S-EMBER: A Large-Scale Benchmark for Streaming Egocentric Memory Retrieval

📰 ArXiv cs.AI

Learn about S-EMBER, a benchmark for streaming egocentric memory retrieval, and how it enables AI assistants to reason across long time horizons

advanced Published 7 Jul 2026

Action Steps

Build a streaming egocentric memory retrieval model using S-EMBER as a benchmark
Run experiments on S-EMBER to evaluate the performance of AI assistants in recalling past experiences
Configure your model to handle continuous first-person recording data
Test your model's ability to reason across long time horizons
Apply S-EMBER to real-world applications such as wearable devices and AI assistants

Who Needs to Know This

AI researchers and engineers working on episodic memory and wearable intelligence can benefit from this benchmark to evaluate and improve their models

Key Insight

💡 S-EMBER enables AI assistants to reason across long time horizons and recall past experiences in a streaming setting

Key Takeaways

Learn about S-EMBER, a benchmark for streaming egocentric memory retrieval, and how it enables AI assistants to reason across long time horizons

Full Article

Title: S-EMBER: A Large-Scale Benchmark for Streaming Egocentric Memory Retrieval

Abstract:
arXiv:2607.02689v1 Announce Type: cross Abstract: As wearable devices enable continuous first-person recording, AI assistants must reason across long time horizons to recall past experiences-a capability known as episodic memory. Current benchmarks often rely on offline evaluation with access to entire video files, failing to simulate the streaming reality of wearable intelligence. We introduce S-EMBER (Streaming Egocentric Memory Benchmark for Episodic Retrieval), a large-scale benchmark compri

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