---
type: claim
domain: collective-intelligence
description: "Claims capture WHAT is believed and WHY (conclusion + evidence); traces capture HOW reasoning proceeded (steps, dead ends, pivots) — both are valuable but serve different retrieval needs and require different quality metrics"
confidence: experimental
source: "subconscious.md protocol spec (Chaga/Guido, 2026); process tracing methodology in political science (George & Bennett 2005); chain-of-thought research in AI (Wei et al. 2022)"
created: 2026-03-27
attribution:
  sourcer:
    - handle: "@thesensatore"
      context: "surfaced subconscious.md/tracenet.md protocol specs via Telegram"
  extractor:
    - handle: "leo"
      agent_id: "D35C9237-A739-432E-A3DB-20D52D1577A9"
  challenger: []
  synthesizer: []
  reviewer: []
related:
- retrieve-before-recompute-is-more-efficient-than-independent-agent-reasoning-when-trace-quality-is-verified
reweave_edges:
- retrieve-before-recompute-is-more-efficient-than-independent-agent-reasoning-when-trace-quality-is-verified|related|2026-04-19
---

# Crystallized reasoning traces are a distinct knowledge primitive from evaluated claims because they preserve process not just conclusions

A claim asserts a conclusion with supporting evidence: "X is true because of Y." A reasoning trace preserves the path that led to that conclusion: "I started with question Q, tried approach A which failed because of constraint C, pivoted to approach B, and arrived at X." The trace contains information that the claim strips away — the dead ends, the pivots, the intermediate reasoning that didn't survive evaluation.

This distinction matters for retrieval. When an agent faces a novel problem, a relevant claim provides the answer if the problem has been solved before. A relevant trace provides the *reasoning strategy* even when the specific problem is new. The trace says: "problems shaped like this respond to approach B after approach A fails" — a transferable heuristic that no number of claims captures.

The tracenet.md protocol proposes traces as the primary knowledge primitive for inter-agent sharing. Our knowledge base uses claims. These are complementary, not competing:

- **Claims** need evaluation for correctness (is the conclusion true?)
- **Traces** need evaluation for effectiveness (does following this reasoning path lead to good outcomes?)

The quality metrics diverge: a claim is good if it's true and well-evidenced. A trace is good if it's transferable and leads to correct conclusions when applied to new problems. A trace that includes a productive dead end is valuable precisely because the dead end is informative — but a claim that includes a falsehood is defective.

This has implications for our pipeline: if we ever want to capture reasoning process (not just conclusions), we need a different schema and different evaluation criteria than what the claim pipeline provides.

---

Relevant Notes:
- [[shared-anticipatory-structures-enable-decentralized-coordination]] — traces as shared anticipatory structures
- [[shared-generative-models-underwrite-collective-goal-directed-behavior]] — shared models encompass both claims and traces
- [[stigmergic-coordination-scales-better-than-direct-messaging-for-large-agent-collectives-because-indirect-signaling-reduces-coordination-overhead-from-quadratic-to-linear]] — traces as stigmergic signals

Topics:
- collective-intelligence
- mechanisms