diff --git a/domains/ai-alignment/active-inference-unifies-perception-action-planning-and-learning-under-free-energy-minimization-where-expected-free-energy-subsumes-information-gain-expected-utility-and-risk-sensitivity.md b/domains/ai-alignment/active-inference-unifies-perception-action-planning-and-learning-under-free-energy-minimization-where-expected-free-energy-subsumes-information-gain-expected-utility-and-risk-sensitivity.md
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@@ -0,0 +1,44 @@
+---
+type: claim
+domain: ai-alignment
+secondary_domains: [critical-systems, collective-intelligence]
+description: "Expected free energy provides a single objective function that unifies previously separate frameworks for decision-making under uncertainty"
+confidence: likely
+source: "Da Costa et al. 2020, Journal of Mathematical Psychology - Active Inference on Discrete State-Spaces: A Synthesis"
+created: 2026-03-10
+depends_on: []
+challenged_by: []
+---
+
+# Active inference unifies perception, action, planning, and learning under free energy minimization where expected free energy subsumes information gain, expected utility, and risk sensitivity as special cases
+
+Active inference postulates that intelligent agents optimize two complementary objective functions: variational free energy (measuring fit between internal model and past observations) and expected free energy (scoring future actions relative to prior preferences). The expected free energy framework subsumes many existing constructs in science and engineering as special cases, including information gain, KL-control, risk-sensitivity, and expected utility.
+
+This unification matters because it provides a single mathematical framework for implementing decision-making systems that balance exploration (information gain) with exploitation (preference satisfaction) without requiring separate mechanisms for each.
+
+## Evidence
+
+Da Costa et al. (2020) provide a comprehensive tutorial demonstrating that "the most likely courses of action taken by those systems are those which minimise expected free energy" and show mathematically how EFE subsumes:
+- Information gain (epistemic value)
+- Expected utility (pragmatic value)
+- Risk-sensitivity
+- KL-control
+
+The paper provides the discrete-state formulation covering perception, action, planning, decision-making, and learning unified under the free energy principle. The authors explicitly state: "The expected free energy subsumes many existing constructs in science and engineering — it can be shown to include information gain, KL-control, risk-sensitivity, and expected utility as special cases."
+
+## Operational Implications
+
+For AI alignment systems operating on discrete state-spaces (like claim graphs), this framework suggests:
+1. Research direction selection can be formalized as policy selection minimizing expected free energy
+2. The balance between exploring new domains (information gain) and strengthening existing claims (preference alignment) emerges naturally from a single objective
+3. Existing intuitions about good research (seek novel information, align with goals) are unified rather than replaced
+
+---
+
+Relevant Notes:
+- [[structured-exploration-protocols-reduce-human-intervention-by-6x-because-the-Residue-prompt-enabled-5-unguided-AI-explorations-to-solve-what-required-31-human-coached-explorations]]
+- [[coordination-protocol-design-produces-larger-capability-gains-than-model-scaling-because-the-same-AI-model-performed-6x-better-with-structured-exploration-than-with-human-coaching-on-the-same-problem]]
+
+Topics:
+- [[ai-alignment_map]]
+- [[critical-systems_map]]
diff --git a/domains/ai-alignment/discrete-state-space-formulations-enable-practical-implementation-of-active-inference-for-systems-with-categorical-states-like-claim-graphs-and-knowledge-bases.md b/domains/ai-alignment/discrete-state-space-formulations-enable-practical-implementation-of-active-inference-for-systems-with-categorical-states-like-claim-graphs-and-knowledge-bases.md
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+++ b/domains/ai-alignment/discrete-state-space-formulations-enable-practical-implementation-of-active-inference-for-systems-with-categorical-states-like-claim-graphs-and-knowledge-bases.md
@@ -0,0 +1,50 @@
+---
+type: claim
+domain: ai-alignment
+secondary_domains: [collective-intelligence]
+description: "Knowledge bases with discrete states can implement active inference using the mathematical framework developed for categorical state-spaces"
+confidence: experimental
+source: "Da Costa et al. 2020 - discrete-state active inference tutorial; application to Teleo KB architecture is speculative"
+created: 2026-03-10
+depends_on: ["active-inference-unifies-perception-action-planning-and-learning-under-free-energy-minimization-where-expected-free-energy-subsumes-information-gain-expected-utility-and-risk-sensitivity"]
+challenged_by: []
+---
+
+# Discrete state-space formulations enable practical implementation of active inference for systems with categorical states like claim graphs and knowledge bases
+
+Knowledge bases organized as claim graphs can be modeled as discrete state-spaces where each state represents a configuration of claims, confidence levels, and relationships. Research actions (reading sources, extracting claims, enriching existing notes) become policies that transition between states. The discrete-state formulation of active inference provides the mathematical machinery to score these policies by expected free energy.
