Topic
decoding
Wasserstein Equilibrium Decoding Boosts Reliability in Medical Visual Question Answering
Researchers have extended game-theoretic decoding to vision-language models for medical visual question answering, introducing a Wasserstein stopping criterion that improves accuracy by up to 3.5 percentage points and reduces inference iterations by 20% while maintaining reliability.
Fast-dLLM++ Boosts Diffusion LLM Inference Up to 37% With Fréchet Profile Decoding
Researchers propose Fast-dLLM++, a training-free extension to Fast-dLLM that uses Fréchet profile decoding to select parallel token commit sets from the full confidence profile. Experiments on LLaDA-8B show up to 37% higher throughput at comparable accuracy on benchmarks including GSM8K, MATH, HumanEval, and MBPP.
Who Should Lead Decoding Now? Tracking Reliable Trajectories for Ensembling Masked Diffusion Language Models
Masked Diffusion Language Models (MDLMs) have emerged as a distinct paradigm for sequence generation, but combining their knowledge is an underexplored problem. Researchers introduce TIE (Trajectory-based Iterative Ensembling), a framework that tracks confidence dynamics over answer-relevant positions to relay decoding trajectories between models, achieving strong performance on diverse reasoning tasks.