Knowledge catalogue
This article describes a collaborative hackathon project involving five research labs that developed a financial simulation drama using small language models, focusing on building complex multi-agent
arXiv:2606.03131v1 Announce Type: new Abstract: Reward models are central to large language model (LLM) alignment, but they remain vulnerable to reward hacking. To evaluate reward-model robustness, we
arXiv:2606.02953v1 Announce Type: new Abstract: Usage-based theories of grammars posit that creative productivity of the structures of language is both bolstered and constrained by two distinct freque
arXiv:2602.16601v2 Announce Type: replace-cross Abstract: Machine learning models are increasingly trained or fine-tuned on synthetic data. Recursively training on such data has been observed to signi
arXiv:2605.29223v1 Announce Type: new Abstract: The parameter counts of the most widely used large language models (LLMs) are often withheld by their developers, leaving model size -- a primary refere
arXiv:2503.21510v3 Announce Type: replace-cross Abstract: Ensuring that predictions of machine learning (ML) classification models are accompanied by uncertainty estimates is one of the main pillars o
arXiv:2605.26106v1 Announce Type: new Abstract: Masked diffusion models (MDMs) have emerged as a promising alternative to autoregressive models for language modeling, yet the effective design of trans
arXiv:2512.19373v3 Announce Type: replace-cross Abstract: In developing data-driven modeling methodologies, there is an ongoing need to reconcile the strong predictive performance of opaque black-box
arXiv:2605.19027v1 Announce Type: new Abstract: Medical foundation models (MedFMs) have emerged as transformative tools in healthcare, demonstrating capabilities across diverse clinical applications.
arXiv:2503.02170v2 Announce Type: replace-cross Abstract: Domain shift remains a persistent challenge in deep-learning-based computer vision, often requiring extensive model modifications or large lab
arXiv:2605.16011v1 Announce Type: cross Abstract: Adaptive learning refers to educational technologies that track learners' learning progress and adapt the instructional process based on individual le
Comfyui-Mesh now supports LTX 2.3 with the ability to distribute models across multiple GPUs or networked machines using Nvenc codec for video generation. The update includes significant VRAM optimiza
arXiv:2506.08902v4 Announce Type: replace Abstract: Large-scale pre-training has fundamentally changed how machine learning research is done today: large foundation models are trained once, and then c
arXiv:2605.09951v1 Announce Type: new Abstract: ML models in healthcare are typically evaluated using curated real-world EHR data. A key limitation of such evaluations is that they may fail to assess
arXiv:2605.08495v1 Announce Type: new Abstract: Deep learning and large public datasets have recently catalyzed the proliferation of AI models for processing brain recordings. However, systematically
arXiv:2605.07783v1 Announce Type: new Abstract: Large language models (LLMs) achieve strong performance but remain costly to deploy in resource-constrained settings. Training small language models (SL
arXiv:2605.03871v1 Announce Type: new Abstract: Language models encode substantial evaluative knowledge from pretraining, yet current post-training methods rely on external supervision (human annotati
arXiv:2605.04831v1 Announce Type: new Abstract: Story generation aims to automatically produce coherent, structured, and engaging narratives. Although large language models (LLMs) have significantly a
It feels like agent harness evolution runs on two axes that usually get conflated. There’s the temporal axis: simplify as models improve, stripping components that compensated for limitations the new
arXiv:2407.13278v3 Announce Type: replace Abstract: Time series, characterized by a sequence of data points organized in a discrete-time order, are ubiquitous in real-world scenarios. Unlike other dat
arXiv:2506.13687v2 Announce Type: replace-cross Abstract: Probabilistic forecasts are typically obtained using state-of-the-art statistical and machine learning models, with model parameters estimated
arXiv:2605.00907v1 Announce Type: new Abstract: Large language models (LLMs) and multimodal large models (MLLMs) are increasingly used for transportation tasks such as regulation question answering, t
arXiv:2510.08049v3 Announce Type: replace-cross Abstract: Although Large Language Models (LLMs) exhibit advanced reasoning ability, conventional alignment remains largely dominated by outcome reward m
arXiv:2604.26160v1 Announce Type: cross Abstract: Nonlinear Mixed Effects models (NLME) models are widely used in pharmacometrics and related fields to analyze hierarchical and longitudinal data. Howe
arXiv:2604.26130v1 Announce Type: cross Abstract: Every RLHF-trained language model is shaped by a reward model, yet the mechanistic interpretability toolkit -- logit lens, direct logit attribution, a
arXiv:2603.12118v2 Announce Type: replace Abstract: Any-to-Any models are an emerging class of multimodal models that accept combinations of multimodal data (e.g., text, image, video, audio) as input
arXiv:2604.24679v1 Announce Type: new Abstract: Pathology foundation models (PFMs) have recently emerged as powerful pretrained encoders for computational pathology, enabling transfer learning across
arXiv:2603.20711v2 Announce Type: replace-cross Abstract: Vision-Language-Action (VLA) models are mainstream in embodied intelligence but face high inference costs. Edge-Cloud Collaborative (ECC) depl
arXiv:2604.19966v1 Announce Type: cross Abstract: Vision-language models (VLMs) are increasingly used in settings where sensitivity to low-level image degradations matters, including content moderatio
arXiv:2512.15808v2 Announce Type: replace-cross Abstract: Foundation models (FMs) are driving a prominent shift in biomedical imaging from task-specific models to unified backbone models for diverse t
