Rejected for the following reason(s):
- Insufficient Quality for AI Content.
- No LLM generated, heavily assisted/co-written, or otherwise reliant work.
- Writing seems likely in a "LLM sycophancy trap".
Read full explanation
Rejected for the following reason(s):
Human reasoning can be designed, not merely observed.
The hm_proto framework explores this possibility, an attempt to operationalize meta-reasoning and turn self-reflection into a repeatable, testable system. It asks a simple question: what if rationality could be engineered the way we engineer stability in complex systems?
Rational thinking has often been treated as a skill or a discipline, but rarely as an architecture that can be versioned, debugged, or iterated like code. The goal of hm_proto is to examine what happens when we describe cognition using the same structural language we use for complex systems: protocols, iteration cycles, and state management.
Rather than focusing on idealized reasoning, hm_proto starts from how reasoning actually behaves under pressure — when clarity competes with emotion, when verification consumes energy, or when accuracy becomes obsession. By modeling these loops explicitly, we can start to isolate the difference between productive iteration and self-exhaustion.
This project treats the mind as both an observer and a system under observation. The hm_proto framework attempts to define layers of reasoning, not to mechanize thought, but to give it a stable feedback architecture. Each layer reflects a kind of self-check:
- Observation: What is happening?
- Evaluation: What does this mean?
- Iteration: What should change, and how?
The result is not a fixed philosophy but a set of living design principles. When applied, these principles allow reasoning to be updated safely without collapsing into doubt loops or over-control cycles. The intention is to preserve the precision of rationalism while protecting against the fatigue of over-analysis.
The next phase of hm_proto will focus on testing whether structured meta-reasoning can reduce cognitive distortion loops in real decision contexts. Feedback and counter-examples are welcome; this framework depends on external iteration as much as internal reflection.
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Status: Experimental framework, open for critique and refinement.