Search: “models”

Galaxy Phase-Space and Field-Level Cosmology: The Strength of Semi-Analytic Models

arXiv:2512.10222v2 Announce Type: replace-cross Abstract: Semi-analytic models are a widely used approach to simulate galaxy properties within a cosmological framework, relying on simplified yet physi

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Model Releases

HelloWorld: Enabling Socially Interactive Characters in Video World Models

DGX agent

arXiv:2608.05070v1 Announce Type: new Abstract: Despite the remarkable recent progress of video world models, social interaction between users and the characters within these worlds remains unsupporte

model-releasesarxiv-cs-cv

6 Aug 2026

Radar4D-VLM: Proposal-Grounded Temporal 4D Radar Reasoning Across Frozen Language Models

arXiv:2608.04130v1 Announce Type: new Abstract: Vision-language models for autonomous driving primarily rely on cameras and LiDAR, leaving 4D radar largely unexplored as a standalone perceptual modali

SIGNPOST-Bench: Benchmarking Text-Vision Conflict Resolution in Multimodal Large Language Models

arXiv:2608.04244v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) make grounded predictions in real-world scenes by combining visual and textual cues, yet existing benchmarks

Simile Understanding in Text-to-Image Models: An Evaluation Framework

arXiv:2608.04750v1 Announce Type: cross Abstract: Similes provide a compact and expressive way to describe visual characteristics in text prompts. Recent text-to-image models (t2i models) can produce

researcharxiv-cs-cl

Toward Federated Large Language Models in Medicine: A Parameter-Efficient Framework for Privacy-Preserving, Multi-Institutional Adaptation

arXiv:2601.22124v2 Announce Type: replace Abstract: Large language models (LLMs) are increasingly adapted for medical applications, but most are trained using data from a single institution because pr

Confident but Unreliable: A Behavioral Safety Audit of Vision-Language Models on Brain MRI

arXiv:2608.02790v1 Announce Type: new Abstract: Vision-language models (VLMs), including medical specialists, are increasingly proposed for medical imaging, yet their stated confidence is rarely evalu

5 Aug 2026

TimeRLM: Recursive Language Models Enable Precise Anomaly Localization in Long-Context Time-Series

arXiv:2608.03391v1 Announce Type: new Abstract: Precise anomaly localization over long-context time series is a crucial task in monitoring applications across clinical care, industrial operations, fin

5 Aug 2026

DE-NER : Zero-shot Named Entity Recognition via Dialogue Elicitation of Large Language Models

arXiv:2608.00538v1 Announce Type: new Abstract: Recent advancements of zero-shot Named Entity Recognition (NER) establish strong baselines by formulating sequence labeling into question answering wher

Exploring More to Solve More: Boosting Diversity in Text Diffusion Models via Entropy-Based Guidance

arXiv:2608.00024v1 Announce Type: new Abstract: Although diffusion models have revolutionized continuous domains like image synthesis through high quality generations and controllable guidance mechani

researcharxiv-cs-cl

From field-scale to large-scale spectral libraries: Tabular foundation models in soil spectroscopy

arXiv:2608.00608v1 Announce Type: new Abstract: Visible and near-infrared (vis-NIR) and mid-infrared (MIR) spectroscopy enable rapid, cost-effective prediction of soil properties. Yet, translating hig

Harnessing Adversarial Distillation to Customise Debiased, Disease-Specific Pathology Foundation Models for Breast Cancer

arXiv:2608.01356v1 Announce Type: new Abstract: Pathology foundation models (PFMs) provide strong tissue representations and have become central to digital pathology. However, deployment in disease-sp

If you missed today's K3 on Fireworks webinar, we've got you covered. We'll be partnering with @arizeai to chat more models on Thursday, Aug…

model-releasesfireworks-ai--x

If you missed today's K3 on Fireworks webinar, we've got you covered. We'll be partnering with @arizeai to chat more models on Thursday, August 6th. Sign up below! Next session, August 6 we'll be co l

Models as Tools: An Agentic Coordination Framework for Unified Multimodal Visual Tracking

arXiv:2608.00847v1 Announce Type: new Abstract: Most current visual trackers adopt a matching-based architecture trained exclusively on tracking datasets, whose performance gains depend heavily on the

Quality-Diversity Red-Teaming: Automated Generation of High-Quality and Diverse Attackers for Large Language Models

arXiv:2506.07121v2 Announce Type: replace Abstract: Ensuring the safety and robustness of large language models (LLMs) is a fundamental challenge and a critical prerequisite for the responsible deploy

