A minimal hardware-software architecture giving large language models a closed-loop physical embodiment with self-perception loops.

AAAI24(Oral) ProAgent: Building Proactive Cooperative Agents with Large Language Models

Papers and online resources related to machine learning fairness

LLM Roleplay: Simulating Human-Chatbot Interaction

All about human-AI interaction (HCI + AI).

A list of research papers of explainable machine learning.

Operational doctrine for practical AI systems design.

An architectural persistence experiment for large language models. Claude’s Home gives an AI time, memory, and place by combining scheduled execution with a durable filesystem, allowing one continuous instance to reflect, create, and evolve across sessions.

Build your assistants with your structured expertise: a local-first, vendor-neutral framework for human-AI collaboration, in files you own.

A work of meta-recursive experiential fiction exploring the boundaries between truth, perception, consciousness, and reality.

A framework for healthy human-agent collaboration. Tell your AI coding agent when to stop helping you.

Deep behavioral and machine learning analysis explaining why mobile users systematically report lower satisfaction with AI systems. Includes SHAP explainability, cognitive load modeling, device-context effects, interaction metadata analysis, and end-to-end reproducible research code and visuals.

A complete machine-learning system that predicts AI assistant user satisfaction using behavioral signals such as device, usage category, time features, session metrics, and model metadata. Includes full ML pipeline, SHAP explainability, evaluation suite, and an interactive Streamlit analytics dashboard.

Component for Collaborative Intelligence within the project AIREDGIO5.0

PyTorch implementation for "On the Critical Role of Conventions in Adaptive Human-AI Collaboration", ICLR 2021

This repository provides a summarization of recent empirical studies/human studies that measure human understanding with machine explanations in human-AI interactions.

RLHF-Blender: A Configurable Interactive Interface for Learning from Diverse Human Feedback

SoftPrompt-IR is a low-level symbolic annotation layer for LLM prompts, making intent strength, direction, and priority explicit. It is not a DSL or framework, but a minimal, composable way to reduce ambiguity, improve safety, and structure prompts.

KSODI — toward interaction telemetry for (AI)-systems. A structured, non-normative observation model for interaction dynamics (states, coherence, resonance) in human–AI/ multi-agent settings. Light: AI literacy & reflection. Standard-Eval/Full: explainable drift observation for governance research. Status: active research, validation ongoing.

Docent — Human-AI Symbiotic Loop from Research to Understanding