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Learning to Evaluate Before Improving: Automatic Rubric Induction for Automatic Research Agents

Paper recorded by Signals 4 on 2026-08-31 in cs.AI. Abstract reproduced from arXiv; link to the original below.

Published 2026-08-31 on arXiv · recorded by Signals 4 on 2026-09-01

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

Abstract

Autonomous scientific research agents are increasingly applied to end-to-end scientific workflows, including literature review, data analysis, experimentation, and report generation. However, open-ended research tasks often do not clearly specify the analyses, methods, and success criteria required to complete the task. As a result, agents may miss important analyses, use inappropriate methods, or

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#250 most recent of 300 cs.AI papers we have recorded · ↑ newer: Reconciling Process Supervision with Outcome-Based Credit in Agentic P · ↓ older: Scaling Large Reasoning Models beyond Human Supervision: A Path toward
Cite this page: Learning to Evaluate Before Improving: Automatic Rubric Induction for Automatic Research Agents: the #250 most recent of 300 cs.AI papers we have recorded (as of 2026-08-31). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/learning-to-evaluate-before-improving-automatic-rubric-induction-for-automatic-r.html
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