Search: “models”

arXiv:2610.17456v1 Announce Type: cross Abstract: Language models are often evaluated alone but increasingly deployed together, routed between, made to debate, ensembled, or collaboratively trained. F

arXiv:2610.18862v1 Announce Type: cross Abstract: Reasoning models achieve strong performance through long chains of thought (CoT), but often spend more tokens than needed to solve a problem. Existing

arXiv:2510.27042v3 Announce Type: replace-cross Abstract: Increasing the thinking budget of AI models can significantly improve accuracy, but not all questions warrant the same amount of reasoning. Us

arXiv:2610.16118v1 Announce Type: new Abstract: Hour-long videos, streaming feeds, and movie-length content are pushing vision-language models (VLMs) past their breaking point -- the standard recipe o

arXiv:2610.15871v1 Announce Type: cross Abstract: We study stationary discrete-time mean-field games with finite state and action spaces under the average-reward criterion, with an entropy-regularized

arXiv:2610.16329v1 Announce Type: new Abstract: Accurate breast cancer risk prediction from screening mammography is essential for improved risk stratification and personalized screening. It can benef

arXiv:2610.14899v1 Announce Type: cross Abstract: The sudden growth in AI data centers is raising numerous environmental concerns. Low-frequency noise pollution is a concern for local residents, that

arXiv:2610.14098v1 Announce Type: new Abstract: Facade articulation at building scale is usually resolved manually or through parametric systems that encode design intent as explicit rules. This paper

This continues the small-model browser experiments from the previous posts. Two standard, off-the-shelf models ran locally on the same Samsung Galaxy S21: Gemma 4 E2B, Q3_K_M as the reasoner Qwen3.5-0

arXiv:2610.18834v1 Announce Type: new Abstract: Visual reasoning is an important capability for vision-language models (VLMs) to understand visual evidence, spatial structure, constraints, and state c

arXiv:2511.18606v2 Announce Type: replace-cross Abstract: Latent safety filters extend Hamilton-Jacobi (HJ) reachability to operate on latent state representations and dynamics learned directly from h

I built BitRun because I wanted to run 1-bit ternary LLMs on ordinary laptops without melting my battery, spinning up a dedicated GPU, or wrestling with complex build environments. Right now, BitRun g

arXiv:2610.18051v1 Announce Type: new Abstract: This work investigates the application of the Membership Inference Test (MINT) to large language models (LLMs) fine-tuned with LoRA, aiming to determine

arXiv:2610.18730v1 Announce Type: new Abstract: Manufacturers often need to characterize a new material, process, or product before enough operating data are available to train reliable predictive mod

arXiv:2610.18572v1 Announce Type: cross Abstract: Automated post-training transforms pre-trained large language models (LLMs) into models capable of handling diverse domains by automatically construct

arXiv:2610.18535v1 Announce Type: new Abstract: Video understanding benchmarks are the primary yardstick for measuring progress in video-language modeling, and a growing body of work relies on them to

arXiv:2610.15073v1 Announce Type: new Abstract: Physical flexibility plays a significant role not only in human motions but also in robot motions. For example, a humanoid robot with passive and flexib

arXiv:2610.18829v1 Announce Type: new Abstract: With the rapid development of multimodal large language models (MLLMs), evaluating their video understanding capabilities has become increasingly import

arXiv:2610.13982v1 Announce Type: new Abstract: Mixture-of-Experts (MoE) models have emerged as an effective paradigm for scaling large language models, yet their massive parameter footprint poses sev

arXiv:2610.15983v1 Announce Type: new Abstract: In this work we present a probabilistic model for predicting the temporal evolution of wildfire size using conditional generative algorithms, namely flo

arXiv:2610.15614v1 Announce Type: new Abstract: Multi-label learning typically pursues model construction through the joint optimization of label fitting, correlation modeling, and structural regulari

arXiv:2610.14039v1 Announce Type: cross Abstract: Existing vision-language-action (VLA) models benefit from large-scale vision-language pretraining, but tend to memorize training trajectories, limitin

arXiv:2610.15427v1 Announce Type: new Abstract: Foundation models are at the forefront of artificial intelligence. A tokenizer, converting the raw input into discrete representations that the model ca

