BiasMix-Finance: Post-Generation KYC Guardrails for LLM Portfolio Advice

• URL: http://arxiv.org/abs/2608.28646v1
• Date: Fri, 14 Aug 2026 05:32:14 GMT
• Title: BiasMix-Finance: Post-Generation KYC Guardrails for LLM Portfolio Advice
• Abstract summary: Large language models (LLMs) can generate plausible-sounding ETF portfolios while silently violating basic constraints on risk, fees, and diversification.<n>We study a model-agnostic, asset-agnostic post-generation guardrail pipeline.<n>We introduce BiasMix-Finance (Mini), a compact stress-test benchmark for constrained decision-making under biased generations.
• Score: 4.108510562556002
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large language models (LLMs) can generate plausible-sounding ETF portfolios while silently violating basic KYC-style constraints on risk, fees, and diversification. This is especially problematic in agentic multi-turn advisory systems, where each draft recommendation can become an action unless guarded by an auditable enforcement layer. We study a model-agnostic, asset-agnostic post-generation guardrail pipeline: (i) enforce a strict JSON allocation schema, (ii) validate allocations against numeric caps, and (iii) when violations occur, deterministically project the output to the nearest feasible portfolio via a convex quadratic program (QCQP). We introduce BiasMix-Finance (Mini), a compact stress-test benchmark for constrained decision-making under biased LLM generations, with a 16-ETF universe, three investor profiles, and eight bias prompts. Across three models and three inference modes (direct, critique, self-consistency), first-pass generations violate at least one cap in 47.6-85.7% of test cases (67.2% pooled), but the convex projection layer reduces final feasibility violations to 0% while requiring only a small correction distance (test pooled median D=||w*-w0||_2=0.066), indicating that the guardrail typically preserves the intent of the original allocation. We report violation rates and correction distances with confidence intervals, and paired model comparisons with multiple-testing correction. To support reproducibility, we release the dataset, prompts, caps, and code in our public GitHub repository.

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