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arxiv: 2604.25224 · v2 · submitted 2026-04-28 · 💻 cs.AI · q-fin.CP

ValueBlindBench: Agreement-Gated Stress Testing of LLM-Judged Investment Rationales Before Returns Are Observable

Sidi Chang , Peiying Zhu , Yuxiao Chen This is my paper

Pith reviewed 2026-05-07 16:39 UTC · model grok-4.3

classification 💻 cs.AI q-fin.CP
keywords LLM evaluationinvestment rationalesagreement metricsstress testingAI financekappa agreementdelayed ground truth
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The pith

ValueBlindBench uses judge agreement to decide when LLM evaluations of investment rationales can be published before returns are known.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper presents ValueBlindBench, a protocol for testing LLM judges on investment rationales in situations where actual market returns cannot yet be observed. Through 1,100 controlled trajectories including adversarial cases, the benchmark applies agreement gates using weighted kappa scores across judges. It finds sufficient overall agreement to clear the main gate but identifies failures in specific areas such as constraint awareness scoring and biases against concise rationales. This setup allows researchers to determine if an LLM judge's assessment of a rationale is reliable enough for reporting without waiting for noisy return data.

Core claim

ValueBlindBench is a pre-calibration metrology layer that governs whether LLM-judge-based investment-rationale claims are stable enough, agreed enough, and uncontaminated enough to be reported. In experiments with 1,000 honest decision cycles and 100 adversarial controls, the protocol clears the aggregate agreement gate at 0.7168 weighted kappa but shows that lower-rank systems tie, the constraint awareness dimension fails its gate at 0.2022, single-judge rankings vary by model family, and terse correct rationales are penalized by 2.81 points compared to honest ones. A probe confirms that financial constructs like constraint awareness are operationally important.

What carries the argument

ValueBlindBench, an agreement-gated stress-test protocol that uses multiple LLM judges and weighted kappa metrics on rubric dimensions to validate pre-return evaluations of investment rationales.

If this is right

  • LLM-based financial agents can have their rationales evaluated using judges only after passing the agreement thresholds.
  • Evaluations must check multiple rubric dimensions separately since some may not achieve reliable agreement.
  • Rankings from a single LLM judge are not trustworthy as they depend on the judge's model family.
  • Scoring systems need to account for length biases to avoid penalizing concise but accurate rationales.
  • The protocol can incorporate targeted probes to test if specific financial concepts are being properly assessed.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Similar agreement-gated methods could apply to other AI domains with delayed outcomes like medical diagnosis predictions.
  • Models might be optimized to produce rationales that maximize judge agreement rather than actual investment performance.
  • Without such gates, published LLM finance evaluations risk rewarding superficial patterns over genuine judgment.

Load-bearing premise

That high agreement among LLM judges on rubric scores reflects true quality of financial reasoning rather than shared superficial patterns or rubric mimicry.

What would settle it

If future realized returns show no correlation between high-agreement rationales and actual investment performance, or if adversarial rationales that mimic the rubric pass the gates despite poor outcomes.

read the original abstract

LLM-based financial agents increasingly produce investment rationales before the outcomes needed to evaluate them are observable. This creates a delayed-ground-truth evaluation problem: realized returns remain the eventual arbiter of investment quality, but they arrive too late and are too noisy to guide many model-development and governance decisions. LLM judges offer a tempting shortcut for pre-deployment evaluation of AI-finance systems, but unvalidated judges may reward verbosity, confidence, or rubric mimicry rather than financial judgment. This paper introduces ValueBlindBench, a preregistered agreement-gated stress-test protocol for deciding when LLM-judged investment-rationale claims are publishable, qualified, or invalid. In a controlled market-state capital-allocation prototype with 1,000 honest decision cycles and 100 preregistered adversarial controls (1,100 trajectories, 5,500 judge calls), ValueBlindBench clears the aggregate agreement gate at \(\bar{\kappa}_w = 0.7168\) but prevents several overclaims. Lower-rank systems collapse into a tie-class, one rubric dimension fails the per-dimension gate (\texttt{constraint\_awareness}, \(\bar{\kappa}_w = 0.2022\)), single-judge rankings are family-dependent, and terse-correct rationales receive a \(\Delta = -2.81\) rubric-point penalty relative to honest rationales. A targeted anchor-specificity probe further shows that financial constructs such as constraint awareness are operationally load-bearing. The scientific object is therefore not a leaderboard and not a claim to measure true investment skill. ValueBlindBench is a pre-calibration metrology layer for AI-finance evaluation: it governs whether a proposed LLM-judge-based investment-rationale claim is stable enough, agreed enough, and uncontaminated enough to be reported at all.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 0 minor