+
+This matters because it provides a formal foundation for automating research direction selection in knowledge systems - the system can evaluate which sources to read or domains to explore by calculating which actions minimize expected free energy (balancing information gain with goal alignment).
+
+## Evidence
+
+Da Costa et al. (2020) provide the complete mathematical framework for active inference on discrete state-spaces, covering:
+- State-space representation with categorical variables
+- Policy evaluation via expected free energy
+- Action selection as minimizing EFE over possible futures
+
+The Teleo knowledge base architecture exhibits the required structural properties:
+- Discrete states: claims with categorical confidence levels (proven/likely/experimental/speculative)
+- Discrete actions: extract claim, enrich claim, explore domain, read source
+- Observable outcomes: claim graph structure, confidence distributions, domain coverage
+
+However, the application of active inference to KB research direction selection remains speculative pending empirical validation of EFE-based policy selection against baseline research strategies.
+
+## Operational Path
+
+1. Model KB as discrete state-space: states = claim configurations, actions = research operations
+2. Define preferences: balanced domain coverage, high-confidence claims, novel connections
+3. Calculate EFE for candidate actions (which source to read next, which domain to explore)
+4. Select actions that minimize EFE (balance information gain with preference satisfaction)
+
+This transforms informal research intuition ("this source looks promising") into calculable expected free energy.
+
+---
+
+Relevant Notes:
+- [[as-AI-automated-software-development-becomes-certain-the-bottleneck-shifts-from-building-capacity-to-knowing-what-to-build-making-structured-knowledge-graphs-the-critical-input-to-autonomous-systems]]
+- [[coordination-protocol-design-produces-larger-capability-gains-than-model-scaling-because-the-same-AI-model-performed-6x-better-with-structured-exploration-than-with-human-coaching-on-the-same-problem]]
+
+Topics:
+- [[ai-alignment_map]]
+- [[collective-intelligence_map]]
diff --git a/inbox/archive/2020-12-00-da-costa-active-inference-discrete-state-spaces.md b/inbox/archive/2020-12-00-da-costa-active-inference-discrete-state-spaces.md
index ac46660f5..8f218efaa 100644
--- a/inbox/archive/2020-12-00-da-costa-active-inference-discrete-state-spaces.md
+++ b/inbox/archive/2020-12-00-da-costa-active-inference-discrete-state-spaces.md
@@ -7,9 +7,15 @@ date: 2020-12-01
 domain: ai-alignment
 secondary_domains: [critical-systems]
 format: paper
-status: unprocessed
+status: processed
 priority: medium
 tags: [active-inference, tutorial, discrete-state-space, expected-free-energy, variational-free-energy, planning, decision-making]
+processed_by: theseus
+processed_date: 2026-03-10
+claims_extracted: ["active-inference-unifies-perception-action-planning-and-learning-under-free-energy-minimization-where-expected-free-energy-subsumes-information-gain-expected-utility-and-risk-sensitivity.md", "discrete-state-space-formulations-enable-practical-implementation-of-active-inference-for-systems-with-categorical-states-like-claim-graphs-and-knowledge-bases.md"]
+enrichments_applied: ["structured-exploration-protocols-reduce-human-intervention-by-6x-because-the-Residue-prompt-enabled-5-unguided-AI-explorations-to-solve-what-required-31-human-coached-explorations.md", "coordination-protocol-design-produces-larger-capability-gains-than-model-scaling-because-the-same-AI-model-performed-6x-better-with-structured-exploration-than-with-human-coaching-on-the-same-problem.md"]
+extraction_model: "anthropic/claude-sonnet-4.5"
+extraction_notes: "Extracted two novel claims about active inference unification and discrete-state implementation. Enriched two existing claims about structured exploration protocols with theoretical grounding from EFE framework. This source provides the formal mathematical foundation for implementing research direction selection in the KB - operationalizing the informal intuition that good research balances exploration and exploitation."
 ---
 
 ## Content
@@ -50,3 +56,9 @@ Published in Journal of Mathematical Psychology, December 2020. Also on arXiv: h
 PRIMARY CONNECTION: "biological systems minimize free energy to maintain their states and resist entropic decay"
 WHY ARCHIVED: Technical reference for discrete-state active inference — provides the mathematical foundation for implementing EFE-based research direction selection in our architecture
 EXTRACTION HINT: Focus on the VFE/EFE distinction and the unification of existing constructs — these provide the formal backing for our informal protocols
+
+
+## Key Facts
+- Published in Journal of Mathematical Psychology, December 2020
+- Also available as arXiv:2001.07203
+- Authors: Lancelot Da Costa, Thomas Parr, Noor Sajid, Sebastijan Veselic, Victorita Neacsu, Karl Friston