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Wide Learning: Learning to Reach Evidence

Paper recorded by Signals 4 on 2026-08-30 in cs.AI. Abstract reproduced from arXiv; link to the original below.

Published 2026-08-30 on arXiv · recorded by Signals 4 on 2026-09-01

Category: cs.AI · 人工智能 · first seen 2026-09-01

Abstract

Machine learning is usually evaluated after an evidence interface has been fixed. A dataset, sensor suite, query language, action set, or experimental protocol determines which observations can be obtained, and learning is judged by what it extracts from them. We study a complementary capability. A learner's state can determine which evidence-generating experiments it can reliably realise under bo

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#270 most recent of 300 cs.AI papers we have recorded · ↑ newer: LLMs Interpret, Embeddings Organize, Graphs Emerge: Agent-Driven Compi · ↓ older: Towards a Systems Foundation for Agentic Skills: Architecture, Lifecyc
Cite this page: Wide Learning: Learning to Reach Evidence: the #270 most recent of 300 cs.AI papers we have recorded (as of 2026-08-30). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/wide-learning-learning-to-reach-evidence.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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