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cs.CL2026
How Language Models Process Negation
Zhejian Zhou, Tianyi Zhou, Robin Jia +1
We study how Large Language Models (LLMs) process negation mechanistically. First, we establish that even though open-weight models often provide wrong answers to questions involvi…
cs.CL2026
Sampling More, Getting Less: Calibration is the Diversity Bottleneck in LLMs
Amin Banayeeanzade, Qingchuan Yang, Dhruv Tarsadiya +6
Diversity is essential for language-model applications ranging from creative generation to scientific discovery, yet modern LLMs often collapse into a narrow subset of plausible ou…
cs.SE2026
Generating Complex Code Analyzers from Natural Language Questions
Amirmohammad Nazari, Sadra Sabouri, Wang Bill Zhu +3
Many software development tasks, such as implementing features and fixing bugs, begin with developers posing questions about a codebase. However, answering questions about codebase…