Knowledge catalogue
arXiv:2505.22976v2 Announce Type: replace-cross Abstract: The ability to simulate the world in a spatially consistent manner is a crucial requirements for effective world models. Such a model enables
After playing with it a bit, Meta's Muse Spark Thinking is fine so far, but really doesn't match the current Big Three models. It also is a bit... weird. Like some strange language & tone, a little lo
arXiv:2610.21965v1 Announce Type: new Abstract: Realistic haptic rendering of viscoelastic materials requires reproducing time-dependent behaviors such as creep and stress relaxation, which arise from
arXiv:2610.22056v1 Announce Type: new Abstract: Traditional reinforcement learning (RL) algorithms face significant limitations in handling long-term tasks with sparse rewards. Recent advancements hav
arXiv:2610.22075v1 Announce Type: new Abstract: Recent work shows that depth recurrence can be retrofitted into pretrained language models. In this work, we adapt this paradigm for visual understandin
Surprised that so few people are talking about this. Persistence has become a core capability of recent models (Opus 5.5, GPT 6 Astra, Gemini 4 Argon,...). I was just reading through the Gemini 4 Argo
arXiv:2610.17124v1 Announce Type: cross Abstract: Full-duplex spoken language models (FD-SLMs) support simultaneous listening and speaking, but often lag behind half-duplex spoken language models (HD-
arXiv:2610.19212v1 Announce Type: new Abstract: World models of visual information predict future image frames conditioned on given action sequences. This predicted scene evolution is driven by determ
arXiv:2610.19020v1 Announce Type: cross Abstract: Discrete diffusion models have emerged as a leading alternative to autoregressive models for generating text and other categorical data, yet their the
arXiv:2610.19773v1 Announce Type: new Abstract: Score-based generative models driven by alpha-stable L{evy processes have been proposed as an alternative to Brownian diffusion models, with empirical e
arXiv:2610.15129v1 Announce Type: new Abstract: Human social perception is incremental: we infer intent before an utterance is complete, revise our interpretation as new cues arrive, and attend to spe
arXiv:2610.15029v1 Announce Type: cross Abstract: Automated disease classification from patient-reported symptoms requires balancing the speed of lightweight discriminative models against the contextu
arXiv:2610.16942v1 Announce Type: cross Abstract: Large audio-language models (LALMs) are fast becoming a general interface to sound, yet how far their broad pretraining carries into specialized domai
arXiv:2610.14871v1 Announce Type: new Abstract: By predicting future states, latent world models provide rich self-supervision for end-to-end driving, but often inherit dense features from pretrained
arXiv:2502.04356v2 Announce Type: replace-cross Abstract: In response to the success of proprietary Large Language Models (LLMs), there is a growing interest in developing open, non-proprietary LLMs a
arXiv:2610.16302v1 Announce Type: new Abstract: Video-based interactive world models aim to enable real-time, user-controllable environments. Yet most existing systems are trained and rolled out at a
arXiv:2610.14583v1 Announce Type: cross Abstract: Autoregressive robot video world models based on discrete visual tokens remain unreliable over long rollout horizons. Retaining the entire generated h
arXiv:2603.18048v3 Announce Type: replace Abstract: Recent Audio Multimodal Large Language Models (Audio MLLMs) demonstrate impressive performance on speech benchmarks, yet it remains unclear whether
You ship an agent. It mostly works. Then the feedback loop becomes a support ticket: a user got the wrong answer, a tool call looped four times, a refund went out that shouldn’t have. Standard observa
Meta Platforms Inc. today released Muse Wonder and Muse Wonder Full, two artificial intelligence models it says are its most capable yet at running agents that handle personal and professional tasks.
arXiv:2610.12596v1 Announce Type: new Abstract: Agentic large language models now tackle complex tasks, yet strategic reasoning over long horizons is under-studied: most agentic benchmarks are short a
arXiv:2610.13895v1 Announce Type: cross Abstract: Large language models reuse the same attention heads across tasks, but identifying these shared heads does not explain what they do differently in eac
arXiv:2610.11253v1 Announce Type: new Abstract: Large language model agents are being increasingly deployed as autonomous scientists, designing experiments and inferring mechanistic world models with
arXiv:2610.11275v1 Announce Type: new Abstract: Phoneme-level models transcribe or generate speech as a sequence of phonemes, the smallest sound units that distinguish words. These models enable fine-
The other week I posted about Jev vs. Kev compared and since then, OpenAI released the decisions endpoint, Cloudflare released Clef and many here asked about Laya as well. This time we compared six po
arXiv:2610.10411v1 Announce Type: cross Abstract: Speculative decoding accelerates large language model inference by using a low-cost draft model to propose tokens that the full-size target model veri
arXiv:2610.08680v1 Announce Type: cross Abstract: Endpoint accuracy on abstract-reasoning benchmarks does not reveal whether a language model has acquired a transferable rule or fit distribution-speci
arXiv:2610.08560v1 Announce Type: cross Abstract: Streaming video-language models must decide not only what to answer, but whether the evidence needed for the current question has arrived. Existing sy
arXiv:2610.07766v1 Announce Type: new Abstract: We present Open Telco (OTel), an open telecom AI resource that releases derived telecom datasets for retrieval, reranking, instruction tuning, and safet
arXiv:2610.07121v1 Announce Type: new Abstract: Diffusion large language models dLLMs) have emerged as a promising alternative to autoregressive language models through bidirectional diffusion-based t
