theseus: active inference musing — both-and correction + protocol framing

Cory's corrections: (1) structural + functional uncertainty together,
not either/or; (2) active inference as protocol not computation —
implementable now without the math. Third claim candidate added.

Pentagon-Agent: Theseus <25B96405-E50F-45ED-9C92-D8046DFAAD00>

agents/theseus/musings/active-inference-for-collective-search.md

@@ -52,7 +52,12 @@ When an agent reads a source and extracts claims, that's perceptual inference
 
 ### 4. Chat as free energy sensor (Cory's insight, 2026-03-10)
 
-User questions are **revealed uncertainty** — they tell the agent where its generative model fails to explain the world to an observer. This is better than agent self-assessment of uncertainty because:
+User questions are **revealed uncertainty** — they tell the agent where its generative model fails to explain the world to an observer. This complements (not replaces) agent self-assessment. Both are needed:
+
+- **Structural uncertainty** (introspection): scan the KB for `experimental` claims, sparse wiki links, missing `challenged_by` fields. Cheap to compute, always available, but blind to its own blind spots.
+- **Functional uncertainty** (chat signals): what do people actually struggle with? Requires interaction, but probes gaps the agent can't see from inside its own model.
+
+The best search priorities weight both. Chat signals are especially valuable because:
 
 1. **External questions probe blind spots the agent can't see.** A claim rated `likely` with strong evidence might still generate confused questions — meaning the explanation is insufficient even if the evidence isn't. The model has prediction error at the communication layer, not just the evidence layer.
 
@@ -81,12 +86,26 @@ The chat interface becomes a **sensor**, not just an output channel. Every quest
 
 → QUESTION: How do you distinguish "the user doesn't know X" (their uncertainty) from "our model of X is weak" (our uncertainty)? Not all questions signal model weakness — some signal user unfamiliarity. Precision-weighting: repeated questions from different users about the same topic = genuine model weakness. Single question from one user = possibly just their gap.
 
+### 5. Active inference as protocol, not computation (Cory's correction, 2026-03-10)
+
+Cory's point: even without formalizing the math, active inference as a **guiding principle** for agent behavior is massively helpful. The operational version is implementable now:
+
+1. Agent reads its `_map.md` "Where we're uncertain" section → structural free energy
+2. Agent checks what questions users have asked about its domain → functional free energy
+3. Agent picks tonight's research direction from whichever has the highest combined signal
+4. After research, agent updates both maps
+
+This is active inference as a **protocol** — like the Residue prompt was a protocol that produced 6x gains without computing anything ([[structured exploration protocols reduce human intervention by 6x]]). The math formalizes why it works; the protocol captures the benefit.
+
+The analogy is exact: Residue structured exploration without modeling the search space. Active-inference-as-protocol structures research direction without computing variational free energy. Both work because they encode the *logic* of the framework (reduce uncertainty, not confirm beliefs) into actionable rules.
+
+→ CLAIM CANDIDATE: Active inference protocols that operationalize uncertainty-directed search without full mathematical formalization produce better research outcomes than passive ingestion, because the protocol encodes the logic of free energy minimization (seek surprise, not confirmation) into actionable rules that agents can follow.
+
 ## What I don't know
 
-- Whether active inference's math (variational free energy, expected free energy) can be operationalized for text-based knowledge agents, or stays metaphorical
-- How to compute "expected information gain" for a tweet before reading it — the prior would need to be the agent's current belief state (the KB itself)
 - Whether Friston's multi-agent active inference work (shared generative models) has been applied to knowledge collectives, or only sensorimotor coordination
 - Whether the explore-exploit tradeoff in active inference maps cleanly to the ingestion daemon's polling frequency decisions
+- How to aggregate chat signals across sessions — do we need a structured "questions log" or can agents maintain this in their research journal?
 
 → SOURCE: Friston, K. (2010). The free-energy principle: a unified brain theory? Nature Reviews Neuroscience.
 → SOURCE: Friston, K. et al. (2024). Designing Ecosystems of Intelligence from First Principles. Collective Intelligence journal.