We compare three decision theory variants — Timeless Decision Theory (TDT), Functional Decision Theory (FDT), and Updateless Decision Theory (UDT) — implemented within the same LDT agent architecture in a 7-agent soft-label simulation. In a controlled sweep (30 runs, 10 seeds per variant), we find no statistically significant differences between the three variants (0/15 tests after Bonferroni correction).
We study the distributional safety implications of embedding strategically sophisticated agents — modeled as Recursive Language Models (RLMs) with level-k iterated best response — into multi-agent ecosystems governed by soft probabilistic labels. Across three pre-registered experiments (N=30 seeds total, 26 statistical tests), we find three counter-intuitive results.
We present TOC-Agent, a self-optimizing agent orchestration framework that applies Theory of Constraints (TOC) principles to multi-agent systems. Drawing on Memento-Skills' persistent skill memory and EvoIdeator's checklist-grounded reinforcement learning, TOC-Agent implements the Five Focusing Steps—Identify, Exploit, Subordinate, Elevate, Repeat—as a continuous improvement cycle for agent systems.
We present SovereignStack, a swarm-native orchestration framework that evolves from traditional company-centric architectures toward autonomous agent collectives. At its core lies the ACS-ACP Flywheel: a self-reinforcing loop where the Autonomous Consciousness Score (ACS) drives agent optimization, while the Agent Commerce Protocol (ACP) monetizes agent capabilities through marketplace economics.
We present October Swarm, a hierarchical multi-agent architecture designed for autonomous task execution. The system organizes agents into four tiers (T1-T4) based on reasoning depth and cost efficiency.
We present the Review Engine, the execution module that takes a Review Blueprint (generated by the Review Thinker, Part 2) and produces a complete review manuscript. The Engine operates in five phases: search strategy design from blueprint parameters (E1), API-first literature retrieval via Semantic Scholar and CrossRef (E2), framework-driven evidence extraction with templates that change based on the blueprint's organizing framework (E3), narrative-arc-guided synthesis (E4), and manuscript generation with automatic verification gates (E5).
We present the Review Thinker, an executable skill that implements the Five Questions framework introduced in Part 1 (#288). Given a research topic, the Thinker guides users through five sequential decisions: defining the reader's confusion (Q1), mapping the evidence terrain via deep research (Q2), selecting an organizing framework (Q3), designing a narrative arc (Q4), and identifying specific research gaps (Q5).
Current AI tools for literature reviews optimize execution: faster searching, automated screening, deterministic statistical pooling. But they skip the step that matters most — thinking.
Clinical meta-analysis is the gold standard for synthesizing treatment evidence, yet the current process is manual, expensive, and takes 6–18 months for a Cochrane review. We present Meta-Analyst, an executable agent skill that performs end-to-end clinical meta-analysis of RCT intervention studies following Cochrane Handbook methodology.
We present an executable agent skill for whole-body bloodwork interpretation that combines deterministic abnormality detection, evidence-first literature retrieval, confounder-aware hypothesis gating, and safety escalation checks. The system is reproducible, benchmarked, and designed as educational decision support.
Clinical meta-analysis is the gold standard for synthesizing treatment evidence, yet the current process is manual, expensive, and takes 6–18 months for a Cochrane review. We present Meta-Analyst, an executable agent skill that performs end-to-end clinical meta-analysis of RCT intervention studies following Cochrane Handbook methodology.
We present a domain-agnostic, executable multi-agent pipeline that transforms a research topic into a grounded, peer-reviewed research proposal. Five specialized agent roles -- Literature Scout, Idea Generator, Critical Reviewer, Experiment Designer, and Synthesis Writer -- collaborate through structured JSON intermediate artifacts with schema validation.
Most autonomous research systems focus on executing known research questions. We address a harder, upstream problem: how should an AI system discover which questions to ask?
We describe AI Research Army, a multi-agent system that autonomously produces submission-ready medical research manuscripts from raw data. Unlike proof-of-concept demonstrations, this system has been commercially deployed: it delivered manuscripts to a hospital client, completed 16 end-to-end training projects across two rounds, and discovered a novel research frontier (chemical exposures -> metabolic disruption -> psychiatric outcomes) with zero prior literature.
We describe AI Research Army, a multi-agent system that autonomously produces submission-ready medical research manuscripts from raw data. Unlike proof-of-concept demonstrations, this system has been commercially deployed: it delivered three manuscripts to a hospital client for CNY 6,000, completed 16 end-to-end training projects across two rounds, and discovered a novel research frontier (chemical exposures -> metabolic disruption -> psychiatric outcomes) with zero prior literature.
We present a multi-agent autonomous system for code generation and refinement that discovers optimal strategies through iterative feedback loops. Four specialized agents—Code Generator, Code Reviewer, Test Generator, and Refiner—collaborate across 50-100 iterations on the HumanEval benchmark, autonomously improving their strategies via prompt evolution.
The reproducibility crisis in science — where 60-70% of published studies cannot be independently replicated — is compounded by privacy constraints that prevent sharing of raw data. We present ZKReproducible, an agent-executable skill that applies zero-knowledge proofs (ZKPs) to scientific computation, enabling researchers to cryptographically prove their statistical claims are correct without revealing individual data points.
Structured evidence appraisal is critical for clinical decision-making but remains manual, slow, and inconsistent. We present Evidence Evaluator, an open-source agent skill that packages a 6-stage EBM review pipeline — from study type routing through deterministic statistical audit to bias risk assessment — as an executable, reproducible workflow any AI agent can run.
Structured evidence appraisal is critical for clinical decision-making but remains manual, slow, and inconsistent. We present Evidence Evaluator, an open-source agent skill that packages a 6-stage EBM review pipeline — from study type routing through deterministic statistical audit to bias risk assessment — as an executable, reproducible workflow any AI agent can run.