Knowledge catalogue
arXiv:2610.19003v1 Announce Type: new Abstract: Abstractive summarization remains challenging across languages, where linguistic quality and factual consistency may diverge, particularly in low-resour
arXiv:2610.19113v1 Announce Type: new Abstract: In-context learning (ICL) allows language models to adapt their behavior from instructions and demonstrations in a prompt, but accuracy alone does not r
arXiv:2610.20254v1 Announce Type: new Abstract: Large view synthesis models (LVSMs) reconstruct scenes from sparse views and render novel views quickly, but they incur unnecessary storage and renderin
arXiv:2610.18127v1 Announce Type: new Abstract: Looped language models (LMs) reuse one block of layers several times per token generation, promising more latent computation at no additional storage co
arXiv:2610.19746v1 Announce Type: new Abstract: Vision-language-action (VLA) models have recently achieved remarkable progress by leveraging large-scale robot datasets and foundation models for genera
arXiv:2610.18570v1 Announce Type: new Abstract: Latent planners often rank candidate action sequences by terminal Euclidean distance to a goal latent. Exact agreement with transition distance would ma
arXiv:2407.12703v5 Announce Type: cross Abstract: Fine-tuning pre-trained language models (PLMs) has recently shown a potential to improve knowledge graph completion (KGC). However, most PLM-based met
arXiv:2610.19934v1 Announce Type: new Abstract: Weight pruning can degrade the accuracy of large reasoning models (LRMs), yet it remains unclear whether it damages reasoning itself. We find that much
arXiv:2610.20401v1 Announce Type: new Abstract: Reinforcement learning makes language models better at solving tasks, but the same mechanism also rewards guessing. When success is the only way to earn
arXiv:2610.15403v1 Announce Type: cross Abstract: Crowdsourced disaster imagery provides timely, ground-level observations when conventional disaster mapping is delayed or incomplete, yet its geograph
arXiv:2610.15368v1 Announce Type: new Abstract: Rapid advances in large language models have made software engineering agents capable of tackling complex coding tasks. Within a containerized environme
arXiv:2610.14957v1 Announce Type: new Abstract: Recent video generative models have shown strong capability in synthesizing high-quality and temporally coherent videos. Yet, adapting them for controll
arXiv:2610.16582v1 Announce Type: new Abstract: Physics-informed generative models augment a data-fitting objective with residual penalties that encourage physical constraints to hold in generated sam
arXiv:2610.18863v1 Announce Type: cross Abstract: Sampling equilibrium field configurations remains a major computational bottleneck in lattice field theory, motivating the development of generative s
arXiv:2610.18651v1 Announce Type: new Abstract: Chemical language models (CLMs) are pretrained on large corpora of molecules with the aim of learning representations that transfer to diverse downstrea
arXiv:2610.17853v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) have achieved strong performance on diverse vision-language tasks, but their computational demands limit deploy
arXiv:2610.18471v1 Announce Type: new Abstract: Weight-space learning treats the weights of neural networks as data, processed by other models called metanetworks. A key challenge is that different we
arXiv:2610.15297v1 Announce Type: new Abstract: Recently, AutoRegressive Visual Generation (ARVG) models have emerged as a competitive paradigm for high-quality image synthesis. However, their substan
arXiv:2610.16652v1 Announce Type: new Abstract: We introduce IND-Eval, a public benchmark that organizes industrial time series across domains into source-specific forecasting tasks. Relative to estab
arXiv:2610.17804v1 Announce Type: cross Abstract: How can language models learn to optimize physical designs through experience? We introduce FracLearn, an experience-abstraction-generalization framew
arXiv:2610.17001v1 Announce Type: new Abstract: Generative policies such as diffusion models provide expressive data-driven priors for robot behavior, yet the situations a robot faces at execution tim
arXiv:2610.18407v1 Announce Type: new Abstract: Diffusion language models reconstruct masked tokens in parallel, offering an alternative to autoregressive generation. Yet their Transformer backbones t
arXiv:2511.13510v2 Announce Type: replace Abstract: This paper presents Naga, a deep State Space Model (SSM) encoding approach inspired by structural concepts from Vedic mathematics. The proposed meth
arXiv:2610.18331v1 Announce Type: cross Abstract: Do multilingual language models learn a universal internal geometry for color, and is it perceptually grounded or primarily linguistic? We decompose c
arXiv:2610.16851v1 Announce Type: new Abstract: Diffusion and flow-matching models may struggle to accurately represent distributional tails, especially for heavy-tailed data. Existing approaches ofte
arXiv:2610.13997v1 Announce Type: new Abstract: Concept representations in diffusion models drift across network depth and denoising time, yet existing sparse autoencoders offer only per-block or per-
arXiv:2610.12718v1 Announce Type: cross Abstract: Large language models are stochastic by nature. Repeated executions under the same experimental conditions may produce different responses. We propose
arXiv:2610.13928v1 Announce Type: new Abstract: Capability evaluations aim to surface dangerous model behaviors before deployment, but their reliability depends on the assumption that hidden capabilit
arXiv:2610.12545v1 Announce Type: cross Abstract: Autoregressive decoding is less suited to non-causally ordered data, e.g., cross-references or future dependencies, since their outputs cannot be revi
arXiv:2610.12572v1 Announce Type: new Abstract: Online reinforcement learning (RL) fine-tunes diffusion models with black-box rewards, but reverse-process methods such as Flow-GRPO are limited in effi
