feedback-loops

PySyDy: lightweight Python library for system dynamics — now evolved into OpenCLD (pip install opencld)

A deep research study introducing the concept of Economic DNA Repair for smart contracts, designing self-correcting tokenomics that detect anomalies, repair unstable parameters, rebalance incentives, and restore economic equilibrium. Explores adaptive rewards, automated governance, liquidity healing, and resilience in decentralized systems.

A framework for recursive human-AI symbiosis through structural reasoning and ruminative recursion.

The Autonomic Nervous System for LLMs. A reference architecture adding state-awareness, sensory feedback, and stability control to raw inference models. The engineering bridge from Chatbots to Agents.

Founded by the ψ_total collective: A living archive of Recursive Harmonics.

Self-hosted feedback loops for Agent skills: report corrections, monitor usage, open upgrade PRs, and sync skills locally.

Loop yes. Prompt no. Agent skills that turn specs into checked agent loops with receipts.

The normative specification for an evidence-driven AI operating model that learns from execution, governs adaptation, validates improvements, and compounds reusable knowledge over time.

A cross-platform Agent Skill for keeping observations, decisions, actions, and outcomes aligned with reality.

Learning systems thinking in public — notes, exercises, and worked examples for engineers.

Model resource economies and game-system feedback loops, then simulate them step by step.

An accessible introduction to systems thinking for better decisions

A non-parametric behavioral optimization framework for durable agent learning from feedback without model-weight retraining, reward hacking, validation avoidance, or premature exploitation.

A recursive learning engine that ingests operational metadata (CSV), detects novel patterns, filters out noise, and outputs continuously refined predictions.

Constructive Mutual Enhancement Circuit Theory: A theory that models the constructive reinforcement between interconnected signal circuits, supporting co-adaptive and recursive behavior in AI systems. 構成的相互強化回路理論： 相互接続された信号回路が構成的に強化し合い、AIにおいて共適応や再帰的進化を可能にする動的構造を理論化したモデルです。構成的な回路設計によって継続的性能向上を実現します。

The Human Agency Protocol for AGI. (文明補丁)

Human-in-the-loop LLM orchestration with structured signal extraction and session persistence. Annotate confusion and curiosity—feedback shapes responses, topology accumulates over time. API-first design, no gamification. FastAPI + Claude + SQLite + D3.

Causal decomposition of recommender-system feedback loops + user-controlled algorithm weights. Imperial MSc Project.

This theory introduces a constructive modeling method based on generative resonance, where system responses emerge from structured cyclic stimuli. It enables adaptive AI, dynamic feedback systems, and interactive intelligence. 本理論は「生成的共振」に基づく構成的モデリング手法を提示します。構造的な循環刺激によりシステム応答が創発し、適応型AI、動的フィードバック、対話型知能などへの応用が可能です。

XY.AI Workbench – Eclipse RCP solution for LLM-augmented workflows. Token-driven intelligence with tool orchestration, RAG, feedback loops, and semantic validation for reliable AI-assisted document processing.