Search: “models”

Arena AI Agentic User Benchmark Ranking

Arena AI's agentic benchmark ranks AI models on how well they orchestrate tools for real-world agentic tasks, based on signals like tool reliability, task completion, and steerability. The leaderboard

model-releasesr-chatgpt

All Blog X Post Paper YouTube Reddit GitHub

Clear filters

At least until (if?) rapid improvement stops, it seems less likely someone is going to catch the Big Three AI Labs. Microsoft and Meta relea…

DGX agent

At least until (if?) rapid improvement stops, it seems less likely someone is going to catch the Big Three AI Labs. Microsoft and Meta released their models, which were fine, but not frontier. SpaceX

model-releasesethan-mollick--x

5 Jun 2026

CLEAR: Cognition and Latent Evaluation for Adaptive Routing in End-to-End Autonomous Driving

model-releasesarxiv-cs-ro

arXiv:2606.06219v1 Announce Type: new Abstract: End-to-end autonomous driving models often struggle to balance multi-modal maneuver generation with real-time inference constraints. While diffusion mod

CollabBench: Benchmarking and Unleashing Collaborative Ability of LLMs with Diverse Players via Proactive Engagement

arXiv:2606.05793v1 Announce Type: new Abstract: While LLM-based agents excel at individual tasks, effective collaboration with realistic human partners remains challenging. Most of the existing conver

DocHop-QA: Towards Multi-Hop Reasoning over Multimodal Document Collections

arXiv:2508.15851v2 Announce Type: replace Abstract: Despite rapid progress in large language models (LLMs), current QA benchmarks still overlook the core challenge of real-world scientific information

Tutorials

Formal Concept Lattices are Good Semantic Scaffolds for Concept-Based Learning

arXiv:2606.05471v1 Announce Type: new Abstract: Learning semantics is essential for deep learning models to be interpretable and better aligned with human reasoning. Concept-based models approach this

tutorialsarxiv-cs-cv

From Self to Other: Evaluating Demographic Perspective-Taking in LLM Hate Speech Annotation

arXiv:2606.06266v1 Announce Type: new Abstract: Hate speech detection is inherently subjective: people from different demographic groups perceive the same content very differently. Collecting enough a

Generic Triple-Latent Compression with Gated Associative Retrieval

arXiv:2606.05175v1 Announce Type: new Abstract: We study generic triple-latent sequence models that maintain a running token state and compressed pair-memory pathway to capture higher-order token inte

Local Ai

https://ollama.com/library/gemma4/tags

Gemma4 is a language model available through Ollama's model library with multiple tagged versions for different use cases and configurations. The Ollama platform enables users to run open-source large

local-aiollama--x

IA-RAG: Interval-Algebra-Driven Temporal Reasoning for Dynamic Knowledge Retrieval

arXiv:2606.06044v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) has shown strong effectiveness in grounding Large Language Models (LLMs) with external knowledge. However, existing

Local Ai

Improving Heart-Focused Medical Question Answering in LLMs via Variance-Aware Rubric Rewards with GRPO

arXiv:2606.05174v1 Announce Type: new Abstract: Large Language Models (LLMs) have shown strong promise in healthcare applications. Yet deploying general-purpose models in real-world settings remains d

local-aiarxiv-cs-cl

Research

Learning to Route LLMs from Implicit Cost-Performance Preferences via Meta-Learning

arXiv:2606.06178v1 Announce Type: cross Abstract: Large language models (LLMs) present a trade-off between performance and cost, where more powerful models incur greater expense. LLM routing aims to m

researcharxiv-cs-cl

LoRi: Low-Rank Distillation for Implicit Reasoning

arXiv:2606.05315v1 Announce Type: new Abstract: Implicit chain-of-thought (iCoT) methods aim to internalize reasoning in large language models, but often underperform explicit CoT prompting. We empiri

Predict and Reconstruct: Joint Objectives for Self-Supervised Language Representation Learning

arXiv:2606.05173v1 Announce Type: new Abstract: Masked language modelling (MLM) has been the dominant pre-training objective for text encoders since BERT, yet it encourages representations that are st

ProSPy: A Profiling-Driven SQL-Python Agentic Framework for Enterprise Text-to-SQL

arXiv:2606.05836v1 Announce Type: new Abstract: Large language models have substantially advanced Text-to-SQL systems, yet applying them to enterprise-scale databases remains challenging. Real-world d

Reducing Hallucinations in Complex Question Answering using Simple Graph-based Retrieval-Augmented Generation (long version)

arXiv:2606.05901v1 Announce Type: new Abstract: Large language models (LLMs) have fundamentally transformed the landscape of Natural Language Processing. Despite these advances, LLMs and LLM-based sys

