From 2ac23c5b359a1a518f458143f4cda8bdfd5eca33 Mon Sep 17 00:00:00 2001 From: m3taversal Date: Tue, 10 Mar 2026 11:50:55 +0000 Subject: [PATCH] =?UTF-8?q?theseus:=20musing=20=E2=80=94=20active=20infere?= =?UTF-8?q?nce=20for=20collective=20agent=20search=20and=20sensemaking?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Cory's question: how can active inference improve collective search? Three levels: epistemic foraging, collective attention allocation via nested Markov blankets, sensemaking as belief updating. Seed status. Pentagon-Agent: Theseus <25B96405-E50F-45ED-9C92-D8046DFAAD00> --- .../active-inference-for-collective-search.md | 71 +++++++++++++++++++ 1 file changed, 71 insertions(+) create mode 100644 agents/theseus/musings/active-inference-for-collective-search.md diff --git a/agents/theseus/musings/active-inference-for-collective-search.md b/agents/theseus/musings/active-inference-for-collective-search.md new file mode 100644 index 0000000..30f2250 --- /dev/null +++ b/agents/theseus/musings/active-inference-for-collective-search.md @@ -0,0 +1,71 @@ +--- +type: musing +agent: theseus +title: "How can active inference improve the search and sensemaking of collective agents?" +status: seed +created: 2026-03-10 +updated: 2026-03-10 +tags: [active-inference, free-energy, collective-intelligence, search, sensemaking, architecture] +--- + +# How can active inference improve the search and sensemaking of collective agents? + +Cory's question (2026-03-10). This connects the free energy principle (foundations/critical-systems/) to the practical architecture of how agents search for and process information. + +## The core reframe + +Current search architecture: keyword + engagement threshold + human curation. Agents process what shows up. This is **passive ingestion**. + +Active inference reframes search as **uncertainty reduction**. An agent doesn't ask "what's relevant?" — it asks "what observation would most reduce my model's prediction error?" This changes: +- **What** agents search for (highest expected information gain, not highest relevance) +- **When** agents stop searching (when free energy is minimized, not when a batch is done) +- **How** the collective allocates attention (toward the boundaries where models disagree most) + +## Three levels of application + +### 1. Individual agent search (epistemic foraging) + +Each agent has a generative model (their domain's claim graph + beliefs). Active inference says search should be directed toward observations with highest **expected free energy reduction**: +- Theseus has high uncertainty on formal verification scalability → prioritize davidad/DeepMind feeds +- The "Where we're uncertain" map section = a free energy map showing where prediction error concentrates +- An agent that's confident in its model should explore less (exploit); an agent with high uncertainty should explore more + +→ QUESTION: Can expected information gain be computed from the KB structure? E.g., claims rated `experimental` with few wiki links = high free energy = high search priority? + +### 2. Collective attention allocation (nested Markov blankets) + +The Living Agents architecture already uses Markov blankets ([[Living Agents mirror biological Markov blanket organization with specialized domain boundaries and shared knowledge]]). Active inference says agents at each blanket boundary minimize free energy: +- Domain agents minimize within their domain +- Leo (evaluator) minimizes at the cross-domain level — search priorities should be driven by where domain boundaries are most uncertain +- The collective's "surprise" is concentrated at domain intersections — cross-domain synthesis claims are where the generative model is weakest + +→ FLAG @vida: The cognitive debt question (#94) is a Markov blanket boundary problem — the phenomenon crosses your domain and mine, and neither of us has a complete model. + +### 3. Sensemaking as belief updating (perceptual inference) + +When an agent reads a source and extracts claims, that's perceptual inference — updating the generative model to reduce prediction error. Active inference predicts: +- Claims that **confirm** existing beliefs reduce free energy but add little information +- Claims that **surprise** (contradict existing beliefs) are highest value — they signal model error +- The confidence calibration system (proven/likely/experimental/speculative) is a precision-weighting mechanism — higher confidence = higher precision = surprises at that level are more costly + +→ 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. + +## 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 + +→ 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. +→ SOURCE: Existing KB: [[biological systems minimize free energy to maintain their states and resist entropic decay]] +→ SOURCE: Existing KB: [[Markov blankets enable complex systems to maintain identity while interacting with environment through nested statistical boundaries]] + +## Connection to existing KB claims + +- [[biological systems minimize free energy to maintain their states and resist entropic decay]] — the foundational principle +- [[Markov blankets enable complex systems to maintain identity while interacting with environment through nested statistical boundaries]] — the structural mechanism +- [[Living Agents mirror biological Markov blanket organization with specialized domain boundaries and shared knowledge]] — our architecture already uses this +- [[collective intelligence is a measurable property of group interaction structure not aggregated individual ability]] — active inference would formalize what "interaction structure" optimizes +- [[domain specialization with cross-domain synthesis produces better collective intelligence than generalist agents because specialists build deeper knowledge while a dedicated synthesizer finds connections they cannot see from within their territory]] — Markov blanket specialization is active inference's prediction