@inproceedings{bharati-etal-2026-frost,
    title = "{FROST}: Factual Reasoning via Optimized Stochastic Trajectories in Large Language Models during Inference",
    author = "Bharati, Soumedhik  and
      Shabbir, Ebad  and
      Gao, Jiechao",
    editor = "Li, Yunyao  and
      Rehm, Georg  and
      Tu, Mei",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics ({ACL} 2026)",
    month = jul,
    year = "2026",
    address = "San Diego, California, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-industry.77/",
    pages = "1105--1113",
    ISBN = "979-8-89176-394-4",
    abstract = "Large language models face a trade-off between factual consistency and reasoningdiversity: deterministic decoding prioritizes reliability but may miss alternativesolution paths, while high-temperature sampling increases exploration at the costof accuracy. We present FROST (Factual Reasoning via Optimized StochasticTrajectories), an inference-time framework that balances exploration andexploitation without additional training or context augmentation. FROST combinesdeterministic inference from a large model with targeted stochastic sampling froma smaller model, selecting outputs via multi-criteria validation over coherence,factual grounding, and semantic novelty. Across HotpotQA, CommonsenseQA, andMMLU, FROST achieves 2{--}5 percentage point improvements over standard chain-of-thoughtprompting and reduces unsupported outputs by 40{\%} relative to Standard CoT. Comparedto Self-Consistency ensembles, FROST delivers comparable accuracy at 28{\%} lowerinference cost through strategic delegation to smaller models. On an adversarialsubset with unanswerable queries, FROST abstains on 34{\%} of cases versus 8{\%} forstandard chain-of-thought, reducing false positives by 45{\%}. Task-stratifiedevaluation shows that exploration benefits scale with problem ambiguity.Generalization to mathematical reasoning, code generation, and multimodal domainsremains future work."
}