One Reflection Is Not Enough: Self-Correcting Autonomous Research via Multi-Hypothesis Failure Attribution
Abstract Overview
This paper presents SAGE, a self-correcting autonomous research system designed to recover from failed experiments more reliably than standard single-reflection agents. Its core mechanism, Multi-Hypothesis Failure Attribution (MHFA), frames recovery as structured causal diagnosis: the system generates multiple evidence-grounded explanations for failure, ranks them with an independent critic, and deterministically routes intervention at the hypothesis, design, or implementation level. SAGE also includes a grounded reporting mechanism that restricts numeric tables to values traceable to measured results and redacts unsupported numbers. On a 12-topic benchmark spanning five domains, the study evaluates both recovery behavior and end-to-end artifact quality, while also emphasizing that scientific writing quality and method-provenance grounding remain unresolved bottlenecks.
Novelty
The main novelty is treating autonomous experiment recovery as multi-hypothesis causal attribution rather than a single free-form reflection. The work also combines hierarchy-aware deterministic routing with explicit numeric grounding, making recovery decisions and reported results more auditable.
Results
On the 12-topic, 5-domain benchmark, SAGE increased metrics-bearing outputs from 42% (5/12) under the reflection baseline to 92% (11/12). In blind artifact-level evaluation, it scored 52.0 overall versus 48.2 for AI-Scientist-v2 and 24.8 for the host reflection baseline, with gains concentrated in code development and execution. Under AR-Eval, the canonical SAGE deliverables averaged 6.75/10 versus 5.00/10 for the baseline, although both systems remained below a strict main-conference-style paper quality bar.
Key Points
- SAGE replaces monolithic reflection with MHFA, which generates, scores, and routes among multiple evidence-grounded failure explanations.
- The system adds grounded reporting that sanitizes numeric tables against measured result registries, aiming to reduce fabricated or unsupported quantitative claims.
- Empirically, SAGE substantially improves recovery to metrics-bearing experiments and overall artifact quality, but the paper identifies prose-level method-provenance grounding and scientific writing quality as continuing limitations.