Self-Correction Bench: Uncovering and Addressing the Self-Correction Blind Spot in Large Language Models

arXiv:2507.02778v3 Announce Type: replace Abstract: Although large language models (LLMs) have transformed AI, they still make errors and follow unproductive reasoning paths. Self-correction is vital

safetyarxiv-cs-cl

Video Models as Native 4D Renderers: World-Grounded Conditioning from Animated Mesh

arXiv:2608.00094v1 Announce Type: new Abstract: Pretrained video diffusion models can act as renderers when the desired scene state is already specified by an animated mesh, a camera trajectory, and a

Why Large Language Models Fail at Tabular Prediction

arXiv:2608.02412v1 Announce Type: new Abstract: Large language models (LLMs) have become the default tool for a remarkable range of tasks, yet they have had conspicuously little success at one of the

Can Large Language Models Derive New Knowledge? A Dynamic Benchmark for Biological Knowledge Discovery

arXiv:2603.03322v2 Announce Type: replace-cross Abstract: Recent advancements in Large Language Model (LLM) agents have demonstrated remarkable potential in automatic knowledge discovery. However, rig

even within math, frontier models are far from being general intelligences. and we still need people to assess which of their output are tru…

model-releasesgary-marcus--x

even within math, frontier models are far from being general intelligences. and we still need people to assess which of their output are trustworthy and which are not. All frontier AI models (includin

Agents

Mixture-of-Translators: Translating KV Caches Across Heterogeneous Large Language Models

arXiv:2607.28979v1 Announce Type: new Abstract: Heterogeneous Large Language Model (LLM) systems increasingly rely on shared contexts, retrieved evidence, and multi-agent dialogue histories, yet their

agentsarxiv-cs-cl

Local Ai

[NEW MODELS!] Supra2-100M Base and Instruct - go check them out!

Hey guys! After a LOT of good feedback on our previous models like Supra-50M-Instruct and -Reasoning, many community likes, follows and upvotes we saw many community requests asking for new models. We

local-air-localllama

Beyond Classification: Pathology Foundation Models as Detection Encoders for Mitotic Figures

arXiv:2607.28007v1 Announce Type: new Abstract: Pathology foundation models (FMs) are models trained on vast amounts of typically unlabeled data and have been shown to yield regularized latent spaces

researcharxiv-cs-cv

From Single- to Cross-Document: Benchmarking Multi-Granularity Event Analysis of Large Language Models

arXiv:2607.27654v1 Announce Type: new Abstract: Event analysis is an essential and fundamental direction of information extraction, involving various event-centric tasks at different granularity of do

IFHierBench: Hierarchical Instruction Following for Large Language Models

arXiv:2607.27912v1 Announce Type: cross Abstract: Instruction-following ability is critical for deploying large language models in real-world applications, where downstream components depend on the ou

Memory Efficient Tabular Foundation Models

arXiv:2607.27546v1 Announce Type: new Abstract: Tabular Foundation Models, such as TabPFN, have received a large amount of recent attention due to their performance on in-context tabular machine learn

researcharxiv-cs-lg

WhisperRec: Latent Reasoning for Efficient Foundation Recommendation Models

arXiv:2607.26621v2 Announce Type: cross Abstract: Large language models (LLMs) have demonstrated strong reasoning capabilities, motivating their adoption as backbones for foundation recommendation mod

HoF-Bench: Rediscovering Real AI-Discovered CVEs Without Frontier Models

arXiv:2607.27030v1 Announce Type: cross Abstract: LLM-based analyzers have begun finding real vulnerabilities in mature open-source projects: AISLE's analyzer is credited with more than 280 CVEs acros

30 Jul 2026

Migrate your prompts to new models and optimize them on Amazon Bedrock

model-releasesaws-ml-blog

Amazon Bedrock Advanced Prompt Optimization optimizes your prompts for up to 5 models at once and compares original versus optimized performance across quality, latency, and cost. Migrate to a new mod

30 Jul 2026

TPCD: Tone-Pressure Contrastive Decoding and the Label-Free Gating Bottleneck in Vision-Language Models

arXiv:2607.26536v1 Announce Type: new Abstract: High-pressure prompts can push vision-language models (VLMs) into unsupported commitments, such as reading illegible text, reporting indeterminate times

30 Jul 2026

Decision-Level Hijacking: Injecting Cognitive Bias into Large Language Models via Bit-Flip Attacks

arXiv:2607.25227v1 Announce Type: cross Abstract: Large Language Models (LLMs) have been widely applied in high-stakes decision-making scenarios such as corporate strategy, and users are increasingly