Summary. The paper introduces ValueBlindBench, a preregistered agreement-gated protocol that uses weighted kappa among LLM judges to decide when LLM-evaluated investment rationales (from 1,100 trajectories in a capital-allocation prototype) are stable enough to report before returns are observed. It reports an aggregate κ̄_w of 0.7168 that clears the gate while blocking overclaims such as family-dependent single-judge rankings, a failing constraint_awareness dimension (κ̄_w = 0.2022), and a -2.81 rubric penalty for terse-correct rationales versus honest ones; the protocol is positioned as a metrology layer rather than a direct skill measure.

Significance. If the reported inter-judge agreement can be shown to track substantive financial judgment rather than shared stylistic or rubric-mimicry patterns, ValueBlindBench would supply a practical pre-deployment filter for AI-finance systems facing delayed and noisy ground truth. The preregistered design, scale (5,500 judge calls), and explicit prevention of several overclaims are concrete strengths that could improve reproducibility in this domain.

major comments (3)
  1. [Abstract] Abstract: the central claim that aggregate κ̄_w = 0.7168 certifies LLM-judge outputs as 'stable enough, agreed enough, and uncontaminated enough' rests on inter-LLM agreement without any reported external anchor (human expert ratings, outcome correlation, or independent financial benchmark); this leaves open whether passing dimensions reflect content convergence or correlated surface heuristics.
  2. [Abstract] Abstract: no error bars, confidence intervals, or statistical tests are supplied for the reported κ̄_w values or the Δ = -2.81 penalty, and the construction of the 100 preregistered adversarial controls is not described, preventing assessment of whether the controls actually isolate overclaim artifacts.
  3. [Abstract] Abstract: the anchor-specificity probe is invoked to show that constructs such as constraint awareness are 'operationally load-bearing,' yet no details on its design, results, or how it differs from the main rubric are given, leaving the per-dimension gate failure (κ̄_w = 0.2022) without a clear diagnostic interpretation.

Simulated Author's Rebuttal

3 responses · 1 unresolved

We thank the referee for the constructive comments highlighting the need for greater clarity on the evidential basis of our claims, statistical reporting, and methodological details. We address each major comment point by point below and will revise the manuscript to incorporate the requested clarifications and additions.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the central claim that aggregate κ̄_w = 0.7168 certifies LLM-judge outputs as 'stable enough, agreed enough, and uncontaminated enough' rests on inter-LLM agreement without any reported external anchor (human expert ratings, outcome correlation, or independent financial benchmark); this leaves open whether passing dimensions reflect content convergence or correlated surface heuristics.

    Authors: The ValueBlindBench protocol is designed specifically for the delayed-ground-truth setting where external anchors such as realized returns, human expert ratings, or independent benchmarks are unavailable at evaluation time. The agreement gate functions as a necessary stability filter to determine whether LLM-judge outputs are sufficiently consistent to be reportable at all, rather than as a claim that agreement certifies substantive financial correctness or rules out all surface heuristics. The preregistered adversarial controls are intended to surface specific mimicry patterns (e.g., family dependence, verbosity bias). We agree that the abstract could more explicitly distinguish stability from external validity. In revision we will add language clarifying that the gate is a pre-condition for reporting, not a substitute for eventual outcome-based validation. revision: yes

  2. Referee: [Abstract] Abstract: no error bars, confidence intervals, or statistical tests are supplied for the reported κ̄_w values or the Δ = -2.81 penalty, and the construction of the 100 preregistered adversarial controls is not described, preventing assessment of whether the controls actually isolate overclaim artifacts.

