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Capability-Gated Planning: Cost-to-Goal Discovery and the Limits of Myopic Experiment Selection

arXiv 2026 52.8 method

TLDR

This paper proves that myopic information-maximizing planners fail in scientific discovery when constructive actions acquire capabilities, formulating goal-directed discovery as stochastic shortest-path and demonstrating unbounded approximation ratios.

Reasoning

The paper offers a strong theoretical contribution by identifying a structural limitation of myopic experiment selection and proving formal guarantees via a capability-indistinguishability lemma. However, it appears to lack real-world experiments or empirical validation, and the abstract is truncated, limiting visibility of broader implications.

Read-first score

Read-first score 52.8, weighted from topical fit, citation, graph, method, reproducibility, and recency signals. Original total remains 49.

Recency 8%
100

Uses a gentle age decay so recent papers surface without erasing older foundations. 2026

Methodology quality 25%
80

Screens visible abstract and analysis fields for experiment, dataset, baseline, metric, and limitation evidence. markers=analysis,experiment,result

Topical relevance 42%
40.8

Uses existing LLM keyword relevance scores normalized to 0-100. AI scientist,automated scientific discovery,autonomous research agent,automated research,literature review agent,survey generation,automated experimentation,experiment design agent,AI for scientific research,paper writing agent,research automation,scientific discovery agent

Reproducibility 25%
30

Screens links and visible text for paper, code, dataset, artifact, and repository signals. pdf=True; code=False; dataset=False; markers=none

Field roles

FrontierMethodology anchor

Rank sensitivity

Stability: volatile; rank range: 40.

Keyword Scores

automated scientific discovery
8
automated research
6
automated experimentation
6
AI for scientific research
6
research automation
6
autonomous research agent
5
scientific discovery agent
5
experiment design agent
4
AI scientist
3
literature review agent
0
survey generation
0
paper writing agent
0

Deep Analysis

Innovations

  • Identifies a structural limitation of myopic experiment selection: constructive actions that acquire epistemic capabilities (instruments, assays, pipelines, simulators, abstractions) are undervalued because their value lies in enabling future actions, not immediate information gain.
  • Formulates goal-directed discovery as a stochastic shortest-path problem in belief space where constructive experiments change the downstream action graph.
  • Proves that for any lookahead depth d, every myopic information-maximizing planner has an unbounded approximation ratio and can fail to reach the goal, using a capability-indistinguishability lemma.
  • Introduces CG-Plan, an incremental replanner with a capability-aware cost-to-go heuristic h = h_cap + h_exp.
  • Demonstrates in a controlled testbed that the performance gap appears only under capability gating, persists for every fixed horizon, and arises with near-miss hypotheses from a data-consistent proposer.

Methodology

The paper formulates discovery planning as a stochastic shortest-path problem in belief space, where constructive actions alter the action graph. Theoretical analysis proves limits of bounded-horizon myopic planners via a capability-indistinguishability lemma. The proposed CG-Plan uses a heuristic combining capability cost and expected experiment cost, and is evaluated in a controlled testbed.

Key Results

Myopic information-maximizing planners have unbounded approximation ratios and can fail to reach the goal under capability gating, while CG-Plan effectively addresses this with a capability-aware heuristic; the performance gap emerges only under gating, persists across fixed horizons, and is tied to near-miss hypotheses from a data-consistent proposer.

Limitations

  • The analysis is based on constructed instances; real-world applicability may vary.
  • The heuristic h_cap requires domain-specific capability estimation, which may not be generally available.
  • The controlled testbed may not reflect the complexity of open-ended scientific discovery.
  • Results are shown primarily for myopic information-maximizing planners; other planner types are not compared.

Resources

Paper

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