Knowledge catalogue
arXiv:2401.00139v3 Announce Type: replace-cross Abstract: This paper introduces a causal attribution model to enhance the interpretability of large language models (LLMs) and improve their causal reas
I would push back a little: because the models are so good & improving, they don't have to be the product. But it is the model that is the prime mover. If they weren't so generally capable, the harnes
arXiv:2506.16950v2 Announce Type: replace Abstract: Out-of-distribution (OOD) robustness is a desired property of computer vision models. Improving model robustness requires high-quality signals from
arXiv:2602.13466v2 Announce Type: replace-cross Abstract: The ability of machine learning models to store input information in hidden layer vector embeddings, analogous to the concept of `memory', is
Ivan Mehta / TechCrunch: Stability AI releases a new family of audio models called Stable Audio 3.0 that is trained on licensed data; the top model can generate six-minute songs — Stability AI, the co
Yann LeCun says that within a year to 18 months, we'll have a general method for training hierarchical world models These models would learn from video and real-world data, then help plan actions in r
arXiv:2411.00471v3 Announce Type: replace-cross Abstract: This paper introduces Dirichlet process mixtures of block g priors for model selection and prediction in linear models. These priors are exten
arXiv:2605.07533v1 Announce Type: new Abstract: Large Language Models (LLMs) have recently demonstrated strong performance in machine translation (MT). However, most prior work focuses on improving or
arXiv:2605.04051v1 Announce Type: cross Abstract: In complex real-world settings, optimization is challenged by the presence of diverse models of differing fidelity. In many optimization problems, a s
arXiv:2605.04447v1 Announce Type: new Abstract: Medical foundation models pre-trained on large-scale datasets have shown powerful versatile performance. However, when adapting medical foundation model
arXiv:2605.03299v1 Announce Type: new Abstract: Cross-lingual topic modeling aims to discover shared semantic structures across languages, yet existing models depend on sparse bilingual resources and
arXiv:2603.28489v2 Announce Type: cross Abstract: The rapid evolution of video generation has enabled models to simulate complex physical dynamics and long-horizon causalities, positioning them as pot
arXiv:2605.01870v1 Announce Type: new Abstract: Large Language Models (LLMs) have substantially advanced the field of Natural Language Processing (NLP), achieving state-of-the-art performance across a
I hear this take (usually from the model labs), but that will just mean more people turn to open source models, no? They’re already cheaper, if they have similar performance (because they’re less coup
arXiv:2601.03423v3 Announce Type: replace-cross Abstract: Adapting language models to the clinical domain through continued pretraining and instruction tuning requires costly retraining for each new m
Clem Delangue shares his experience using OpenAI's GPT 5.5 within an ML internship context for practical machine learning tasks including dataset creation, model training, and model optimization, as w
Our cofounder @the_bunny_chen joined @GregorVand to talk about open models, production inference, and how RFT is unlocking model customization for teams without a dedicated ML org. They covered custom
Financial Times: Sources: Goldman Sachs stopped its bankers in Hong Kong from using Anthropic's models; Anthropic says its models were never officially “supported” in Hong Kong — Employees were unable
Today @poolsideai is releasing Laguna M.1 & Laguna XS.2, our latest generation models and first public models We started Poolside because we believed that to build truly capable coding agents, you nee
arXiv:2510.07632v2 Announce Type: replace Abstract: Frontier AI models have achieved remarkable progress, yet recent studies suggest they struggle with compositional reasoning, often performing at or
arXiv:2604.19784v1 Announce Type: cross Abstract: Recently, it has been found that frontier AI models can resist their own shutdown, a behavior known as self-preservation. We extend this concept to th
arXiv:2604.17310v1 Announce Type: new Abstract: Discrete diffusion models form a powerful class of generative models across diverse domains, including text and graphs. However, existing approaches fac
arXiv:2410.17448v3 Announce Type: replace Abstract: Large Language Models (LLMs) are transformer-based machine learning models that have shown remarkable performance in tasks for which they were not e
