RESEARCHMonitorWATCHLIST
TokenSwap: Benchmarking and Reducing the Modality Gap in Multimodal LLMs
arXiv cs.CL — Computation and Language
Factual evidence
What the source reports
Researchers introduce TokenSwap to benchmark and mitigate the 'modality gap'—inconsistent outputs in multimodal LLMs for equivalent inputs.
Inspect the evidence
- Inclusion basis
- Enterprise AI
- Publisher and source type
- arXiv cs.CL — Computation and Language · RESEARCH
- Published by source
- 12 October 2026
- Collected by OneBench
- 3 Aug 2026, 09:18 UK
Stored source excerpt
arXiv:2607.28640v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) should generate consistent responses given semantically equivalent inputs across modalities. However, we observe a systematic…
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