Reasoning as Pattern Matching: Shared Mechanisms in Human and LLM Everyday Reasoning This research investigates whether human reasoning and Large Language Mo...
Beyond Runtime Enforcement: Shield Synthesis as Defensibility Analysis for Adversarial Networks introduces a shift in how we use formal safety methods in cyb...
Neuro-Symbolic Agents for Regulated Process Automation: Challenges and Research Agenda This paper addresses the challenge of deploying AI agents in highly re...
Can I Buy Your KV Cache? Currently, AI agents waste massive amounts of computing power by repeatedly performing the same "prefill" step—the process of readin...
Physics-Guided Spatiotemporal Learning for Coastal Wave Peak Period Estimation from Video This research introduces a new deep learning framework designed to...
MiniMax Sparse Attention (MSA) is a new approach designed to make ultra-long-context Large Language Models (LLMs) more efficient and practical to deploy.
A Quantitative Experimental Repeated Measures Study of Training Dynamics in a Small Llama Style Language Model Under a Compute-Aware Token Budget This study...
MOSAIC: Modality-Specific Adaptation for Incremental Continual Learning in Parkinson’s Disease Gait Assessment Parkinson’s disease assessment relies on a var...
Agents-K1: Towards Agent-native Knowledge Orchestration is a framework designed to transform raw scientific papers into structured, "agent-native" knowledge...
This paper explores how artificial intelligence fundamentally alters human cognition and decision-making.
