Research System · Main-Line Layer 5
Review Layer
The honesty to examine itself — every day it checks yesterday's views against what really happened, recording both right and wrong, both what it can and can't see
The review layer is the system's self-examination stage. Each day it takes what actually happened in the market and looks back, mechanically and objectively, at the views and predictions it made the day before — was the read right, did the move it expected actually play out, where did it drift — recording all of it faithfully and handing the conclusions to the self-learning engine. It exists not to prove the system right, but to find where it can do better.
Review Flow
Retrieve yesterday's views, compare against real market action, judge objectively, mark limits honestly, then feed back to evolution — a look-back the system owes itself.
Retrieve yesterday's views
Pulls the research views the system recorded the day before, as the subject of this review
Compare against real action
Uses the market action that actually unfolded afterward, matched line by line against the original views, checking whether the moves it expected actually played out
Judge objectively
Outcomes are judged by fixed rules — the AI doesn't grade its own work; right is right, wrong is wrong
Mark limits honestly
States plainly what this review can and cannot see — no overstating, no avoiding
Feed back to evolution
Structures the conclusions and hands them to the self-learning engine, as grounds for the system to improve
Core Capabilities
The value of review isn't in looking back, but in looking back honestly, objectively, and usefully.
Mechanical, objective judgment
Outcomes are judged by fixed rules rather than letting the AI that made the view rate itself — avoiding the distortion of grading one's own work too kindly.
No self-grading distortion — the yardstick is independent of what it measures
Upstream first, then downstream
Checks whether the directional, upstream view was right before looking downstream — tracing the causal chain link by link, checking whether each view and expectation held, rather than fixating on the final result.
Not just outcome but cause — which link failed becomes visible
Honest about limits
The review reflects only what can be compared mechanically; details like real-time execution adjustments that it can't see, it says it can't see.
No distortion or self-flattery — what isn't seen isn't counted as seen
Structured retention
Every review is organized into a uniform data format, accumulating over time into material for ongoing improvement.
No throwaway reviews — every look-back leaves value behind
The Creed of Review
Review isn't for proving yourself right — it's for finding where you were wrong
Many systems only look back when they win. The review layer does the opposite — it examines every view alike: when it wins, know why; when it's wrong, know exactly which link broke. Judgment runs on fixed rules and never lets the side that made the view rate itself — because only then is review honest.
It also never pretends to know everything: what can be compared mechanically is recorded faithfully; what it can't see, it plainly admits it can't. Only a system willing to own its limits can truly improve — and that is exactly why review exists.
Design Principles
Objective before flattering
Judgment answers only to rules and real market action, never bending the yardstick to make results look good.
Honest before perfect
Better to admit a limit than to overstate what the review can see.
In service of evolution
Review doesn't end in a recap — it ends in the system doing better next time.
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