Search: “context”

European 78B A3.4B model is now supported by llama.cpp, I am waiting for some GGUFs https://huggingface.co/Aleph-Alpha/Kolibri-1 Kolibri is Aleph Alpha's mixture-of-experts (MoE) reasoning model, with

arXiv:2610.21389v1 Announce Type: new Abstract: Vision-language-action (VLA) policies commonly place a multi-billion-parameter model inside the feedback loop, repeatedly recomputing task context whene

Knowledge graphs are becoming a critical context and reasoning layer for agentic AI, helping enterprises connect data and ground agents in key areas of business expertise. For diversified financial se

arXiv:2510.03419v3 Announce Type: replace-cross Abstract: Neural diffusion processes provide a scalable, non-Gaussian approach to modelling distributions over functions, but existing formulations are

arXiv:2610.21113v1 Announce Type: cross Abstract: Large-scale multi-agent ecosystems require mechanisms for discovering suitable agents without exposing entire registries to a large language model (LL

arXiv:2610.14818v1 Announce Type: new Abstract: Agentic AI moves generative AI applications toward systems that coordinate reasoning, enterprise context, model inference, and external actions. Integra

arXiv:2610.15371v1 Announce Type: cross Abstract: Gnani-Evon-v3.3-30B-A3B is a long-context hybrid Mamba-Transformer mixture-of-experts model for reasoning, instruction following, and tool use in Engl

arXiv:2610.18498v1 Announce Type: cross Abstract: Autoformalization (AF) requires more than compilation: formal statements must preserve the meaning of their informal sources, a property known as fait

arXiv:2610.18818v2 Announce Type: cross Abstract: Pre-trained Large Language Model (LLMs) are effective on many tasks in zero-shot and few-shot settings. For machine translation of low-resource langua

arXiv:2610.15677v1 Announce Type: new Abstract: Continual learning aims to adapt pretrained large language models (LLMs) to evolving data while preserving their pretrained capabilities. A central chal

Meta’s Muse Spark Open, a high‑capacity US open‑weight model for agentic software engineering, is now live on Together AI as a Day 0 launch partner. The model supports a 1‑million‑token context window

arXiv:2606.16710v2 Announce Type: replace-cross Abstract: Multi-agent systems (MAS) are credited with solving complex problems, yet their agents routinely act on context that may be false. Prior work

Muse Spark Open is now rolled out to Pro, Max, Pay-as-you-go, and Teams plans. Muse Spark Open is now on Ollama. @Meta's open-weight model for coding and tool use, with a 1M-token context window. Run

arXiv:2610.18612v1 Announce Type: cross Abstract: System One models such as Jev change how software makes decisions by mapping open-ended semantic contexts into structured judgments that directly driv

arXiv:2610.15259v1 Announce Type: new Abstract: Low-bit quantization reduces the cost of serving reasoning models but can substantially degrade long chains of thought. Each step reads cached keys and

arXiv:2610.19268v1 Announce Type: new Abstract: Benchmarks shape how model capabilities are measured and interpreted, yet their scores can obscure whose conventions, contexts, and judgments define suc

arXiv:2610.17639v1 Announce Type: new Abstract: Aesthetic image cropping improves composition by balancing subjects, context, and image boundaries. Large vision-language models bring semantic knowledg

arXiv:2610.17191v1 Announce Type: new Abstract: Linear-time sequence models typically store long-context information in a recurrent state. A fixed-size state limits how much information can be stored,

arXiv:2610.14207v1 Announce Type: new Abstract: Under a fixed context budget, which objects and relations should a 3D scene-grounded VLM see first? On ScanNet indoor referring benchmarks (Dai et al.,

Here is how i serve 40 people (10 of them developers) with local AI services. n8n for automatisation whisper and Vexa AI, a bot joins Teams and makes transcripts, n8n later extracts decisions and todo

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:2610.13794v1 Announce Type: new Abstract: Discourse particles subtly shape the flow of discourse by reflecting conversational context and interlocutor attitudes, often leaving the truth-conditio

arXiv:2610.13334v1 Announce Type: new Abstract: Motivated by the success of large language models (LLMs) with broad generalizability and robustness to noisy or unreliable pre-training data, we explore