Self-supervised User Profile Generation for Personalization

arXiv:2606.05336v1 Announce Type: new Abstract: Personalizing large language models (LLMs) has become a central challenge as LLMs are deployed across recommendation, search, dialogue, and content gene

TextWand: A Unified Framework for Scene Text Editing

model-releasesarxiv-cs-cv

arXiv:2606.05730v1 Announce Type: new Abstract: We propose TextWand, a general-purpose framework that unifies scene text removal, generation, and replacement into a single model. By decomposing comple

The Mirage of Performance Gains: Why Contrastive Decoding Fails to Mitigate Object Hallucinations in MLLMs?

arXiv:2504.10020v4 Announce Type: replace Abstract: Contrastive decoding strategies are widely used to reduce object hallucinations in multimodal large language models (MLLMs). These methods work by c

Safety

When AI Says It Feels

arXiv:2606.05734v1 Announce Type: cross Abstract: Large language models (LLMs) are generally constrained from expressing feelings through human-preference alignment in post-training processes. This po

safetyarxiv-cs-cl

YouZhi: Towards High-Concurrency Financial LLMs via Adaptive GQA-to-MLA Transition

arXiv:2606.05868v1 Announce Type: new Abstract: Large language models (LLMs) drive significant financial innovations, yet their high-concurrency deployment is severely bottlenecked by KV cache memory

100-LongBench: Are de facto Long-Context Benchmarks Literally Evaluating Long-Context Ability?

arXiv:2505.19293v2 Announce Type: replace-cross Abstract: Long-context capability is considered one of the most important abilities of LLMs, as a truly long context-capable LLM enables users to effort

A Systematic Evaluation of Positional Bias in Multi-Video Summarization with MLLMs

arXiv:2606.04596v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) are increasingly used for video understanding, yet their reliability under multi-video inputs remains poorly un

Activation-Based Active Learning for In-Context Learning: Challenges and Insights

arXiv:2606.05134v1 Announce Type: new Abstract: Deep active learning has previously been explored for LLM in-context sample selection, but not with methods that utilise recent advances in understandin

Analysis-Driven Procedural Generation of an Engine Sound Dataset with Embedded Control Annotations

model-releasesarxiv-cs-lg

arXiv:2603.07584v2 Announce Type: replace-cross Abstract: Computational engine sound modeling is central to the automotive audio industry, particularly for active sound design applications and virtual

Bayesian learning for the stochastic shortest path problem

model-releasesarxiv-cs-lg

arXiv:2606.04845v1 Announce Type: cross Abstract: Sequential decision-making problems are often modelled as a Markov decision process (MDP). We focus on the stochastic shortest path (SSP) problem, whi

Building The Ph(ysical)AI Layer Of Machine Intelligence

arXiv:2606.04106v1 Announce Type: cross Abstract: Foundation models achieve generalization through massive-scale training on diverse data, but have limitations with transfer to truly unseen domains wi

DetectZoo: A Unified Toolkit for AI-Generated Content Detection Across Text, Audio, and Image Modalities

arXiv:2606.04205v1 Announce Type: cross Abstract: The growing popularity and capacity of generative models have eroded the distinction between human and machine-generated content, motivating a growing

Dive into the Scene: Breaking the Perceptual Bottleneck in Vision-Language Decision Making via Focus Plan Generation

arXiv:2606.04046v1 Announce Type: cross Abstract: In embodied vision-language decision making tasks such as robotic manipulation and navigation, Vision-Language and Vision-Language-Action Models (VLMs

Research

Evaluating Autoformalization Robustness via Semantically Similar Paraphrasing

arXiv:2511.12784v3 Announce Type: replace Abstract: Large Language Models (LLMs) have recently emerged as powerful tools for autoformalization. Despite their impressive performance, these models can s

researcharxiv-cs-cl

Applications

Federated Learning for Multi-Center Sepsis Early Prediction with Privacy-Preserving

arXiv:2606.04338v1 Announce Type: new Abstract: Privacy-sensitive and distributed characteristics of multi-center medical data bring severe obstacles to centralized modeling for accurate early predict

applicationsarxiv-cs-lg

FinTradeBench: A Financial Reasoning Benchmark for LLMs

arXiv:2603.19225v3 Announce Type: replace-cross Abstract: Real-world financial decision-making is a challenging problem that requires reasoning over heterogeneous signals, including company fundamenta

GPT-5.5 dominates $1,500 LLM hacking test while Gemini refuses to even try

A security researcher spent 1,500 running 13+ AI models against a deliberately vulnerable app, with GPT-5.5 achieving a 70% solve rate while Gemini refused to engage almost entirely. The test app cont

model-releasesr-chatgpt

How to Fine-Tune Nemotron 3.5 ASR for Your Language, Domain, or Accent

model-releaseshugging-face

This guide explains how to adapt NVIDIA's Nemotron 3.5 Automatic Speech Recognition (ASR) model to specific languages, domains, or accents through fine-tuning techniques. It likely covers the fine-tun