Evaluation of Adversarial Robustness in Arabic Language Models

arXiv:2607.25814v1 Announce Type: new Abstract: The emergence of the recent outstanding capabilities of Arabic Language Models has opened doors for exposing their vulnerabilities. One of the major sec

safetyarxiv-cs-cl

GAUGE: Grading Agent-Built Financial Models Without a Golden Answer

arXiv:2607.24889v1 Announce Type: cross Abstract: Financial models combine public disclosures with analyst assumptions to produce forecasts and valuations. While some components can be checked mechani

Instruction-based Image Editing: A Survey on Data, Models, Evaluation, and Applications

arXiv:2607.25642v1 Announce Type: cross Abstract: Instruction-based Image Editing (IIE) aims to transform a given image into a new one based on textual instructions. Advances in Large Language Models

Minimizing Targeted Activations: Input-Only Suppression of Evaluation-Awareness Latents in Large Language Models

arXiv:2607.25907v1 Announce Type: cross Abstract: Activation steering controls model behavior by editing internal activations at inference time. We study its input-side dual: optimizing a fluent promp

Neuromorphic Diffusion Language Models: Addressing Compute and Memory Bottlenecks via Sparsity and Block Denoising

arXiv:2607.24841v1 Announce Type: new Abstract: Autoregressive (AR) large language models (LLMs) are inherently inefficient at inference time because each generated token requires accessing the full s

Cheap Probes Predict Expensive Training in 3D-CT Vision--Language Models

arXiv:2607.22771v1 Announce Type: cross Abstract: Picking the frozen image encoder for a 3D~CT vision--language model (VLM), together with the token-compression scheme on top of it, is a search over m

DailyBench: A Unified Benchmark for AI-Generated and Manipulated Images from Modern Generative Models

arXiv:2607.24016v1 Announce Type: new Abstract: Recent advances in generative models have shifted AI-generated image detection from identifying easily distinguishable, fully synthetic images to identi

Fast Cross-Scenario Adaptation of CSI Models via Channel Conditional Parameter Generation

arXiv:2607.22637v1 Announce Type: new Abstract: Deep learning has shown strong potential for massive multiple-input multiple-output (Massive MIMO) physical-layer tasks, including channel state informa

From Score Learning to Discretized Sampling: An End-to-End Generalization Analysis of Diffusion Models

arXiv:2607.23226v1 Announce Type: new Abstract: Despite the empirical success of score-based diffusion models, a complete theoretical understanding of how finite-sample learning, network parameterizat

researcharxiv-cs-lg

Interpreting Quantum Learning Models via Stochastic Processes

arXiv:2607.17327v2 Announce Type: replace-cross Abstract: Quantum machine learning models define probabilistic input--output maps through coherent quantum evolution and measurement. While such models

researcharxiv-cs-lg

MIITA: Memory-Induced Inference-Time Adaptation for Continual Learning with Small Language Models

arXiv:2607.22556v1 Announce Type: new Abstract: Continual learning (CL) is essential for small language models (SLMs) to adapt to evolving real-world needs in resource-constrained deployments. However

What do Reward Models Memorize?

arXiv:2607.24484v1 Announce Type: cross Abstract: This paper studies what discriminatively trained reward models (RMs) memorize by measuring counterfactual memorization on two human preference dataset

researcharxiv-cs-cl

Hardware

Attackers have frontier AI. Defenders need a frontier AI ecosystem—the best open and closed models, force-multiplied by a global community. …

Attackers have frontier AI. Defenders need a frontier AI ecosystem—the best open and closed models, force-multiplied by a global community. During the Hugging Face incident, closed AI blocked essentia

hardwareclem-delangue--x

Interpretable Anomaly and Drift Detection with Gaussian Mixture Models

arXiv:2607.16811v2 Announce Type: replace Abstract: We revisit Gaussian Mixture Models (GMMs) as a lightweight, interpretable tool for anomaly detection and, in particular, for detecting distributiona

On the Identifiability of Controlled World Models

arXiv:2607.22430v1 Announce Type: new Abstract: Learning world models that infer environment dynamics from high-dimensional observations and predict outcomes under candidate actions is central to plan

safetyarxiv-cs-lg

Spectral Prior for Reducing Exposure Bias in Diffusion Models

arXiv:2607.22091v1 Announce Type: new Abstract: Diffusion models typically suffer from error accumulation during iterative sampling, commonly referred to as exposure bias. We reveal systematic frequen

safetyarxiv-cs-cv

Applications

Token-Operations-Oriented Inference Optimization Techniques for Large Models

arXiv:2606.20295v2 Announce Type: replace-cross Abstract: Large model inference optimization serves as a key foundation for supporting the scalable, low-cost, and highly stable operation of large mode

applicationsarxiv-cs-cl