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cs.CL2026
TokenSwap: Benchmarking and Reducing the Modality Gap in Multimodal LLMs
Andong Hua, Colton Bishop, Igor Mordatch +5
Multimodal large language models (MLLMs) should generate consistent responses given semantically equivalent inputs across modalities. However, we observe a systematic discrepancy i…
cs.CL2026
The Path Matters: Learning a Token-Commitment Policy for Diffusion Language Models
Bohang Sun, Max Zhu, Francesco Caso +5
Diffusion large language models promise faster generation by refining many token positions in parallel, but this parallelism introduces a hidden control problem: which proposed tok…
cs.CL2025
Flaw or Artifact? Rethinking Prompt Sensitivity in Evaluating LLMs
Andong Hua, Kenan Tang, Chenhe Gu +3
Prompt sensitivity, referring to the phenomenon where paraphrasing (i.e., repeating something written or spoken using different words) leads to significant changes in large languag…