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Mak Sò
Mak Sò

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🧠Loop of Truth: From Loose Tricks to Structured Reasoning

#ai #redis #opensource #machinelearning

The myth of "new" ideas

AI research has a short memory. Every few months, we get a new buzzword: Chain of Thought, Debate Agents, Self Consistency, Iterative Consensus. None of this is actually new.

  • Chain of Thought is structured intermediate reasoning.
  • Iterative consensus is verification and majority voting.
  • Multi agent debate echoes argumentation theory and distributed consensus.

Each is valuable, and each has limits. What has been missing is not the ideas but the architecture that makes them work together reliably.

The Loop of Truth (LoT) is not a breakthrough invention. It is the natural evolution: the structured point where these techniques converge into a reproducible loop.

The three ingredients

1. Chain of Thought

CoT makes model reasoning visible. Instead of a black box answer, you see intermediate steps.

Strength: transparency.
Weakness: fragile - wrong steps still lead to wrong conclusions.

agents:
  - id: cot_agent
    type: local_llm
    prompt: |
      Solve step by step:
      {{ input }}
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2. Iterative consensus

Consensus loops, self consistency, and multiple generations push reliability by repeating reasoning until answers stabilize.

Strength: reduces variance.
Weakness: can be costly and sometimes circular.

3. Multi agent systems

Different agents bring different lenses: progressive, conservative, realist, purist.

Strength: diversity of perspectives.
Weakness: noise and deadlock if unmanaged.

Why LoT matters

LoT is the execution pattern where the three parts reinforce each other:

  1. Generate - multiple reasoning paths via CoT.
  2. Debate - perspectives challenge each other in a controlled way.
  3. Converge - scoring and consensus loops push toward stability.

Repeat until a convergence target is met. No magic. Just orchestration.

OrKa Reasoning traces

A real trace run shows the loop in action:

  • Round 1: agreement score 0.0. Agents talk past each other.
  • Round 2: shared themes emerge, for example transparency, ethics, and human alignment.
  • Final loop: agreement climbs to about 0.85. Convergence achieved and logged.

Memory is handled by RedisStack with short term and long term entries, plus decay over time. This runs on consumer hardware with Redis as the only backend.

{
  "round": 2,
  "agreement_score": 0.85,
  "synthesis_insights": ["Transparency, ethical decision making, human aligned values"]
}
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Architecture: boring, but essential

Early LoT runs used Kafka for agent communication and Redis for memory. It worked, but it duplicated effort. RedisStack already provides streams and pub or sub.

So we removed Kafka. The result is a single cohesive brain:

  • RedisStack pub or sub for agent dialogue.
  • RedisStack vector index for memory search.
  • Decay logic for memory relevance.

This is engineering honesty. Fewer moving parts, faster loops, easier deployment, and higher stability.

Understanding the Loop of Truth

Loop of Truth diagram

The diagram shows how LoT executes inside OrKa Reasoning. Here is the flow in plain language:

  1. Memory Read

    • The orchestrator retrieves relevant short term and long term memories for the input.

  2. Binary Evaluation

    • A local LLM checks if memory is enough to answer directly.
    • If yes, build the answer and stop.
    • If no, enter the loop.

  3. Router to Loop

    • A router decides if the system should branch into deeper debate.

  4. Parallel Execution: Fork to Join

    • Multiple local LLMs run in parallel as coroutines with different perspectives.
    • Their outputs are joined for evaluation.

  5. Consensus Scoring

    • Joined results are scored with the LoT metric: Q_n = alpha * similarity + beta * precision + gamma * explainability, where alpha + beta + gamma = 1.
    • The loop continues until the threshold is met, for example Q >= 0.85, or until outputs stabilize.

  6. Exit Loop

    • When convergence is reached, the final truth state T_{n+1} is produced.
    • The result is logged, reinforced in memory, and used to build the final answer.

Why it matters: the diagram highlights auditable loops, structured checkpoints, and traceable convergence. Every decision has a place in the flow: memory retrieval, binary check, multi agent debate, and final consensus. This is not new theory. It is the first time these known concepts are integrated into a deterministic, replayable execution flow that you can operate day to day.

Why engineers should care

LoT delivers what standalone CoT or debate cannot:

  • Reliability - loops continue until they converge.
  • Traceability - every round is logged, every perspective is visible.
  • Reproducibility - same input and same loop produce the same output.

These properties are required for production systems.

LoT as a design pattern

Treat LoT as a design pattern, not a product.

  • Implement it with Redis, Kafka, or even files on disk.
  • Plug in your model of choice: GPT, LLaMA, DeepSeek, or others.
  • The loop is the point: generate, debate, converge, log, repeat.

MapReduce was not new math. LoT is not new reasoning. It is the structure that lets familiar ideas scale.

OrKa Reasoning v0.9.0

For the latest implementation notes and fixes, see the OrKa Reasoning v0.9.0 changelog:
https://github.com/marcosomma/orka-reasoning

This release refines multi agent orchestration, optimizes RedisStack integration, and improves convergence scoring. The result is a more stable Loop of Truth under real workloads.

Closing thought

LoT is not about branding or novelty. Without structure, CoT, consensus, and multi agent debate remain disconnected tricks. With a loop, you get reliability, traceability, and trust. Nothing new, simply wired together properly.

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