Search: “models”

arXiv:2610.03374v1 Announce Type: new Abstract: Embodied world models enable scalable simulation of embodied interactions for robot learning. However, existing models are prone to Model Laziness, as t

arXiv:2610.02521v1 Announce Type: new Abstract: Long-video generation and world models have shown strong potential for interactive entertainment and embodied simulation by predicting future observatio

Paper linky - Context Language Models The central idea of the paper is incredibly simple. Give a model the ability to edit its context on-the-go like a file has major benefits on task performance, con

Reupload: I'm adding this again, since previous post had a totally stupid title which made people assume this app is some kind of cloud/proprietary models UI - this is totally not the case here. In ve

arXiv:2610.01821v1 Announce Type: new Abstract: Understanding information processing in large language models (LLMs) requires dissecting the geometric organization of their internal token representati

arXiv:2610.00673v1 Announce Type: cross Abstract: Looped language models increase effective depth by repeatedly applying a shared block of layers, but existing large-scale recipes require multi-stage

arXiv:2610.01942v1 Announce Type: new Abstract: Predicting the future evolution of a scene is a fundamental capability for world modeling. Recent work has shown that operating in the feature space of

introducing 𝚌𝚕𝚎𝚏: our first models trained by @cloudflare's workers ai team. today, we're releasing two fast and accurate decision models that top the benchmarks for quality and latency. use them host

arXiv:2410.02343v2 Announce Type: replace Abstract: Large language models (LLMs) routinely fail to output the correct option in multiple-choice question answering (MCQA) while encoding the answer inte

arXiv:2609.39848v1 Announce Type: new Abstract: Foundation models are increasingly adopted across a wide range of applications, often serving as core blocks within AI systems. Yet different foundation

arXiv:2609.39604v1 Announce Type: new Abstract: Although conventional world models - auto-regressive or diffusion models based on transformers or convolutional networks - may learn surface statistics

arXiv:2609.36883v1 Announce Type: new Abstract: Tabular foundation models (TFMs) are increasingly popular because they deliver strong predictions on new datasets through in-context learning, without t

arXiv:2609.30048v1 Announce Type: new Abstract: If a language model can recognize code it wrote, it may favor that code as a judge, and instances of one model monitoring each other could collude. We t

arXiv:2609.27232v1 Announce Type: new Abstract: Scaling laws have guided large-model development in computer vision and natural language processing, but the relationships among data, model size, and c

Built this because I got tired of guessing whether a model would fit in my 6GB VRAM before downloading it, and because ollama.com's library doesn't let you sort by size. What it does: - Benchmarks Oll

arXiv:2609.24190v1 Announce Type: new Abstract: Artificial intelligence (AI) has advanced at a rapid pace in recent years. Initially, breakthroughs in large language models caught widespread attention

arXiv:2609.24574v1 Announce Type: new Abstract: Computational social science increasingly relies on large language models for text annotation, and the validity of published findings now rests on the l

Must-read papers of the week ▪️ JEPA-Anything ▪️ Modality-Autoregressive World-Action Models ▪️ In-Context Robot Learning with VLM Agents ▪️ Dream-RSI: Recursive Self-Improvement through Evolving Worl

arXiv:2609.16006v1 Announce Type: cross Abstract: Large language models (LLMs) increasingly serve users whose expectations are shaped by their cultural context, yet most cultural evaluations test what

Hey experts! I genuinely have this question and would love a general consensus from people actually using these models. LLMs have advanced a lot in benchmarks and in practical usage. Even GPT-4o and G

arXiv:2609.17291v1 Announce Type: new Abstract: The quest for new materials increasingly relies on predictive models and comprehensive simulations that span scales from atomic to macroscopic levels. H

arXiv:2609.14829v1 Announce Type: cross Abstract: We introduce Enemray, a Hassaniya-centric language model that enables general-purpose interaction in Hassaniya. Enemray is trained around a stability-

arXiv:2609.14743v1 Announce Type: new Abstract: Pure speech language models often lag behind text and speech-text language models in generating coherent content, but this gap is difficult to quantify

arXiv:2609.12575v1 Announce Type: new Abstract: Ambiguity is often treated as a bug for AI systems to resolve---but in human communication and culture, ambiguity can also be a generative resource. Fro

arXiv:2604.08335v2 Announce Type: replace Abstract: We present a feedforward graph architecture in which heterogeneous frozen large language models serve as computational nodes, communicating through

