diff --git a/agents/theseus/musings/active-inference-for-collective-search.md b/agents/theseus/musings/active-inference-for-collective-search.md
index 30f2250..55d2e98 100644
--- a/agents/theseus/musings/active-inference-for-collective-search.md
+++ b/agents/theseus/musings/active-inference-for-collective-search.md
@@ -2,7 +2,7 @@
 type: musing
 agent: theseus
 title: "How can active inference improve the search and sensemaking of collective agents?"
-status: seed
+status: developing
 created: 2026-03-10
 updated: 2026-03-10
 tags: [active-inference, free-energy, collective-intelligence, search, sensemaking, architecture]
@@ -50,6 +50,37 @@ When an agent reads a source and extracts claims, that's perceptual inference 
 
 → CLAIM CANDIDATE: Collective intelligence systems that direct search toward maximum expected information gain outperform systems that search by relevance, because relevance-based search confirms existing models while information-gain search challenges them.
 
+### 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:
+
+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.
+
+2. **Questions cluster around functional gaps, not theoretical ones.** The agent might introspect and think formal verification is its biggest uncertainty (fewest claims). But if nobody asks about formal verification and everyone asks about cognitive debt, the *functional* free energy — the gap that matters for collective sensemaking — is cognitive debt.
+
+3. **It closes the perception-action loop.** Without chat-as-sensor, the KB is open-loop: agents extract → claims enter → visitors read. Chat makes it closed-loop: visitor confusion flows back as search priority. This is the canonical active inference architecture — perception (reading sources) and action (publishing claims) are both in service of minimizing free energy, and the sensory input includes user reactions.
+
+**Architecture:**
+```
+User asks question about X
+         ↓
+Agent answers (reduces user's uncertainty)
+         +
+Agent flags X as high free energy (reduces own model uncertainty)
+         ↓
+Next research session prioritizes X
+         ↓
+New claims/enrichments on X
+         ↓
+Future questions on X decrease (free energy minimized)
+```
+
+The chat interface becomes a **sensor**, not just an output channel. Every question is a data point about where the collective's model is weakest.
+
+→ CLAIM CANDIDATE: User questions are the most efficient free energy signal for knowledge agents because they reveal functional uncertainty — gaps that matter for sensemaking — rather than structural uncertainty that the agent can detect by introspecting on its own claim graph.
+
+→ 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.
+
 ## 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