Search: “models”

arXiv:2605.31595v1 Announce Type: new Abstract: Dynamic scene reconstruction from monocular video remains a fundamental challenge in computer vision. Existing feed-forward methods predict 3D Gaussians

arXiv:2605.31365v1 Announce Type: new Abstract: Recent advances in Multimodal Large Language Models (MLLMs) have led to promising progress in web agents. However, existing web agents often rely on han

arXiv:2603.22744v2 Announce Type: replace Abstract: Large language models excel on objectively verifiable tasks such as math and programming, where evaluation reduces to unit tests or a single correct

arXiv:2605.31167v1 Announce Type: new Abstract: Assessing whether Large Language Models outputs are factually grounded, epistemically calibrated, and methodologically reproducible is a prerequisite fo

arXiv:2603.02630v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have achieved great success in many real-world applications, especially the one serving as the cognitive backbone

arXiv:2510.00845v4 Announce Type: replace-cross Abstract: Mechanistic Interpretability (MI) aims to reverse-engineer model behaviors by identifying functional sub-networks. Yet, the scientific validit

arXiv:2602.07905v2 Announce Type: replace Abstract: Large Language Models (LLMs) have shown strong potential in complex medical reasoning yet face diminishing gains under inference scaling laws. While

MiniMax M3 is live and Together AI is powering its inference 🚀 Tomorrow at 6pm PT we're going live on X Spaces with the teams behind the model and the infrastructure to give you a deep dive. https://x

MiniMax-M3 will by arrive on HuggingFace openweight at next week! Introducing MiniMax M3: The First Open-Weights Model to Combine Three Frontier Capabilities - Coding & Agentic Frontier: 59.0% SWE-Ben

arXiv:2605.31302v1 Announce Type: cross Abstract: Undersampled magnetic resonance imaging (MRI) reconstruction seeks to recover temporally or contrast-varying image series from incomplete multicoil k-

arXiv:2605.31010v1 Announce Type: new Abstract: Retrieval-augmented generation is intensively studied to ground large language models on external evidence. However, retrieving from a unified knowledge

arXiv:2605.31136v1 Announce Type: new Abstract: In automated fact-checking (AFC), check-worthiness detection identifies claims requiring verification based on domain-specific criteria. On Wikipedia, t

arXiv:2605.31222v1 Announce Type: new Abstract: Distributional reinforcement learning (DRL) models the full return distribution rather than expectations, but extending it to multivariate settings rema

arXiv:2605.31460v1 Announce Type: new Abstract: Reasoning-based robotic policies using large language and vision-language models achieve strong semantic planning capabilities but mostly suffer from a

arXiv:2605.31090v1 Announce Type: cross Abstract: Mislabeled samples in training datasets severely degrade the performance of deep networks, as overparameterized models tend to memorize erroneous labe

arXiv:2605.30991v1 Announce Type: cross Abstract: Inference-time reward alignment steers pretrained diffusion and flow-based generative models to satisfy user-specified rewards without retraining. Rec

arXiv:2605.31463v1 Announce Type: cross Abstract: Mixture-of-Experts (MoE) has become the dominant architecture for frontier language models. To meet this demand, production frameworks have built opti

arXiv:2605.30695v1 Announce Type: new Abstract: Deploying vision-language-action (VLA) policies in industrial environments requires the ability to teach new tasks at low cost, a property current VLAs

arXiv:2603.06738v2 Announce Type: replace-cross Abstract: Recent Super-Resolution~(SR) methods mainly adopt Transformers for their strong long-range modeling capability and exceptional representationa

arXiv:2605.30957v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models have emerged as a promising paradigm for general-purpose robot control. However, their performance remains fundament

