Search: “models”

Enhancing Video Representations with Spatiotemporal-Semantic Residual to Mitigate Hallucinations in Video Large Multimodal Models

arXiv:2601.22574v2 Announce Type: replace-cross Abstract: Although Video Large Multimodal Models have achieved strong performance in video understanding, they still suffer from hallucination. Existing

All Blog X Post Paper YouTube Reddit GitHub

Clear filters

Research

How Language Models Fail: Token-Level Signatures of Committed and Persistent Reasoning Failures

DGX agent

arXiv:2606.06635v1 Announce Type: cross Abstract: Failures in language model reasoning emerge through distinct processes that leave identifiable signatures in the reasoning trace. We characterize thes

researcharxiv-cs-ai

8 Jun 2026

Introducing the Third Generation of Apple’s Foundation Models

local-aiapple-ml-research

Our next generation of Apple Intelligence is centered around our users, integrated deeply into our operating systems, and powered by a bold new architecture with privacy at its core. At the heart of t

MatterDoor: Sampling Zero-shot Spatio-semantic Priors using Generative Models

model-releasesarxiv-cs-ai

arXiv:2510.11014v2 Announce Type: replace-cross Abstract: Autonomous robots often view rooms only partially, through a doorway, where the walls and scene structure hide the geometry and task-relevant

Modeling Nonlinear Feature Interactions with Product-Unit Residual Networks

model-releasesarxiv-cs-ai

arXiv:2606.06861v1 Announce Type: cross Abstract: Understanding nonlinear feature interactions is crucial in science and engineering, yet standard multilayer perceptrons (MLPs) often capture such inte

Multi-Objective Preference Optimization: Improving Human Alignment of Generative Models

model-releasesarxiv-cs-lg

arXiv:2505.10892v2 Announce Type: replace Abstract: Post-training LLMs with RLHF and preference optimization methods (e.g., DPO, IPO) has greatly improved alignment, yet these approaches assume a sing

Applications

Narrative violation: according to @Stanford research, local models can answer 71.3% of real-world chat and reasoning queries accurately, up …

applicationsclem-delangue--x

Narrative violation: according to @Stanford research, local models can answer 71.3% of real-world chat and reasoning queries accurately, up from 23.2% in 2023. Obviously at a fraction of the cost and

Phonetic Error Analysis of Raw Waveform Acoustic Models

arXiv:2606.07030v1 Announce Type: cross Abstract: We analyse error patterns of raw waveform acoustic models on TIMIT phone recognition beyond the overall phone error rate (PER). PER is decomposed acro

Robots Need More than VLA and World Models

arXiv:2606.06556v1 Announce Type: new Abstract: Generalist robot intelligence is often framed as a policy-scaling problem: collect more robot demonstrations, train larger Vision-Language-Action (VLA)

safetyarxiv-cs-ro

Should You Use Your Large Language Model to Explore or Exploit?

arXiv:2502.00225v4 Announce Type: replace-cross Abstract: We evaluate the ability of the current generation of large language models (LLMs) to help a decision-making agent facing an exploration-exploi

agentsarxiv-cs-ai

Small Language Model Agents Enable Efficient and High-Quality Knowledge Mining

arXiv:2510.01427v3 Announce Type: replace Abstract: At the core of Deep Research is knowledge mining, the task of extracting structured information from massive unstructured text in response to user i

agentsarxiv-cs-ai

The Dark Regulome: Disentangling Predictability from Regulation in Genomic Foundation Models

arXiv:2606.06834v1 Announce Type: new Abstract: High-grade gliomas integrate into neural circuits through functional synapses with neurons, raising the question of which noncoding elements shape synap

local-aiarxiv-cs-cl

VALUEFLOW: Toward Pluralistic and Steerable Value-based Alignment in Large Language Models

arXiv:2602.03160v2 Announce Type: replace Abstract: Aligning Large Language Models (LLMs) with the diverse spectrum of human values remains a central challenge: preference-based methods often fail to

RREDCoT: Segment-Level Reward Redistribution for Reasoning Models

arXiv:2606.06475v1 Announce Type: cross Abstract: Recent advancements in reasoning language models have been driven by Reinforcement Learning (RL) fine-tuning. Most often, these rely on the Group Rela

6 Jun 2026

A Survey on Diffusion Language Models

arXiv:2508.10875v3 Announce Type: replace Abstract: Diffusion Language Models (DLMs) are rapidly emerging as a powerful and promising alternative to the dominant autoregressive (AR) paradigm. By gener

Biomazon: A Multimodal Dataset for 3D Forest Structure and Biomass Modeling in the Amazon Basin

model-releasesarxiv-cs-cv

arXiv:2606.05368v1 Announce Type: new Abstract: Accurate, spatially explicit characterization of tropical forest structure is essential for carbon accounting and ecosystem monitoring, yet most ML pipe

