ITMO University

Diffusion language model agents can pass compressed denoising states to each other without decoding text first. A sender packs its hidden state into four signed dense vectors, and the receiver keeps its own context and trajectory while denoising. On QASC, matched messages raise accuracy from 72.6% to 80.8%, a message from another question drops it to 46.8%, and one late transfer reaches 87.8%, 7.0 points above four recurrent exchanges.

Diffusion language model agents can pass compressed denoising states to each other without decoding text first. A sender packs its hidden state into four signed dense vectors, and the receiver keeps its own context and trajectory while denoising. On QASC, matched messages raise accuracy from 72.6% to 80.8%, a message from another question drops it to 46.8%, and one late transfer reaches 87.8%, 7.0 points above four recurrent exchanges.

Event sequence forecasting has long been ranked by prediction accuracy alone, even though generative models can produce many possible futures. A benchmark for heterogeneous event sequences such as card transactions measures accuracy and distributional realism together and finds no method leads on both: diffusion models lead on realism, dedicated predictors on accuracy. A post-hoc aggregation called Wasserstein BaryBooster collapses a diffusion model's samples into one trajectory, reaching the highest TMS on three datasets while keeping non-trivial cDFS.

Event sequence forecasting has long been ranked by prediction accuracy alone, even though generative models can produce many possible futures. A benchmark for heterogeneous event sequences such as card transactions measures accuracy and distributional realism together and finds no method leads on both: diffusion models lead on realism, dedicated predictors on accuracy. A post-hoc aggregation called Wasserstein BaryBooster collapses a diffusion model's samples into one trajectory, reaching the highest TMS on three datasets while keeping non-trivial cDFS.