Search: “models”

Machine Learning and Time-Series Foundation Models for Day-Ahead Electricity Forecasting in a University Building

model-releasesarxiv-cs-ai

arXiv:2610.12638v1 Announce Type: cross Abstract: Day-ahead electricity forecasts can help building operators schedule heating, ventilation, and other services around anticipated demand. Choosing a fo

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Research

Mesh-Native Tokenisation: Retrofitting Foundation Models to Unstructured Data

DGX agent

arXiv:2610.12618v1 Announce Type: cross Abstract: Physics foundation models are an increasingly popular approach for building versatile emulators for spatiotemporal systems. They require tokenisation

researcharxiv-cs-ai

12 Oct 2026

Safety

Mind the Temporal Gap: Evaluating Video Models for Temporal Understanding in Driving

arXiv:2610.12575v1 Announce Type: cross Abstract: Modern video-language models are increasingly treated as general-purpose video reasoners, yet it remains unclear whether they robustly capture tempora

safetyarxiv-cs-ro

Physics-inspired Generative AI models via real hardware-based noisy quantum diffusion

arXiv:2505.22193v2 Announce Type: replace-cross Abstract: Quantum Diffusion Models (QDMs) are an emerging paradigm in Generative AI that aims to use quantum properties to improve the performances of t

researcharxiv-cs-ai

Hardware

Tabular Foundation Models Are Effectively Shallow

arXiv:2610.13253v1 Announce Type: cross Abstract: In-context-learning Tabular Foundation Models (TFMs) are uniquely suited to scenarios with limited resources or privacy constraints, as they solve new

hardwarearxiv-cs-ai

Tools

Together AI integration: Evaluate open models and optimize evaluation costs in Arize AX

Evaluate open models with Together AI in Arize AX. Compare model performance, run LLM-as-a-Judge evaluations, and explore lower-cost alternatives. The post Together AI integration: Evaluate open model

toolsarize-ai

Weight Clustering Reveals the Importance of Relative Rank in Large Language Models

arXiv:2603.17917v2 Announce Type: replace-cross Abstract: Large language models (LLMs) contain billions of parameters, but many exact weight values are not essential. We apply scalar K-means clusterin

BeliefScope: Diagnosing Evidence-Driven Revision and Pressure-Induced Shifts in Large Language Models

arXiv:2610.11305v1 Announce Type: new Abstract: A language model may revise the same proposition after receiving genuinely relevant evidence or after receiving directional user pressure that adds no r

Diffu-LoRA: A Novel Low-Rank Adaptation for Personalized Diffusion Models

arXiv:2610.10550v1 Announce Type: new Abstract: Personalizing text-to-image diffusion models from a few reference images requires preserving subject identity while following prompts that describe new

KDFP: A first-principles approach to knowledge distillation in large language models

model-releasesarxiv-cs-lg

arXiv:2610.10854v1 Announce Type: new Abstract: Knowledge distillation is an established technique for improving the capabilities of small, efficient student models by training them with the represent

Learning to Retrieve: Internalizing Memory Retrieval for Video World Models

arXiv:2610.11444v1 Announce Type: new Abstract: Video world models aim to generate explorable, 3D-consistent scene videos conditioned on camera trajectories. Existing approaches often rely on external

researcharxiv-cs-cv

Mid-Training Language Models on Raw Video

arXiv:2610.11019v1 Announce Type: cross Abstract: Multimodal large language models learn mostly from paired image-text data or annotated video, and raw web video is rarely used to further train an exi

tutorialsarxiv-cs-ai

Safety

One Word Opens the Gate: The Option-Channel Attack on Typed Decision Models as Agent Guardrails

arXiv:2610.12292v1 Announce Type: new Abstract: A typed decision model reads a piece of text and returns a probability over caller-defined options, each with a short written definition, generating no

safetyarxiv-cs-ai

Similar Predictive Fit but Different Latent Dynamics: Characterizing Learned Dynamical Structure in Personalized Models of Brain Disorders

arXiv:2610.10850v1 Announce Type: new Abstract: As AI models move toward clinical decision-making and personalized treatment, understanding what a model learns is important beyond predictive accuracy

Applications

A Review Of Robotic World Models For Dynamic Environments Based On Factor And Scene Graphs

arXiv:2610.08800v1 Announce Type: cross Abstract: Models based on graphs have emerged in robotics as a powerful foundation for internal world representations, where factor and scene graphs are among t

applicationsarxiv-cs-cv

Anthropic launches the Critical Infrastructure Defense Program to provide AI models, threat research, and on-site support, starting with CrowdStrike and others (Sam Sabin/Axios)

