Search: “models”

Simple Self-Conditioning Adaptation for Masked Diffusion Models

arXiv:2604.26985v1 Announce Type: cross Abstract: Masked diffusion models (MDMs) generate discrete sequences by iterative denoising under an absorbing masking process. In standard masked diffusion, if

researcharxiv-cs-ai

1 May 2026

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Combating Visual Neglect and Semantic Drift in Large Multimodal Models for Enhanced Cross-Modal Retrieval

DGX agent

arXiv:2604.25273v1 Announce Type: new Abstract: Despite significant progress in Unified Multimodal Retrieval (UMR) powered by Large Multimodal Models (LMMs), existing embedding methods primarily focus

model-releasesarxiv-cs-cv

29 Apr 2026

Comparing Data Assimilation and Likelihood-Based Inference on Latent State Estimation in Agent-Based Models

arXiv:2509.17625v2 Announce Type: replace Abstract: In this paper, we present the first systematic comparison of Data Assimilation (DA) and Likelihood-Based Inference (LBI) in the context of an Agent-

29 Apr 2026

Heterogeneous Variational Inference for Markov Degradation Hazard Models: Discretized Mixture with Interpretable Clusters

arXiv:2604.24818v1 Announce Type: new Abstract: Bayesian finite mixture models can identify discrete risk clusters (low-risk vs. high-risk equipment), but face three critical bottlenecks: (1) insuffic

applicationsarxiv-cs-lg

29 Apr 2026

Thinking About Thinking: Evaluating Reasoning in Post-Trained Language Models

arXiv:2510.16340v2 Announce Type: replace Abstract: Recent advances in post-training techniques have endowed Large Language Models (LLMs) with enhanced capabilities for tackling complex, logic-intensi

safetyarxiv-cs-cl

29 Apr 2026

DreamAudio: Customized Text-to-Audio Generation with Diffusion Models

arXiv:2509.06027v3 Announce Type: replace-cross Abstract: With the development of large-scale diffusion-based and language-modeling-based generative models, impressive progress has been achieved in te

Evolve: A Persistent Knowledge Lifecycle for Small Language Models

arXiv:2604.23424v1 Announce Type: cross Abstract: Evolve pairs a small local language model with a persistent, teacher-compiled knowledge store -- refined through sleep consolidation and usage-driven

FreqCache: Accelerating Embodied VLN Models with Adaptive Frequency-Guided Token Caching

arXiv:2604.24391v1 Announce Type: new Abstract: Vision-Language-Navigation (VLN) models exhibit excellent navigation accuracy but incur high computational overhead. Token caching has emerged as a prom

researcharxiv-cs-ro

In-Sync: Adaptation of Speech Aware Large Language Models for ASR with Word Level Timestamp Predictions

arXiv:2604.22817v1 Announce Type: cross Abstract: Recent advances in speech-aware language models have coupled strong acoustic encoders with large language models, enabling systems that move beyond tr

safetyarxiv-cs-cl

IntentVLM: Open-Vocabulary Intention Recognition through Forward-Inverse Modeling with Video-Language Models

arXiv:2604.24002v1 Announce Type: cross Abstract: Improving the effectiveness of human-robot interaction requires social robots to accurately infer human goals through robust intention understanding.

researcharxiv-cs-ai

Local Ai

LAMP: Extracting Local Decision Surfaces From Large Language Models

arXiv:2505.11772v3 Announce Type: replace Abstract: We introduce LAMP (Local Attribution Mapping Probe), a method that shines light onto a black-box language model's decision surface and studies how r

local-aiarxiv-cs-lg

LLMind: Bio-inspired Training-free Adaptive Visual Representations for Vision-Language Models

arXiv:2603.14882v2 Announce Type: replace Abstract: Vision-Language Models (VLMs) typically assume a uniform spatial fidelity across the entire field of view of visual inputs, dedicating equal precisi

researcharxiv-cs-cv

Scaling Properties of Continuous Diffusion Spoken Language Models

arXiv:2604.24416v1 Announce Type: cross Abstract: Speech-only spoken language models (SLMs) lag behind text and text-speech models in performance, with recent discrete autoregressive (AR) SLMs indicat

Tutorials

Tandem: Riding Together with Large and Small Language Models for Efficient Reasoning

arXiv:2604.23623v1 Announce Type: new Abstract: Recent advancements in large language models (LLMs) have catalyzed the rise of reasoning-intensive inference paradigms, where models perform explicit st

tutorialsarxiv-cs-ai

Towards Holistic Evaluation of Large Audio-Language Models: A Comprehensive Survey

arXiv:2505.15957v4 Announce Type: replace-cross Abstract: With advancements in large audio-language models (LALMs), which enhance large language models (LLMs) with auditory capabilities, these models

safetyarxiv-cs-ai

Ulterior Motives: Detecting Misaligned Reasoning in Continuous Thought Models

arXiv:2604.23460v1 Announce Type: new Abstract: Chain-of-Thought (CoT) reasoning has emerged as a key technique for eliciting complex reasoning in Large Language Models (LLMs). Although interpretable,