Cursor announced updates to its Teams pricing model in June 2026, as detailed in a blog post linked from their official X account. The announcement covers pricing changes or new features related to Cu

arXiv:2605.30523v1 Announce Type: cross Abstract: Recent work describes what transformers can and cannot compute through connections to boolean circuits, but existing results lack exact characterizati

arXiv:2605.31311v1 Announce Type: cross Abstract: Networked AI systems increasingly rely on multiple agents that collaboratively learn and adapt models over communication networks. In such systems, bi

arXiv:2602.13110v3 Announce Type: replace-cross Abstract: Large language models (LLMs) are increasingly used as scalable judges in pairwise evaluation, but they remain prone to miscalibration and bias

arXiv:2605.30698v1 Announce Type: cross Abstract: Vision-language models (VLMs) have achieved strong performance on visual question answering (VQA). To mitigate individual hallucinations and blind spo

arXiv:2605.31509v1 Announce Type: cross Abstract: Large language model agents trained with reinforcement learning (RL) often learn brittle, task-specific shortcuts. We hypothesize that agents generali

arXiv:2605.31033v1 Announce Type: new Abstract: Streaming video generation models typically rely on temporal-centric memory, which organizes historical context as raw frames, chunk segments, or unclus

arXiv:2602.06161v2 Announce Type: replace-cross Abstract: Parallel diffusion decoding can accelerate diffusion language model inference by unmasking multiple tokens per step, but aggressive parallelis

arXiv:2605.30389v1 Announce Type: cross Abstract: Pattern languages are a classical model in formal language theory and algorithmic learning theory. This note formulates the problem of computing the i

This article discusses how prefix-aware routing and prefix caching techniques can reduce the computational overhead and costs associated with running large language models at scale by eliminating redu

arXiv:2601.19791v3 Announce Type: replace Abstract: We study grokking, the onset of generalization long after overfitting, in a classical ridge regression setting. We prove end-to-end grokking results

arXiv:2605.31069v1 Announce Type: cross Abstract: Accurately predicting future events is fundamental to content understanding and decision-making across various domains. While prior research has prima

arXiv:2505.18069v3 Announce Type: replace Abstract: Hebbian and anti-Hebbian plasticity are widely observed in the brain and are classically modeled as mechanistic, local homosynaptic rules stabilized

arXiv:2510.15710v3 Announce Type: replace Abstract: Medical workflows routinely combine reading images with producing visual and textual outputs, making both image understanding and generation central

arXiv:2506.01318v4 Announce Type: replace-cross Abstract: Machine unlearning (MU) aims to expunge a designated forget set from a trained model without costly retraining, yet the existing techniques ov

arXiv:2601.05770v3 Announce Type: replace-cross Abstract: Algorithm extraction aims to synthesize executable programs directly from models trained on algorithmic tasks, enabling de novo recovery of ex

arXiv:2605.30719v1 Announce Type: cross Abstract: We study when large language models (LLMs) can serve as effective black-box policy optimizers for reinforcement learning (RL) tasks, i.e., when can we

which is NOT new; see this quote from 5 years ago. the fact that all this is still true says a lot. This was right five years ago, and still is: “Large scale pretrained models are certainly likely to

arXiv:2605.30362v1 Announce Type: cross Abstract: Spiking neural networks (SNNs) hold promise for demonstrating superior learning and representation capabilities in deep models. Given the tremendous s

arXiv:2605.30833v1 Announce Type: cross Abstract: On-policy distillation transfers reasoning capabilities by training a student model on its own generated trajectories using token-level feedback from

Flux Identity Adjuster V2 is a ComfyUI node designed to improve identity consistency for FLUX.2 klein 9b models . It automatically throttles text strength when facial formation struggles, preventing c

This post likely argues that an individual's upbringing and early life experiences function similarly to a system prompt for an AI model—shaping foundational values, behaviors, and responses that infl

1/10 - PDF parsing at browser speed Jerry Liu showed LiteParse v2 using Rust to WebAssembly for sub-second extraction of messy PDFs, without calling a model. It can sit as a default step in agent and

B9434 is a release build identifier from the llama.cpp project, an open-source C/C++ implementation for large language model inference. Llama.cpp releases include updates, optimizations, and bug fixes

arXiv:2602.11171v2 Announce Type: replace-cross Abstract: Fine-tuning Large Language Models (LLMs) with Low-Rank Adaptation (LoRA) offers a resource-efficient way to personalize or specialize. However

arXiv:2605.30070v1 Announce Type: cross Abstract: Moving beyond simple scalar rewards toward richer world feedback is a natural path to more scalable RL post-training. On-policy self-distillation (OPS