arXiv:2604.20354v1 Announce Type: new Abstract: Text-to-Image generation has seen significant advancements in output realism with the advent of diffusion models. However, diffusion models encounter di
arXiv:2604.18775v1 Announce Type: new Abstract: Detecting jailbreak behaviour in large language models remains challenging, particularly when strongly aligned models produce harmful outputs only rarel
arXiv:2604.19394v1 Announce Type: new Abstract: This paper narrows the performance gap between small, specialized models and significantly larger general-purpose models through domain adaptation via c
Speculative decoding is a technique that accelerates language model inference by using a smaller draft model to generate candidate tokens, which are then verified by a larger model, reducing latency w
arXiv:2604.19642v1 Announce Type: new Abstract: Edge devices such as smartwatches and smart glasses cannot continuously run even the smallest 100M-1B parameter language models due to power and compute
arXiv:2604.18827v1 Announce Type: cross Abstract: Scaling data and artificial neural networks has transformed AI, driving breakthroughs in language and vision. Whether similar principles apply to mode
arXiv:2506.12606v2 Announce Type: replace Abstract: While Mamba has demonstrated strong performance in language modeling, its potential as a speech self-supervised learning (SSL) model remains underex
arXiv:2512.02636v3 Announce Type: replace-cross Abstract: Log-likelihood evaluation enables important capabilities in generative models, including model comparison, certain fine-tuning objectives, and
arXiv:2601.03331v2 Announce Type: replace Abstract: Recent advances in Vision-Language Models (VLMs) have improved performance in multi-modal learning, raising the question of whether these models tru
arXiv:2604.15395v1 Announce Type: new Abstract: Over the recent years, the field of robotics has been undergoing a transformative paradigm shift from fixed, single-task, domain-specific solutions towa
arXiv:2509.01728v4 Announce Type: replace-cross Abstract: Recent advances in the development of robotic foundation models have led to promising end-to-end and general-purpose capabilities in robotic s
arXiv:2604.14001v1 Announce Type: new Abstract: Diffusion language models have recently emerged as a leading alternative to standard language models, due to their ability for bidirectional attention a
arXiv:2603.26259v2 Announce Type: replace-cross Abstract: While Late Interaction models exhibit strong retrieval performance, many of their underlying dynamics remain understudied, potentially hiding
arXiv:2509.15406v2 Announce Type: replace Abstract: AI generative models leave implicit traces in their generated images, which are commonly referred to as model fingerprints and are exploited for sou
arXiv:2604.10905v1 Announce Type: cross Abstract: We present Audio Flamingo Next (AF-Next), the next-generation and most capable large audio-language model in the Audio Flamingo series, designed to ad
Reuters: AWS launches Amazon Bio Discovery, an AI-powered application designed to speed up drug development, giving scientists access to biological foundation models — Amazon's (AMZN.O) cloud unit on
arXiv:2508.08574v3 Announce Type: replace Abstract: We introduce DeepFleet, a suite of foundation models designed to support coordination and planning for large-scale mobile robot fleets. These models
arXiv:2604.09609v1 Announce Type: new Abstract: Human behavior models are essential as behavior references and for simulating human agents in virtual safety assessment of automated vehicles (AVs), yet
arXiv:2604.10337v1 Announce Type: new Abstract: Employee attrition presents a major challenge for organizations, increasing costs and reducing productivity. Predicting attrition accurately enables pro
arXiv:2604.11035v1 Announce Type: new Abstract: Diffusion language models promise parallel generation, yet still lag behind autoregressive (AR) models in quality. We stem this gap to a failure of intr
arXiv:2604.09791v1 Announce Type: new Abstract: Small language models are attractive for production deployment due to their low cost, fast inference, and ease of specialization. However, adapting them
arXiv:2509.20868v2 Announce Type: replace-cross Abstract: Structured reasoning can improve the inference performance of large language models (LLMs), but it also introduces computational cost and cont
arXiv:2604.10720v1 Announce Type: new Abstract: Artificial models that simulate how learners act and respond within educational systems are a promising tool for evaluating tutoring strategies and feed
arXiv:2604.09244v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models have emerged as the mainstream of embodied intelligence. Recent VLA models have expanded their input modalities fr
arXiv:2512.02231v2 Announce Type: replace-cross Abstract: Multimodal large language models (MLLMs) are expected to jointly interpret vision, audio, and language, yet existing video benchmarks rarely a
arXiv:2604.08780v1 Announce Type: cross Abstract: World models promise a paradigm shift in robotics, where an agent learns the underlying physics of its environment once to enable efficient planning a
arXiv:2511.22475v2 Announce Type: replace-cross Abstract: We present adversarial flow models, a class of generative models that belongs to both the adversarial and flow families. Our method supports n
arXiv:2306.02781v4 Announce Type: replace-cross Abstract: Generative artificial intelligence, and large language models in particular, have emerged as one of the most transformative paradigms in moder
arXiv:2604.07282v1 Announce Type: cross Abstract: Automated face recognition has made rapid strides over the past decade due to the unprecedented rise of deep neural network (DNN) models that can be t
arXiv:2604.06233v1 Announce Type: new Abstract: Safety-trained language models routinely refuse requests for help circumventing rules. But not all rules deserve compliance. When users ask for help eva