing conservative constraints

Local Ai

Breaking the Reasoning Horizon in Entity Alignment Foundation Models

arXiv:2601.21174v2 Announce Type: replace Abstract: Entity alignment (EA) is critical for knowledge graph (KG) fusion. Existing EA models lack transferability and are incapable of aligning unseen KGs

local-aiarxiv-cs-lg

Complacent, Not Sycophantic: Reframing Large Language Models and Designing AI Literacy for Complacent Machines

arXiv:2605.14544v1 Announce Type: new Abstract: Large language models are often described as sycophantic, in the sense that they appear to flatter users or mirror their beliefs. We argue that this lab

Applications

Enhanced and Efficient Reasoning in Large Learning Models

arXiv:2605.14036v1 Announce Type: new Abstract: In current Large Language Models we can trust the production of smoothly flowing prose on the basis of the principles of machine learning. However, ther

applicationsarxiv-cs-ai

GhostCite: A Large-Scale Analysis of Citation Validity in the Age of Large Language Models

arXiv:2602.06718v2 Announce Type: replace-cross Abstract: Citations provide the basis for trusting scientific claims; when they are invalid or fabricated, this trust collapses. With the advent of Larg

How Sensitive Are Radiomic AI Models to Acquisition Parameters?

arXiv:2605.14667v1 Announce Type: new Abstract: A main barrier for the deployment of AI radiomic systems in clinical routine is their drop in performance under heterogeneous multicentre acquisition pr

K-Models: a Flexible and Interpretable Method for Ordinal Clustering with Application to Antigen-Antibody Interaction Profiles

arXiv:2605.14828v1 Announce Type: cross Abstract: Existing clustering methods for functional data often prioritize partitioning accuracy over interpretability, making it challenging to extract meaning

Measuring and Mitigating Toxicity in Large Language Models: A Comprehensive Replication Study

arXiv:2605.14087v1 Announce Type: new Abstract: Large Language Models (LLMs), when trained on web-scale corpora, inherently absorb toxic patterns from their training data. This leads to ``toxic degene

safetyarxiv-cs-cl

Mitigating Mask Prior Drift and Positional Attention Collapse in Large Diffusion Vision-Language Models

arXiv:2605.14530v1 Announce Type: new Abstract: Large diffusion vision-language models (LDVLMs) have recently emerged as a promising alternative to autoregressive models, enabling parallel decoding fo

safetyarxiv-cs-cv

Polaris: A Godel Agent Framework for Small Language Models through Experience-Abstracted Policy Repair

arXiv:2603.23129v2 Announce Type: replace Abstract: Godel agent realize recursive self-improvement: an agent inspects its own policy and traces and then modifies that policy in a tested loop. We intro

Rethinking Layer Relevance in Large Language Models Beyond Cosine Similarity

arXiv:2605.14075v1 Announce Type: cross Abstract: Large language models (LLMs) have revolutionized natural language processing. Understanding their internal mechanisms is crucial for developing more i

researcharxiv-cs-cl

Tutorials

What Do EEG Foundation Models Capture from Human Brain Signals?

arXiv:2605.11410v2 Announce Type: replace Abstract: Clinical electroencephalogram (EEG) analysis rests on a hand-crafted feature catalog refined over decades, e.g., band power, connectivity, complexit

tutorialsarxiv-cs-ai

Differences in Text Generated by Diffusion and Autoregressive Language Models

arXiv:2605.12522v1 Announce Type: cross Abstract: Diffusion language models (DLMs) are promising alternatives to autoregressive language models (ARMs), yet the intrinsic differences in their generated

researcharxiv-cs-ai

Early Semantic Grounding in Image Editing Models for Zero-Shot Referring Image Segmentation

arXiv:2605.13122v1 Announce Type: new Abstract: Instruction-based image editing (IIE) models have recently demonstrated strong capability in modifying specific image regions according to natural langu

Exemplar-Free Continual Learning for State Space Models

arXiv:2505.18604v3 Announce Type: replace Abstract: State-Space Models (SSMs) excel at capturing long-range dependencies with structured recurrence, making them well-suited for sequence modeling. Howe

researcharxiv-cs-lg

Guide, Think, Act: Interactive Embodied Reasoning in Vision-Language-Action Models

model-releasesarxiv-cs-cv

arXiv:2605.13632v1 Announce Type: cross Abstract: In this paper, we propose GTA-VLA(Guide, Think, Act), an interactive Vision-Language-Action (VLA) framework that enables spatially steerable embodied

interwhen: A Generalizable Framework for Steering Reasoning Models with Test-time Verification

arXiv:2602.11202v3 Announce Type: replace-cross Abstract: Reasoning models produce long traces of intermediate decisions and tool calls, making test-time verification important for ensuring correctnes

Is Video Anomaly Detection Misframed? Evidence from LLM-Based and Multi-Scene Models

arXiv:2605.12725v1 Announce Type: new Abstract: Recent video anomaly detection research has expanded rapidly with an emphasis on general models of normality intended to work across many different scen

safetyarxiv-cs-cv

Mind the Gap: How Elicitation Protocols Shape the Stated-Revealed Preference Gap in Language Models

arXiv:2601.21975v2 Announce Type: replace Abstract: Recent work identifies a stated-revealed (SvR) preference gap in language models (LMs): a mismatch between the values models endorse and the choices

researcharxiv-cs-ai

PaMM: Periodic Motif Memory for Atomistic Models with an Explicit Local-Structure Interface

arXiv:2605.13297v1 Announce Type: new Abstract: Periodic crystals repeatedly instantiate similar local coordination motifs across translated cells and chemically related structures, but current equiva

Radial Compensation: Fixing Radius Distortion in Chart-Based Generative Models on Riemannian Manifolds

arXiv:2511.14056v2 Announce Type: replace-cross Abstract: We study the base distribution in chart-based generative models on Riemannian manifolds. Standard methods sample in Euclidean tangent space an

researcharxiv-cs-ai

Tutorials

Do AI models really learn physics, or just learn what physics looks like? Flatiron's @_helenqu, with @PolymathicAI & CDS researchers includi…

Do AI models really learn physics, or just learn what physics looks like? Flatiron's @_helenqu, with @PolymathicAI & CDS researchers including @ylecun, finds that models predicting in latent space rec

tutorialsyann-lecun--x

From Message-Passing to Linearized Graph Sequence Models

arXiv:2605.12358v1 Announce Type: new Abstract: Message-passing based approaches form the default backbone of most learning architectures on graph-structured data. However, the rapid progress of moder

safetyarxiv-cs-lg

Grid Games: The Power of Multiple Grids for Quantizing Large Language Models

arXiv:2605.12327v1 Announce Type: new Abstract: A major recent advance in quantization is given by microscaled 4-bit formats such as NVFP4 and MXFP4, quantizing values into small groups sharing a scal

Hyperbolic Concept Bottleneck Models

arXiv:2605.06440v2 Announce Type: replace-cross Abstract: Concept Bottleneck Models (CBMs) have become a popular approach to enable interpretability in neural networks by constraining classifier input

Investigating Thinking Behaviours of Reasoning-Based Language Models for Social Bias Mitigation

arXiv:2510.17062v2 Announce Type: replace Abstract: While reasoning-based large language models excel at complex tasks through an internal, structured thinking process, a concerning phenomenon has eme

safetyarxiv-cs-cl