Eliya Nachmani, Omer Luxembourg, Haim Permuter
We lifted 5 functions out of this paper's own repositories and ran 4 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| ML-GSAI/LLaDA | — | 2 of 3 |
| copy not recorded | — | 1 of 1 |
| omerlux/DUS | — | 1 of 1 |
| Function | Status | Where it lives |
|---|---|---|
| apply_end_think_logit_boost | Ran | ML-GSAI/LLaDA/generate.py pointer only (licence: NONE) · get_code("b8d1edd147098bad") |
| contains_token_sequence | Ran | this paper's copy was not recorded; identical code first harvested from ml-gsai/llada pointer only · get_code("f3fec99b5aa1aa8e") |
| dilated_unmask_levels | Ran | omerlux/DUS/generate.py code served (permissive licence) · get_code("9d2036ccca0be595") |
| get_next_sequence_token_id | Ran | ML-GSAI/LLaDA/generate.py pointer only (licence: NONE) · get_code("94947c39a3c54f82") |
| generate | Not yet run | ML-GSAI/LLaDA/generate.py pointer only (licence: NONE) · get_code("6f9738ad733e164b") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Masked diffusion language models (MDLMs) promise fast, non-autoregressive text generation, yet existing samplers, which pick tokens to unmask based on model confidence, ignore interactions when unmasking multiple positions in parallel and effectively reduce to slow, autoregressive behavior. We propose the Dilated Unmasking Scheduler (DUS), an inference-only, plannermodel-free method that partitions sequence positions into non-adjacent dilated groups and unmasks them in parallel so as to minimize an upper bound on joint entropy gain at each denoising step. By explicitly trading off the number of network calls against generation quality, DUS recovers most of the performance lost under traditional parallel unmasking strategies. Across math (GSM8K, MATH500), code (HumanEval, MBPP), general-knowledge (BBH, MMLU-Pro), and instruction following (IFEval) benchmarks, DUS outperforms confidence-based planners and turns the diffusion-specific quality-speed trade-off into a deterministic, predictable speedup set by the block size B, yielding up to 5.8× wall-clock speedup over token-by-token MDLM decoding without modifying the underlying denoiser. Applied as a drop-in post-filter, dilated spacing also improves adaptive samplers. Code is available at https://github.com/omerlux/DUS.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2506.19037")
get_code_for_paper("2506.19037")
have("2506.19037")
Connect an agent — have() is free.