When to Commit? Towards Variable-Size Self-Contained Blocks for Discrete Diffusion Language Models

arXiv:2604.23994v1 Announce Type: cross Abstract: Discrete diffusion language models (dLLMs) enable parallel token updates with bidirectional attention, yet practical generation typically adopts block

Atlas-Alignment: Making Interpretability Transferable Across Language Models

arXiv:2510.27413v2 Announce Type: replace-cross Abstract: Interpretability is crucial for building safe, reliable, and controllable language models, yet existing interpretability pipelines remain cost

27 Apr 2026

RedVLA: Physical Red Teaming for Vision-Language-Action Models

arXiv:2604.22591v1 Announce Type: new Abstract: The real-world deployment of Vision-Language-Action (VLA) models remains limited by the risk of unpredictable and irreversible physical harm. However, w

safetyarxiv-cs-ro

27 Apr 2026

Low-Rank Adaptation Redux for Large Models

arXiv:2604.21905v1 Announce Type: new Abstract: Low-rank adaptation (LoRA) has emerged as the de facto standard for parameter-efficient fine-tuning (PEFT) of foundation models, enabling the adaptation

24 Apr 2026

Graph-Theoretic Models for the Prediction of Molecular Measurements

arXiv:2604.19840v1 Announce Type: new Abstract: Graph-theoretic approaches offer simplicity, interpretability, and low computational cost for molecular property prediction. Among these, the model prop

23 Apr 2026

What Language Models Know But Don't Say: Non-Generative Prior Extraction for Generalization

arXiv:2601.17609v2 Announce Type: replace Abstract: In domains like medicine and finance, large-scale labeled data is costly and often unavailable, leading to models trained on small datasets that str

applicationsarxiv-cs-cl

23 Apr 2026

Are Large Language Models Economically Viable for Industry Deployment?

arXiv:2604.19342v1 Announce Type: new Abstract: Generative AI-powered by Large Language Models (LLMs)-is increasingly deployed in industry across healthcare decision support, financial analytics, ente

Deep sprite-based image models: An analysis

model-releasesarxiv-cs-cv

arXiv:2604.19480v1 Announce Type: new Abstract: While foundation models drive steady progress in image segmentation and diffusion algorithms compose always more realistic images, the seemingly simple

Regression with Large Language Models for Materials and Molecular Property Prediction

arXiv:2409.06080v2 Announce Type: replace-cross Abstract: We demonstrate the ability of large language models (LLMs) to perform material and molecular property regression tasks, a significant deviatio

Remask, Don't Replace: Token-to-Mask Refinement in Masked Diffusion Language Models

arXiv:2604.18738v1 Announce Type: new Abstract: Masked diffusion language models such as LLaDA2.1 rely on Token-to-Token (T2T) editing to correct their own generation errors: whenever a different toke

Visual Adversarial Attack on Vision-Language Models for Autonomous Driving

arXiv:2411.18275v2 Announce Type: replace Abstract: Vision-language models (VLMs) have significantly advanced autonomous driving (AD) by enhancing reasoning capabilities. However, these models remain

safetyarxiv-cs-cv

Bi-LoRA: Efficient Sharpness-Aware Minimization for Fine-Tuning Large-Scale Models

arXiv:2508.19564v2 Announce Type: replace Abstract: Fine-tuning large-scale pre-trained models with limited data presents significant challenges for generalization. While Sharpness-Aware Minimization

DeepRitzSplit Neural Operator for Phase-Field Models via Energy Splitting

arXiv:2604.18261v1 Announce Type: cross Abstract: The multi-scale and non-linear nature of phase-field models of solidification requires fine spatial and temporal discretization, leading to long compu

researcharxiv-cs-lg

Grokking of Diffusion Models: Case Study on Modular Addition

arXiv:2604.17673v1 Announce Type: new Abstract: Despite their empirical success, how diffusion models generalize remains poorly understood from a mechanistic perspective. We demonstrate that diffusion

applicationsarxiv-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

SpeakerSleuth: Can Large Audio-Language Models Judge Speaker Consistency across Multi-turn Dialogues?

arXiv:2601.04029v2 Announce Type: replace Abstract: Large Audio-Language Models (LALMs) as judges have emerged as a prominent approach for evaluating speech generation quality, yet their ability to as

