Spectral partitioning on pairwise mutual-information graphs from agent hidden states detects representational coalitions that behavioral measures miss in multi-agent AI.
From the Phenomenology to the Mechanisms of Consciousness: Integrated Information Theory 3.0
5 Pith papers cite this work. Polarity classification is still indexing.
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Information defined as maximum-caliber deviation derives IIT 3.0 cause-effect repertoires from constrained entropy maximization and equates to prediction error under CLT and LDT.
Introduces four synergy-based measures of spacetime integration from partial information decomposition and finds them more suitable than current IIT practice for simple deterministic networks.
Proposes a body-grounded perspective model for AI agents using interoceptive viability signals, a Fisher-style metric on fused states, and conative alignment to produce stable body-directed behavior in a reward-free gridworld.
Introduces intrinsic difference from maximal specification to assess cause-effect repertoire availability, establishing a necessary differentiation-specification tradeoff for intrinsic existence in IIT.
citing papers explorer
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Hidden Coalitions in Multi-Agent AI: A Spectral Diagnostic from Internal Representations
Spectral partitioning on pairwise mutual-information graphs from agent hidden states detects representational coalitions that behavioral measures miss in multi-agent AI.
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Information as Maximum-Caliber Deviation: A bridge between Integrated Information Theory and the Free Energy Principle
Information defined as maximum-caliber deviation derives IIT 3.0 cause-effect repertoires from constrained entropy maximization and equates to prediction error under CLT and LDT.
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Quantifying Spacetime Integration across a Partition with Synergy
Introduces four synergy-based measures of spacetime integration from partial information decomposition and finds them more suitable than current IIT practice for simple deterministic networks.
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Body-Grounded Perspective Formation and Conative Attunement in Artificial Agents
Proposes a body-grounded perspective model for AI agents using interoceptive viability signals, a Fisher-style metric on fused states, and conative alignment to produce stable body-directed behavior in a reward-free gridworld.
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Intrinsic cause-effect power: the tradeoff between differentiation and specification
Introduces intrinsic difference from maximal specification to assess cause-effect repertoire availability, establishing a necessary differentiation-specification tradeoff for intrinsic existence in IIT.