When Absence Is Evidence: Evaluating Completeness-Sensitive Negative Reasoning in Large Language Models

arXiv:2608.04591 · cs.CL, cs.AI · Submitted 2026-08-05 · Read on arXiv

Byoungjae Min, Kennedy Edemacu, Sae-Hong Cho, Yoonhyuk Choi, Beakcheol Jang, Jong Wook Kim

cs.CL, cs.AI

Submitted: 2026-08-05

Comments: 19 pages, 2 figures, 20 tables

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

Benchmark — 15× in this paper · explained in 2 episodes
Abstention — 12× in this paper · explained in 1 episode
Accuracy — 11× in this paper · explained in 1 episode
Core — 11× in this paper · explained in 2 episodes
RAG — 6× in this paper · explained in 3 episodes
Sufficiency — 5× in this paper · explained in 2 episodes
Language Models (LLMs) — 3× in this paper · explained in 2 episodes
Large Language Models (LLMs) — 3× in this paper · explained in 9 episodes
Robustness — 3× in this paper · explained in 6 episodes
Failure Modes — 2× in this paper · explained in 3 episodes
Hallucination — 2× in this paper · explained in 4 episodes
Trace — 2× in this paper · explained in 2 episodes
Benchmarking — 1× in this paper · explained in 4 episodes
Conformal Prediction — 1× in this paper · explained in 11 episodes
Consistency — 1× in this paper · explained in 3 episodes
Evaluation Framework — 1× in this paper · explained in 3 episodes
Faithfulness — 1× in this paper · explained in 2 episodes
Foundation Model — 1× in this paper · explained in 3 episodes
Foundation Models — 1× in this paper · explained in 8 episodes
Incremental Updates — 1× in this paper · explained in 2 episodes
LLM judge — 1× in this paper · explained in 2 episodes
Papers — 1× in this paper · explained in 3 episodes
Selective Prediction — 1× in this paper · explained in 2 episodes

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