Search: “models”

Algebraic Language Models for Inverse Design of Metamaterials via Diffusion Transformers

arXiv:2507.15753v2 Announce Type: replace-cross Abstract: Generative machine learning models have revolutionized material discovery by capturing complex structure-property relationships, yet extending

researcharxiv-cs-ai

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Context Unrolling in Omni Models

DGX agent

arXiv:2604.21921v1 Announce Type: new Abstract: We present Omni, a unified multimodal model natively trained on diverse modalities, including text, images, videos, 3D geometry, and hidden representati

researcharxiv-cs-cv

24 Apr 2026

DenoiseRank: Learning to Rank by Diffusion Models

arXiv:2604.20852v1 Announce Type: cross Abstract: Learning to rank (LTR) is one of the core tasks in Machine Learning. Traditional LTR models have made great progress, but nearly all of them are imple

Latent Denoising Improves Visual Alignment in Large Multimodal Models

model-releasesarxiv-cs-cv

arXiv:2604.21343v1 Announce Type: new Abstract: Large Multimodal Models (LMMs) such as LLaVA are typically trained with an autoregressive language modeling objective, providing only indirect supervisi

Open-H-Embodiment: A Large-Scale Dataset for Enabling Foundation Models in Medical Robotics

arXiv:2604.21017v1 Announce Type: cross Abstract: Autonomous medical robots hold promise to improve patient outcomes, reduce provider workload, democratize access to care, and enable superhuman precis

Process Supervision via Verbal Critique Improves Reasoning in Large Language Models

arXiv:2604.21611v1 Announce Type: cross Abstract: Inference-time scaling for LLM reasoning has focused on three axes: chain depth, sample breadth, and learned step-scorers (PRMs). We introduce a fourt

Projected Gradient Unlearning for Text-to-Image Diffusion Models: Defending Against Concept Revival Attacks

arXiv:2604.21041v1 Announce Type: new Abstract: Machine unlearning for text-to-image diffusion models aims to selectively remove undesirable concepts from pre-trained models without costly retraining.

Schoenfeld's Anatomy of Mathematical Reasoning by Language Models

arXiv:2512.19995v2 Announce Type: replace-cross Abstract: Large language models increasingly expose reasoning traces, yet their underlying cognitive structure and steps remain difficult to identify an

researcharxiv-cs-ai

Seeing Isn't Believing: Uncovering Blind Spots in Evaluator Vision-Language Models

arXiv:2604.21523v1 Announce Type: cross Abstract: Large Vision-Language Models (VLMs) are increasingly used to evaluate outputs of other models, for image-to-text (I2T) tasks such as visual question a

Thinking Like a Botanist: Challenging Multimodal Language Models with Intent-Driven Chain-of-Inquiry

arXiv:2604.20983v1 Announce Type: cross Abstract: Vision evaluations are typically done through multi-step processes. In most contemporary fields, experts analyze images using structured, evidence-bas

Transient Turn Injection: Exposing Stateless Multi-Turn Vulnerabilities in Large Language Models

arXiv:2604.21860v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly integrated into sensitive workflows, raising the stakes for adversarial robustness and safety. This pape

Value-Conflict Diagnostics Reveal Widespread Alignment Faking in Language Models

arXiv:2604.20995v1 Announce Type: new Abstract: Alignment faking, where a model behaves aligned with developer policy when monitored but reverts to its own preferences when unobserved, is a concerning

safetyarxiv-cs-ai

Assessing the Robustness of Climate Foundation Models under No-Analog Distribution Shifts

arXiv:2603.23043v2 Announce Type: replace-cross Abstract: The accelerating pace of climate change introduces profound non-stationarities that challenge the ability of Machine Learning based climate em

Evaluating the Quality of the Quantified Uncertainty for (Re)Calibration of Data-Driven Regression Models

arXiv:2508.17761v3 Announce Type: replace Abstract: In safety-critical applications data-driven models must not only be accurate but also provide reliable uncertainty estimates. This property, commonl

ParetoSlider: Diffusion Models Post-Training for Continuous Reward Control

arXiv:2604.20816v1 Announce Type: cross Abstract: Reinforcement Learning (RL) post-training has become the standard for aligning generative models with human preferences, yet most methods rely on a si

PR-CAD: Progressive Refinement for Unified Controllable and Faithful Text-to-CAD Generation with Large Language Models

arXiv:2604.19773v1 Announce Type: cross Abstract: The construction of CAD models has traditionally relied on labor-intensive manual operations and specialized expertise. Recent advances in large langu

