Knowledge catalogue
arXiv:2605.16377v1 Announce Type: cross Abstract: Transparent and standardized reporting is essential for reproducible scientific research, yet adherence to reporting guidelines remains inconsistent b
arXiv:2605.18257v1 Announce Type: cross Abstract: Multimodal representation alignment is pivotal for large language models and robotics. Traditional methods are often hindered by cross-modal informati
arXiv:2605.17410v1 Announce Type: new Abstract: Token economics has emerged as a useful lens for understanding resource allocation, value creation, and pricing in large language model systems. While r
arXiv:2604.01658v2 Announce Type: replace Abstract: Large language model (LLM)-based evolution is a promising approach for open-ended discovery, where progress requires sustained search and knowledge
arXiv:2307.08643v4 Announce Type: replace Abstract: Corruption is notoriously widespread in data collection. Despite extensive research, the existing literature predominantly focuses on specific setti
arXiv:2605.17651v1 Announce Type: new Abstract: Counterfactual explanations (CFEs) provide actionable recourse, but most methods assume a static framework with fixed data and a trained classifier. Thi
Curious finding while creating evals and benchmarks for long-horizon (100+ turn) agents While it’s generally thought that a direct swap to open source models can bring immediate cost savings, that’s n
arXiv:2605.18753v1 Announce Type: cross Abstract: Current hierarchical attention methods, such as NSA and InfLLMv2, select the top-k relevant key-value (KV) blocks based on coarse attention scores and
datasette-llm is a plugin that integrates LLM functionality into Datasette for language model operations. Version 0.1a8 represents an alpha release of this plugin that extends Datasette's capabilities
arXiv:2603.00952v2 Announce Type: replace Abstract: High-fidelity reconstruction of dynamic scenes is an important yet challenging problem. While recent 4D Gaussian Splatting (4DGS) has demonstrated t
Deep Agents now integrates with @nebiusai Token Factory. Now, you can run agent workloads on production-grade AI infrastructure with open-source models, dedicated endpoints, real-time search, and full
arXiv:2605.18722v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models have recently become a central direction in embodied AI, but current systems are restricted to either dual-gripper c
arXiv:2605.17729v1 Announce Type: cross Abstract: Deep learning models achieved high accuracy in pneumonia detection from chest X-rays. However, their generalization across clinical domains remains li
arXiv:2604.07393v2 Announce Type: replace-cross Abstract: Accurate forecasting of industrial time series requires balancing predictive accuracy with physical plausibility under non-stationary operatin
arXiv:2605.18165v1 Announce Type: new Abstract: Unlike autoregressive models, which generate one token at a time, dLLMs denoise a chunk of [MASK] tokens jointly and sample one or more tokens per step;
AI infrastructure is no longer just about faster computing. It is becoming the foundation for a new enterprise operating model where data comes alive, digital workers reshape workflows and leadership
arXiv:2605.17721v1 Announce Type: new Abstract: Large language model (LLM)-based agents have demonstrated strong capabilities in complex reasoning and problem solving through multi-step interactions,
In this post, we demonstrate how you can extend the conversational memory of Kiro CLI by implementing a custom Model Context Protocol (MCP) server that integrates with Amazon Bedrock AgentCore Memory.
arXiv:2605.17361v1 Announce Type: cross Abstract: Multi-agent systems (MAS) powered by large language models (LLMs) have emerged as a powerful paradigm for complex problem solving, where performance c
arXiv:2508.14769v2 Announce Type: replace Abstract: Federated distillation has emerged as a promising collaborative machine learning approach, offering enhanced privacy protection and reduced communic
arXiv:2602.00470v2 Announce Type: replace Abstract: Individual tree crown segmentation is an important task in remote sensing for forest biomass estimation and ecological monitoring. However, accurate
arXiv:2508.20836v3 Announce Type: replace Abstract: In this letter, we report the first experimental demonstration of the recently emerged new paradigm in hovering and flapping flight physics called (
Vercel announced a flat-rate CDN pricing model entering limited beta, offering predictable content delivery costs instead of usage-based pricing. This new offering allows developers to pay a fixed fee
arXiv:2605.16319v1 Announce Type: new Abstract: Medium-horizon Alzheimer's disease progression prediction is difficult because future clinical scores can remain tied to baseline severity, while biomar
arXiv:2605.16293v1 Announce Type: cross Abstract: Artificial intelligence has advanced rapidly in biomedicine through large-scale multimodal data integration, enabling increasingly accurate prediction
arXiv:2605.18271v1 Announce Type: cross Abstract: With the rapid emergence of personal AI agents based on Large Language Models (LLMs), implementing them on-device has become essential for privacy and
arXiv:2605.17229v1 Announce Type: new Abstract: Automated driving system deployment requires rigorous validation across safety-critical vehicle-pedestrian interactions, yet real-world datasets rarely
arXiv:2505.20914v2 Announce Type: replace Abstract: General object composition (GOC) aims to seamlessly integrate a target object into a background scene with desired geometric properties, while simul
arXiv:2605.16435v1 Announce Type: cross Abstract: Heatwaves are an important problem in cities, and climate change makes this problem more difficult. In this paper, we present a GPU-based deep learnin
arXiv:2605.16598v1 Announce Type: cross Abstract: Agentic retrieval improves multi-hop question answering by giving language models autonomy to iteratively gather evidence. Recent work augments these
