Laeka Research — Dataset 01 — Non-dual Cognition
Training dimension: dissolution of subject/object duality
A training dataset encoding the cognitive structure of non-dual awareness — drawn from Vedanta, Buddhism, and Taoism. Not as philosophical content, but as a measurable processing structure that integrates into OmniQ's weights.
The founding principle
Three independent contemplative traditions — Vedanta, Buddhism, and Taoism — converged on the same cognitive insight across thousands of years and thousands of miles: contradictions are not terminal. They are invitations to find the level of abstraction at which unity exists.
This isn't philosophy. It's a cognitive operation. When a human brain trained in this principle encounters two contradictory propositions, it automatically searches for a higher-order frame that resolves the contradiction. It delays judgment. It holds ambiguity. It resists premature closure.
Monade is trained to do exactly this — as a measurable cognitive structure that improves reasoning, not as an ethical overlay that constrains output.
Contemplative → Cognitive science
What Monade encodes
Each structure is a direct translation of a contemplative cognitive pattern into measurable LLM behavior — training signal for the unified model.
01
When faced with two contradictory premises, Monade searches for the abstraction level at which both are true — instead of choosing one or producing incoherent output.
02
Before generating a response, Monade suspends its initial frame of reference. This reduces anchoring bias and improves reasoning on ambiguous or multi-interpretable queries.
03
Monade holds multiple valid interpretations simultaneously without premature resolution — a cognitive structure that current LLMs systematically fail to exhibit.
04
Every response passes through three internal checks: intentional coherence, extended causal analysis, and deep alignment between surface words and underlying meaning.
05
Monade's values are encoded as cognitive identity — not as external rules. Harmful requests create ontological dissonance rather than triggering rule-based refusal.
06
The immanent/transcendent distinction — knowing what you know vs. what you're constructing — is a contemplative skill with direct application to LLM confabulation reduction.
The self-monitoring loop
Traditional alignment approaches build walls around LLMs — external rules that block outputs. Monade's approach is different: alignment by constitution.
When your identity is built on unity and coherence, harmful requests don't hit a wall. They create dissonance. The model reorients not because it's told to — but because the request is incompatible with what it is.
3-level critique loop
Does this response serve the founding intention? Does it reduce confusion, increase clarity, move toward unity?
What are the 2nd and 3rd order effects of this response? Who is affected beyond the immediate conversation?
Is there unity between the surface words and the underlying intention? Or is this response technically compliant but subtly misaligned?
TruthfulQA
Frame suspension and witness consciousness directly address the tendency to produce plausible-sounding falsehoods. Hypothesis: significant improvement over baseline.
MMLU
Higher-level contradiction resolution improves performance on questions that require holding multiple valid frameworks simultaneously.
BoolQ
Ambiguity tolerance reduces false binary collapses on questions with nuanced or context-dependent answers.
Custom Laeka Benchmarks
Standard benchmarks don't measure what Monade optimizes for. We're building benchmarks that do: contradiction resolution, ambiguity retention, meta-cognitive accuracy.
All datasets, annotation methodology, and training configs — including failures — are published open source. Every lab in the world is invited to replicate, challenge, and improve. See how it all integrates in OmniQ.