DRIFT: A Residual Flow Adapter for Decoding Continuous Outputs in Vision-Language Models

arXiv:2606.05758v1 Announce Type: new Abstract: Many modern vision-language models (VLMs) build on autoregressive decoding of discrete tokens. While text-based output interfaces enable scalable pretra

Let It Be Simple: One-Step Action Generation for Vision-Language-Action Models

arXiv:2606.05737v1 Announce Type: new Abstract: Diffusion-based vision-language-action (VLA) models often inherit the image-generation view: actions are generated by iterative denoising. We argue that

safetyarxiv-cs-cv

PLAN-S: Bridging Planning with Latent Style Dynamics for Autonomous Driving World Models

arXiv:2606.06014v1 Announce Type: cross Abstract: Latent world models (LWMs) have strengthened end-to-end autonomous driving by forecasting compact scene dynamics for downstream planning. However, exi

agentsarxiv-cs-ro

Tutorials

Self-Augmenting Retrieval for Diffusion Language Models

arXiv:2606.06474v1 Announce Type: new Abstract: Discrete diffusion language models generate text by iteratively denoising an entire response in parallel. At each step, they predict tentative tokens fo

tutorialsarxiv-cs-cl

Tutorials

ADAPTOOD: Uncertainty-Aware Fine-Tuning for Out-of-Distribution ECG Time Series Models

arXiv:2606.04164v1 Announce Type: cross Abstract: Data samples used for training often differ from those encountered during fine-tuning and deployment, and while ML models show promise, their performa

tutorialsarxiv-cs-ai

Beyond Pixel Histories: World Models with Persistent 3D State

arXiv:2603.03482v2 Announce Type: replace-cross Abstract: Interactive world models continually generate video by responding to a user's actions, enabling open-ended generation capabilities. However, e

Tutorials

Can VLMs Predict Future States? Bootstrapping World Models from Inverse Dynamics

arXiv:2506.06006v3 Announce Type: replace-cross Abstract: Can unified vision-language models (VLMs) perform forward dynamics prediction (FDP), i.e., predicting the future state (in image form) given t

tutorialsarxiv-cs-ai

Industry

Critical Hugging Face Transformers flaw ran attacker code on a routine model load

Pluto Security Inc. today disclosed a critical remote code execution vulnerability in Hugging Face Inc.’s Transformers library that allowed attacker-controlled artificial intelligence models to run ar

industrysiliconangle

Global Sketch-Based Watermarking for Diffusion Language Models

arXiv:2606.04486v1 Announce Type: cross Abstract: Watermarking methods for language models have been studied extensively in the autoregressive setting, where tokens are generated sequentially. These w

safetyarxiv-cs-cl

https://ollama.com/library/nemotron-3-ultra

Nemotron-3-Ultra is a large language model available through Ollama's model library, likely representing an advanced iteration in NVIDIA's Nemotron model series optimized for performance and capabilit

model-releasesollama--x

On Forgetting and Stability of Score-based Generative models

arXiv:2601.21868v2 Announce Type: replace-cross Abstract: Understanding the stability and long-time behavior of generative models is a fundamental problem in modern machine learning. This paper provid

researcharxiv-cs-lg

Supportive Token Revealing for Fast Diffusion Language Model Decoding

arXiv:2606.04236v1 Announce Type: cross Abstract: Discrete diffusion language models can generate text efficiently by updating multiple masked positions in parallel, but this parallelism introduces a

ViewMask-1-to-3: Multi-View Consistent Image Generation via Multimodal Discrete Diffusion Models

arXiv:2512.14099v3 Announce Type: replace Abstract: Motivated by discrete diffusion's success in language-vision modeling, we explore its potential for multi-view generation, a task dominated by conti

we're taking an early bet on open models specifically, because they're SO much cheaper one point of reference: an app outputting 10M tokens/…

we're taking an early bet on open models specifically, because they're SO much cheaper one point of reference: an app outputting 10M tokens/day costs roughly 250/day on Opus 4.8 versus ~24/day for Min

agentsharrison-chase--x

Worker Utility as Hysteresis: A Preisach Model of Transaction Acceptance in Gig Labour Markets

arXiv:2606.04916v1 Announce Type: new Abstract: Worker utility is not observed -- only its consequence is. Each gig transaction produces a single bit: accepted or rejected. We argue this structure poi

researcharxiv-cs-lg

A Sparse Bayesian Learning Algorithm for Estimation of Interaction Kernels in Motsch-Tadmor Model

arXiv:2505.07068v2 Announce Type: replace-cross Abstract: In this paper, we investigate the data-driven identification of asymmetric interaction kernels in the Motsch-Tadmor model based on observed tr

researcharxiv-cs-lg

An Improved Method for Personalizing Diffusion Models

arXiv:2407.05312v2 Announce Type: replace Abstract: Diffusion models have demonstrated impressive image generation capabilities. Personalized approaches, such as textual inversion and Dreambooth, enha

