Search: “models”

arXiv:2604.22518v1 Announce Type: new Abstract: We introduce NONSAC (Non-Minimal Sampling and Consensus), a general framework for robust and scalable model estimation from arbitrarily large datasets c

arXiv:2511.05456v2 Announce Type: replace Abstract: Graph network-based simulators (GNS) have demonstrated strong potential for learning particle-based physics (such as fluids, deformable solids, and

arXiv:2112.02604v3 Announce Type: replace-cross Abstract: Accurately modeling pedestrian intention and understanding driver decision-making processes are critical for the development of safe and socia

arXiv:2602.12469v2 Announce Type: replace Abstract: Deep ensemble methods often improve predictive performance, yet they suffer from three practical limitations: redundancy among base models that infl

arXiv:2604.22082v1 Announce Type: cross Abstract: As AI systems begin to automate complex tasks, supervision increasingly relies on weaker models or limited human oversight that cannot fully verify ou

arXiv:2604.22226v1 Announce Type: new Abstract: Sports videos are a challenging domain for multimodal understanding because they involve complex and dynamic human activities. Despite rapid progress in

arXiv:2604.22554v1 Announce Type: new Abstract: Transformations produced by image and video generation models often evolve in a highly non-linear manner: long stretches where the content barely change

I actually switched my personal Claude subscription to this (currently using Mimo v2) Nous Portal offers everything you need to build with Hermes Agent in one easy subscription: → 300+ models from eve

THIS GUY LOST 200 IN ONE DAY BECAUSE THE STRING 'HERMES.md' WAS IN HIS GIT COMMITS HERMES.md is a real convention used in AI agent projects. it's a system prompt specification file. not some obscure e

arXiv:2604.21442v1 Announce Type: new Abstract: The development of 3D scanning technology has enabled the acquisition of massive point cloud models with diverse structures and large scales, thereby pr

arXiv:2604.21101v1 Announce Type: new Abstract: For autoregressive modeling of chaotic dynamical systems over long time horizons, the stability of both training and inference is a major challenge in b

arXiv:2604.21766v1 Announce Type: new Abstract: Existing audio question answering benchmarks largely emphasize sound event classification or caption-grounded queries, often enabling models to succeed

arXiv:2601.10863v3 Announce Type: replace Abstract: Traditional time series forecasting methods optimize for accuracy alone. This objective neglects temporal consistency, in other words, how consisten

arXiv:2604.21277v1 Announce Type: new Abstract: We introduce MMTR-Bench, a benchmark designed to evaluate the intrinsic ability of Multimodal Large Language Models (MLLMs) to reconstruct masked text d

arXiv:2507.03806v3 Announce Type: replace-cross Abstract: This paper presents a learning-based framework for approximating an exact magnetic-field interaction model, supported by both numerical and ex

arXiv:2602.00931v2 Announce Type: replace-cross Abstract: Large language model reasoning is often treated as a monolithic capability, relying on binary preference supervision that fails to capture par

arXiv:2601.22703v2 Announce Type: replace Abstract: Detecting out-of-distribution (OOD) inputs is a critical safeguard for deploying machine learning models in the real world. However, most post-hoc d

arXiv:2603.22823v3 Announce Type: replace Abstract: Context. The problem of comparative evaluation of communication protocols for task orchestration by large language model (LLM) agents is considered.

arXiv:2604.21890v1 Announce Type: new Abstract: Event extraction identifies the central aspects of events from text. It supports event understanding and analysis, which is crucial for tasks such as in

arXiv:2604.21555v1 Announce Type: new Abstract: Sentence embedding techniques aim to encode key concepts of a sentence's meaning in a vector space. However, the majority of evaluation approaches for s

arXiv:2604.20902v1 Announce Type: cross Abstract: While standard flow-matching models transport noise to data uniformly, incorporating an explicit generation order - specifically, establishing coarse,

arXiv:2604.21786v1 Announce Type: new Abstract: Social media platforms have become primary arenas for climate communication, generating millions of images and posts that - if systematically analysed -

arXiv:2604.21495v1 Announce Type: cross Abstract: Numerical reasoning over expert-domain tables often exhibits high in-domain accuracy but limited robustness to domain shift. Models trained with super

arXiv:2604.21638v1 Announce Type: new Abstract: Dataset condensation constructs compact synthetic datasets that retain the training utility of large real-world datasets, enabling efficient model devel

