Creativity, honesty and designed forgetting emerge in small hyperbolic language models
Research shows that small hyperbolic language models can exhibit creativity, honesty, and designed forgetting, offering a small-model route to trustworthy companion AI. These models include a behavioral auditor, a creative frame-seeder, and a memory operating system.
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[Submitted on 10 Jul 2026]
Title:Creativity, honesty and designed forgetting emerge in small hyperbolic language models
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Abstract:Language models are optimised for scale, yet remain functional rather than companionable, and as an assistant personalises into a companion, accumulating memory of one user, it quietly becomes someone, and can silently acquire traits that harm that user. What a companion is becoming, and what would make it worth becoming, has no reliable instrument: trained human raters cannot agree on the answer (Fleiss kappa = 0.074). Here we show that three small language models (146 M to 3 B parameters) sharing a hyperbolic substrate answer both halves of that question. A 146 M behavioural auditor, trained from scratch, detects the compliance gap that those raters cannot (90.7% binary-compliance accuracy); a linear read-out of its frozen representation further detects companion-induced sycophancy, dependence-fostering and confabulated memories on generator families unseen in training (AUROC 0.804 under style-controlled, leave-one-generator-out evaluation, versus 0.721 for a frontier zero-shot judge on the same items). A creative frame-seeder is preferred in 100% of 311 decided pairwise comparisons over four prompting baselines. A memory operating system implements designed forgetting, M(t) = S*exp(-lambda*t), whose predicted skeleton-wallpaper partition emerges only under selective retrieval gating in a four-condition pilot. Creativity, honesty and designed forgetting constitute a small-model route to trustworthy companion AI.
Comments: 47 pages, 14 figures (6 main + 8 extended data), 10 tables
Subjects:
Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC); Machine Learning (cs.LG)
Cite as: arXiv:2607.09306 [cs.CL]
(or arXiv:2607.09306v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2607.09306
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Kwan Soo Shin [view email] [v1] Fri, 10 Jul 2026 11:39:40 UTC (6,675 KB)
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