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arXiv cs.AI· Weihan Li, Tianshi Zheng, Yangqiu Song, Ginny Y. Wong, Simon See·· 3 天前AI 评分31

ULTRADISCOVERY:互联、认知开放宇宙中的溯因探索

ULTRADISCOVERY: Abductive Exploration in an Interconnected, Epistemically Open Universe

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研究者提出 ULTRADISCOVERY,一个含五个领域的交互式世界,用于考察智能体在认知开放与结构互联条件下的溯因探索能力。在 2×2 设计中,表征开放时十一个模型常撤回被教授的公理,且无一引入未观测实体或重写变量;披露表征使干预请求增至三倍。没有任何系统能在 200 次付费动作内做出精确预测,更大预算下仅两种辅助同时具备时才出现一次精确预测。

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Abstract:Scientific discovery often begins when scattered clues call for a new way of describing the world. Such abductive exploration can require constructing the representation in which an explanation is stated, when the world is epistemically open, and composing evidence scattered across contexts, when it is structurally interconnected. Existing benchmarks rarely separate these two demands or control them independently. We introduce ULTRADISCOVERY, an interactive world of five domains in which an agent revises an initially successful theory and predicts the outcome of an unseen cross-domain intervention. A $2 \times 2$ design leaves the representation open or discloses it, and leaves the evidence distributed or aligns it, with the latent dynamics fixed. With the representation open, agents across eleven models often retract the axiom they were taught, and none introduces the unobserved entity or rewrites the variables that a replacement requires. Disclosure triples intervention requests and adds about one of the eighteen findings the world affords, and alignment adds less. Two vendor-harness systems carry discovery into more domains, and one of them rewrites the variables in Open episodes. No system makes the exact prediction within 200 paid actions. At larger budgets one exact prediction appears with both aids, while every Open episode remains inexact. The results locate the difficulty in the step from accumulating evidence to composing it into a representation that transfers.
Comments: 46 pages, 18 figures, 16 tables
Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
Cite as: arXiv:2610.03092 [cs.LG]
  (or arXiv:2610.03092v2 [cs.LG] for this version)
  https://doi.org/10.48550/arXiv.2610.03092

arXiv-issued DOI via DataCite

Submission history

From: Weihan Li [view email]
[v1] Fri, 2 Oct 2026 10:12:34 UTC (1,432 KB)
[v2] Tue, 13 Oct 2026 09:01:26 UTC (1,602 KB)

来源:arXiv cs.AI · arxiv.org