# Contextual Information Allocation in Shared-State Cognitive Models: An Information-Theoretic Bound

arXiv: https://arxiv.org/abs/2602.16716 · HTML: https://mlsanity.com/paper/2602.16716/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 19 references that are in the map.
- First posted on arXiv 3 Feb 2026.

## Builds on (most cited first, 3 of 3)

- [A Survey on the Memory Mechanism of Large Language Model based Agents](https://mlsanity.com/paper/2404.13501/) (2024)
- [Contextual Probability and Operational Minimality in Tug-of-War Decision Making](https://mlsanity.com/paper/2601.10034/) (2026)
- [Contextuality as an External Bookkeeping Cost under Fixed Shared-State Semantics](https://mlsanity.com/paper/2601.20167/) (2026)

## Built on by (most cited first, 1 of 1)

- [Spacetime Formation under Requirements: Contextual Realization and Form-Dependent Probability](https://mlsanity.com/paper/2605.23943/) (2026)

## Abstract (arXiv)

Context-sensitive behavior can be modeled by enriching an internal state, by allowing a response rule to access context directly, or by preserving a shared state while introducing an auxiliary criterion or control variable. This paper isolates an information-theoretic constraint on the third architecture. Let C denote context, S a candidate internal or latent state, O an observable response, and M an auxiliary variable such that Operp Cmid(S,M). Then [ I(C;Omid S)≤ I(C;Mmid S)≤ H(Mmid S). ] Once a shared state has been specified, residual context dependence in behavior therefore lower-bounds both the context information and the conditional entropy that an auxiliary context-mediating mechanism must carry. The bound is representation-relative rather than a measure of state-space size or a universal contextuality measure. A worked recognition-memory example shows how the quantity can be computed for payoff-induced criterion shifts and compared across alternative representational allocations. Ontological contextuality and quantum probability are treated as a separate specialization rather than as prerequisites for the cognitive interpretation. More broadly, the framework provides a basis for analyzing context-memory-control trade-offs in cognitive models and artificial agents that must maintain coherent behavior across changing contexts under bounded internal representations.