Private Etymology: Designing Relational Reuse of Shared Symbols in Long-Term Human-AI Interaction

• URL: http://arxiv.org/abs/2608.08443v2
• Date: Thu, 13 Aug 2026 09:48:15 GMT
• Title: Private Etymology: Designing Relational Reuse of Shared Symbols in Long-Term Human-AI Interaction
• Abstract summary: Private Etymology is a machine-representable relational provenance.<n>It records how a dyad-specific symbolic expression is proposed, interpreted, negotiated, repaired, reused, revised, stabilized, contested, forgotten, or retired over time.<n>Private Etymology is proposed as infrastructure for conversational agents to participate in changing relational microcultures.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Previous studies have shown that people can develop shared symbols, partner-specific expressions, personal idioms, inside jokes, and other parts of a relational microculture. Recent work has also examined how humans and conversational AI negotiate and revise symbolic meanings. However, long-term human-AI systems still lack a clear design model for recording how a dyad-specific expression gains meaning, checking whether both sides still accept that meaning, and safely reusing the expression in later sessions. This concept-and-prototype paper introduces Private Etymology, a machine-representable relational provenance that records how a dyad-specific symbolic expression is proposed, interpreted, negotiated, repaired, reused, revised, stabilized, contested, forgotten, or retired over time. I also propose relational reuse: reactivating a dyad-specific expression in a later session without fully explaining its meaning again. The contribution is not the invention of shared symbols or relational microcultures. Instead, this paper integrates prior ideas into persistent, revisable, and evidence-grounded symbolic units for human-AI relationships. I present a lifecycle model, an illustrative machine-readable schema, a working Apple Watch prototype, and a longitudinal research agenda. In the prototype, a language model classifies discrete conversational evidence, while deterministic local code decides whether a Shared Symbol can be updated. This prevents a free-form model confidence score or an AI proposal by itself from directly updating the persisted symbol. Private Etymology is proposed as infrastructure for conversational agents to participate in changing relational microcultures without inventing their origins or treating relational meaning as a fixed memory value.

Related papers

• Language Has Two Parameters: Narrative-Induced Semantic Plasticity and Phase-Sensitive Interpretation [0.0]
  This paper proposes that encounters alter persistent relations among simultaneously active meanings.<n>It argues that future AI agents will need persistent semantic states indexed to particular individuals and relationships.
  arXiv  Detail & Related papers  (2026-08-18T17:34:25Z)
• Recon: Reconstruction-Guided Reasoning Synthesis for User Modeling [30.893319122518587]
  We propose Recon, which uses action reconstruction to score reasoning traces by their predictive power.<n>We show that Recon-synthesized reasoning transfers across models, and improves user modeling beyond the reconstruction model.
  arXiv  Detail & Related papers  (2026-05-26T12:55:11Z)
• PERMA: Benchmarking Personalized Memory Agents via Event-Driven Preference and Realistic Task Environments [72.02445514666428]
  We introduce PERMA, a benchmark designed to evaluate persona consistency over time beyond static preference recall.<n>PerMA consists of temporally ordered interaction events spanning multiple sessions and domains, with preference-related queries inserted over time.<n> Experiments demonstrate that by linking related interactions, advanced memory systems can extract more precise preferences and reduce token consumption.
  arXiv  Detail & Related papers  (2026-03-24T14:04:11Z)
• Learning-Infused Formal Reasoning: From Contract Synthesis to Artifact Reuse and Formal Semantics [0.0]
  Vision paper articulates a long-term research agenda for formal methods at the intersection with artificial intelligence.<n>It advances a forward-looking perspective on the next generation of formal methods based on the integration of automated contract synthesis, semantic artifact reuse, and refinement-based theory.
  arXiv  Detail & Related papers  (2026-02-02T22:39:02Z)
• How Notations Evolve: A Historical Analysis with Implications for Supporting User-Defined Abstractions [27.219003138635728]
  Recent AI systems promise a new set of informal interactions with computers through natural language and other notational forms.<n>These informal interactions can then lead to formal representations, but depend upon pre-existing formalisms known to both humans and AI.<n>We conduct a comparative historical analysis of notation development to identify some relevant characteristics.
  arXiv  Detail & Related papers  (2026-02-02T01:38:05Z)
• Priors in Time: Missing Inductive Biases for Language Model Interpretability [58.07412640266836]
  We show that Sparse Autoencoders impose priors that assume independence of concepts across time, implying stationarity.<n>We introduce a new interpretability objective -- Temporal Feature Analysis -- which possesses a temporal inductive bias to decompose representations at a given time into two parts.<n>Our results underscore the need for inductive biases that match the data in designing robust interpretability tools.
  arXiv  Detail & Related papers  (2025-11-03T18:43:48Z)
• SaFiRe: Saccade-Fixation Reiteration with Mamba for Referring Image Segmentation [58.80001825332851]
  Referring Image (RIS) aims to segment the target object in an image given a natural language expression.<n>Recent methods predominantly focus on simple expressions like "red car" or "left girl"
  arXiv  Detail & Related papers  (2025-10-11T10:50:58Z)
• Modeling Open-World Cognition as On-Demand Synthesis of Probabilistic Models [93.1043186636177]
  We explore the hypothesis that people use a combination of distributed and symbolic representations to construct bespoke mental models tailored to novel situations.<n>We propose a computational implementation of this idea -- a Model Synthesis Architecture''<n>We evaluate our MSA as a model of human judgments on a novel reasoning dataset.
  arXiv  Detail & Related papers  (2025-07-16T18:01:03Z)
• SymbolicThought: Integrating Language Models and Symbolic Reasoning for Consistent and Interpretable Human Relationship Understanding [21.30887121514266]
  SymbolicThought is a human-in-the-loop framework that combines large language models with symbolic reasoning.<n>It constructs editable character relationship graphs, refines them using seven types of logical constraints, and enables real-time validation and conflict resolution.<n> Experiments show that SymbolicThought improves annotation accuracy and consistency while significantly reducing time cost.
  arXiv  Detail & Related papers  (2025-07-05T23:46:35Z)
• Emergent Graphical Conventions in a Visual Communication Game [80.79297387339614]
  Humans communicate with graphical sketches apart from symbolic languages. We take the very first step to model and simulate such an evolution process via two neural agents playing a visual communication game. We devise a novel reinforcement learning method such that agents are evolved jointly towards successful communication and abstract graphical conventions.
  arXiv  Detail & Related papers  (2021-11-28T18:59:57Z)
• Prototypical Representation Learning for Relation Extraction [56.501332067073065]
  This paper aims to learn predictive, interpretable, and robust relation representations from distantly-labeled data. We learn prototypes for each relation from contextual information to best explore the intrinsic semantics of relations. Results on several relation learning tasks show that our model significantly outperforms the previous state-of-the-art relational models.
  arXiv  Detail & Related papers  (2021-03-22T08:11:43Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.