mlsanityData to 14 Oct 2026

BudgetVerify: Budget-Tiered Verification for Financial QA

arXiv 2609.33052 ↗First posted 27 Sep 2026cs.AI cs.LGcited by 1

New in LLM reasoning; no direction yet. LLM reasoning

Abstract (arXiv)

Financial question answering often requires precise numerical extraction, unit handling, and arithmetic over tables and text, but applying expensive verification uniformly wastes test-time compute. We propose BudgetVerify, a budget-tiered generator-verifier framework that routes each generated answer to one of three verification tiers: no verification, lightweight check-and-revise, or higher-cost solve-first-then-compare verification. The router is trained from offline correctness and token-cost outcomes and, at test time, selects a verification tier using information available before verification, including the question, context statistics, the generated answer, and associated generator metadata. The selected tier either returns the generated answer directly or invokes the corresponding verifier. Across six commercial and open-weight base models, BudgetVerify consistently produces more efficient accuracy-cost Pareto frontiers than fixed verification policies by selectively allocating stronger verification only when it is useful. Although absolute performance varies across models, these efficiency gains and the resulting qualitative frontier shape are consistent across generator models.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 14 of its 22 references that are in the map. First posted on arXiv 27 Sep 2026.

Builds on

14 of 22 references.

References · 14

  1. 2022Self-Consistency Improves Chain of Thought Reasoning in Language Modelscited by 5,708
  2. 2024Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameterscited by 1,986
  3. 2024Large Language Monkeys: Scaling Inference Compute with Repeated Samplingcited by 991
  4. 2024RouteLLM: Learning to Route LLMs with Preference Datacited by 682
  5. 2025Why Do Multi-Agent LLM Systems Fail?cited by 604
  6. 2021FinQA: A Dataset of Numerical Reasoning over Financial Datacited by 562
  7. 2024Generative Verifiers: Reward Modeling as Next-Token Predictioncited by 480
  8. 2023FinanceBench: A New Benchmark for Financial Question Answeringcited by 270
  9. 2025Evaluating Judges as Evaluators: The JETTS Benchmark of LLM-as-Judges as Test-Time Scaling Evaluatorscited by 44
  10. 2025CARROT: A Cost Aware Rate Optimal Routercited by 39
  11. 2025Hard2Verify: A Step-Level Verification Benchmark for Open-Ended Frontier Mathcited by 22
  12. 2025Variation in Verification: Understanding Verification Dynamics in Large Language Modelscited by 19
2 more
  1. 2026Adaptive Test-Time Compute Allocation for Reasoning LLMs via Constrained Policy Optimizationcited by 9
  2. 2025S2J: Bridging the Gap Between Solving and Judging Ability in Generative Reward Modelscited by 5

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  1. 2026From Documents to Reasoning: A Validated Synthetic Data Pipeline and Semantic-Aware Fine-Tuning for Financial Numerical Reasoning
  2. 2023BizBench: A Quantitative Reasoning Benchmark for Business and Finance
  3. 2026Towards Expert Financial QA via Self-Improving RAG
  4. 2026Fighting Numerical Hallucinations via Data-centric Compilation for Online Financial QA
  5. 2026V-FiLLM: Verified Financial LLM Reasoning Benchmark
  6. 2024FinTextQA: A Dataset for Long-form Financial Question Answering
  7. 2026FinInteract: Benchmarking Clarification and Intent Integration in Ambiguous Financial Question Answering
  8. 2026Enhancing Financial Question Answering: A Novel Benchmark Dataset of Banks' financial statements