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Scaling Discovery Requires Forgetting

Paper recorded by Signals 4 on 2026-10-14 in cs.AI. Abstract reproduced from arXiv; link to the original below.

Published 2026-10-14 on arXiv · recorded by Signals 4 on 2026-10-15

Category: cs.AI · 人工智能 · first seen 2026-10-15

Abstract

Automated research with LLM agents has shown promising results in various domains. Through iterative refinement and scored verifiers, LLM agents are capable of discovering new ideas. There are different ways to scale automated research: let one agent iterate for longer, run several agents in parallel, or further let the parallel agents communicate and build on one another's candidates. However, we

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#311 most recent of 960 cs.AI papers we have recorded · ↑ newer: Recovering Reliable Supervision via Critique-Guided On-Policy Distilla · ↓ older: PhySGA: A Physics-Space Grounded Agentic Framework for Generalizable a

Cite this page: Scaling Discovery Requires Forgetting: the #311 most recent of 960 cs.AI papers we have recorded (as of 2026-10-14). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/scaling-discovery-requires-forgetting.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json

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