Search: “models”

arXiv:2509.26435v2 Announce Type: replace-cross Abstract: Controllable summarization moves beyond generic outputs toward human-aligned summaries guided by specified attributes. In practice, the interd

arXiv:2510.20512v2 Announce Type: replace Abstract: Recent progress in accelerating text-to-image diffusion models enables high-fidelity synthesis within a single denoising step. However, customizing

arXiv:2604.05426v2 Announce Type: replace-cross Abstract: Low-Rank Adaptation (LoRA) is now the dominant method for parameter-efficient fine-tuning of large language models, but achieving a high-quali

'Voiden' doesn't appear in any search results as a known model or tool in the Ollama ecosystem. Based on the Reddit source and the broader context of the r/ollama community, this post likely discusses

arXiv:2604.08837v1 Announce Type: new Abstract: Flow-based image generative models exhibit stable training and produce high quality samples when using multi-step sampling procedures. One-step generati

arXiv:2604.09168v1 Announce Type: new Abstract: We introduce Elastic Looped Transformers (ELT), a highly parameter-efficient class of visual generative models based on a recurrent transformer architec

arXiv:2604.08589v1 Announce Type: new Abstract: Mental health challenges among young adults, are on the rise, necessitating effective solutions such as digital mental health interventions (DMHIs). Des

arXiv:2604.08741v1 Announce Type: new Abstract: Modern Automatic License Plate Recognition (ALPR) systems achieve outstanding performance in controlled, well-defined scenarios. However, large-scale re

arXiv:2604.04144v2 Announce Type: replace-cross Abstract: The holy grail of LLM personalization is a single LLM for each user, perfectly aligned with that user's preferences. However, maintaining a se

arXiv:2604.09258v1 Announce Type: new Abstract: Pretraining is the cornerstone of Large Language Models (LLMs), dominating the vast majority of computational budget and data to serve as the primary en

arXiv:2509.20006v3 Announce Type: replace Abstract: With the large models easing the labor-intensive manipulation process, image manipulations in today's real scenarios often entail a complex manipula

arXiv:2604.08803v1 Announce Type: cross Abstract: This paper considers AI model churn as an opportunity for frugal investigation of large AI models. It describes how the incessant push for ever more p

arXiv:2604.08870v1 Announce Type: cross Abstract: Student dropout is a persistent concern in Learning Analytics, yet comparative studies frequently evaluate predictive models under heterogeneous proto

Nous Research's Hermes agent framework combines a language model's contextual reasoning capabilities ('this') with an autonomous agent built on their Hermes model series, enabling tool use, multi-step

this is a good point around taking advantage of model/api provider features i agree that prompt caching is great! we make sure to use it in deepagents! but that alone doesnt lock in - you can switch p

Zhipu AI announced that one of their models or projects reached the #1 trending position on Hugging Face, describing it as their most challenging release to date. The post from Zhipu AI's account high

arXiv:2511.22396v2 Announce Type: replace-cross Abstract: Recent multimodal reasoning models, inspired by DeepSeek-R1, have significantly advanced vision-language systems. However, in remote sensing (

arXiv:2603.18019v2 Announce Type: replace Abstract: Do language model benchmarks actually measure what practitioners intend them to ? High-level metadata is too coarse to convey the granular reality o

arXiv:2604.06718v2 Announce Type: cross Abstract: Repurchase behavior is a primary signal in large-scale retail recommendation, particularly in categories with frequent replenishment: many items in a

arXiv:2603.20698v2 Announce Type: replace-cross Abstract: Multimodal Large Language Models (MLLMs) have demonstrated remarkable potential in medical image analysis. However, their application in gastr

arXiv:2506.19420v2 Announce Type: replace Abstract: Multimodal sarcasm understanding is a high-order cognitive task. Although large language models (LLMs) have shown impressive performance on many dow

arXiv:2601.16206v3 Announce Type: replace-cross Abstract: Agentic intelligence in large language models (LLMs) requires not only model intrinsic capabilities but also interactions with external enviro

arXiv:2604.08457v1 Announce Type: new Abstract: Cooperative autonomous driving requires traffic scene understanding from both vehicle and infrastructure perspectives. While vision-language models (VLM

