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Entropy in Conversational AI: Structured Unpredictability as Inferrable Interiority

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Computer Science > Computation and Language

arXiv:2609.19044 (cs)
[Submitted on 20 Jul 2026]

Title:Entropy in Conversational AI: Structured Unpredictability as Inferrable Interiority

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Abstract:Sampling can increase response diversity without producing history-dependent behavior. We formalize a different design target, structured unpredictability, as conditional dependence between an output and a persistent hidden state beyond what an observer can infer from the transcript. A selection layer updates a low-dimensional style-and-attention state from a capacity-limited stream, generates several responses with a fixed base model, and selects for novelty and state affinity. Evaluation uses scripted sequences of independent prompt turns: the base model receives the current turn and rendered state, but not the preceding dialogue; cross-turn dependence resides in the wrapper state and response selector. A synthetic implementation validates the pipeline and matches four prospectively hash-frozen divergence features at point level. In the final real-model grid (mlx-community/Qwen2.5-1.5B-Instruct-4bit; 56 sequences per arm), the mechanism increased lexical novelty over the low-variance and consistency-only controls by 0.073 and 0.023, respectively. Its stylometric-consistency contrast with novelty-matched sampling was equivalent to zero under the registered smallest-effect rule, so the joint novelty-consistency criterion failed. The original two-part accumulation criterion also failed; a revised final-grid contrast, frozen after the powered grid, found higher consistency than the memory-reset ablation (0.028, 95% CI [0.018,0.039]), but does not establish path dependence. Twin separation was not established (0.003, 95% CI [-0.011,0.019]); the mean curve's saturating curvature matched the frozen prediction, which without separation does not support path dependence. Probe-level capability equivalence held within +/-0.10 on a near-ceiling battery, while output quality was not evaluated. All outcomes are machine-scored; no claims about perceived mind or consciousness are tested.
Comments: 18 pages, 5 figures, 6 tables. Ancillary files contain the complete artifact: code, frozen protocol, per-sequence and per-item outcome files, and deterministic analysis scripts. Code archive: this https URL Final-grid pre-registration: this https URL
Subjects: Computation and Language (cs.CL); Human-Computer Interaction (cs.HC)
Cite as: arXiv:2609.19044 [cs.CL]
  (or arXiv:2609.19044v1 [cs.CL] for this version)
  https://doi.org/10.48550/arXiv.2609.19044
arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Sebastian Cochinescu PhD (ABD) [view email]
[v1] Mon, 20 Jul 2026 23:03:28 UTC (1,193 KB)
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