Hardware

Temporally Extended Mixture-of-Experts Models

arXiv:2604.20156v1 Announce Type: new Abstract: Mixture-of-Experts models, now popular for scaling capacity at fixed inference speed, switch experts at nearly every token. Once a model outgrows availa

hardwarearxiv-cs-lg

The Imperfective Paradox in Large Language Models

arXiv:2601.09373v2 Announce Type: replace Abstract: Do Large Language Models (LLMs) genuinely grasp the compositional semantics of events, or do they rely on surface-level probabilistic heuristics? We

Understanding Overparametrization in Survival Models through Interpolation

arXiv:2512.12463v3 Announce Type: replace-cross Abstract: Classical statistical learning theory predicts a U-shaped relationship between test loss and model capacity, driven by the bias-variance trade

safetyarxiv-cs-lg

Efficient Autoregressive Inference for Transformer Probabilistic Models

arXiv:2510.09477v2 Announce Type: replace-cross Abstract: Set-based transformer models for amortized probabilistic inference and meta-learning, such as neural processes, prior-fitted networks, and tab

researcharxiv-cs-lg

Mitigating Judgment Preference Bias in Large Language Models through Group-Based Polling

arXiv:2510.08145v2 Announce Type: replace Abstract: Large Language Models (LLMs) as automatic evaluators, commonly referred to as LLM-as-a-Judge, have also attracted growing attention. This approach p

ORCA: An Agentic Reasoning Framework for Hallucination and Adversarial Robustness in Vision-Language Models

arXiv:2509.15435v2 Announce Type: replace-cross Abstract: Large Vision-Language Models (LVLMs) exhibit strong multimodal capabilities but remain vulnerable to hallucinations from intrinsic errors and

RDP LoRA: Geometry-Driven Identification for Parameter-Efficient Adaptation in Large Language Models

arXiv:2604.19321v1 Announce Type: cross Abstract: Fine-tuning Large Language Models (LLMs) remains structurally uncertain despite parameter-efficient methods such as Low-Rank Adaptation (LoRA), as the

ATLAS: Constitution-Conditioned Latent Geometry and Redistribution Across Language Models and Neural Perturbation Data

arXiv:2604.17663v1 Announce Type: cross Abstract: Constitution-conditioned post-training can be analysed as a structured perturbation of a model's learned representational geometry. We introduce ATLAS

BengaliMoralBench: A Benchmark for Auditing Moral Reasoning in Large Language Models within Bengali Language and Culture

arXiv:2511.03180v2 Announce Type: replace Abstract: As multilingual Large Language Models (LLMs) gain traction across South Asia, their alignment with local ethical norms, particularly for Bengali, sp

Class-specific diffusion models improve military object detection in a low-data domain

arXiv:2604.18076v1 Announce Type: new Abstract: Diffusion-based image synthesis has emerged as a promising source of synthetic training data for AI-based object detection and classification. In this w

DOSE: Data Selection for Multi-Modal LLMs via Off-the-Shelf Models

arXiv:2604.16979v1 Announce Type: cross Abstract: High-quality and diverse multimodal data are essential for improving vision-language models (VLMs), yet existing datasets often contain noisy, redunda

Efficient Inference for Coupled Hidden Markov Models in Continuous Time and Discrete Space

arXiv:2510.12916v2 Announce Type: replace-cross Abstract: Systems of interacting continuous-time Markov chains are a powerful model class, but inference is typically intractable in high dimensional se

researcharxiv-cs-lg

Local Ai

FM-CAC: Carbon-Aware Control for Battery-Buffered Edge AI via Time-Series Foundation Models

arXiv:2604.16448v1 Announce Type: cross Abstract: As edge AI deployments scale to billions of devices running always-on, real-time compound AI pipelines, they represent a massive and largely unmanaged

local-aiarxiv-cs-lg

Forecast Sports Outcomes under Efficient Market Hypothesis: Theoretical and Experimental Analysis of Odds-Only and Generalised Linear Models

arXiv:2604.17194v1 Announce Type: cross Abstract: Converting betting odds into accurate outcome probabilities is a fundamental challenge in order to use betting odds as a benchmark for sports forecast

