claim

active

claim:causal-emergence-may-be-a-previously-undisclosed-axis-of-reorganization-of-neural-representations-in-rl-agents

Causal emergence may be a previously undisclosed axis of reorganization of neural representations in RL agents.

Authors' interpretive assertion that the observed alignment reveals a novel organizing principle of neural representation dynamics.

Source paper

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The Causally Emergent Alignment Hypothesis: Causal Emergence Aligns with and Predicts Final Reward in Reinforcement Learning Agents

(2026) · Federico Pigozzi · Michael Levin

Neighborhood — ranked by edge-count

Hypotheses (1)

hypothesis

Causally Emergent Alignment Hypothesis
extends
The hypothesis that successful RL agents will display causal emergence that is predictive of final reward early in training and whose representational dynamics align with reward improvement.

Communities (3)

community

Causal emergence in biological systems
members_of
Examines how macro-scale causal power exceeds micro-scale in living and learning systems.
Causal emergence in learning agents
members_of
Uses effective information (EI) and coarse-graining to link causal emergence with RL and biological learning.
Causal emergence in learning and adaptation
members_of
Framework measuring how coarse-grained causal structure increases during learning across biological and artificial agents, using effective information and interventional methods.

Related by similarity (8)

cosine ≥ 0.65 · no typed edge

Entities in the same semantic neighborhood but without a typed relation to this one — candidates for new edges or unrecognized duplicates.

Causal emergence can enable causal interventions to create better RL agents.claim0.871
Assertion that understanding causal emergence may lead to methods for manipulating agent representations to improve performance.
Biological and artificial agents share causal emergence as an axis of learning and reorganization.claim0.862
Interpretive assertion bridging Levin's biological cognition work with artificial RL; extends 'minds at all scales' thesis.
Successful RL agents exhibit causal emergence that predicts final reward early in training and aligns representational dynamics with reward improvement.hypothesis0.858
Central finding: causal emergence serves as a previously undisclosed axis of neural representation reorganization in learning agents.
Causal emergence predictive of final reward early in RL training across multiple algorithms, architectures, and environments.finding0.852
Empirical result: CE measurements correlate with and predict learning performance in RL agents.
Causal emergence alignment with learning is a shared axis comparing biological and artificial creatures.claim0.849
Assertion that the correlation between causal emergence and learning constitutes another way biological and artificial intelligences converge.
Causal emergence provides new perspectives for causal representation learning, interpreting latent variables as emergent causalities.claim0.838
Cross-fertilization claim made in discussion.
Biological agents increase causal emergence after learning new memories.claim0.827
Prior empirical observation from biological systems; motivates investigation in artificial agents.
successful agents exhibited causal emergence that was consistently predictive of final reward early in training and whose representational dynamics aligned with reward improvement in most tasks.quote0.826
Load-bearing summary of the main empirical finding that anchors the Causally Emergent Alignment Hypothesis.