Sam Sabin / Axios: Anthropic launches the Critical Infrastructure Defense Program to provide AI models, threat research, and on-site support, starting with CrowdStrike and others — Anthropic is launch

model-releasestechmeme

Batch Augmentation with Unimodal Fine-tuning for Multimodal Fusion of Large Language Models

arXiv:2505.06592v2 Announce Type: replace Abstract: In this paper, we propose batch augmentation with unimodal fine-tuning for multimodal learning. We start with pre-trained unimodal models. We fine-t

researcharxiv-cs-cv

Best practices when running a benchmark on online models [D]

model-releasesr-machinelearning

I'm developing a benchmark for a low resource language and I don't want it to be leaked and used for training when it is being used to get predictions. For locally run models it shouldn't be a problem

CoDR: Training-Free Confidence-Drift Remasking for Diffusion Language Models

arXiv:2610.08833v1 Announce Type: new Abstract: Masked diffusion language models (MDLMs) decode by repeatedly committing tokens to masked positions, but these commitments are usually irreversible. A t

researcharxiv-cs-cl

HarnessIR: Harnessing Multimodal Foundation Models for Universal Real-World Image Restoration

arXiv:2610.10133v1 Announce Type: new Abstract: Real-world low-quality images suffer from complex mixed degradations, including but not limited to noise, blur, atmospheric effects, etc. Recent agentic

Noise Your Prompt: Noising Conditioning Tokens in Continuous Diffusion Language Models

arXiv:2610.09145v1 Announce Type: cross Abstract: We revisit a standard accepted practice in the continuous diffusion language model literature of fixing conditioning prompt tokens clean during traini

researcharxiv-cs-cl

SEER: Self-Enhancing Chain-of-Thought Compression for Reasoning Models

arXiv:2509.14093v3 Announce Type: replace-cross Abstract: Chain-of-Thought (CoT) prompting can substantially improve the reasoning ability of large language models (LLMs), but it often comes with high

Self-Consuming Generative Models with Co-Evolving Human Preferences

arXiv:2610.09415v1 Announce Type: new Abstract: Generative models are increasingly trained in self-consuming iterative loops, where users curate preferred samples from model-generated candidates and t

U-Space: Uncovering When and Why Uncertainty Arises in Language Models

arXiv:2610.09087v1 Announce Type: new Abstract: Large language models are informing decisions with ever-higher stakes. As the consequences of their errors grow, a central question becomes harder to ig

researcharxiv-cs-cl

Attenuated in-context identification in time-series foundation models: diagnosis under counterfactual inputs and repair by synthetic forced-system fine-tuning

arXiv:2610.08118v1 Announce Type: new Abstract: Covariate-aware time-series foundation models (TSFMs) promise training-free what-if answers for instrumented plants: the change in output that a differe

DIPrune: Task-Aware Token Pruning with Dual Importance for Efficient Multimodal Language Models

arXiv:2610.08341v1 Announce Type: new Abstract: Recent training-free pruning approaches for Multimodal Large Language Models (MLLMs) effectively cut computational overhead by exploiting visual redunda

Dynamic Positional Attention Modulation for Parameter-Efficient Fine-Tuning of Large Language Models

model-releasesarxiv-cs-ai

arXiv:2610.07848v1 Announce Type: cross Abstract: Parameter-efficient fine-tuning (PEFT) has become a standard approach for adapting large language models to downstream tasks. However, most existing P

MARCO: The Radioactive Watermark for Protein Generative Models

arXiv:2610.08316v1 Announce Type: cross Abstract: Protein Generative Models (PGMs) have revolutionized structural biology by enabling the design of complex 3D protein structures from sequence data. Ho

researcharxiv-cs-ai

Preserving Unstable Modes Through Inverse Dynamics in JEPA World Models

arXiv:2610.07540v1 Announce Type: new Abstract: Robotic systems often exhibit unstable modes, along which small perturbations and disturbances can cause unbounded growth unless corrected through feedb

Rethinking Fine-Tuning: Unlocking Hidden Capabilities in Vision-Language Models

arXiv:2512.23073v2 Announce Type: replace-cross Abstract: Fine-tuning has become the dominant paradigm for adapting Vision-Language Models (VLMs), yet most approaches rely on explicit weight updates t

RSJEV: Discriminative Remote Sensing Scene Classification with Multimodal Large Language Models

arXiv:2610.08539v1 Announce Type: new Abstract: Remote sensing scene classification is a fundamental task in Earth observation and geospatial analysis. Existing approaches mainly follow three paradigm

Local Ai

ThinkFuse: Trajectory-Aware Test-Time Fusion for Small Reasoning Models

arXiv:2610.07803v1 Announce Type: new Abstract: Small reasoning models (SRMs) have shown strong performance on complex reasoning tasks by generating extended chain-of-thought trajectories, but they of

local-aiarxiv-cs-ai

Towards In-Parameter Memory Augmentation for Large Language Models

arXiv:2610.08630v1 Announce Type: new Abstract: Recently Large Language Models (LLMs) and LLM-based agents increasingly need to incorporate knowledge acquired after pretraining, e.g., domain facts, us