arXiv:2603.20421v2 Announce Type: replace-cross Abstract: We present Hawkeye, a system for analyzing and reproducing GPU-level arithmetic operations. Using our framework, anyone can re-execute on a CP
This post likely discusses techniques and methodologies for building AI agents capable of performing AI engineering tasks, such as code generation, model optimization, or system design. It probably co
arXiv:2605.17426v1 Announce Type: cross Abstract: We propose a framework for predicting the effects of mobility introduction measures using a human-flow digital twin. This digital twin incorporates a
arXiv:2605.16527v1 Announce Type: cross Abstract: Hypergraphs model higher-order relations that drive real-world decisions, from drug prescriptions to recommendations. A central structural signal in s
arXiv:2605.17865v1 Announce Type: new Abstract: LiDARs are being increasingly deployed for consumer imaging in handheld, wearable, and robotic applications. These sensors can capture the time-of-fligh
arXiv:2605.18500v1 Announce Type: new Abstract: Large language models (LLMs) have increasingly leveraged tool invocation to enhance their reasoning capabilities. However, existing approaches typically
arXiv:2605.18324v1 Announce Type: cross Abstract: Representation Autoencoders (RAE) replace traditional VAE with pretrained vision encoders. In this paper, we systematically investigate several design
arXiv:2509.18150v2 Announce Type: replace-cross Abstract: Multimodal Large Language Models (MLLMs) have demonstrated outstanding performance across a variety of domains. However, training MLLMs is oft
arXiv:2605.17829v1 Announce Type: new Abstract: AI evaluation is undergoing a structural change. Large language models (LLMs) are increasingly deployed as systems that act over time through tools, env
Databricks introduced AI spend controls as part of its Unity AI Gateway, enabling organizations to monitor, manage, and optimize costs associated with AI model usage and API calls. The feature likely
arXiv:2508.00712v2 Announce Type: replace-cross Abstract: We introduce JSON Bag-of-Tokens model (JSON-Bag) as a method to generically represent game trajectories by tokenizing their JSON descriptions
arXiv:2605.16896v1 Announce Type: new Abstract: Contextual Automatic Speech Recognition (ASR) faces challenges with large-scale keyword dictionaries, as excessive irrelevant candidates introduce noise
arXiv:2605.16899v1 Announce Type: new Abstract: A fundamental challenge in embodied AI is verifying if agents build internal models of spatial structure or merely learn to mimic task-specific expert t
arXiv:2601.01123v2 Announce Type: replace-cross Abstract: Graph Neural Networks (GNNs) have demonstrated remarkable success in various domains such as social networks, molecular chemistry, and more. A
arXiv:2605.17359v1 Announce Type: new Abstract: Large language model (LLM)-based multi-agent systems have shown strong potential for complex reasoning by coordinating specialized agents through struct
arXiv:2503.02574v2 Announce Type: replace-cross Abstract: In this paper, we argue that current safety alignment research efforts for large language models are hindered by many intertwined sources of n
arXiv:2605.17603v1 Announce Type: cross Abstract: High-resolution precipitation information is essential for climate impact assessment, yet global climate models remain too coarse to resolve key small
arXiv:2508.06670v2 Announce Type: replace-cross Abstract: By applying interpretable machine learning methods such as decision trees, we study how simple models can classify the Galois groups of Galois
arXiv:2605.17478v1 Announce Type: new Abstract: Visual Geometry Grounded Transformers (VGGT) have set new benchmarks in high-fidelity 3D scene reconstruction. However, as the sequence length increases
arXiv:2605.06017v2 Announce Type: replace Abstract: Sequence-level evaluations in autoregressive Large Language Models (LLMs) rely on highly dependent token generation. Establishing tight concentratio
arXiv:2605.17230v1 Announce Type: cross Abstract: Quantum error correction (QEC) is indispensable for realizing fault-tolerant quantum computation, yet its effectiveness hinges critically on the class
arXiv:2605.16464v1 Announce Type: cross Abstract: Brain tumors exhibit high heterogeneity in morphology and multimodal contrast, making manual slice-by-slice de lineation time-consuming and experience
arXiv:2605.17743v1 Announce Type: new Abstract: Continual test-time adaptation adapts a source-pretrained model to non-stationary, unlabeled target streams while retaining past competence, yet texture
arXiv:2605.17885v1 Announce Type: cross Abstract: Although artificial intelligence (AI) now matches or exceeds human performance across numerous cognitive tasks, creativity remains a highly contested
arXiv:2605.16456v1 Announce Type: new Abstract: Understanding how objects relate to each other in space is fundamental to scene understanding, yet most contrastive pre-training approaches only model p
arXiv:2605.17624v1 Announce Type: cross Abstract: We investigate the potential of invariant and equivariant semi-supervised learning for addressing the challenges of training multi-task models on part
The Nuxt MCP Toolkit has been updated to support MCP apps, expanding its capabilities for developers working with Nuxt and Model Context Protocol applications. This enhancement likely enables better i
arXiv:2605.17862v1 Announce Type: cross Abstract: Scaling on-policy distillation (OPD) for large language models (LLMs) confronts a fundamental tension: asynchronous execution is necessary for system
arXiv:2605.17488v1 Announce Type: new Abstract: The landscape of joint audio and video generation has been fundamentally transformed by the advent of powerful foundation models. Despite these strides,
arXiv:2512.12572v2 Announce Type: replace Abstract: Data attribution aims to explain model predictions by estimating how they would change if certain training points were removed, and is used in a wid