Stability-Weighted Decoding for Diffusion Language Models

arXiv:2604.17068v1 Announce Type: new Abstract: Diffusion large language models (dLLMs) enable parallel text generation by iteratively denoising a fully masked sequence, unmasking a subset of masked t

Do Vision-Language Models Truly Perform Vision Reasoning? A Rigorous Study of the Modality Gap

arXiv:2604.16256v1 Announce Type: cross Abstract: Reasoning in vision-language models (VLMs) has recently attracted significant attention due to its broad applicability across diverse downstream tasks

Elucidating the SNR-t Bias of Diffusion Probabilistic Models

arXiv:2604.16044v1 Announce Type: new Abstract: Diffusion Probabilistic Models have demonstrated remarkable performance across a wide range of generative tasks. However, we have observed that these mo

safetyarxiv-cs-cv

LLaMo: Scaling Pretrained Language Models for Unified Motion Understanding and Generation with Continuous Autoregressive Tokens

arXiv:2602.12370v2 Announce Type: replace Abstract: Recent progress in large models has led to significant advances in unified multimodal generation and understanding. However, the development of mode

researcharxiv-cs-cv

Sketching the Readout of Large Language Models for Scalable Data Attribution and Valuation

arXiv:2604.16197v1 Announce Type: new Abstract: Data attribution and valuation are critical for understanding data-model synergy for Large Language Models (LLMs), yet existing gradient-based methods s

applicationsarxiv-cs-lg

Agents

Some Mac Mini and Mac Studio models are unavailable or facing up to 12-week wait times in the US, with analysts citing strong demand from AI agent power users (Nicole Nguyen/Wall Street Journal)

Nicole Nguyen / Wall Street Journal: Some Mac Mini and Mac Studio models are unavailable or facing up to 12-week wait times in the US, with analysts citing strong demand from AI agent power users — Th

agentstechmeme

18 Apr 2026

Correcting Suppressed Log-Probabilities in Language Models with Post-Transformer Adapters

arXiv:2604.14174v1 Announce Type: new Abstract: Alignment-tuned language models frequently suppress factual log-probabilities on politically sensitive topics despite retaining the knowledge in their h

17 Apr 2026

Reasoning Dynamics and the Limits of Monitoring Modality Reliance in Vision-Language Models

arXiv:2604.14888v1 Announce Type: new Abstract: Recent advances in vision language models (VLMs) offer reasoning capabilities, yet how these unfold and integrate visual and textual information remains

safetyarxiv-cs-cl

17 Apr 2026

Local Ai

Can you use Ollama models with the Codex app on Windows?

Yes, Ollama models can be used with the Codex app on Windows. Ollama supports all major operating systems, including Windows , and open models can be used with OpenAI's Codex CLI through Ollama — Code

local-air-ollama

Cracking the Code of Juxtaposition: Can AI Models Understand the Humorous Contradictions

arXiv:2405.19088v3 Announce Type: replace Abstract: Recent advancements in large multimodal language models have demonstrated remarkable proficiency across a wide range of tasks. Yet, these models sti

Diffusion Sequence Models for Generative In-Context Meta-Learning of Robot Dynamics

arXiv:2604.13366v1 Announce Type: new Abstract: Accurate modeling of robot dynamics is essential for model-based control, yet remains challenging under distributional shifts and real-time constraints.

researcharxiv-cs-lg

FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction

arXiv:2604.13453v1 Announce Type: new Abstract: Traffic forecasting requires modeling complex temporal dynamics and long-range spatial dependencies over large sensor networks. Existing methods typical

researcharxiv-cs-lg

Indexing Multimodal Language Models for Large-scale Image Retrieval

arXiv:2604.13268v1 Announce Type: cross Abstract: Multimodal Large Language Models (MLLMs) have demonstrated strong cross-modal reasoning capabilities, yet their potential for vision-only tasks remain

Neural Chain-of-Thought Search: Searching the Optimal Reasoning Path to Enhance Large Language Models

arXiv:2601.11340v2 Announce Type: replace Abstract: Chain-of-Thought reasoning has significantly enhanced the problem-solving capabilities of Large Language Models. Unfortunately, current models gener

TLoRA+: A Low-Rank Parameter-Efficient Fine-Tuning Method for Large Language Models

arXiv:2604.13368v1 Announce Type: new Abstract: Fine-tuning large language models (LLMs) aims to adapt pre-trained models to specific tasks using relatively small and domain-specific datasets. Among P

Benchmarking Foundation Models with Retrieval-Augmented Generation in Olympic-Level Physics Problem Solving

arXiv:2510.00919v3 Announce Type: replace-cross Abstract: Retrieval-augmented generation (RAG) with foundation models has achieved strong performance across diverse tasks, but their capacity for exper