I've been using Claude Opus 5.5 to speed up Qwen3.8-27B on my PC (rtx 3090), it wrote a CUDA megakernel that is 1.4-1.9x faster than llama.cpp depending on the task/context length. The results and cod

arXiv:2610.11379v1 Announce Type: new Abstract: JEV models make multimodal decisions by directly scoring candidates. Although the common context is encoded once, candidate evaluation can still repeat

arXiv:2610.10795v1 Announce Type: new Abstract: We study ranking models by population-average utility from pairwise comparisons when preferences vary across users and tasks. Prior work shows that a si

In the dawn of the LLM craze, when wild Llama2's roamed free and 4k tokens was considered a fairly long context for local models, I forked an interactive fiction and MUD library called Tale to experim

arXiv:2610.11592v1 Announce Type: new Abstract: Current frontier LLMs can theoretically process long contexts with 1M tokens or more. But to what extent can they go beyond simple retrieval and perform

arXiv:2610.12005v1 Announce Type: new Abstract: Which forms of test-time compute improve the predictions of strong pretrained tabular foundation models (TFMs)? We systematically study this along three

arXiv:2610.10429v1 Announce Type: new Abstract: Autoregressive video generation requires denoising the current frames while writing their key-value representations as context for future predictions. H

arXiv:2610.09027v1 Announce Type: cross Abstract: Modern language model systems operate autonomously over increasingly long contexts containing untrusted text and images. Can an adversarial input cont

arXiv:2610.07962v1 Announce Type: cross Abstract: Where generalization capacity--the ability to extract context-invariant relational structures--first emerges remains a central question in AI and neur

arXiv:2610.07006v1 Announce Type: new Abstract: Learning effective representations helps characterize the structure and dynamics of equity markets from financial data with a low signal-to-noise ratio.

arXiv:2610.06938v1 Announce Type: new Abstract: Medical image segmentation remains fragmented along two axes: segmentation paradigms and data dimensionality. Existing methods are typically developed s

arXiv:2610.05269v1 Announce Type: new Abstract: We develop a Unified Scaling Law and a Unified Theory of Time Series Learning to understand how model capacity and historical information support foreca

Co-authors:Stenal Jolly, Strategic Cloud Engineer, GoogleAnubhav Dhawan, Software Engineer, Google Following the landmark announcement of MCP Toolbox v1.0, we're thrilled to announce that the MCP Tool

arXiv:2610.03769v1 Announce Type: new Abstract: Multi-agent LLM systems pair a sender with broad context and an executor with a limited local view. We study when a short message improves the executor'

arXiv:2610.03945v1 Announce Type: new Abstract: A constrained optimization problem may involve a parameter in its objective and active constraints, yet the final decision may remain insensitive to sma

arXiv:2610.06672v1 Announce Type: new Abstract: Long-video understanding places substantial demands on memory, as answering questions often requires retrieving information distributed across extended

Banger paper from Microsoft and colleagues. If you run agents that search a large document collection, this one is worth your time. (bookmark it) They introduce CorpusMap, which resolves recurring ent

arXiv:2610.02858v1 Announce Type: new Abstract: Language model agents are deployed with a harness, the software around the model that manages its context, tools, and feedback. When such an agent is di

arXiv:2610.03027v1 Announce Type: cross Abstract: The key-value (KV) cache becomes a major memory bottleneck in long-context LLM inference, placing substantial pressure on memory capacity and bandwidt

In our #NeurIPS2026 paper “A Minimal Interpretable Architecture for Zero-Shot Reconstruction of Dynamical Systems (DS)” (preprint: https://arxiv.org/abs/2607.14937) we reduce a DS foundation model to

I wanted to know if theres anything that can improve creative writing for our Local Models? I specificly am asking for qwen models as they are way better when it comes to researching and writing html

ggml-openvino: update to 2026.4.1, optimize performance, expand ops, improve device listing. (#29852) ggml-openvino : Qwen3.5 MoE perf (#312) Squash of ravi9#312: ggml-openvino: add detailed inference

arXiv:2610.01435v1 Announce Type: new Abstract: Tabular foundation models (TFMs) achieve strong predictive performance through in-context learning, yet repeatedly conditioning on labeled data makes in