All Articles
19242 articles total
FPGA-Based Neural Network Accelerators for Space Applications: A Survey
A comprehensive survey on using FPGA-based neural network accelerators to enhance onboard computing systems for space missions.
UniversalRAG: Retrieval-Augmented Generation over Corpora of Diverse Modalities and Granularities
UniversalRAG is an any-to-any Retrieval-Augmented Generation framework that integrates knowledge from heterogeneous sources across diverse modalities and granularities.
The Accountability Paradox: How Platform API Restrictions Undermine AI Transparency Mandates
An analysis of how platform API restrictions on social media hinder algorithmic transparency and compliance with the EU Digital Services Act.
Fractured Chain-of-Thought Reasoning
Fractured Sampling is introduced as a way to reduce token costs in Chain-of-Thought reasoning by truncating reasoning traces without significant loss in accuracy.
MASLab: A Unified and Comprehensive Codebase for LLM-based Multi-Agent Systems
MASLab is a unified codebase that integrates over 20 LLM-based multi-agent system methods to provide a standardized environment for research and evaluation.
Amazon Announces Multibillion-Dollar Data Center in Missouri
Amazon is building a multibillion-dollar data center in Missouri.
Cohere's First Model for Developers
Cohere releases its first AI model specifically tailored for developers.
Reviews have become expensive, rewrites have become cheap
An exploration of the shifting cost dynamics between reviewing existing code versus rewriting it, especially in the context of AI.
Humanity isn't ready for the coming intelligence explosion
A speculative discussion on humanity's readiness for a potential intelligence explosion.
The 90-year-old idea behind JEPA models: Canonical Correlation Analysis
An analysis of the role of Canonical Correlation Analysis in the development of JEPA models.
Under What Conditions Can a Machine Be Called Genuinely Creative?
A theoretical paper defining the requirements for genuine machine creativity using a framework from Designics.
MiniMax Sparse Attention
Introduction of MiniMax Sparse Attention (MSA), a blockwise sparse attention mechanism that significantly reduces compute and increases speed for long-context LLMs.
Is It You or Your Environment? A Bayesian Inference Framework for Genomically-Anchored Personalized Physiological Interpretation
A Bayesian inference framework that uses genomic profiles as priors to provide personalized physiological interpretation for health AI.
EurekAgent: Agent Environment Engineering is All You Need For Autonomous Scientific Discovery
Introduction of EurekAgent, an environment-engineered agent system that optimizes autonomous scientific discovery through constrained execution and artifact management.
Application of Artificial Intelligence and Machine Learning in Libraries: A Systematic Review
A systematic review of the application of AI and ML within the library science domain.
Learning Developmental Scaffoldings to Guide Self-Organisation
Researchers propose a model that jointly learns self-organisation rules and pre-patterns using Neural Cellular Automata (NCA) and SIREN to improve robustness and encoding capacity in biological development simulation.
Planning with the Views via Scene Self-Exploration
The ViewSuite framework introduces iterative self-exploration and view graph distillation to help VLMs plan complex multi-turn camera movements in 3D environments.
VikingMem: A Memory Base Management System for Stateful LLM-based Applications
VikingMem is introduced as a memory base management system built on VikingDB to provide stateful, long-term interaction memory for LLM-based applications through selective extraction and temporal compression.
Evidence-Gated LLM Priors for Multi-Objective Bayesian Optimization
This paper proposes an objective-wise reputation-market mechanism to calibrate LLM-generated expert priors in multi-objective Bayesian optimization, reducing reliance on uncalibrated LLM confidence.
EvoTrainer: Co-Evolving LLM Policies and Training Harnesses for Autonomous Agentic Reinforcement Learning
EvoTrainer is an autonomous training framework that co-evolves LLM policies and training harnesses through empirical feedback to improve performance in agentic RL, particularly for software engineering tasks.