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Cognitive Prosthetic: An AI-Enabled Multimodal System for Episodic Recall in Knowledge Work

arXiv:2603.02072v1h-index: 17
Originality Synthesis-oriented
AI Analysis

This addresses episodic recall challenges for knowledge workers, but it is incremental as it builds on existing multimodal and retrieval concepts without claiming broad SOTA gains.

The paper tackles the problem of episodic memory strain in knowledge work by developing the Cognitive Prosthetic Multimodal System (CPMS), an AI-enabled proof-of-concept that integrates speech, physiological, and gaze data into queryable episodic records for natural language retrieval, demonstrating technical feasibility with a modular and privacy-aware architecture.

Modern knowledge workplaces increasingly strain human episodic memory as individuals navigate fragmented attention, overlapping meetings, and multimodal information streams. Existing workplace tools provide partial support through note-taking or analytics but rarely integrate cognitive, physiological, and attentional context into retrievable memory representations. This paper presents the Cognitive Prosthetic Multimodal System (CPMS) --an AI-enabled proof-of-concept designed to support episodic recall in knowledge work through structured episodic capture and natural language retrieval. CPMS synchronizes speech transcripts, physiological signals, and gaze behavior into temporally aligned, JSON-based episodic records processed locally for privacy. Beyond data logging, the system includes a web-based retrieval interface that allows users to query past workplace experiences using natural language, referencing semantic content, time, attentional focus, or physiological state. We present CPMS as a functional proof-of-concept demonstrating the technical feasibility of transforming heterogeneous sensor data into queryable episodic memories. The system is designed to be modular, supporting operation with partial sensor configurations, and incorporates privacy safeguards for workplace deployment. This work contributes an end-to-end, privacy-aware architecture for AI-enabled memory augmentation in workplace settings.

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