Aggregate Disambiguation Systems
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ReSolve is a training‑free inference method that reuses candidate reasoning by selectively moderating generative outputs. It examines existing derivations when candidates disagree or lack a parseable answer, then incorporates new solutions into a bounded loop. On 130 competition‑mathematics problems, ReSolve achieves 100 and 99 correct answers with significantly fewer tokens than eight‑sample self‑consistency, while a controlled ablation shows that visible derivations improve accuracy.
arXiv:2606. 08098v1 Announce Type: new Abstract: Majority voting over sampled answers is the dominant unsupervised aggregator for multi-sample LLM inference.
arXiv:2608.23086v1 Announce Type: new Abstract: Black-box large language models need confidence scores that can separate likely-correct from likely-incorrect outputs, enabling systems to prioritize h...
The paper introduces Dual-Seed Comparison (DSC), a protocol that uses two independent LLM-generated seeds to reduce systematic bias in probabilistic sampling. DSC constructs a bit sequence from the character-level ordinal values of the seeds, normalizes it into a pseudo-uniform variate, and maps it to the target distribution via the inverse cumulative distribution function. Empirical results show DSC outperforms existing methods in 96% of evaluated settings and enhances distributional control in tasks like MCQ generation and attribute-constrained text-to-image prompting.
The paper introduces a formal framework for Simulation-Augmented Generation (SAGE), a method that simulates individual viewpoints to answer contentious queries more representatively. By applying the metric proportional justified representation+ (mPJR+) axiom from proportional clustering, the authors prove that only a small number of simulations (n ≪ n_H) and dynamic routing to an even smaller subset (k ≪ n) are sufficient to approximate proportional representation for a large population. Empirical results on political and personal advice domains show that their routing algorithm outperforms k‑means and random selection baselines in achieving higher mPJR+ satisfaction rates.
arXiv:2608. 14420v1 Announce Type: new Abstract: Power Sampling sharpens a language model's distribution over complete generation trajectories, offering a verifier-free way to improve reasoning at inference time.