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cs.AI2026
Post-Training Large Language Models with LLMON
Basel Shbita, Michael Hind, Chad DeLuca +8
Large Language Models (LLMs) treat all context tokens uniformly, lacking explicit separation between executable instructions and passive data. This leads to prompt injection vulner…
cs.AI2026
Flow Reasoning Models: Turning Flows Into Efficient Recurrent Reasoners
Alec Helbling, Andrey Bryutkin, Mauro Martino +3
Structured reasoning requires making and revising interdependent decisions to reach a globally consistent solution. Existing architectures struggle with this: autoregressive models…