Search: “models”

This article analyzes the performance differences between open-source and closed-source AI models in the current landscape, examining factors that influence their relative capabilities and market posi

arXiv:2603.20410v2 Announce Type: replace Abstract: Scientific machine learning is increasingly used to build surrogate models, yet most models are trained under a restrictive assumption in which futu

A user reported satisfaction with Gemma 4 Cloud but switched to GLM 5.1 due to API errors, noting that the alternative model consumes significantly more resources. The post likely discusses the perfor

Morning everyone! I put this merge together yesterday, put it on huggingface to share with Jackrong, and a bunch of people discovered it when it got cloned into Jackrongs repo, and it’s going a bit vi

arXiv:2604.14570v1 Announce Type: new Abstract: Deepfake detectors face growing challenges in generalization as new image synthesis techniques emerge. In particular, deepfakes generated by diffusion m

arXiv:2604.14626v1 Announce Type: new Abstract: Mixture-of-Experts (MoE) models have become the dominant architecture for large-scale language models, yet on-premises serving remains fundamentally mem

Ernie Image Turbo is a text-to-image generation model that can run efficiently on consumer-grade hardware like RTX 30 series GPUs with limited VRAM by using INT8 quantization. The post discusses techn

arXiv:2604.14188v1 Announce Type: cross Abstract: Large language models have demonstrated impressive performance across many domains of mathematics and physics. One natural question is whether such mo

arXiv:2511.02135v2 Announce Type: replace Abstract: Large language models (LLMs) have become a popular approach for simulating human behaviors, yet it remains unclear if LLMs are necessary for all sim

arXiv:2511.01014v3 Announce Type: replace Abstract: Instruction-following is a fundamental ability of Large Language Models (LLMs), requiring their generated outputs to follow multiple constraints imp

arXiv:2601.18675v2 Announce Type: replace Abstract: We investigate whether temporal embedding models trained on longitudinal electronic health records can learn clinically meaningful representations w

arXiv:2604.15149v1 Announce Type: new Abstract: As reinforcement Learning with Verifiable Rewards (RLVR) has become the dominant paradigm for scaling reasoning capabilities in LLMs, a new failure mode

LM Studio version 0.4.12 introduces support for Alibaba's Qwen 3.6 model, improved PDF export functionality for chat conversations, and fixes for MCP (Model Context Protocol) servers with OAuth authen

arXiv:2510.17568v5 Announce Type: replace Abstract: Recent 3D feed-forward models, such as the Visual Geometry Grounded Transformer (VGGT), have shown strong capability in inferring 3D attributes of s

arXiv:2503.21970v3 Announce Type: replace Abstract: State-Space Models (SSMs) have attracted considerable attention in Image Restoration (IR) due to their ability to scale linearly sequence length whi

arXiv:2604.14616v1 Announce Type: new Abstract: Clinical value set authoring -- the task of identifying all codes in a standardized vocabulary that define a clinical concept -- is a recurring bottlene

arXiv:2604.14865v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly exposed to adaptive jailbreaking, particularly in high-stakes Chemical, Biological, Radiological, and Nucl

arXiv:2604.14558v1 Announce Type: new Abstract: This paper presents the NTIRE 2026 image super-resolution (imes4) challenge, one of the associated competitions of the NTIRE 2026 Workshop at CVPR 2026.

arXiv:2604.13737v1 Announce Type: cross Abstract: Recommender systems have historically developed along two largely independent paradigms: feature interaction models for modeling correlations among mu

arXiv:2604.03307v2 Announce Type: replace Abstract: Multimodal Large Language Models (MLLMs) have achieved remarkable success, yet they remain prone to perception-related hallucinations in fine-graine

arXiv:2603.29088v2 Announce Type: replace-cross Abstract: Recent progress in large language models (LLMs) has substantially advanced automatic code generation and formal theorem proving, yet software

arXiv:2604.14883v1 Announce Type: new Abstract: Recent advances in Deep Learning (DL) have strengthened data-driven System Identification (SysID), with Neural and Fuzzy Ordinary Differential Equation

arXiv:2604.13316v1 Announce Type: new Abstract: Neural operators have emerged as fast surrogate models for physics simulations, yet they remain acutely vulnerable to adversarial perturbations, a criti

