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Dialectical Reasoning Templates

A deep dive into the structured reasoning templates used by the CNS 2.0 synthesis engine to ensure logical consistency and mitigate hallucination.

The Challenge: Unconstrained LLM Reasoning

One of the greatest challenges in working with Large Language Models (LLMs) is their tendency to “hallucinate” or generate fluent but logically inconsistent text. When tasked with a complex reasoning problem like synthesizing two opposing narratives, an unconstrained LLM might take shortcuts, ignore critical evidence, or invent new information to create a plausible-sounding but ultimately flawed output.

For a system like CNS 2.0, which must be reliable and transparent, this is unacceptable. We cannot treat the LLM as an infallible black box. Instead, we must structure its reasoning process to make it more rigorous, consistent, and auditable.

The Solution: Structured Reasoning Templates

To solve this, CNS 2.0 employs structured reasoning templates for its dialectical synthesis phase. As detailed in Section 4.4 of our Ideas Paper, these templates are sophisticated, meta-prompts that guide the LLM through a formal, step-by-step dialectical process.

By forcing the LLM to “show its work” within a pre-defined logical structure, we achieve two critical goals:

  1. Improved Reliability: The template constrains the LLM, reducing the likelihood of logical fallacies and ensuring that all parts of the problem (thesis, antithesis, shared evidence) are explicitly addressed.
  2. Enhanced Transparency: The structured output allows a human user (or another AI component) to easily audit the LLM’s reasoning process. We can see exactly how it analyzed the conflict and arrived at its conclusion, rather than just seeing the final answer.

The Hegelian Dialectical Template

Our primary template is based on the Hegelian dialectic of thesis, antithesis, synthesis. It forces the LLM to move beyond simple summarization and engage in a process of higher-order resolution.

DIALECTICAL_SYNTHESIS_TEMPLATE = """
Given the following validated inputs:
- THESIS: {thesis_claims} [Supported by evidence: {thesis_evidence}]
- ANTITHESIS: {antithesis_claims} [Supported by evidence: {antithesis_evidence}]
- SHARED_EVIDENCE: {shared_evidence_list}
- CONFLICT_POINTS: {identified_contradictions}

REQUIRED_PROCESS:
1. CONTRADICTION_ANALYSIS:
   - Identify the fundamental source of disagreement.
   - Analyze how the shared evidence is interpreted differently to support opposing conclusions.
   - Determine if the contradiction is a genuine paradox or merely an apparent conflict.

2. EVIDENCE_SYNTHESIS:
   - Reconcile the interpretation of the shared evidence.
   - Identify which specific pieces of evidence support aspects of both the thesis and the antithesis.
   - Determine what additional evidence, if found, would be most likely to resolve the core dispute.

3. HIGHER_ORDER_RESOLUTION:
   - Formulate a new synthesis that preserves the valid insights from both the thesis and antithesis.
   - Ensure the synthesis directly addresses the root cause of the contradiction identified in the analysis phase.
   - Generate novel insights or a new conceptual framework that transcends the original disagreement.

4. LOGICAL_VALIDATION:
   - Verify that the final synthesis is internally logically consistent.
   - Confirm that all claims within the synthesis are supported by the provided evidence.
   - Ensure that no logical fallacies have been introduced during the reasoning process.

CONSTRAINTS:
- Must preserve and explain all high-quality shared evidence.
- Cannot introduce new claims that are unsupported by the provided evidence.
- Must explicitly address all major points of contradiction.
- Cannot resort to simple averaging, compromise, or "splitting the difference."

OUTPUT_FORMAT: [Structured synthesis with explicit reasoning chains for each of the four process steps.]
"""

Breakdown of the Template’s Function

  • Contradiction Analysis: This forces the LLM to begin by diagnosing the nature of the conflict, rather than immediately jumping to a solution. This is a critical step in deep reasoning.
  • Evidence Synthesis: This step grounds the entire process in the available data. The LLM must explicitly map the evidence to the competing claims, preventing it from ignoring inconvenient facts.
  • Higher-Order Resolution: This is the core of the creative synthesis process. It explicitly forbids simple compromises and pushes the LLM to generate a genuinely novel perspective that reframes the original problem.
  • Logical Validation: This final step acts as a self-check, forcing the LLM to review its own work for consistency and fallacies before producing the final output.

By using this structured, transparent, and rigorous approach, we transform the LLM from a potentially unreliable text generator into a more disciplined and accountable reasoning engine, which is an essential requirement for building a trustworthy knowledge synthesis system.