arXiv:2610.16338v1 Announce Type: new Abstract: Recent years have witnessed rapid progress in joint audio--visual generation. However, audio--visual synchronization remains a challenge, as generated s

arXiv:2610.18104v1 Announce Type: new Abstract: Hybrid attention is rapidly becoming the dominant architectural choice for frontier models. Despite their successes, such architectures complicate model

arXiv:2610.18638v1 Announce Type: cross Abstract: Combining pretrained models is a common strategy for building AI systems, whether by aligning a vision encoder with a language model or merging fine-t

arXiv:2411.14865v2 Announce Type: replace-cross Abstract: Optical flow estimation is a fundamental task in dynamic scene understanding and computer vision. While recent deep models achieve impressive

arXiv:2610.17383v1 Announce Type: new Abstract: Large-scale repositories of wearable sensor data have enabled the development of foundation models for human activities and behavior. However, existing

arXiv:2610.14158v1 Announce Type: new Abstract: Understanding and tracking a dynamic world are fundamental to visual intelligence. Existing video benchmarks provide limited insight into whether models

arXiv:2610.15556v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models commonly generate robot actions token by token through Large Language Model (LLM) decoders, causing inference late

arXiv:2610.15299v1 Announce Type: new Abstract: Misclassification is a critical problem in many machine learning applications. Since even the classifier models with high accuracy (e.g., > 95%) still i

Follow-up to last week's post about streaming MoE experts from disk in a browser tab. That ran on hand-written engines for two models. This week the same disk tier sits under llama.cpp's own WebGPU ba

It's confirmed. OpenAI has been secretly replacing Astra with increasingly shit models since release. My evidence is overwhelming. Three Reddit comments, a benchmark that fluctuated 4%, and a guy who

tl;dr: running Qwen3.8FN Q4 locally is cheaper for me than every cloud model except Luna and the newer Haiku at mid-peak and peak electricity pricing where I live, and becomes the absolute cheapest mo

arXiv:2610.13600v1 Announce Type: cross Abstract: Large Audio Language Models (LALMs) achieve strong performance on speech-text tasks, but standard audio encoders produce hundreds of tokens for even s

arXiv:2610.13025v1 Announce Type: new Abstract: Self-evolving agents are increasingly used to improve language model systems without human intervention through either weight updates or harness improve

arXiv:2610.13437v1 Announce Type: new Abstract: Anomaly detection, the identification of collision events that deviate from Standard Model expectations, is one of the most promising model-agnostic str

arXiv:2610.12522v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used in molecular discovery and structure elucidation, where decisions depend on physical observations.

arXiv:2610.13386v1 Announce Type: new Abstract: Stance detection remains challenging for large language models (LLMs) due to implicit opinions, sarcasm, and context-dependent expressions of stance. Al

arXiv:2610.13008v1 Announce Type: new Abstract: In repeated human-AI interaction, a single observation can be quoted, summarized, and returned to a model as multiple reports, making repetition look li

Here're results from my final test of coding, coding (panda/numpy), data analysis, data-driven decision-making, and local RAG domains I interact with most often. This test, which is refined from a 500

What's Changed llama: update llama.cpp to b11521 for decision models and EmbeddingGemma 2 by @jmorganca in #18917 server: reuse verified manifests and loaded models in inference routes by @dhiltgen in

arXiv:2610.12995v1 Announce Type: cross Abstract: Supervised fine-tuning (SFT) remains a widely used approach for post-training large language models, because fixed demonstrations can directly introdu

There is a lot of discussion which quant to use. Especially a lot of post how much speed you gain with FP4, but not much about quality. So i tested some of the models that I use on my R9700. Models: Q

Hey r/LocalLLaMA! Maintainer here. Some of you might remember OpenMed from our 1.8 post a few months back. Quick recap: it's an open-source (Apache-2.0) medical AI toolkit with one rule we never break

DigUp is a free Mac app that runs Google DeepMind’s new EmbeddingGemma 2 locally over your own files. The model puts text, images, audio and video in one space, so you describe what you remember and l