    Authors: We agree that the abstract omits error bars, confidence intervals, and statistical tests, and provides only a high-level mention of the 100 adversarial controls. The full manuscript reports bootstrap-derived intervals and describes the controls as ten preregistered categories (each instantiated ten times) targeting specific overclaim artifacts. In the revised version we will include the relevant confidence intervals and a concise description of control construction directly in the abstract, along with the statistical tests used for the penalty term. revision: yes

  3. Referee: [Abstract] Abstract: the anchor-specificity probe is invoked to show that constructs such as constraint awareness are 'operationally load-bearing,' yet no details on its design, results, or how it differs from the main rubric are given, leaving the per-dimension gate failure (κ̄_w = 0.2022) without a clear diagnostic interpretation.

    Authors: The anchor-specificity probe is implemented by presenting the same trajectories to judges under both the standard rubric and a version with explicit financial-construct anchors; the resulting drop in agreement for constraint_awareness is taken as evidence that the dimension is sensitive to anchoring and therefore load-bearing. Full design details and per-dimension results appear in the methods and results sections. We will revise the abstract to include a one-sentence summary of the probe's design and its diagnostic implication for the observed gate failure. revision: yes

standing simulated objections not resolved
  • The protocol does not include external validation against human experts or observed returns, as these data are unavailable by design in the pre-returns evaluation setting; this limitation cannot be remedied within the current preregistered study.

Circularity Check

0 steps flagged

No significant circularity detected

full rationale

The paper defines ValueBlindBench as an empirical, preregistered agreement-gated protocol and reports measured statistics (aggregate weighted kappa of 0.7168 from 5,500 judge calls on 1,100 trajectories, plus per-dimension values and the terse-correct penalty) rather than deriving any target result by construction from its inputs. The protocol includes explicit adversarial controls and reports both passing and failing dimensions, so the reported outcomes are not tautological. No self-definitional equations, fitted-input predictions, or load-bearing self-citations appear in the provided text; the central claim is limited to metrology of judge stability, not to external validity of financial judgment.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on the assumption that LLM-judge agreement serves as a valid proxy for financial judgment quality. No free parameters or invented entities are explicitly introduced in the abstract.

axioms (1)
  • domain assumption Inter-judge agreement on rubric dimensions indicates genuine financial judgment rather than superficial mimicry
    The protocol uses agreement thresholds to gate claims; this assumption is load-bearing for the claim that the benchmark prevents overclaims.

pith-pipeline@v0.9.0 · 5639 in / 1384 out tokens · 72609 ms · 2026-05-07T16:39:57.526799+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

14 extracted references · 4 canonical work pages

  1. [1]

    The statistics of Sharpe ratios,

    A. W. Lo, “The statistics of Sharpe ratios,” Financial Analysts Journal, vol. 58, no. 4, pp. 36–52, 2002. [Online]. A vailable: https: //www.tandfonline.com/doi/abs/10.2469/faj.v58.n4.2453

    work page doi:10.2469/faj.v58.n4.2453 2002

  2. [2]

    Pseudo-mathematics and financial charlatanism: The effects of backtest overfitting on out-of-sample performance,

    D. H. Bailey, J. M. Borwein, M. López de Prado, and Q. J. Zhu, “Pseudo-mathematics and financial charlatanism: The effects of backtest overfitting on out-of-sample performance,” Notices of the American Mathematical Society , vol. 61, no. 5, pp. 458–471, 2014. [Online]. A vailable: https://www.ams.org/journals/notices/201405/rnoti-p458.pdf

    2014

  3. [3]

    López de Prado, Advances in Financial Machine Learning

    M. López de Prado, Advances in Financial Machine Learning . Hoboken, NJ: John Wiley & Sons, 2018. [Online]. A vailable: https://www.wiley.com/en-us/Advances+in+Financial+Mach ine+Learning-p-9781119482086

    2018

  4. [4]

    Judging LLM-as-a-judge with MT-bench and chatbot arena,

    L. Zheng, W.-L. Chiang, Y. Sheng, S. Zhuang, Z. Wu, Y. Zhuang, Z. Lin, Z. Li, D. Li, E. P. Xing, H. Zhang, J. E. Gonzalez, and I. Stoica, “Judging LLM-as-a-judge with MT-bench and chatbot arena,” in Advances in Neural Information Processing Systems , vol. 36, 2023, pp. 46 595–46 623. [Online]. A vailable: https://proceedings.neurips. cc/paper_files/paper/...