Applications

Uniform Discrete Diffusion Models are Minimax Optimal for Estimating Distributions with Small Effective Support Size

arXiv:2610.07655v1 Announce Type: cross Abstract: Discrete diffusion models have emerged as a practically successful framework for generative modeling on discrete product spaces, yet their statistical

applicationsarxiv-cs-lg

Universe of Thoughts: A Computational Framework for Creative Reasoning in Large Language Models

model-releasesarxiv-cs-ai

arXiv:2511.20471v3 Announce Type: replace Abstract: Recent advances in Large Language Model (LLM) reasoning have improved conventional problem solving, but creative reasoning remains comparatively und

Weight Oracles: Reading Neural Network Weights with Language Models

model-releasesarxiv-cs-lg

arXiv:2610.07334v1 Announce Type: new Abstract: Interpretability methods for neural networks are predominantly reactive: they analyse activations produced during specific forward passes, requiring kno

Industry

We're releasing pplx-embed-v2-late, two late-interaction embedding models that retrieve text, images, and pages with a shared embedding spac…

We're releasing pplx-embed-v2-late, two late-interaction embedding models that retrieve text, images, and pages with a shared embedding space for cross-model querying. Both models achieve frontier per

industryclem-delangue--x

Anthropic expands its Cyber Verification Program by integrating Project Glasswing and offering three tiers, all with access to its most capable Claude models (Anthropic)

Anthropic: Anthropic expands its Cyber Verification Program by integrating Project Glasswing and offering three tiers, all with access to its most capable Claude models — We're launching a new, expand

model-releasestechmeme

Bayesian Entropy-based Reordering for Calibrated Diffusion Language Models

arXiv:2610.05125v1 Announce Type: new Abstract: Masked Diffusion Language Models (MDLMs) generate sequences by iteratively replacing masked tokens with model predictions. At each denoising step, the d

tutorialsarxiv-cs-lg

Safety

Considering Context: When World Models Need Context Encoders

arXiv:2610.06651v1 Announce Type: new Abstract: Methods for generalization in model-based reinforcement learning typically assume that an agent cannot recover the latent context governing the environm

safetyarxiv-cs-lg

Frozen in a Frame: The Velocity Blind Spot in JEPA World Models

arXiv:2610.04585v1 Announce Type: new Abstract: Joint-embedding predictive architectures (JEPAs) for world modeling train an encoder so a predictor maps a current embedding and action to the next fram

Integrating Survival-Based Aging Models with Data-Driven RUL Prognostics

arXiv:2610.06128v1 Announce Type: new Abstract: Predictive maintenance requires reliable remaining useful life (RUL) estimation. Existing methods mainly follow two paradigms: wear-based aging models t

MMLongCite: A Benchmark for Evaluating Faithfulness of Long-Context Vision-Language Models

arXiv:2510.13276v2 Announce Type: replace Abstract: The rapid advancement of long-context vision language models (LCVLMs) has led to a significant expansion of their context windows. However, an exten

Questioning the Questions: Sustaining Self-Evolution in Reasoning Models

arXiv:2610.04299v1 Announce Type: new Abstract: Self-evolving reasoning models learn from their own generated questions, yet repeated self-training can lead to performance collapse. In this paper, we

tutorialsarxiv-cs-lg

Local Ai

RealtimeWAM: One-Step Asynchronous World Action Models

arXiv:2610.06617v1 Announce Type: new Abstract: World Action Models (WAMs) incorporate visual representations from video generation backbones to guide action prediction. Recent efficient WAMs adopt Mi

local-aiarxiv-cs-cv

Saying, Not Knowing: Aggressively GGUF-Quantized Small Language Models Still Write Rare Words They Can No Longer Define

model-releasesarxiv-cs-lg

arXiv:2610.04403v1 Announce Type: cross Abstract: Post-training quantization to the GGUF format's mixed-precision K-quants is commonly how open-weight language models reach consumer hardware, yet its

SimAuthor: Harnessing Foundation Models for Persistent Scientific Simulator Authoring

arXiv:2610.06257v1 Announce Type: new Abstract: Foundation models can generate scientific code, but authoring a scientific simulator (an executable program encoding hypotheses about how mechanisms gen

tutorialsarxiv-cs-lg

SpecFold: Folding Multi-Branch Redundancy for Faster Speculative Decoding in Diffusion Language Models

arXiv:2610.04875v1 Announce Type: cross Abstract: Diffusion large language models (DLLMs) generate text through iterative block denoising, and multi-branch speculative decoding accelerates this proces