HalluSAE: Detecting Hallucinations in Large Language Models via Sparse Auto-Encoders

arXiv:2604.16430v1 Announce Type: new Abstract: Large Language Models (LLMs) are powerful and widely adopted, but their practical impact is limited by the well-known hallucination phenomenon. While re

Identifying Ethical Biases in Action Recognition Models

arXiv:2604.17971v1 Announce Type: new Abstract: Human Action Recognition (HAR) models are increasingly deployed in high-stakes environments, yet their fairness across different human appearances has n

safetyarxiv-cs-cv

Inertia in Moral and Value Judgments of Large Language Models

arXiv:2408.09049v3 Announce Type: replace Abstract: Large Language Models (LLMs) behave non-deterministically, and prompting has become a common method for steering their outputs. A popular strategy i

Measuring Representation Robustness in Large Language Models for Geometry

arXiv:2604.16421v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly evaluated on mathematical reasoning, yet their robustness to equivalent problem representations remains po

OmniZip: Audio-Guided Dynamic Token Compression for Fast Omnimodal Large Language Models

arXiv:2511.14582v2 Announce Type: replace Abstract: Omnimodal large language models (OmniLLMs) have attracted increasing research attention of late towards unified audio-video understanding. However,

OPSDL: On-Policy Self-Distillation for Long-Context Language Models

arXiv:2604.17535v1 Announce Type: new Abstract: Extending the effective context length of large language models (LLMs) remains a central challenge for real-world applications. While recent post-traini

Parallel Test-Time Scaling for Latent Reasoning Models

arXiv:2510.07745v4 Announce Type: replace Abstract: Parallel test-time scaling (TTS) is a pivotal approach for enhancing large language models (LLMs), typically by sampling multiple token-based chains

Tutorials

Plausibility as Commonsense Reasoning: Humans Succeed, Large Language Models Do not

arXiv:2604.04825v2 Announce Type: replace Abstract: Large language models achieve strong performance on many language tasks, yet it remains unclear whether they integrate world knowledge with syntacti

tutorialsarxiv-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

Program Structure-aware Language Models: Targeted Software Testing beyond Textual Semantics

arXiv:2604.17715v1 Announce Type: cross Abstract: Recent advances in large language models for test case generation have improved branch coverage via prompt-engineered mutations. However, they still l

Revisiting Change VQA in Remote Sensing with Structured and Native Multimodal Qwen Models

model-releasesarxiv-cs-cv

arXiv:2604.18429v1 Announce Type: new Abstract: Change visual question answering (Change VQA) addresses the problem of answering natural-language questions about semantic changes between bi-temporal r

S2H-DPO: Hardness-Aware Preference Optimization for Vision-Language Models

model-releasesarxiv-cs-cv

arXiv:2604.18512v1 Announce Type: new Abstract: Vision-Language Models (VLMs) have demonstrated remarkable progress in single-image understanding, yet effective reasoning across multiple images remain

Test-Time Adaptation for EEG Foundation Models: A Systematic Study under Real-World Distribution Shifts

arXiv:2604.16926v1 Announce Type: new Abstract: Electroencephalography (EEG) foundation models have shown strong potential for learning generalizable representations from large-scale neural data, yet

When Background Matters: Breaking Medical Vision Language Models by Transferable Attack

arXiv:2604.17318v1 Announce Type: new Abstract: Vision-Language Models (VLMs) are increasingly used in clinical diagnostics, yet their robustness to adversarial attacks remains largely unexplored, pos

When Choices Become Risks: Safety Failures of Large Language Models under Multiple-Choice Constraints

arXiv:2604.16916v1 Announce Type: new Abstract: Safety alignment in large language models (LLMs) is primarily evaluated under open-ended generation, where models can mitigate risk by refusing to respo

When Seeing Overrides Knowing: Disentangling Knowledge Conflicts in Vision-Language Models

arXiv:2507.13868v2 Announce Type: replace Abstract: Vision-language models (VLMs) increasingly combine visual and textual information to perform complex tasks. However, conflicts between their interna

Local Ai

AdaVFM: Adaptive Vision Foundation Models for Edge Intelligence via LLM-Guided Execution

arXiv:2604.15622v1 Announce Type: new Abstract: Language-aligned vision foundation models (VFMs) enable versatile visual understanding for always-on contextual AI, but their deployment on edge devices

local-aiarxiv-cs-cv

20 Apr 2026

FineSteer: A Unified Framework for Fine-Grained Inference-Time Steering in Large Language Models