GPT-5.5 is now available in Devin as an Agent Preview! GPT-5.5 has set a new bar for what's possible with Devin. It runs longer and more autonomously than any GPT model we've tested, surfacing bugs no

Grok Voice is used by @Starlink Introducing Grok Voice Think Fast 1.0 A state-of-the-art voice model built for complex, multi-step workflows with snappy responses and high accuracy. It takes the top s

arXiv:2509.18629v3 Announce Type: replace-cross Abstract: Foundation models excel across diverse tasks, but adapting them to specialized applications often requires fine-tuning, an approach that is me

arXiv:2604.21334v1 Announce Type: new Abstract: Do large language models (LLMs) exhibit systematic ideological bias when reasoning about economic causal effects? As LLMs are increasingly used in polic

arXiv:2604.21593v1 Announce Type: new Abstract: As LLMs reduce English-centric bias, a surprising trend emerges: non-English responses sometimes outperform English on reasoning tasks. We hypothesize t

arXiv:2604.21102v1 Announce Type: cross Abstract: We present a novel framework for automatically evaluating building conditions nationwide in the United States by leveraging large language models (LLM

arXiv:2509.25868v3 Announce Type: replace Abstract: The mechanisms underlying scientific confabulation in Large Language Models (LLMs) remain poorly understood. We introduce ReFACT (Reddit False And C

arXiv:2604.21139v1 Announce Type: new Abstract: Language models must bind entities to the attributes they possess and maintain several such binding relationships within a context. We study how multipl

arXiv:2604.21700v1 Announce Type: cross Abstract: The growing application of large language models (LLMs) in safety-critical domains has raised urgent concerns about their security. Many recent studie

arXiv:2506.12721v2 Announce Type: replace Abstract: Scaling test-time compute has emerged as an effective strategy for improving the performance of large language models. However, existing methods typ

arXiv:2604.21327v1 Announce Type: cross Abstract: Test-time reinforcement learning (TTRL) always adapts models at inference time via pseudo-labeling, leaving it vulnerable to spurious optimization sig

arXiv:2604.21911v1 Announce Type: cross Abstract: Despite impressive progress in capabilities of large vision-language models (LVLMs), these systems remain vulnerable to hallucinations, i.e., outputs

arXiv:2604.19974v1 Announce Type: cross Abstract: Large language models can be uncertain yet correct, or confident yet wrong, raising the question of whether their output-level uncertainty and their a

arXiv:2501.18873v4 Announce Type: replace Abstract: Reinforcement Learning from Human Feedback (RLHF) has emerged as a powerful approach for aligning generative models, but its reliance on learned rew

arXiv:2604.20606v1 Announce Type: cross Abstract: Vision Mamba, as a state space model (SSM), employs a zero-order hold (ZOH) discretization, which assumes that input signals remain constant between s

arXiv:2505.14984v2 Announce Type: replace Abstract: Open-Domain Table Question Answering (TQA) involves retrieving relevant tables from a large corpus to answer natural language queries. Traditional d

arXiv:2604.19859v1 Announce Type: cross Abstract: Edge-scale deep research agents based on small language models are attractive for real-world deployment due to their advantages in cost, latency, and

arXiv:2604.20429v1 Announce Type: new Abstract: Remote sensing (RS) image-text retrieval plays a critical role in understanding massive RS imagery. However, the dense multi-object distribution and com

arXiv:2604.20487v1 Announce Type: cross Abstract: Large language models (LLMs) encode knowledge in parametric weights, making it costly to update or extend without retraining. Retrieval-augmented gene

arXiv:2604.16756v2 Announce Type: replace-cross Abstract: Prompt-induced cognitive biases are changes in a general-purpose AI (GPAI) system's decisions caused solely by biased wording in the input (e.

🚨 OpenAI just launched GPT-5.5. The OpenAI team was nice enough to give me early access over the last several weeks, and I just want to flag: there is a certain class of models (one that we’re hitting

arXiv:2604.20835v1 Announce Type: new Abstract: Modern language models demonstrate impressive coding capabilities in common programming languages (PLs), such as C++ and Python, but their performance i