Hyper-ICL: Attention Calibration with Hyperbolic Anchor Distillation for Multimodal In-Context Learning

model-releasesarxiv-cs-cv

arXiv:2606.04434v1 Announce Type: new Abstract: Multimodal In-Context Learning (ICL) has emerged as a practical inference paradigm for Multimodal Large Language Models, where a small set of interleave

Tutorials

J-RAS: Mutual Adaptation for Medical Image Segmentation via Contrastive Retrieval-Augmented Joint Optimization

arXiv:2510.09953v3 Announce Type: replace Abstract: Manual medical image segmentation by clinicians, though accurate, is time-consuming and variable across experts, whereas AI-based models automate th

tutorialsarxiv-cs-cv

Safety

(Mis)generalization of Helpful-only Fine-tuning

arXiv:2606.04413v1 Announce Type: new Abstract: Helpful-only models, that is, models that are trained to always follow user intent, are valuable for dangerous capability evaluations and other areas of

safetyarxiv-cs-lg

Nemotron 3 Ultra!

model-releasesclem-delangue--x

Nemotron 3 Ultra is an advanced language model released by NVIDIA, representing an improvement over previous versions in the Nemotron series with enhanced capabilities for various NLP tasks. The annou

Neural Galerkin Normalizing Flows for Bayesian Inference of Diffusions with Inaccessible Boundaries

model-releasesarxiv-cs-lg

arXiv:2606.04324v1 Announce Type: new Abstract: One of the primary challenges in Bayesian inference on the parameters of a diffusion model from discrete observations is the unavailability of an analyt

@nvidia @nebiustf More info on the new Nemotron 3 Ultra: https://x.com/NVIDIAAI/status/2062521325076299981?s=20

model-releasesnous-research--x

@nvidia @nebiustf More info on the new Nemotron 3 Ultra: https://x.com/NVIDIAAI/status/2062521325076299981?s=20 Today we're shipping Nemotron 3 Ultra. A 550B MoE frontier-intelligence open model built

NVIDIA Nemotron 3 Ultra now available on Amazon SageMaker JumpStart

model-releasesaws-ml-blog

The search results primarily discuss the Nemotron 3 Nano Omni model rather than Nemotron 3 Ultra. However, I found a recent NVIDIA blog post reference that indicates Nemotron 3 Ultra is available thro

NVIDIA Nemotron 3 Ultra Powers Faster, More Efficient Reasoning for Long-Running Agents

model-releasesnvidia-developer

NVIDIA's Nemotron 3 Ultra is a 550B-parameter Mixture-of-Experts model with 55B active parameters, optimized for orchestrating complex, long-running agent workflows by combining frontier reasoning and

Physics-Informed Video Generation via Mixture-of-Experts Latent Alignment

model-releasesarxiv-cs-cv

arXiv:2606.04737v1 Announce Type: new Abstract: Large-scale video generation models have made remarkable progress in semantic consistency and visual quality, producing videos that are increasingly coh

Reasoning over Boundaries: Enhancing Specification Alignment via Test-time Deliberation

arXiv:2509.14760v3 Announce Type: replace Abstract: Large language models (LLMs) are increasingly applied in diverse real-world scenarios, each governed by bespoke behavioral and safety specifications

Self-Evolving Deep Research via Joint Generation and Evaluation

arXiv:2606.04507v1 Announce Type: cross Abstract: Large Language Models (LLMs) have become increasingly adopted in daily applications, with deep research standing out as a particularly important capab

StepPRM-RTL: Stepwise Process-Reward Guided LLM Fine-Tuning for Enhanced RTL Synthesis

arXiv:2606.04246v1 Announce Type: new Abstract: Automatic generation of RTL code for digital hardware designs remains challenging due to long-horizon reasoning, multi-step dependencies, and strict cor

Applications

The Differentiable Auditory Loop (DAL): An ML Framework for Hyper-Personalized Hearing Aids

arXiv:2606.04103v1 Announce Type: cross Abstract: Conventional hearing aids rely on fixed, frequency-dependent amplification and compression to manage reduced sensitivity, which often fails to provide

applicationsarxiv-cs-ai

The Meta-Agent Challenge: Are Current Agents Capable of Autonomous Agent Development?

arXiv:2606.04455v1 Announce Type: new Abstract: Current AI benchmarks evaluate agents on task execution within human-designed workflows. These evaluations fundamentally fail to measure a critical next