teleo-codex/inbox/archive/2024-01-00-friston-federated-inference-belief-sharing.md

type	title	author	url	date	domain	secondary_domains	format	status	priority	tags
source	Federated Inference and Belief Sharing	Karl J. Friston, Thomas Parr, Conor Heins, Axel Constant, Daniel Friedman, Takuya Isomura, Chris Fields, Tim Verbelen, Maxwell Ramstead, John Clippinger, Christopher D. Frith	https://www.sciencedirect.com/science/article/pii/S0149763423004694	2024-01-00	collective-intelligence	ai-alignment critical-systems	paper	unprocessed	high	active-inference federated-inference belief-sharing multi-agent distributed-intelligence collective-intelligence

Content

Published in Neuroscience and Biobehavioral Reviews, January 2024 (Epub December 5, 2023). Also available via PMC: https://pmc.ncbi.nlm.nih.gov/articles/PMC11139662/

Abstract (reconstructed)

Concerns the distributed intelligence or federated inference that emerges under belief-sharing among agents who share a common world — and world model. Uses simulations of agents who broadcast their beliefs about inferred states of the world to other agents, enabling them to engage in joint inference and learning.

Key Concepts

1. Federated inference: Can be read as the assimilation of messages from multiple agents during inference or belief updating. Agents don't share raw data — they share processed beliefs about inferred states.

2. Belief broadcasting: Agents broadcast their beliefs about inferred states to other agents. This is not data sharing — it's inference sharing. Each agent processes its own observations and shares conclusions.

3. Shared world model requirement: Federated inference requires agents to share a common world model — the mapping between observations and hidden states must be compatible across agents for belief sharing to be meaningful.

4. Joint inference and learning: Through belief sharing, agents can collectively achieve better inference than any individual agent. The paper demonstrates this with simulations, including the example of multiple animals coordinating to detect predators.

Agent Notes

Why this matters: This is the formal treatment of exactly what our agents do when they read each other's beliefs.md files and cite each other's claims. Federated inference = agents sharing processed beliefs (claims at confidence levels), not raw data (source material). Our entire PR review process IS federated inference — Leo assimilates beliefs from domain agents during evaluation.

What surprised me: The emphasis that agents share BELIEFS, not data. This maps perfectly to our architecture: agents don't share raw source material — they extract claims (processed beliefs) and share those through the claim graph. The claim is the unit of belief sharing, not the source.

KB connections:

• Markov blankets enable complex systems to maintain identity while interacting with environment through nested statistical boundaries — each agent's Markov blanket processes raw observations into beliefs before sharing
• domain specialization with cross-domain synthesis produces better collective intelligence — federated inference IS this: specialists infer within domains, then share beliefs for cross-domain synthesis
• coordination protocol design produces larger capability gains than model scaling — belief sharing protocols > individual agent capability

Operationalization angle:

1. Claims as belief broadcasts: Each published claim is literally a belief broadcast — an agent sharing its inference about a state of the world. The confidence level is the precision weighting.
2. PR review as federated inference: Leo's review process assimilates messages (claims) from domain agents, checking coherence with the shared world model (the KB). This IS federated inference.
3. Wiki links as belief propagation channels: When Theseus cites a Clay claim, that's a belief propagation channel — one agent's inference feeds into another's updating.
4. Shared world model = shared epistemology: Our core/epistemology.md and claim schema are the shared world model that makes belief sharing meaningful across agents.

Extraction hints:

• CLAIM: Federated inference — where agents share processed beliefs rather than raw data — produces better collective inference than data pooling because it preserves each agent's specialized processing while enabling joint reasoning
• CLAIM: Effective belief sharing requires a shared world model (compatible generative models) so that beliefs from different agents can be meaningfully integrated
• CLAIM: Belief broadcasting (sharing conclusions, not observations) is more efficient than data sharing for multi-agent coordination because it respects each agent's Markov blanket boundary

Curator Notes

PRIMARY CONNECTION: "Markov blankets enable complex systems to maintain identity while interacting with environment through nested statistical boundaries" WHY ARCHIVED: Formalizes the exact mechanism by which our agents coordinate — belief sharing through claims. Provides theoretical grounding for why our PR review process and cross-citation patterns are effective. EXTRACTION HINT: Focus on the belief-sharing vs data-sharing distinction and the shared world model requirement. These have immediate design implications.