mlsanityData to 7 Oct 2026

From Checker to Forecaster: Code-Owned Evaluation of Model-Generated Strategic Routes Under Delayed Ground Truth

arXiv 2607.10972 ↗First posted 13 Jul 2026cs.AIcited by 0

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Abstract (arXiv)

Many evaluations of model outputs rely either on contracts checkable at evaluation time or on feedback that arrives within the operating loop. We study the complementary setting in which ground truth is delayed, censored, or private, so deterministic code cannot check correctness at scoring time and must instead issue a code-owned provisional forecast. RouteCast instantiates this regime for model-generated typed strategic routes: models propose candidate routes and structured factors; point-in-time evidence, reference classes, and deterministic transformations produce a provisional forecast-ranking; later outcomes evaluate the forecast. In a retrospective venture pilot on 21 binary-outcome cases (6 positive, 15 negative), the whole-packet RouteCast score showed preliminary retrospective discrimination (AUC 0.756, 95% CI [0.471,0.980]), while a blind LLM judge reached AUC 0.678 [0.419,0.897] and an identity-exposed LLM judge reached AUC 0.761 [0.515,0.944], consistent with recognition- or outcome-related leakage risk. A preregistered decomposition ablation on the same binary subset found that converting the identical inputs into typed staged routes was indistinguishable from the whole-packet score (Delta AUC = -0.144, 95% CI [-0.471,0.176]) and from a deterministic heuristic (Delta AUC = -0.089, 95% CI [-0.412,0.278]). The pilot establishes an auditable feasibility result and exposes failure modes; it does not establish prospective calibration, causal decision improvement, route-decomposition advantage, or cross-domain validity.

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References · 9

  1. 2017On Calibration of Modern Neural Networkscited by 4,860
  2. 2024DeLLMa: Decision Making Under Uncertainty with Large Language Modelscited by 20
  3. 2026Look-Ahead-Bench: a Standardized Benchmark of Look-ahead Bias in Point-in-Time LLMs for Financecited by 11
  4. 2024SSFF: Investigating LLM Predictive Capabilities for Startup Success through a Multi-Agent Framework with Enhanced Explainability and Performancecited by 7
  5. 2026GroundEval: A Deterministic Replacement for LLM-as-Judge in Stateful Agent Evaluationcited by 4
  6. 2026The Strategic Foresight of LLMs: Evidence from a Fully Prospective Venture Tournamentcited by 2
  7. 2026DIALECTIC: A Multi-Agent System for Startup Evaluationcited by 2
  8. 2026Separating Diagnosis from Control: Auditable Policy Adaptation in Agent-Based Simulations with LLM-Based Diagnosticscited by 1
  9. 2026CARE: Context-Aware Ranking Evolution with Executable Scoring Programs for Budgeted Reaction Optimizationcited by 1

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