Chabod

Canonical Research Library

Foundational technical papers that shape how I think about intelligent systems, quantitative modeling, and engineering design.

01 HYPERAGENTS — Coordinated Agent Workflows at Scale Click to expand document Released: March 19th, 2026

Paper I · Agentic Systems

Overview: HYPERAGENTS explores architectures where multiple specialized agents collaborate through structured task decomposition, memory sharing, and orchestration loops.

Why it matters:

  • Demonstrates how agent specialization improves reliability on complex tasks.
  • Highlights orchestration and verification as first-class system components.
  • Offers design patterns useful for production-grade AI tooling.

Key ideas: decomposition, planner-executor separation, tool-aware routing, and iterative refinement.

02 Why AI Systems Don’t Learn — and What To Do About It Click to expand document Released: March 16th, 2026

Paper III · Learning Systems

Overview: This paper examines why many AI systems fail to reliably improve from experience, and presents practical mechanisms for feedback loops, evaluation discipline, and iterative system-level learning.

Why it matters:

  • Explains core failure modes that block continuous improvement in deployed AI systems.
  • Connects learning quality to data, evaluation design, and organizational workflows.
  • Provides concrete guidance for building systems that actually get better over time.

Key ideas: closed-loop feedback, measurable learning objectives, robust evals, and operational iteration.

03 TURBOQUANT — Fast, Practical Methods for Quantitative Modeling Click to expand document Released: April 29th, 2025

Paper II · Quantitative Intelligence

Overview: TURBOQUANT focuses on accelerating quantitative workflows by combining efficient model design, robust estimation, and deployment-minded optimization.

Why it matters:

  • Bridges research-grade quant methods with implementation constraints.
  • Improves turnaround for testing ideas in noisy market environments.
  • Emphasizes practical performance under real-world data limitations.

Key ideas: computational efficiency, stability under uncertainty, and scalable experimentation.

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