Cursor has integrated Claude Opus 4.7 into its platform, highlighting the model's impressive autonomous capabilities and enhanced creative reasoning abilities. The announcement suggests a new feature

arXiv:2504.21751v4 Announce Type: replace-cross Abstract: Modern software development demands code that is maintainable, testable, and scalable by organizing the implementation into modular components

arXiv:2604.13800v1 Announce Type: new Abstract: Embodied AI research is increasingly moving beyond single-task, single-environment policy learning toward multi-task, multi-scene, and multi-model setti

arXiv:2601.08605v2 Announce Type: replace Abstract: Experience intervention in web agents emerges as a promising technical paradigm, enhancing agent interaction capabilities by providing valuable insi

arXiv:2509.18847v3 Announce Type: replace-cross Abstract: Tool-augmented large language models (LLMs) are usually trained with supervised imitation or coarse-grained reinforcement learning that optimi

arXiv:2604.13627v1 Announce Type: cross Abstract: Supervised fine-tuning (SFT) is a common first stage of LLM post-training, teaching the model to follow instructions and shaping its behavior as a hel

Editor’s note: Today we hear from Perry Nightingale, SVP of Creative AI at WPP about the workflow that cuts training time for humanoid robots from days to minutes — plus access to the open-source code

arXiv:2604.13686v1 Announce Type: new Abstract: While Large Language Models (LLMs) have significantly advanced Text-to-SQL performance, existing benchmarks predominantly focus on Western contexts and

arXiv:2604.13201v1 Announce Type: new Abstract: Large language models are emerging as scientific assistants, but evaluating their ability to reason from empirical data remains challenging. Benchmarks

arXiv:2604.13713v1 Announce Type: new Abstract: Metaphor detection models achieve strong benchmark performance, yet it remains unclear whether this reflects transferable generalization or lexical memo

arXiv:2511.16555v3 Announce Type: replace Abstract: Recent advances in stereo matching have focused on accuracy, often at the cost of significantly increased model size. Traditionally, the community h

Release: llm-anthropic 0.25 New model: claude-opus-4.7, which supports thinking_effort: xhigh. #66 New thinking_display and thinking_adaptive boolean options. thinking_display summarized output is cur

arXiv:2604.13066v1 Announce Type: new Abstract: In-context learning has established itself as an important learning paradigm for Large Language Models (LLMs). In this paper, we demonstrate that LLMs c

arXiv:2604.13897v1 Announce Type: new Abstract: We present an open Molecular Crystal (MC) database of Machine-Learned Interatomic Potentials (MLIP) called MolCryst-MLIPs. The first release comprises f

arXiv:2604.13073v1 Announce Type: new Abstract: Modern multimodal large language models (MLLMs) generate fluent responses from interleaved text, image, audio, and video inputs. However, identifying wh

arXiv:2604.13531v1 Announce Type: cross Abstract: Graphical User Interface (GUI) agents show strong capabilities for automating web tasks, but existing interactive benchmarks primarily target benign,

arXiv:2603.22126v3 Announce Type: replace Abstract: Deploying learned robot manipulation policies in industrial settings requires rigorous pre-deployment validation, yet exhaustive testing across high

arXiv:2604.14041v1 Announce Type: new Abstract: Daily scenarios are characterized by visual richness, requiring Multimodal Large Language Models (MLLMs) to filter noise and identify decisive visual cl

arXiv:2604.13787v1 Announce Type: new Abstract: Large Language Models (LLMs) enhance their problem-solving capability by utilizing external tools. However, in open-world scenarios with massive and evo

arXiv:2601.10245v2 Announce Type: replace-cross Abstract: Multi-step reasoning tasks like mathematical problem solving are vulnerable to cascading failures, where a single incorrect step leads to comp

arXiv:2604.06484v2 Announce Type: replace Abstract: Cultural values are expressed not only through language but also through visual scenes and everyday social practices. Yet existing evaluations of cu

1/5 When we saw our Reducer (=LLM judge component in Maestro, our agentic framework, that selects the best output from parallel agent runs) consistently picking gold patches, we were sure Claude Opus

arXiv:2604.12255v1 Announce Type: cross Abstract: Dynamic facial expression recognition in the wild remains challenging due to data scarcity and long-tail distributions, which hinder models from effec