Attention, May I Have Your Decision? Localizing Generative Choices in Diffusion Models

arXiv:2604.06052v2 Announce Type: replace Abstract: Text-to-image diffusion models exhibit remarkable generative capabilities, yet their internal operations remain opaque, particularly when handling p

local-aiarxiv-cs-cv

Beyond Semantics: Modeling Factual and Affective Perceptual Experiences from Vision-Language Data

arXiv:2606.03345v1 Announce Type: cross Abstract: We present P-Topics (Perception Topics) modeling, a novel problem for understanding how images are perceived affectively and across cultures. The goal

BYORn: Bootstrap Your Own Responses to Defend Large Vision-Language Models Against Backdoor Attacks

arXiv:2606.02947v1 Announce Type: cross Abstract: Supervised fine-tuning is the predominant approach for adapting autoregressive vision-language models to downstream tasks. Recent work has shown that

CoralBay: A Self-Supervised CT Foundation Model

model-releasesarxiv-cs-cv

arXiv:2606.03888v1 Announce Type: new Abstract: Self-supervised learning has enabled large-scale pre-training on 2D natural images, producing general-purpose visual representations that transfer effec

DMT-CBT: Longitudinal Therapeutic State Modeling for CBT Counseling

arXiv:2606.03132v1 Announce Type: new Abstract: Large language models (LLMs) have shown growing potential for Cognitive Behavioral Therapy (CBT) counseling. However, most existing approaches still for

local-aiarxiv-cs-cl

Do Explanations Increase the Risk of Decision Logic Leakage? Explanation-Guided Stealing of Graph Models

arXiv:2506.03087v2 Announce Type: replace-cross Abstract: Graph Neural Networks (GNNs) have become essential tools for analyzing graph-structured data in domains such as drug discovery and financial a

FlowGuard: Flow Matching for Identity-Independent Detection of Data-Free Model Stealing Attacks on Energy System Intrusion Detection Systems

arXiv:2606.03430v1 Announce Type: cross Abstract: Artificial Intelligence (AI)-based Intrusion Detection Systems (IDS) deployed in energy infrastructure are vulnerable to model theft attacks, which al

Generalizing Graph Foundation Models via Hyperbolic Retrieval-Augmented Generation

arXiv:2606.03307v1 Announce Type: cross Abstract: Graph foundation models (GFMs) emerged as a dominant paradigm in graph representation learning by leveraging large-scale pre-training for cross-domain

local-aiarxiv-cs-ai

GeoSem-WAM: Geometry- and Semantic-Aware World Action Models

arXiv:2606.03188v1 Announce Type: new Abstract: Recent World Action Models (WAMs) have demonstrated impressive capabilities in embodied decision-making. However, whether their effectiveness stems from

researcharxiv-cs-ro

GuidedBridge: Training-freely Improving Bridge Models with Prior Guidance

arXiv:2606.03119v1 Announce Type: cross Abstract: Guidance methods, such as classifier-free guidance (CFG) and auto-guidance (AG), have advanced noise-to-data generation in diffusion models. Recently,

KBQA-R1: Reinforcing Large Language Models for Knowledge Base Question Answering

arXiv:2512.10999v3 Announce Type: replace Abstract: Knowledge Base Question Answering (KBQA) challenges models to bridge the gap between natural language and strict knowledge graph schemas by generati

safetyarxiv-cs-cl

Language Models Compare Quantities Using Number-specific and Unit-specific Heuristics

arXiv:2606.03982v1 Announce Type: new Abstract: Quantities with measurement units, such as 110 cm and 1.2 m, require language models (LMs) to combine a numeral with a symbolic unit scale. Here, we stu

Language Models Need Sleep: Learning to Self-Modify and Consolidate Memories

arXiv:2606.03979v1 Announce Type: cross Abstract: The past few decades have witnessed significant advances in the design of machine learning algorithms, from early studies on task-specific shallow mod

Neural Attention Search Linear: Towards Adaptive Token-Level Hybrid Attention Models

arXiv:2602.03681v2 Announce Type: replace Abstract: The quadratic computational complexity of softmax transformers has become a bottleneck in long-context scenarios. In contrast, linear attention mode

Applications

NewtPhys: Do Foundation Models Understand Newtonian Physics?

arXiv:2606.03986v1 Announce Type: new Abstract: Previous work has evaluated physics reasoning in foundation models using synthetic or semi-synthetic scenes and visual question-answering tasks. However

applicationsarxiv-cs-cv