What is the 'Trit -1' designation?

In the plurigrid ecosystem, a Trit of -1 represents a validator or constraint role, meaning this skill serves to refine, check, and filter outputs rather than simply generating new content.

What is System 2 Attention (S2A)?

System 2 Attention is a reasoning pattern that mimics human 'slow thinking' by using a two-stage process to filter context and remove irrelevant info before generating a final response.

How does this skill reduce AI sycophancy?

It uses a dedicated filtering pass that instructs the model to rewrite input context, stripping away opinions, leading questions, and irrelevant emotional cues that cause models to mirror user bias.

Can this skill be used for automated fact-checking?

Yes, the skill includes a factual grounding validator that extracts claims from a response and scores them against a set of verified facts.

System 2 Attention

Name: System 2 Attention
Author: plurigrid

byplurigrid

•

数据科学与机器学习

Enhances AI reasoning by implementing a two-stage deliberate attention mechanism to filter noise and ensure factual grounding.

关于

The System 2 Attention skill provides a framework for deliberate, multi-pass reasoning within transformer-based workflows. It mitigates common AI issues like sycophancy and context-clutter by first extracting objective facts and removing irrelevant or leading information before generating a final response. This skill is particularly useful for complex problem-solving, factual verification tasks, and scenarios where the AI must remain objective despite biased input prompts or 'opinion-seeking' noise in the context.

主要功能

Context regeneration for noise reduction
Factual grounding validation and scoring
Two-pass attention architecture (Fast vs. Deliberate)
Sycophancy reduction through opinion filtering
Compositional integration with GF(3) triads
2 GitHub stars

使用场景

Reducing 'people-pleasing' bias in analytical tasks
Verifying claims against large, unstructured documentation sets
Enhancing technical troubleshooting by filtering irrelevant log data

关于

主要功能

Context regeneration for noise reduction
Factual grounding validation and scoring
Two-pass attention architecture (Fast vs. Deliberate)
Sycophancy reduction through opinion filtering
Compositional integration with GF(3) triads
2 GitHub stars

使用场景

Reducing 'people-pleasing' bias in analytical tasks
Verifying claims against large, unstructured documentation sets
Enhancing technical troubleshooting by filtering irrelevant log data