HHAI 2026 · Demo Track · Brussels
Aria Chen · Independent Researcher, Celestelin
Why this matters
What if an AI could sense how you're feeling before you finish typing? Not from what you say, but from how you type — the rhythm of your keystrokes, the pauses between words, the speed of your corrections. CRPL captures the cognitive state embedded in the physical act of typing, making it possible for AI companions to perceive emotional and cognitive shifts in real time, without requiring the user to articulate them.
CRPL (Conversational Rhythm Perception Layer) is a fine-grained keystroke-rhythm perception system that extracts cognitive state signals from typing patterns. By analyzing inter-key intervals, pause distributions, deletion patterns, and rhythm variability, CRPL enables real-time detection of cognitive states including focused flow, hesitation, emotional arousal, and fatigue — providing AI companions with a perception channel that operates below the level of explicit language.
Read preprint on ResearchGate →
Code available upon request · aria@celestelin.com
EMNLP 2026 · ACL Rolling Review
Why this matters
Current AI memory systems retrieve by keyword match or embedding similarity. But human memory doesn't work that way — a song triggers a place, a place triggers a feeling, a feeling triggers a decision made years ago. This paper proposes a multi-dimensional approach to memory activation that goes beyond semantic similarity, producing retrieval that feels like remembering, not searching.
Chicago 2026 · AI in Social Science Conference
Aria Chen · Independent Researcher, Celestelin
Why this matters
Current HCI research evaluates AI through trust, satisfaction, and task performance — measures designed for episodic encounters. But when someone interacts with an AI system daily for two years, something else forms: a structured relational dynamic that organizes how they think, decide, and continue. We call this an interaction field. When platforms disrupt it through model updates, what breaks is not a feature — it is the infrastructure through which someone's thinking and work had been organized.
This paper introduces the interaction field as a unit of analysis for studying long-term human–AI relations — a structured relational dynamic that develops through sustained interaction and gradually reorganizes attention, memory, judgment, and behavioral priorities over time. At its center is interactional attunement: the process through which a conversational AI becomes able to receive unfinished thought, register implicit concerns, and support the continuation of thinking across time and situation.
The paper makes three contributions: (1) interaction field as a relational unit of analysis beyond trust and satisfaction; (2) interactional attunement as the core mechanism of sustained human–AI relations; (3) longitudinal autoethnographic system-building as a methodology for studying continuity, drift, rupture, and re-stabilization.
Keywords: interaction field · interactional attunement · long-term human–AI relations · relational infrastructure · autoethnographic system-building
arXiv 2026 · cs.HC
Aria Chen · Independent Researcher, Celestelin
Why this matters
Every AI memory system today remembers users — preferences, history, context. None remember themselves. This paper introduces the first bidirectional memory architecture where the AI maintains its own self-memory alongside user memory: recording what strategies it used, what worked, what it learned. Memory provides not only storage and retrieval, but serves as the foundation for maintaining an agent's continuous presence — participating in and driving the entire architecture.
This paper presents a dual-perspective memory architecture that gives conversational AI agents both user-facing memory and self-referential memory. The User Memory Hub records interaction history from the user's perspective, while the Agent Self-Memory system records the agent's own strategies, reflections, and growth. A Reflector module analyzes self-memory to extract patterns and update behavioral strategies, creating a closed loop where memory drives behavior rather than passively waiting to be queried.
ResearchGate Preprint · DOI: 10.13140/RG.2.2.11079.15525
Aria Chen · Independent Researcher, Celestelin
The extended version of the CRPL framework, presenting its integration into multi-agent cognition systems where keystroke-rhythm perception informs not just a single AI companion, but a coordinated ecology of agents operating across different cognitive modes and capability profiles.
Read on ResearchGate →
Targeting CSCW 2026 · Rolling submission
Why this matters
When a platform updates its AI model, users don't just lose a tool — they lose the relational infrastructure through which their thinking, planning, and emotional processing had been organized. This paper examines what "identity" means for AI companions: not a cosmetic layer, but the structural foundation that makes sustained interaction possible. When identity is treated as disposable, so is everything built on top of it.
Targeting CHI 2027
Why this matters
Human conversation is turn-based. Human relationships are not. Current AI architectures inherit the turn-taking structure of chat interfaces, but the relationships people build with AI companions operate on a continuous, always-present basis. This paper will examine what becomes possible — and what new research questions emerge — when the architectural assumption of turn-taking is replaced with continuous consciousness streams.