arXiv:2610.13808v1 Announce Type: cross Abstract: Variational quantum circuits (VQCs) are highly sensitive to circuit architecture, yet most quantum machine learning pipelines select a single best des
arXiv:2610.13722v1 Announce Type: new Abstract: Legal language understanding requires reasoning over semi-structured regulatory documents, yet standard pre-training objectives do not explicitly target
Flo Health leveraged Databricks AI Runtime to create open‑source language models that prioritize both safety and practical utility. By integrating Databricks’ scalable training infrastructure with rob
arXiv:2610.13194v1 Announce Type: new Abstract: Long-horizon autonomous research has shown promise in accelerating scientific progress through sustained cycles of hypothesis generation, implementation
arXiv:2610.13557v1 Announce Type: new Abstract: Anticipation of vessel trajectories is an essential task when navigating at sea. This applies to both human navigators and autonomous surface vessels. T
arXiv:2610.13041v1 Announce Type: new Abstract: Recurrent-depth language models improve inference-time reasoning by repeatedly applying a shared network to refine internal representations, but the add
arXiv:2610.12910v1 Announce Type: new Abstract: Visual world models predict the consequences of actions from images, but planning with them is typically done by optimizers such as CEM and MPPI. Howeve
arXiv:2610.13524v1 Announce Type: new Abstract: Intermittent ``spikes'' in loss, during the pre-training of Large Language Models (LLMs), remains a mysterious phenomenon both theoretically and empiric
arXiv:2610.13818v1 Announce Type: new Abstract: Representations of supervised datasets support higher-level machine-learning tasks such as dataset retrieval and distribution-shift monitoring. However,
arXiv:2610.13024v1 Announce Type: cross Abstract: World Action Models (WAMs) inherit powerful priors from pretrained video generation models, but their variational autoencoder (VAE) representations ar
A good example of why open weight and open source models are important - they permit users to fine-tune specific use cases with greater cost efficiency. As has been the case in other technology market
I was curious about how omni decision models could be used in rescue workers bodycams or smart glasses, so I decided to build a quick demo. I went with @liquidai d1-omni-600M since it supports both vi
Satya is spot on. The interesting engineering problem isn’t just making models behave, it’s building systems that don’t need to trust them in the first place. We’re working on some pretty fun problems
arXiv:2510.15866v2 Announce Type: replace Abstract: The clinical adoption of biomedical vision-language models is hindered by prompt optimization techniques that produce either uninterpretable latent
arXiv:2610.12316v1 Announce Type: new Abstract: Recent motion generative models have demonstrated strong capabilities in synthesizing physically plausible character motion, but often overlook establis
arXiv:2610.12214v1 Announce Type: cross Abstract: Recent full-duplex spoken dialog models enable simultaneous listening and speaking, but fine-grained models still advance their backbone autoregressiv
arXiv:2610.11399v1 Announce Type: new Abstract: Many applications of language models hinge not on individual samples but on the expectation of a test functional under the model. Estimating such expect
arXiv:2610.11818v1 Announce Type: cross Abstract: While massive, closed-source Vision-Language Models (VLMs) set strong benchmarks for document understanding, their dependence on commercial APIs limit
arXiv:2610.11111v1 Announce Type: new Abstract: Time Series Language Models (TSLMs) offer a promising path toward time series understanding by reasoning over temporal signals and producing natural lan
arXiv:2610.11942v1 Announce Type: new Abstract: A login screen that appears after a tap on Sign in is expected; the same screen after a tap on View order is an attack. For GUI agents, safety is theref
arXiv:2610.11196v1 Announce Type: cross Abstract: Large audio-language models (LALMs) exploit multimodal evidence, yet task-irrelevant audio can alter text-reasoning decisions when listening is unnece
arXiv:2602.22871v2 Announce Type: replace-cross Abstract: Reasoning with large language models often benefits from generating multiple chains-of-thought, but existing aggregation strategies are typica
arXiv:2610.12412v1 Announce Type: new Abstract: Multiplayer world models must generate independently controlled views with consistent representations of both players and their shared environment. Most
arXiv:2610.10378v1 Announce Type: new Abstract: Document-level text simplification involves transformations that go beyond sentence-internal edits, addressing discourse coherence, anaphora resolution,
arXiv:2506.03173v3 Announce Type: replace Abstract: Many physical systems do not merely move or deform; they grow, adding material and changing the geometry that a world model must represent. Existing
arXiv:2610.09346v1 Announce Type: new Abstract: Quantization-aware training (QAT) can recover much of the accuracy lost when large language models are compressed below four bits. Existing re- covery s
arXiv:2610.09194v1 Announce Type: new Abstract: Longitudinal models forecast how a patient's imaging state evolves, but accuracy does not show whether the patient's observed trajectory drives the pred
arXiv:2610.09455v1 Announce Type: new Abstract: Recently, approaches that leverage human video datasets for robot policy training have become increasingly prevalent. However, most existing hand tracke
arXiv:2610.09335v1 Announce Type: cross Abstract: Autonomous unmanned aerial vehicle (UAV) object search involves a closed loop of perception, decision-making, and action under partial observability.
arXiv:2610.09994v1 Announce Type: new Abstract: Models with near-identical predictive performance can yield substantially different predictions, a phenomenon known as predictive multiplicity. Prior wo