mlsanityData to 14 Oct 2026

ULTRADISCOVERY: Abductive Exploration in an Interconnected, Epistemically Open Universe

arXiv 2610.03092 ↗First posted 2 Oct 2026cs.LG cs.AIcited by 0

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Abstract (arXiv)

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 × 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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 21 of its 64 references that are in the map. First posted on arXiv 2 Oct 2026.

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21 of 64 references.

References · 21

  1. 2023Self-Refine: Iterative Refinement with Self-Feedbackcited by 3,442
  2. 2026DeepSeek-V4: Towards Highly Efficient Million-Token Context Intelligencecited by 1,040
  3. 2025AlphaEvolve: A coding agent for scientific and algorithmic discoverycited by 867
  4. 2022ScienceWorld: Is your Agent Smarter than a 5th Grader?cited by 327
  5. 2024ScienceAgentBench: Toward Rigorous Assessment of Language Agents for Data-Driven Scientific Discoverycited by 235
  6. 2025ShinkaEvolve: Towards Open-Ended And Sample-Efficient Program Evolutioncited by 174
  7. 2024DISCOVERYWORLD: A Virtual Environment for Developing and Evaluating Automated Scientific Discovery Agentscited by 100
  8. 2025From Automation to Autonomy: A Survey on Large Language Models in Scientific Discoverycited by 93
  9. 2023LILO: Learning Interpretable Libraries by Compressing and Documenting Codecited by 49
  10. 2025Automated Hypothesis Validation with Agentic Sequential Falsificationscited by 44
  11. 2026ARC-AGI-3: A New Challenge for Frontier Agentic Intelligencecited by 37
  12. 2025NewtonBench: Benchmarking Generalizable Scientific Law Discovery in LLM Agentscited by 36
9 more
  1. 2025PhysGym: Benchmarking LLMs in Interactive Physics Discovery with Controlled Priorscited by 15
  2. 2022Towards Understanding How Machines Can Learn Causal Overhypothesescited by 14
  3. 2025Benchmarking World-Model Learning with Environment-Level Queriescited by 10
  4. 2026Simple Baselines are Competitive with Code Evolutioncited by 9
  5. 2026Model Discovery Agent: LLM-assisted Bayesian experiment design for data-efficient discovery of mechanistic world modelscited by 7
  6. 2026CausaLab: A Scalable Environment for Interactive Causal Discovery Toward AI Scientistscited by 4
  7. 2026MineExplorer: Evaluating Open-World Exploration of MLLM Agents in Minecraftcited by 4
  8. 2026LatentGym: A Testbed For Cross-Task Experiential Learning With Controllable Latent Structurecited by 2
  9. 2026Before the Action: Benchmarking LLMs on Prospective Hypothesis Discoverycited by 1

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