pAI-Econ-claude: A Gated Human-in-the-Loop Multi-Agent Architecture for AI-Assisted Economic Theory (arXiv: 2607.21268)
原文の言語: 英語
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SkillsMP は hiyenwong/ai_collection から 4,114 件の skill を収集しています。skill を開くとソースと詳細を確認できます。
hiyenwong/ai_collection収集済み skill 4,114 件中 40 件を表示しています。
pAI-Econ-claude: A Gated Human-in-the-Loop Multi-Agent Architecture for AI-Assisted Economic Theory (arXiv: 2607.21268)
原文の言語: 英語
The Dark Room in the Reward Channel: Dense Prediction Rewards Collapse GRPO-Trained LLM Agents -- an (arXiv: 2607.21273)
原文の言語: 英語
A Comparative Evaluation of Embeddings and LLMs in a Greek Book Publisher Setting - The CUP Dataset (arXiv: 2607.21274)
原文の言語: 英語
Adaptive Depth Sparse Framework: Similarity-Driven Resource Allocation for Pre-Trained LLMs (arXiv: 2607.21291)
原文の言語: 英語
An LLM-Driven Workflow for Automated Process Control Strategy Generation and Tuning from Dynamic Pro (arXiv: 2607.21292)
原文の言語: 英語
Expert Behavior Prior Reinforcement Learning (arXiv: 2607.21302)
原文の言語: 英語
GRADRAG: Cross-Component Prompt Adaptation for Coordinated Multi-Agent RAG (arXiv: 2607.21324)
原文の言語: 英語
Toward cryptographically verifiable authorization for autonomous AI agents: A security hypothesis, p (arXiv: 2607.21325)
原文の言語: 英語
From Static Bibliometrics to Dynamic Knowledge Graphs: An LLM-Powered Framework for Modernizing Scie (arXiv: 2607.21327)
原文の言語: 英語
Regulating autonomous and agentic AI (arXiv: 2607.21345)
原文の言語: 英語
Euclid-MCP: A Model Context Protocol Server for Deterministic Logical Reasoning via Prolog (arXiv: 2607.21412)
原文の言語: 英語
PATS: Policy-Aware Training Scaffolding for Agentic Reinforcement Learning (arXiv: 2607.21419)
原文の言語: 英語
AREX: Towards a Recursively Self-Improving Agent for Deep Research (arXiv: 2607.21461)
原文の言語: 英語
Agentic coding without the cloud: evaluating open-weight large language models on longitudinal data (arXiv: 2607.21482)
原文の言語: 英語
Compact Latent Coordination for Autonomous Vehicles at Unsignalized Intersections (arXiv: 2607.21488)
原文の言語: 英語
Toward Continuous Assurance for the Democratization of AI Agent Creation in Industry (arXiv: 2607.21495)
原文の言語: 英語
Agentic Context Management: Solving Agent Memory and Cost by Treating Them as Lifecycle and Architec (arXiv: 2607.21503)
原文の言語: 英語
Same Dangerous Objective, Opposite Advice: Direct Exposure versus Multi-Agent Mediation (arXiv: 2607.21518)
原文の言語: 英語
GS-Agent: Creating 4D Physical Worlds With Generative Simulation (arXiv: 2607.21522)
原文の言語: 英語
MIRROR: Learning from the Other View for Multi-Modal Reasoning (arXiv: 2607.21552)
原文の言語: 英語
OpenForgeRL: Train Harness-native Agents in Any Environment (arXiv: 2607.21557)
原文の言語: 英語
Workaround for arxiv-search SSL issues when using python httpx
原文の言語: 英語
Skill for understanding and applying the research from arXiv:2607.13644 "Evaluating Encoding Strategies for Closed-Loop Classification in Biological Neural Networks"
原文の言語: 英語
Hybrid Hindsight Self-Distillation for Reinforcement Learning with Verifiable Rewards
原文の言語: 英語
Skill for implementing and understanding the theory-grounded hybrid neural network (HTNN) that integrates artificial neural networks (ANNs) with continuous attractor neural networks (CANNs) for stable visual object tracking, as proposed in arXiv:2606.22604.
原文の言語: 英語
Model-Agnostic Meta Learning (MAML) framework for Differentiable Model Predictive Control (MPC) to enable adaptive control strategies across varying scenarios. Combines meta-learning with differentiable MPC for real-time adaptability without extensive…
原文の言語: 英語
Agentic coding and persistent returns to expertise
原文の言語: 英語
Exploring Brain Networks Using Noninvasive Electrophysiological Measurements: Methods and Applications
原文の言語: 英語
End-to-end deep learning framework for decoding visual semantic categories from ECoG signals during video stimulus presentation.
原文の言語: 英語
Skill for evaluating language models using EEG signals to examine human-like next-word prediction behavior based on arXiv:2607.16549. Enables fine-grained analysis of cognitive plausibility of language models during reading comprehension tasks.
原文の言語: 英語
Skill for exploring brain networks using noninvasive electrophysiological measurements (EEG/MEG) based on arXiv:2607.17602v1. Covers forward/inverse problems, source reconstruction, connectivity measures, and analysis pipelines.
原文の言語: 英語
Spiking Neural Networks for fMRI-Based Visual Semantic Decoding - methodology for using SNN-derived visual features as alternative targets for fMRI-based visual decoding, demonstrating stronger alignment with fMRI responses and improved visual semantic…
原文の言語: 英語
Fast Whole-Brain, Geometry-Aware Functional Alignment for Cross-Subject Decoding using Spectral Optimal Transport (SpectralOT) method for fMRI data analysis
原文の言語: 英語
Methodology for using Spiking Neural Network (SNN)-derived visual features as targets for fMRI-based visual semantic decoding, showing superior alignment with brain activity compared to traditional ANN features.
原文の言語: 英語
STSBench: A Large-Scale Dataset for Modeling Neuronal Activity in the Dorsal Stream of Primate Visual Cortex - Skill for understanding and applying the methods from arXiv:2607.15631
原文の言語: 英語
Skill for understanding and applying the mechanistic model of perceptual inference in visual cortex equivalent to a minimal diffusion model (arXiv:2607.15693). Enables extraction of principles linking sparse coding, recurrent dynamics, and diffusion model…
原文の言語: 英語
Skill for understanding and implementing the discrete tensor-native STDP-based SNN visual place recognition pipeline from arXiv:2607.13584v1. Use when working with spiking neural networks for visual place recognition, loop closure in SLAM, or neuromorphic…
原文の言語: 英語
RG-Flow Transformer for encoding scale-free dynamics in scarce EEG data - uses renormalization-group inductive bias to improve interpretability and spectral exponent recovery from limited neural recordings
原文の言語: 英語
Best practices for safely accessing arXiv programmatically, avoiding common pitfalls with HTTP, SSL, and automated tools.
原文の言語: 英語
Functional alignment method for fMRI using SpectralOT to embed cortical geometry into Laplace-Beltrami eigenmodes for cross-subject decoding
原文の言語: 英語