Search: “models”

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

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

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

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

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

arXiv:2608.29935v1 Announce Type: new Abstract: Simulation of admittance-type models requires physically consistent dynamic models that are rarely available for off-the-shelf, everyday objects, limiti

arXiv:2608.28541v1 Announce Type: new Abstract: A code world model accepted by a sampling gate can be exactly right on everything the gate can see and arbitrarily wrong beyond it. We characterize what

arXiv:2608.28404v1 Announce Type: new Abstract: Video generation for autonomous driving cannot follow the web-scale route: driving data is expensive to collect, bound by privacy requirements, and cann

arXiv:2607.15439v2 Announce Type: replace Abstract: Our previous ARC-AGI-3 agent bundled executable world modeling, prompted simplification, and exact replay verification, leaving their individual con

arXiv:2608.26190v1 Announce Type: new Abstract: World models enable agents to reason about future outcomes and learn policies from their knowledge of state transition, but existing approaches primaril

arXiv:2608.27328v1 Announce Type: new Abstract: High similarity between first-visit and return frames does not necessarily show that a video world model remembered the scene; the intervening rollout m

arXiv:2608.24056v1 Announce Type: new Abstract: Generative and predictive artificial intelligence models are increasingly used to generate geometry and to predict physical fields and scalar quantities

arXiv:2608.21386v1 Announce Type: cross Abstract: Given a task described by a few examples, how should a model be specialized to it? Four mechanisms are available -- zero-shot, in-context attention, t

arXiv:2508.13533v2 Announce Type: replace Abstract: Within a model family, a smaller variant is often deployed as a drop-in replacement for a larger one when their performance is similar. However, per

arXiv:2608.19936v1 Announce Type: cross Abstract: Public benchmarks are important measures of Automatic Speech Recognition (ASR) model capabilities. However, by nature of being public, there is risk o

arXiv:2608.18138v1 Announce Type: cross Abstract: In recent years, the rapid development of language models has transformed the field of Natural Language Processing through a wide range of application

arXiv:2608.17183v1 Announce Type: new Abstract: Small Language Models (SLMs) are increasingly deployed in resource-constrained, privacy-sensitive settings, where safety and bias failures can cause sec

arXiv:2501.05819v2 Announce Type: replace-cross Abstract: Uncrewed Aerial Vehicles (UAVs) are increasingly used in modern communication networks. However, challenges in decision-making and digital mod

arXiv:2608.12486v1 Announce Type: new Abstract: Large language models (LLMs) cannot retain post-deployment experience without parameter updates. We introduce DIVE, a diversity-driven framework that en

arXiv:2608.13049v1 Announce Type: cross Abstract: Large-scale manipulation data is essential for robot learning, yet collecting robot demonstrations remains expensive and difficult to scale. Meanwhile

arXiv:2505.22839v2 Announce Type: replace-cross Abstract: Recent studies suggest that diffusion models significantly improve the empirical adversarial robustness of deep neural network models. While i

arXiv:2608.08634v1 Announce Type: new Abstract: Open-weight language models from Chinese AI labs caught up on benchmarks relative to proprietary frontier models in recent months. Yet their reliability

arXiv:2608.07004v1 Announce Type: new Abstract: Although remarkable capabilities have been demonstrated by Large Language Models (LLMs) across scientific domains, feedback controller design remains un

arXiv:2608.04827v1 Announce Type: cross Abstract: We introduce the Intrinsic Hybrid Latent Diffusion Model (ILDM), a generative framework that integrates probabilistic dimensionality reduction with ge

arXiv:2608.02603v1 Announce Type: new Abstract: Controllable video generation models are increasingly being developed as world models. Accordingly, evaluating them in this role extends beyond the appa

arXiv:2607.27404v1 Announce Type: new Abstract: Existing benchmarks for electrocardiogram foundation models primarily evaluate downstream predictive performance, providing limited insight into whether

arXiv:2607.25110v1 Announce Type: cross Abstract: Early ranking stages in recommendation systems precompute item embeddings and cache them in-model for scoring within strict latency constraints. Becau

arXiv:2607.23806v1 Announce Type: cross Abstract: Improving a language model today means retraining it: enormous compute, a new opaque model each cycle, non-deterministic output. We take the opposite

arXiv:2607.24507v1 Announce Type: cross Abstract: Existing methods mainly adapt pretrained autoregressive (AR) language models to masked diffusion, whereas we directly adapt them to uniform-noise diff

arXiv:2607.22199v1 Announce Type: new Abstract: Score-based diffusion models have achieved remarkable empirical success in generative modeling, yet their approximation-theoretic foundations remain inc

arXiv:2507.18311v3 Announce Type: replace Abstract: Large Vision-Language Models (LVLMs) have shown impressive capabilities across a range of tasks that integrate visual and textual understanding, suc

arXiv:2607.20723v1 Announce Type: cross Abstract: This work presents LeakyLMs, a set of attacks that leak proprietary model, architecture, and deployment information from production language models. L

arXiv:2607.20228v1 Announce Type: new Abstract: We propose a hybrid approach for user-centric modeling of transactional event sequences that combines contrastive representation learning (CoLES) with S

arXiv:2607.13332v1 Announce Type: new Abstract: Training large language models at the multi-billion to trillion parameter scale is confined to datacenters, where data-parallel (DP) and model-parallel