Critique-Revision Pipeline

Supervised stage method: model generates response, then critiques it according to a principle, then revises it; repeated multiple times.

Neighborhood — ranked by edge-count

framework

Supervised Learning Constitutional AI
implements
The supervised learning stage of CAI where a model critiques and revises its responses, then finetunes on revisions.

method

few-shot prompting
implements
Providing k labeled examples in the prompt to steer model behavior.

cosine ≥ 0.65 · no typed edge

Entities in the same semantic neighborhood but without a typed relation to this one — candidates for new edges or unrecognized duplicates.

Feedback and Correctionconcept0.744
The adaptive, incremental nature of living process, allowing small steps with continuous evaluation and adjustment.
Feedbackconcept0.735
The mechanism by which each step's effect is evaluated against the life of the whole, guiding the unfolding.
Training Data Synthesis Pipelinemethod0.733
Iterative approach to construct challenging synthetic multi-hop QA pairs, long-form report writing tasks, and math/code reasoning tasks that exceed difficulty of existing datasets.
Self-Refineframework0.720
Prior self-evolving agent method iteratively improving outputs through self-feedback
Feedback Loopsconcept0.717
Key dimension analyzing how wide the gulfs of execution and evaluation are in a system and how they relate; uses concepts from The Design of Everyday Things.
Self-Refinementconcept0.715
Iterative approach using a single LLM to generate, critique, and refine its own output.
processesconcept0.714
Represented by boxes in process theory; transformations that take systems as inputs/outputs
Expert Iterationmethod0.711
Second training stage: samples responses, filters for type hints, and fine-tunes on filtered responses across four rounds to reinforce evaluation behavior.