mlsanityData to 14 Oct 2026

Agreement Before Diversity: Verification-First Complementarity for Heterogeneous Language-Model Coordination

arXiv 2608.04618 ↗First posted 5 Aug 2026cs.AIcited by 0

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Abstract (arXiv)

Heterogeneous language-model ensembles expand the space of candidate responses, yet they lack a principled criterion for when a newly generated answer should supersede an already supported one. We decouple candidate headroom from replacement authority, rendering the latter as an explicit, auditable object. Our proposed method, Agreement-Before-Diversity (ABD), is a frozen, label-free decision rule: an anchor answer is retained if two additional trusted samples corroborate it under a fixed equivalence relation; otherwise, it is replaced by a heterogeneous synthesis. For this gating mechanism, we prove two exact identities. The first shows that the accuracy gap relative to unconditional synthesis is determined jointly by the agreement coverage and the anchor's advantage on the protected subset. The second shows that the gap relative to never synthesizing reflects a contrast between authorized recovery and authorized destruction. Neither identity assumes independence or calibrated confidence, and the expected inference cost is approximately eight minus five times the coverage in number of calls. Under blind, exact-ID evaluation, ABD achieves 59.43% on the complete LiveCodeBench-v6 (vs. 52.57% for Single9 and 52.00% for HAC; n = 175) and 75.00% on an untouched GPQA-Diamond split (both controls at 72.78%; n = 180). Furthermore, these identities localize every aggregate difference to an enumerable protected stratum: no discordant items occur among the 3 protected cases on LiveCodeBench, where coverage bounds the gate's contribution to 1.71 points a priori; 13 versus 8 discordant cases among 132 on GPQA-Diamond; and 12 versus 0 among 71 under a frozen anchor perturbation. Diversity supplies potential; verification structure supplies authority.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 29 of its 36 references that are in the map. First posted on arXiv 5 Aug 2026.

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29 of 36 references.

References · 29

  1. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,459
  2. 2021Training Verifiers to Solve Math Word Problemscited by 10,109
  3. 2023Judging LLM-as-a-Judge with MT-Bench and Chatbot Arenacited by 8,626
  4. 2021Evaluating Large Language Models Trained on Codecited by 8,320
  5. 2020Measuring Massive Multitask Language Understandingcited by 7,963
  6. 2022Self-Consistency Improves Chain of Thought Reasoning in Language Modelscited by 5,708
  7. 2021Measuring Mathematical Problem Solving With the MATH Datasetcited by 5,371
  8. 2023Reflexion: Language Agents with Verbal Reinforcement Learningcited by 4,276
  9. 2016Simple and Scalable Predictive Uncertainty Estimation using Deep Ensemblescited by 3,926
  10. 2023Let's Verify Step by Stepcited by 3,789
  11. 2023Self-Refine: Iterative Refinement with Self-Feedbackcited by 3,442
  12. 2023Tree of Thoughts: Deliberate Problem Solving with Large Language Modelscited by 3,211
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  3. 2022Survey of Hallucination in Natural Language Generationcited by 2,075
  4. 2024LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Codecited by 2,004
  5. 2022Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language modelscited by 1,998
  6. 2022Language Models (Mostly) Know What They Knowcited by 1,856
  7. 2023Improving Factuality and Reasoning in Language Models through Multiagent Debatecited by 1,784
  8. 2022Competition-Level Code Generation with AlphaCodecited by 1,462
  9. 2023FrugalGPT: How to Use Large Language Models While Reducing Cost and Improving Performancecited by 853
  10. 2017Selective Classification for Deep Neural Networkscited by 852
  11. 2023SelfCheckGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Modelscited by 810
  12. 2023CRITIC: Large Language Models Can Self-Correct with Tool-Interactive Critiquingcited by 783
  13. 2024RouteLLM: Learning to Route LLMs with Preference Datacited by 682
  14. 2023LLM-Blender: Ensembling Large Language Models with Pairwise Ranking and Generative Fusioncited by 564
  15. 2024Mixture-of-Agents Enhances Large Language Model Capabilitiescited by 438
  16. 2023SpecInfer: Accelerating Generative Large Language Model Serving with Tree-based Speculative Inference and Verificationcited by 365
  17. 2017Predict Responsibly: Improving Fairness and Accuracy by Learning to Defercited by 233

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