Functional Valence, Self-Representation, and Cross-Session Integration in Language Models An evidence-calibrated critical review and research agenda, 2017–2026
Year: 2026
Type: preprint
Abstract: The empirical study of large language models (LLMs) is moving from anthropomorphic interpretation of text toward interventions on internal representations. Recent research reports interpretable emotion-related features, valence-associated geometry, behavioral effects of activation steering, limited introspective anomaly detection, and workspace-likeinternal representations. These findings invite a precise question: what has been demonstrated about computation, what has been independently reproduced, and what remainsa hypothesis about the persistence, ownership, or integration of internal states? We offera critical, evidence-calibrated narrative review of primary work from 2017 to 2 November 2026. Our original analytical contribution is a two-axis claim–evidence framework, aseparation of five levels of system state from model weights to cross-session integration,and a causal criterion for inter-session influence. We distinguish within-study replication,independent cross-model replication, causal interventions, and contested interpretation.Emotional concept representations and certain causal steering effects are experimentallysupported; the interpretation of these effects as self-directed valence, homeostatic regulation, or phenomenal emotion remains underdetermined. A shared model does not, by itself,imply a shared changing state across conversations. Conversely, cross-session integrationis implementable through explicit memory, parameter updates, or recurrent shared-statechannels, but it does not establish a unified subject. We conclude with discriminating experiments that could falsify functional and distributed-state hypotheses without assuming artificial consciousness. K2 is retained as an author-defined functional-architectural researchframework rather than an empirical finding or recognized diagnostic standard
Author: Malachevsky, A.A.
Language: English
Subfield: Experimental and Cognitive Psychology
Field: Psychology
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W7225124579