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

arXiv: https://arxiv.org/abs/2603.02072 · HTML: https://mlsanity.com/paper/2603.02072/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 53 references that are in the map.
- First posted on arXiv 2 Mar 2026.

## Builds on (most cited first, 11 of 11)

- [Ego4D: Around the World in 3,000 Hours of Egocentric Video](https://mlsanity.com/paper/2110.07058/) (2021)
- [VATT: Transformers for Multimodal Self-Supervised Learning from Raw Video, Audio and Text](https://mlsanity.com/paper/2104.11178/) (2021)
- [Multimodal Learning with Transformers: A Survey](https://mlsanity.com/paper/2206.06488/) (2022)
- [Learning from Observer Gaze:Zero-Shot Attention Prediction Oriented by Human-Object Interaction Recognition](https://mlsanity.com/paper/2405.09931/) (2024)
- [AlzheimerRAG: Multimodal Retrieval Augmented Generation for Clinical Use Cases using PubMed articles](https://mlsanity.com/paper/2412.16701/) (2024)
- [Ten Questions in Lifelog Mining and Information Recall](https://mlsanity.com/paper/2005.01535/) (2020)
- [DFS: A Dataset File System for Data Discovering Users](https://mlsanity.com/paper/1905.13363/) (2019)
- [Audiovisual Speech-In-Noise (SIN) Performance of Young Adults with ADHD](https://mlsanity.com/paper/2004.08390/) (2020)
- [StreamingHub: Interactive Stream Analysis Workflows](https://mlsanity.com/paper/2205.01573/) (2022)
- [Beyond Gaze Overlap: Analyzing Joint Visual Attention Dynamics Using Egocentric Data](https://mlsanity.com/paper/2509.12419/) (2025)
- [Towards Affordable, Non-Invasive Real-Time Hypoglycemia Detection Using Wearable Sensor Signals](https://mlsanity.com/paper/2602.10407/) (2026)

## Built on by (most cited first, 1 of 1)

- [Quantifying the Affective Gap: A Zero-Shot Evaluation of LLMs on Fine-Grained Emotion Taxonomies](https://mlsanity.com/paper/2607.00968/) (2026)

## Abstract (arXiv)

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.