Publikation
Does This Action Still Explain the Task? Reverse Scoring for Diffusion Language Model Agents
Jiacheng Qiu; Christopher E. Mower; Jan Peters; Haitham Bou-Ammar; Matthieu Zimmer
In: Computing Research Repository eprint Journal (CoRR), Vol. abs/2609.38536, Pages 1-24, arXiv, 2026.
Zusammenfassung
Diffusion-based large language models (dLLMs) promise to break the sequential
latency bottleneck of autoregressive agents through parallel decoding, but recent
evaluations show this efficiency does not transfer to embodied agentic compe-
tence: dLLM-backed agents repeatedly fall into retry loops, re-issuing an action
long after it has failed. We give a mechanistic account of this failure and a training-
free remedy. We trace the retry loop to the adaptivity of masked decoding: the
sampler commits the positions it is most confident about and defers the uncertain
ones, and at a failure state the context already offers a confident fill for the de-
ferred decision, i.e. the failed action itself, so the retry is committed without the
failure feedback ever being confronted. We model the resulting distortion of the
action distribution as a task-blind corruption: contextually salient actions (e.g.,
the action just taken) receive inflated probability by a factor that depends on the
state and the action but not on the task. Under this model, we analyse an invariance
proposition: the task-blind factor cancels exactly from the reverse conditional, i.e.
the likelihood of the task given the state and a candidate action, which coincides
with the task posterior of an idealized uncorrupted model. Masked dLLMs eval-
uate the reverse conditional natively, unlike autoregressive models, by masking
the task tokens and denoising, at the cost of a few parallel passes per candidate.
We instantiate the rule as Reflect Reverse and evaluate it on four multi-turn em-
bodied benchmarks, where it improves task success and progression rates over
forward-scoring baselines.