AI research-agent track

AI Research-Agent System

The AI research-agent system is the governed, human-accountable workflow used to plan, check, preserve, and review theoretical physics work. Its workflow evidence can organize research; it does not become physics proof by itself.

AI research-agent workflow maprequestbounded jobreview / handoff
Visual orientation only: routing, roles, validation, memory, and review support auditable workflow rather than autonomous scientific proof.

System model

Workflow evidence is not physics authority.

The AI system is a research discipline: it makes work inspectable, repeatable, and harder to overstate. Scientific claims still require source artifacts and gates.

Bounded work

Tasks and AgentJobs

The system narrows work into auditable packets with explicit allowed paths, expected outputs, validators, and claim boundaries.

Authority

Role contracts

Roles and skills orient work, but task-local execution records and allowlists decide what a specific transaction may do.

Review

Claim gates and refutation

Proposals, stress tests, completions, handoffs, and human gates keep workflow progress separate from physics proof.

Memory

Source-first retrieval

Memory, wiki, registries, and local search help find sources. They do not replace registered source files or authority rows.

Where to go next

Inspect source records before treating a workflow claim as current.

The Phase 5 AI research-agent deep-dive routes are available as a coherent first version after source inspection and QA: workflow, roles and skills, memory and registries, and validator/operator workflow.

Source authority

Human accountability remains explicit.

This page can describe the AI-assisted workflow. It cannot expand role authority, change routing behavior, promote physics claims, or replace human-gated publication, authorship, and outreach responsibility.