@inproceedings{gupta-etal-2026-0704mis,
    title = "0704mis at {S}em{E}val-2026 Task 11: Single-Call Joint Abstraction for Robust Neuro-Symbolic Retrieval",
    author = "Gupta, Ishita  and
      Goyal, Dhruv  and
      Bedi, Jatin",
    editor = "Kochmar, Ekaterina  and
      Ghosh, Debanjan  and
      North, Kai  and
      Komachi, Mamoru",
    booktitle = "Proceedings of the 20th {I}nternational {W}orkshop on {S}emantic {E}valuation (2026)",
    month = jul,
    year = "2026",
    address = "San Diego, California, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl-workshops/2026.semeval-1.244/",
    pages = "1944--1949",
    ISBN = "979-8-89176-414-9",
    abstract = "Neuro-symbolic Basis for Robust Syllogistic Reasoning Under Distractors.We present our submission to SemEval-2026 Task 11 Subtasks 2 and 4, on syllogistic premise retrieval with distractors. Our system is based on a robustness-first neuro-symbolic pipeline. The key innovation is single-call joint abstraction: rather than parsing all statements independently, one LLM call jointly abstracts all premises and the conclusion into categorical logical forms (A/E/I/O) where symbolic (X/Y/Z) mappings are globally consistent. This allows reliable detection of the shared middle term needed for syllogistic validation. Parsed forms are passed through an exhaustive O(n{\texttwosuperior}) premise-pair search with deterministic validation against the 24 valid Aristotelian syllogistic forms via constant time lookup. Ablation studies show that more theoretically sophisticated variants degrade performance when logical-form extraction is the primary bottleneck. Our approach achieves competitive rankings in both English and multilingual settings while remaining simple, deterministic, and content-invariant."
}