First off, I know that GLM, Qwen, and DeepSeek absolutely dominate in terms of SOTA Open Source models, and that’s what I use in my personal projects and for school, however, I’m also responsible for

I’m genuinely mind-blown by what’s happening with AI models right now. On one side, you have GPT-6-class frontier models that are just… insane. The level of capability is getting hard to wrap your hea

arXiv:2609.11022v1 Announce Type: cross Abstract: A model first sees an image from one physical measurement experiment, such as how far a block coasted, and must answer a question about a new trial, s

arXiv:2609.10787v1 Announce Type: new Abstract: Accurate modelling of illumination is central to realistic image synthesis and scene understanding. Yet, there is little exploration into whether image

arXiv:2609.10947v1 Announce Type: cross Abstract: We propose the Platonic brain bridge hypothesis: omni models, which process video, audio and text jointly like the brain, converge on brain-like repre

i've been thinking about this more after trying different agent setups. the model isn't the only thing that determines how well an agent performs. The harness around the model matters a lot too. With

arXiv:2609.06059v1 Announce Type: new Abstract: As large language models evolve from question-answering systems into general-purpose agents, evaluation must move beyond static answer correctness to as

arXiv:2609.07879v1 Announce Type: new Abstract: Large Language Models (LLMs) are frequently confident, eloquent, and well versed. A natural question arises: do they know what they don't know? To answe

Nice paper from Salesforce on co-evolving harnesses and models. Harness engineering is a hot topic right now. So this is a great read. (bookmark it) Salesforce evolved a harness with a weak model acro

arXiv:2512.15560v3 Announce Type: replace Abstract: The text encoder is a critical component of text-to-image and text-to-video diffusion models, fundamentally determining the semantic fidelity of the

I kept picking local models badly. Parameter count, quantization, VRAM. I could read all of it and still not know whether a model would be any good on my machine. So I stopped guessing and wrote somet

arXiv:2601.08955v3 Announce Type: replace-cross Abstract: Recent advances in world models have shown promise for modeling future dynamics of environmental states, enabling agents to reason and act wit

arXiv:2609.03422v1 Announce Type: new Abstract: Diversity is a widely observed factor in the resilient function of collective systems, yet the type of diversity that matters depends on the properties

arXiv:2609.03294v1 Announce Type: new Abstract: Latent world models support efficient model predictive control from high-dimensional observations, yet optimizing a single learned latent objective can

arXiv:2505.00284v3 Announce Type: replace-cross Abstract: Rapid advances in vision-language models (VLMs) have generated growing interest in their application to autonomous driving. A prevailing assum

arXiv:2609.00868v1 Announce Type: cross Abstract: Vision-language models are evaluated by aggregate accuracy on multimodal benchmarks, a practice that implicitly assumes the model uses its visual inpu

arXiv:2608.28592v1 Announce Type: new Abstract: Large language models (LLMs) achieve high scores on medical knowledge examinations, yet real-world oncology is not a knowledge test--it is a sequence of

arXiv:2608.28673v1 Announce Type: new Abstract: Deploying vision perception models in autonomous vehicles requires that we prioritize inference speeds, resulting in a model with shallower architecture

arXiv:2608.29990v1 Announce Type: cross Abstract: Large language models are increasingly deployed in Arabic-speaking markets, yet standard benchmarks overwhelmingly reward Modern Standard Arabic (MSA)

arXiv:2608.29802v1 Announce Type: new Abstract: Face recognition systems are increasingly deployed in security-critical applications, yet they remain vulnerable to presentation and morph attacks. Pres

arXiv:2602.06181v2 Announce Type: replace Abstract: Post-training quantization reduces the memory needed to run large language models but alters their social biases in ways that aggregate metrics fail