Agreement Before Diversity: Verification-First Complementarity for Heterogeneous Language-Model Coordination
Quick Answer
This paper shows that The Agreement-Before-Diversity (ABD) method enhances heterogeneous language-model coordination by retaining anchor answers corroborated by two trusted samples, achieving 59.43% accuracy on LiveCodeBench-v6 and 75.00% on GPQA-Diamond.
Quick Take
This approach provides a principled criterion for response selection without assuming independence or calibrated confidence, significantly improving decision-making in AI systems.
Key Points
- ABD retains anchor answers supported by two corroborating samples under a fixed equivalence relation.
- Achieved 59.43% accuracy on -v6, outperforming Single9 and HAC.
- 75.00% accuracy on untouched -Diamond split, exceeding control benchmarks.
- Two exact identities prove the accuracy gap is determined by agreement coverage and anchor advantage.
- Expected inference cost is approximately eight minus five times the coverage in number of calls.
DeepSignal Analysis
What happened
The Agreement-Before-Diversity (ABD) method was introduced to improve coordination among heterogeneous language models. This method retains anchor answers supported by two trusted samples, achieving notable accuracy rates on specific benchmarks.
Key evidence
- ABD achieved 59.43% accuracy on the LiveCodeBench-v6 benchmark, outperforming Single9 and HAC models.
- On the GPQA-Diamond split, ABD reached 75.00% accuracy, surpassing control benchmarks that stood at 72.78%.
- The method does not rely on independence or calibrated confidence, focusing instead on a fixed equivalence relation for decision-making.
Why it matters
The ABD method provides a structured approach to response selection in AI systems, addressing the challenge of determining when to replace generated answers. By establishing a verification framework, it enhances decision-making reliability, which is crucial for applications relying on heterogeneous language models.
What to watch
Future developments should focus on the practical implementation of ABD in various AI applications. Observing its performance across different datasets and real-world scenarios will be essential to validate its effectiveness and identify potential limitations.
Paper Resources
📖 Reader Mode
~2 min readAbstract: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.
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2608.04618 [cs.AI] |
| (or arXiv:2608.04618v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2608.04618 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Binjie Guo [view email]
[v1]
Wed, 5 Aug 2026 09:24:39 UTC (1,090 KB)
— Originally published at arxiv.org
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