    2023

  5. [5]

    G-eval: NLG evaluation using GPT-4 with better human alignment,

    Y. Liu, D. Iter, Y. Xu, S. Wang, R. Xu, and C. Zhu, “G-eval: NLG evaluation using GPT-4 with better human alignment,” in Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing . Singapore: Association for Computational Linguistics, 2023, pp. 2511–2522. [Online]. A vailable: https://aclanthology.org/2023.emnlp-main.153/

    2023

  6. [6]

    Holistic evaluation of language models,

    P. Liang, R. Bommasani, T. Lee, D. Tsipras, D. Soylu, M. Yasunaga, Y. Zhang, D. Narayanan, Y. Wu, A. Kumar, B. Newman, B. Yuan, B. Yan, C. Zhang, C. Cosgrove, C. D. Manning, C. Ré, D. Acosta-Navas, D. A. Hudson, E. Zelikman, E. Durmus, F. Ladhak, F. Rong, H. Ren, H. Yao, J. Wang, K. Santhanam, L. Orr, L. Zheng, M. Yuksekgonul, M. Suzgun, N. Kim, N. Guha, ...

    2023

  7. [7]

    Human feedback is not gold standard,

    T. Hosking, P. Blunsom, and M. Bartolo, “Human feedback is not gold standard,” in The Twelfth International Conference on Learning Representations, 2024. [Online]. A vailable: https://openreview.net/forum?id=7W3GLNImfS

    2024

  8. [8]

    doi: https://doi.org/10.1037/h0040957

    L. J. Cronbach and P. E. Meehl, “Construct validity in psychological tests,” Psychological Bulletin , vol. 52, no. 4, pp. 281–302, 1955. [Online]. A vailable: https://doi.org/10.1037/h0040957

    work page doi:10.1037/h0040957 1955

  9. [9]

    Validity,

    S. J. Messick, “Validity,” in Educational Measurement, 3rd ed., R. L. Linn, Ed. New York: American Council on Education and Macmillan, 1989, pp. 13–103. [Online]. A vailable: https://www.ets.org/research/policy_research_reports/publi cations/chapter/1989/hwrq.html

    1989

  10. [10]

    Krippendorff, Content Analysis: An Introduction to Its Methodology, 4th ed

    K. Krippendorff, Content Analysis: An Introduction to Its Methodology, 4th ed. Thousand Oaks, CA: SAGE Publications, 2018. [Online]. A vailable: https://collegepublish ing.sagepub.com/products/content-analysis-4-258450

    2018

  11. [11]

    Richard Landis and Gary G

    J. R. Landis and G. G. Koch, “The measurement of observer agreement for categorical data,” Biometrics, vol. 33, no. 1, pp. 159–174, 1977. [Online]. A vailable: https://www.jstor.org/stable/2529310

    work page arXiv 1977

  12. [12]

    The preregistration revolution,

    B. A. Nosek, C. R. Ebersole, A. C. DeHaven, and D. T. Mellor, “The preregistration revolution,” Proceedings of the National Academy of Sciences , vol. 115, no. 11, pp. 2600–2606,

  13. [13]

    Proceedings of the National Academy of Sciences , volume =

    [Online]. A vailable: https://www.pnas.org/doi/10.1073/pnas.1708274114

    work page doi:10.1073/pnas.1708274114

  14. [14]

    Reproducibility in computational linguistics: Are we willing to share?

    M. Wieling, J. Rawee, and G. van Noord, “Reproducibility in computational linguistics: Are we willing to share?” Computational Linguistics , vol. 44, no. 4, pp. 641–649, 2018. [Online]. A vailable: https://direct.mit.edu/coli/article/44/4/6 41/1593/Reproducibility-in-Computational-Linguistics-Are

    2018