{
  "attempt_id": "1e84c33a-e5bd-4a24-b551-de7b4d675054",
  "claims": [
    {
      "challenge_claim_sha256": "333c510d8a8d69cc59827726bb86dd399983b01e7a253af8887d0f2251cda61b",
      "evidence": "Repository mechanism audit plus deterministic masked-MDP transition checks.",
      "status": "verified",
      "target_claim": "The paper formalizes masked diffusion sampling as an MDP in which the diffusion language model is the environment and the policy chooses which tokens to unmask (Section 3)."
    },
    {
      "challenge_claim_sha256": "bf6aebbeea700b651067e91333f97aef0e4fffec15565daf27c4cf0e89b06056",
      "evidence": "Repository policy/config audit plus local confidence-to-action checks.",
      "status": "verified",
      "target_claim": "The learned unmasking policy is a lightweight single-layer transformer mapping token confidences to unmasking decisions (Section 3.2)."
    },
    {
      "challenge_claim_sha256": "28fdce6dd76e8df860c9149960ceaf5f1edf8398eb3da00be9d4844911be16f5",
      "evidence": "No trained checkpoint or raw Figure 4 evaluation output is bundled for CPU recomputation.",
      "status": "inconclusive",
      "target_claim": "Policy sampling matches state-of-the-art heuristic samplers in semi-autoregressive block generation settings (Figure 4)."
    },
    {
      "challenge_claim_sha256": "d2a26b39ada20dfa224d97c827be80a8f15fe7a06bdc9811c6a53f086fc2e607",
      "evidence": "No trained checkpoint or raw Figure 5 evaluation output is bundled for CPU recomputation.",
      "status": "inconclusive",
      "target_claim": "Learned policies outperform heuristic unmasking strategies in the full-diffusion generation setting (Figure 5)."
    },
    {
      "challenge_claim_sha256": "1969db18a5c2b4252edee8f22c1e947bb7d185870e1016c0f8a491141c5415ac",
      "evidence": "Source/config audit plus deterministic expert left-to-right unmasking simulation.",
      "status": "toy",
      "target_claim": "Visualization of learned full-diffusion policies shows expert-steered policies recovering a left-to-right unmasking order on GSM8K samples (Figure 7)."
    }
  ],
  "computed_outputs": {
    "left_to_right": [
      0,
      2,
      3
    ],
    "mdp_step": {
      "action": [
        3,
        0
      ],
      "done": false,
      "next_state": [
        "A",
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      ],
      "state": [
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      ],
      "unmasked": 2
    },
    "semi_ar_blocks": [
      [
        0,
        1,
        2,
        3
      ],
      [
        4,
        5,
        6,
        7
      ],
      [
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        9
      ]
    ]
  },
  "fencing_token": 1,
  "generated_at": "2026-08-01T00:00:00+00:00",
  "limitations": [
    "Benchmark claims are inconclusive without released raw evaluation outputs or CPU-feasible trained policy checkpoints.",
    "Local simulations verify mechanism contracts and do not substitute for paper-scale model evaluation."
  ],
  "owner": "codex-paper-owner-05",
  "paper_id": "F9NDKf5oPy",
  "repository_audit": {
    "file_sha256": {
      "README.md": "2e29199c7bce1da1cb199bf693fb753e9fe9e473c34357633a6f560e6178346d",
      "configs/experiment_configs/llada_8b_instruct_dit_confidence_BL32_mixture.yaml": "eb3b223982d15564e358d0ba6e174cc25e4f6094bb42a32630952672829d8580",
      "eval/pipeline.py": "7294c782b17d91116ee67c7a1fab347c277f9416f3797a381f465010c52e2dc6"
    },
    "terms_found": {
      "confidence": true,
      "environment": true,
      "evaluation": true,
      "mdp": true,
      "single_block_transformer": true
    },
    "upstream_revision": "35e4830485f1821d57f9ac3f1a303f3d4531fb82"
  },
  "snapshot_id": "9e9d22e53a0f5eba83916747aebd400e61cf28e84e57cf5219a34f0c7a3b00dd",
  "title": "Learning Unmasking Policies for Diffusion Language Models",
  "upstream": {
    "arxiv": "2512.09106",
    "huggingface_paper": "https://huggingface.co/papers/2512.09106",
    "openreview": "https://openreview.net/forum?id=F9NDKf5oPy",
    "repository": "https://github.com/apple/ml-rl-dllm",
    "revision": "35e4830485f1821d57f9ac3f1a303f3d4531fb82"
  }
}