Echo: Learning from Experience Data via User-Driven Refinement
Static "human data" faces inherent limitations: it is expensive to scale and bounded by the knowledge of its creators. Continuous learning from "experience data" - interactions between agents and their environments - promises to transcend these barriers. Today, the widespread deployment of AI agents grants us low-cost access to massive streams of such real-world experience. However, raw interaction logs are inherently noisy, filled with trial-and-error and low information density, rendering them
Record details
Published: 21 May 2026
Source: arXiv
Category: Research
Topics: Agents & autonomy · Environment
Retrieved: 14 July 2026
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ethics.ai (21 May 2026), “Echo: Learning from Experience Data via User-Driven Refinement,” evidence record 3938, https://ethics.ai/record/3938 (originally published by arXiv).
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