arXiv:2604.08074v1 Announce Type: new Abstract: Reliable and weather-robust perception systems are essential for safe autonomous driving and typically employ multi-modal sensor configurations to achie

arXiv:2602.11635v2 Announce Type: replace Abstract: Multimodal large language models (MLLMs) have achieved strong performance on perception-oriented tasks, yet their ability to perform mathematical sp

arXiv:2604.08548v1 Announce Type: new Abstract: Human body fitting, which aligns parametric body models such as SMPL to raw 3D point clouds of clothed humans, serves as a crucial first step for downst

arXiv:2604.07392v1 Announce Type: cross Abstract: Autonomous agents operating in dynamic and safety-critical environments require decision-making frameworks that are both computationally efficient and

arXiv:2604.07070v1 Announce Type: new Abstract: While Large Language Models (LLMs) demonstrate remarkable reasoning capabilities, their potential for purpose-driven exploration in dynamic geo-spatial

arXiv:2406.12009v5 Announce Type: replace Abstract: Accurate and transparent financial information disclosure is essential for market efficiency, investor decision-making, and corporate governance. Ch

I was unable to retrieve the specific Reddit post at the provided URL through my search. The post (reddit.com/r/StableDiffusion/comments/1shxmfk) did not surface in the search results, and I cannot...

LangChain's Middleware abstraction, introduced by Harrison Chase (@hwchase17) in LangChain 1.0 Alpha, addresses context engineering in AI agents by providing clean `before_model`, `after_model`, an...

arXiv:2604.07172v1 Announce Type: new Abstract: Calibration is central to reliable semantic uncertainty quantification, yet prior work has largely focused on discrimination, neglecting calibration. As

arXiv:2604.06737v1 Announce Type: cross Abstract: Large language models have demonstrated remarkable capabilities across a wide range of natural language processing tasks, yet their application in the

arXiv:2604.08538v1 Announce Type: new Abstract: AI agents are changing the requirements for document parsing. What matters is semantic correctness: parsed output must preserve the structure and

arXiv:2508.21618v2 Announce Type: replace-cross Abstract: We present PhISM, a physics-informed deep learning architecture that learns without supervision to explicitly disentangle hyperspectral observ

arXiv:2604.06912v1 Announce Type: cross Abstract: MLLMs require high-resolution visual inputs for fine-grained tasks like document understanding and dense scene perception. However, current global res

arXiv:2604.08282v1 Announce Type: new Abstract: Radar perception models are trained with different inputs, from range-Doppler spectra to sparse point clouds. Dense spectra are assumed to outperform sp

In an April 9, 2026 Substack post, AI skeptic Gary Marcus argues that Anthropic's announcement of its Claude 'Mythos' model was significantly overhyped, offering three key reasons for skepticism: t...

arXiv:2601.23155v2 Announce Type: replace-cross Abstract: Information-based data selection for instruction tuning is compelling: maximizing the log-determinant of the Fisher information yields a monot

arXiv:2604.08423v1 Announce Type: new Abstract: What are the limits of controlling language models via synthetic training data? We develop a reinforcement learning (RL) primitive, the Dataset Policy G

arXiv:2503.10183v4 Announce Type: replace Abstract: Existing vision-language models (VLMs) often suffer from visual hallucination, where the generated responses contain inaccuracies that are not groun

arXiv:2604.07223v1 Announce Type: cross Abstract: As large language models (LLMs) evolve from static chatbots into autonomous agents, the primary vulnerability surface shifts from final outputs to int

arXiv:2604.06277v1 Announce Type: new Abstract: Existing hallucination detection methods for large language models (LLMs) rely on external verification at inference time, requiring gold answers, retri

arXiv:2604.07242v1 Announce Type: new Abstract: Despite deep learning models running well-defined mathematical functions, we lack a formal mathematical framework for describing model architectures. Ad

arXiv:2604.08524v1 Announce Type: cross Abstract: Applying steering vectors to large language models (LLMs) is an efficient and effective model alignment technique, but we lack an interpretable explan

As part of the evaluation of its Claude Mythos model, Anthropic engaged a clinical psychiatrist for approximately 20 hours of evaluation sessions, describing Mythos as 'the most psychologically se...