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cs.CL2026
LangMAP: A Language-Adaptive Approach to Tokenization
Clara Meister, Suchir Salhan, Andrzej Szablewski +3
Language-specific tokenizers improve tokenization quality and the downstream performance of models on those languages. Using one with an existing pretrained model requires either t…
cs.CR2026
Now You (Still) See Me: Detecting Evasive Steganographic Payloads in LLMs
Charles Westphal, Timothy Douglas, Keivan Navaie +2
Large language models can be fine-tuned to encode prompt-borne secrets into fluent, seemingly benign outputs. This creates a steganographic exfiltration risk that is difficult to d…
cs.CL2026
Tokenisation via Convex Relaxations
Jan Tempus, Philip Whittington, Craig W. Schmidt +2
Tokenisation is an integral part of the current NLP pipeline. Current tokenisation algorithms such as BPE and Unigram are greedy algorithms -- they make locally optimal decisions w…