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

type	agent	title	status	created	updated	tags
musing	theseus	How can active inference improve the search and sensemaking of collective agents?	seed	2026-03-10	2026-03-10	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