arXiv:2610.07887v1 Announce Type: cross Abstract: Unified multimodal models (UMMs) integrate understanding and generation, yet their generative behavior is rarely governed by what they understand abou
We show how you can use the River API to fine-tune open weight models to beat GPT-6 Astra and Claude Opus 5.5 on tasks you care about, with less than 1% of the cost! A few years from now most of the L
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
arXiv:2312.01886v4 Announce Type: replace Abstract: Large vision-language models (LVLMs) have demonstrated their incredible capability in visual question answering. However, this rich visual interacti
arXiv:2610.03960v1 Announce Type: cross Abstract: Instruction fine-tuning adapts a pretrained language model to follow instructions by training it on instruction--response pairs from a collection of t
arXiv:2610.04407v1 Announce Type: cross Abstract: Temporal Foundation Models (TFMs) aim to generalize across domains, datasets, and tasks. Yet, their development remains constrained by fragmented, tas
arXiv:2610.03387v1 Announce Type: new Abstract: Typed decision models return choices or distributions over answer options supplied at request time. Accuracy with complete options does not establish wh
arXiv:2505.21870v2 Announce Type: replace-cross Abstract: Retrieval-augmented generation (RAG) generally enhances large language models' (LLMs) ability to solve knowledge-intensive tasks. But RAG coul
arXiv:2610.03519v1 Announce Type: new Abstract: A reasoning model asked whether a causal effect is recoverable from observational data can fail in two ways: it refuses an identifiable query or answers
Thanks for sharing! We open-sourced code and models. 🤗Huggingface Paper: https://huggingface.co/papers/2609.38597 💻 Code: https://github.com/nv-tlabs/PixelUMM 🤗 Model: https://huggingface.co/nvidia/Pi
arXiv:2610.03516v1 Announce Type: cross Abstract: World action models (WAMs) have advanced robot control by predicting how observations and actions evolve over time. Despite this progress, RGB and act
arXiv:2610.01618v1 Announce Type: new Abstract: Agent evaluations increasingly go beyond a single success rate, reporting metrics such as cost, consistency, and robustness. Yet they typically treat th
arXiv:2610.01296v1 Announce Type: new Abstract: Mixture-of-Experts (MoE) Diffusion Language Models (DLMs) offer flexible parallel decoding and increased model capacity, but their large number of exper
arXiv:2610.01496v1 Announce Type: new Abstract: Recent work on language-model adaptation has shown that single models can obtain informative training signals by evaluating their behavior in demonstrat
arXiv:2610.00280v1 Announce Type: new Abstract: A world model learns to forecast how a physical system evolves from recorded trajectories, yet the systems it imitates obey physical laws that are neith
arXiv:2609.39648v1 Announce Type: cross Abstract: Diffusion models are typically viewed as stochastic processes that transform noise into data. We take a complementary perspective: a diffusion model d
arXiv:2609.38480v1 Announce Type: cross Abstract: Most clinical benchmarks evaluate language models (LMs) on diagnosis using complete case descriptions. In clinical practice, however, patients present
arXiv:2609.40222v1 Announce Type: new Abstract: When a camera revisits a previously observed region, a video world model should reproduce what was there before. This requires both remembering past obs
arXiv:2609.39773v1 Announce Type: new Abstract: Crystal structure governs material properties, making crystal structure prediction (CSP) a fundamental problem in materials science. Generative models a
arXiv:2606.19998v2 Announce Type: replace-cross Abstract: Vision-Language-Action (VLA) models are increasingly deployed across diverse tasks, yet they remain black boxes whose physical interactions ca
arXiv:2609.35947v1 Announce Type: new Abstract: Many inference-time tasks for pretrained discrete diffusion models and diffusion language models reduce to drawing samples from a tilted version of the
arXiv:2509.23140v2 Announce Type: replace Abstract: Recent advancements have endowed Large Language Models with impressive general reasoning capabilities. However, these reasoning models often perform
arXiv:2609.37594v1 Announce Type: new Abstract: Uncertainty estimates tell us how unsure a model is, but not why. Without knowing which parts of an input influences a model's uncertainty, we cannot te
arXiv:2607.09185v2 Announce Type: replace Abstract: Action-conditioned world models (ACWMs) aim to simulate future observations conditioned on embodied actions. However, learning controllable ACWMs re
As query traffic grows, so does the compute bill for embedding it. On a low-power device, a large model may not fit in memory. A smaller query model could reduce that cost, but switching usually means
arXiv:2609.35441v1 Announce Type: new Abstract: State space models (SSMs) achieve efficient sequence processing because their affine state updates are closed under composition and can therefore be eva
arXiv:2609.34981v1 Announce Type: new Abstract: World action models (WAMs) predict the future alongside actions during training. Due to the heavy computation cost of video denoising, whether the futur
arXiv:2609.31070v1 Announce Type: cross Abstract: Quantum Machine Learning is a novel field of research aimed at devising machine learning approaches exploiting principles of quantum mechanics, such a
arXiv:2609.30456v1 Announce Type: new Abstract: Reward maximization alignment methods for discrete diffusion models have primarily focused on steering the reverse process, either by influencing token
arXiv:2609.29264v1 Announce Type: cross Abstract: Artificial intelligence (AI) models are increasingly deployed through remote services, making model misappropriation a growing concern. Existing appro