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Interrupting the Loop: Periodic Subject Changes Raise Judged Surprise and Connection in Base Language Models
The research examines where novelty comes from in base language models and what they can perceive in a long text stream. The study dismantles a cognitively inspired generation loop across 24 conditions on three base…
ORACLE: A Multi-Objective Reinforcement Learning-Based Analog Circuit Design Optimizer with Large Language Models-Guided Exploration
The paper presents ORACLE, a multi-objective reinforcement learning-based framework for optimizing analog circuit design. The framework addresses limitations of existing methods by using vector-valued learning and…
Sparse Autoencoders Encode Both Concepts and Functions: The Downstream Geometry of Feature Effects
A study explores the inconsistencies in linking sparse autoencoders (SAEs) with model behavior, focusing on the geometry of changes in model logits caused by feature interventions. The authors propose an unsupervised…
Autonomous Collaborative Learning Among an Ensemble of Tsetlin Machines with Consensus-Based Inference
The researchers propose a decentralized collaborative learning paradigm for an ensemble of Tsetlin Machines (TMs) using consensus-based inference. Each agent has its own private TM model, and they combine individual…
Local Stability and Gaussian Smoothing of Quantized Neural Networks
The authors study the use of Gaussian averaging as a smoother alternative for quantized neural networks. They derive a bound on the difference between the original function and the smoothed version under certain…
Unleashing Multimodal Large Language Models for Training-free HOI Detection in the Wild
The paper presents a new framework called AgentHOI that uses multimodal large language models (MLLMs) to detect human-object interactions (HOIs) in complex scenes without the need for any HOI data during training. The…
LoRA-Based Cascaded Multimodal Fusion for Action Recognition in Medical Training Environments
The paper introduces a new framework for recognizing actions and activities in medical training environments using a cascaded Low-Rank Adaptation (LoRA)-based multimodal fusion approach. This method allows for the…
WebSwarm: Recursive Multi-Agent Orchestration for Deep-and-Wide Web Search
The paper proposes a new framework called WebSwarm for multi-agent search systems. This system aims to improve deep-and-wide web searches by dynamically creating search nodes that can delegate tasks recursively and…
QFedAgent: Quantum-Enhanced Personalized Federated Learning for Multi-Agent Activity Recognition
The paper proposes a new method called QFedAgent for multi-agent activity recognition in privacy-sensitive robotic sensing applications. The method uses a hybrid quantum-classical personalized Federated Learning…
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