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Manas Gaur

ACL 2026×2ICML 2026检索与知识推理评测与基准多模态可解释性
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0 一作 · 3 篇 · ACL 2026 / ICML 2026

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2026 顶会论文

3
作者位次 #2/3ACL 2026Poster· Natural Language Processing

Experiments or Outcomes? Probing Scientific Feasibility in Large Language Models

Seyedali Mohammadi · Manas Gaur · Francis Ferraro

摘要

Scientific feasibility assessment asks whether a claim is consistent with established knowledge and whether experimental evidence could support or refute it. We frame feasibility assessment as a diagnostic reasoning task in which, given a hypothesis, a model predicts feasible or infeasible and ju...

可解释性推理评测与基准OpenReview 项目页
作者位次 #4/4ACL 2026Findings· Findings of ACL

Beyond Memorization: Testing LLM Reasoning on Unseen Theory of Computation Tasks

Shlok Shelat · Jay Raval · Souvik Roy · Manas Gaur

摘要

Large language models (LLMs) have demonstrated strong performance on formal language tasks, yet whether this reflects genuine symbolic reasoning or pattern matching on familiar constructions remains unclear. We introduce a benchmark for deterministic finite automata (DFA) construction from regula...

推理评测与基准检索与知识OpenReview 项目页

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