arXiv:2610.19118v1 Announce Type: new Abstract: Emerging decision models such as JEV provide typed interfaces that map task instructions and observations directly to choices, judgments, and scores. As
Document extraction is an arbitrarily complex reasoning problem for both frontier models and humans. ✅ The schema encodes essentially hundreds of interrelated reasoning tasks ✅ A PDF can be hundreds o
arXiv:2610.19399v1 Announce Type: new Abstract: Korean neologisms arise through diverse mechanisms, a demanding test of language models beyond lexical memorization. To enable systematic evaluation, we
arXiv:2509.09658v3 Announce Type: replace Abstract: Hallucinations in multimodal large language models (MLLMs), where a model generates content inconsistent with the input image, pose significant risk
arXiv:2610.15218v1 Announce Type: new Abstract: A recurrent-depth language model reuses one block of weights several times in a row. The number of repetitions, which we call loops, is chosen when the
arXiv:2610.19095v1 Announce Type: new Abstract: Chatbot Arena ranks language models with the Bradley-Terry model, which gives each model one score and so assumes Luce's choice axiom. Choice theory pre
arXiv:2610.17309v1 Announce Type: cross Abstract: We introduce StartLux-Decision, decision models from 0.8B to 27B parameters that combine efficient single-pass prediction with adaptive, confidence-gu
arXiv:2610.17573v1 Announce Type: new Abstract: Self-supervised pretraining has expanded the pool of 3D medical foundation models, but published evaluations use different datasets, endpoints and fine-
arXiv:2610.16813v1 Announce Type: new Abstract: Vision foundation models (VFMs) provide transferable visual representations, but their inference cost limits deployment in many settings. Fine-tuning sp
arXiv:2610.14635v1 Announce Type: cross Abstract: Large Vision-Language Models (LVLMs) have achieved remarkable progress across a wide range of multimodal tasks. However, object hallucination remains
I spent a decent amount of time with Gemini 4 before launch. Overall, Gemini 4 is a big jump over previous Gemini models and it is the first non-OpenAI/Anthropic model to pass the Fable/Mythos/Astra b
arXiv:2610.06354v2 Announce Type: replace Abstract: Large language models (LLMs) are increasingly explored for graph understanding and decision-making, while System One models such as Jev select direc
arXiv:2610.13106v1 Announce Type: new Abstract: Biological function in RNA is encoded across scales, from local sequence motifs to long-range structural dependencies. Conventional RNA foundation model
arXiv:2610.11896v1 Announce Type: new Abstract: Assembly documentation is a downstream manufacturing artifact that is still usually authored by interpreting CAD models by hand. Structured product data
arXiv:2610.07535v1 Announce Type: cross Abstract: Multi-agent simulations with large language models (LLMs) often operate networks of agents with a single base model. This overlooks the inter-model ef
arXiv:2610.07563v1 Announce Type: new Abstract: Large language models (LLMs) have the potential to meet a key goal in economics: a quantitative model of household decision making, across a variety of
arXiv:2610.08627v1 Announce Type: new Abstract: Long-horizon world-model planning typically relies on autoregressive rollouts, where predicted states are repeatedly fed back into the model. This prese
arXiv:2610.07716v1 Announce Type: new Abstract: Prefill-only decision models inspired by the Jev model score every candidate in a menu during a single forward pass and never decode, which makes one ca
arXiv:2610.08678v1 Announce Type: cross Abstract: Speculative decoding accelerates inference for a large language model (LLM), referred to as the target model, by first using a smaller model, referred
arXiv:2610.07093v1 Announce Type: new Abstract: The rapid proliferation of heterogeneous data sources within the Internet of Things (IoT) across domains such as smart cities, energy management, and en
arXiv:2610.07723v1 Announce Type: cross Abstract: Safety alignment in Large Language Models (LLMs) remains vulnerable to backdoor attacks. Existing LLM backdoors are almost all input-centric: activati
arXiv:2610.06387v1 Announce Type: new Abstract: Large language models (LLMs) have achieved substantial performance gains through increases in model size, training data, and computational resources. Ho
arXiv:2610.06447v1 Announce Type: new Abstract: Deploying model predictive control (MPC) requires constructing or identifying a predictive model for each target system. Time-series foundation 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