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This work introduces a unified conceptual framework that views discrete diffusion models through the construction of the underlying discrete state space, and exposes common design trade-offs across training objectives, inference algorithms, scaling behavior, systems optimization, and evaluation protocols.
This work identifies a robust failure mode that is accompanied by a widening gap between in-loop metric gains and gains measured on independent held-out evaluations and proposes Diversity-Aware Proposal Sampling (DAPS), a lightweight mitigation that combines category-coverage reweighting, persistent edit memory, and a...
Evaluating three frontier VLMs in both homogeneous and cross-model adversarial settings, it is found that even the strongest agent hallucinates 15.1% of its verifiable spatial claims and 11.5% of accusations are strictly unsupported.
BPO is instantiate as Branching Policy Optimization (BPO), a sandbox-native RL algorithm that adaptively snapshots the sandbox at high-entropy decision points along a backbone trajectory, and proves this estimator is unbiased and has strictly lower variance than the trajectory-level baseline, with the reduction equal t...
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