arXiv AI By Jos\'e Mar\'ia Lago, Albert Castellana, Edgars Nem\v{s}e

Aggregate Disambiguation Systems

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arXiv AI
2d ago

ReSolve: Reusing Candidate Reasoning through Selective Generative Moderation

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.

By Bangji Yang, Jiajun Fan, Hongba Ma, Xi Zhu, Weizhi Zhang, Minghao Guo, Ye Li, Hamid Palangi, Jiaxuan You
arXiv AI
Aug 28

Mutual Debiasing via Dual-Seed Comparison for Probabilistic Sampling in Large Language Models

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.

By Zihao Guo, Hongtao Lv, Chaoli Zhang, Laiguo Yin, Lei Liu, Yonghui Xu, Lizhen Cui
arXiv AI
3d ago

Social Choice Foundations for Simulation-Augmented Generation

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.

By Sonja Kraiczy, Smitha Milli, Ratip Emin Berker, Avinandan Bose, Brandon Amos, Jamelle Watson-Daniels, Maximilian Nickel, Edith Elkind, Ariel D. Procaccia