| --- |
| license: mit |
| task_categories: |
| - question-answering |
| - text-generation |
| language: |
| - en |
| tags: |
| - masked-diffusion |
| - diffusion-language-models |
| - reasoning-trajectories |
| - evaluation |
| - dream-7b |
| - llada |
| - arc-challenge |
| - gsm8k |
| pretty_name: "traj-eval: Diffusion LM Denoising Trajectory Benchmarks" |
| size_categories: |
| - n<1K |
| configs: |
| - config_name: per_trajectory_metrics |
| default: true |
| data_files: |
| - split: dream_arcc_direct_64 |
| path: results/metrics/dream_arcc_smoke64_direct_metrics.jsonl |
| - split: dream_arcc_reasoning_64 |
| path: results/metrics/dream_arcc_smoke64_reasoning_metrics.jsonl |
| - split: llada_instruct_arcc_direct_64 |
| path: results/metrics/llada_instruct_arcc_smoke64_direct_metrics.jsonl |
| - split: llada_instruct_arcc_reasoning_64 |
| path: results/metrics/llada_instruct_arcc_smoke64_reasoning_metrics.jsonl |
| - split: llada_15_arcc_direct_64 |
| path: results/metrics/llada_15_arcc_smoke64_direct_metrics.jsonl |
| - split: llada_15_arcc_reasoning_64 |
| path: results/metrics/llada_15_arcc_smoke64_reasoning_metrics.jsonl |
| - split: dream_gsm8k_64 |
| path: results/metrics/dream_gsm8k_smoke64_metrics.jsonl |
| - split: llada_instruct_gsm8k_64 |
| path: results/metrics/llada_instruct_gsm8k_smoke64_metrics.jsonl |
| - split: llada_15_gsm8k_64 |
| path: results/metrics/llada_15_gsm8k_smoke64_metrics.jsonl |
| - config_name: trajectories |
| data_files: |
| - split: dream_arcc_direct_64 |
| path: results/dream_arcc_smoke64_direct.jsonl |
| - split: dream_arcc_reasoning_64 |
| path: results/dream_arcc_smoke64_reasoning.jsonl |
| - split: llada_instruct_arcc_direct_64 |
| path: results/llada_instruct_arcc_smoke64_direct.jsonl |
| - split: llada_instruct_arcc_reasoning_64 |
| path: results/llada_instruct_arcc_smoke64_reasoning.jsonl |
| - split: llada_15_arcc_direct_64 |
| path: results/llada_15_arcc_smoke64_direct.jsonl |
| - split: llada_15_arcc_reasoning_64 |
| path: results/llada_15_arcc_smoke64_reasoning.jsonl |
| - split: dream_gsm8k_64 |
| path: results/dream_gsm8k_smoke64.jsonl |
| - split: llada_instruct_gsm8k_64 |
| path: results/llada_instruct_gsm8k_smoke64.jsonl |
| - split: llada_15_gsm8k_64 |
| path: results/llada_15_gsm8k_smoke64.jsonl |
| --- |
| |
| # traj-eval: Step-by-Step Denoising Trajectory Benchmarks for Masked Diffusion Language Models |
|
|
| **Tagline:** Measure reasoning in the denoising trajectory, not just final-answer accuracy. |
| **Author:** Adele Chinda (Department of Computer Science, Georgia State University) |
| **Code & Evaluation Toolkit:** [https://github.com/arrdel/traj-eval](https://github.com/arrdel/traj-eval) |
| **Project Page:** [https://arrdel.github.io/traj-eval/](https://arrdel.github.io/traj-eval/) |
| **Interactive Hugging Face Space:** [https://huggingface.co/spaces/arrdel/traj-eval](https://huggingface.co/spaces/arrdel/traj-eval) |
| **Hugging Face Collection:** [https://huggingface.co/collections/arrdel/traj-eval-diffusion-lm-reasoning-trajectories-6af321457d8dbd6074548379](https://huggingface.co/collections/arrdel/traj-eval-diffusion-lm-reasoning-trajectories-6af321457d8dbd6074548379) |
|
|
| ## Overview |
|
|
| Two models can have identical final-answer accuracy for very different reasons: |
|
|
| - **Genuine reasoning:** the verdict changes as reasoning develops across intermediate denoising steps, then stabilizes. Reasoning drives the verdict. |
| - **Rationalization:** the verdict is fixed in the first few steps and reasoning text is elaborated afterwards to justify it. Verdict drives reasoning. |
| - **Oscillation:** the verdict flips repeatedly across denoising steps and never settles until generation ends, even if the final answer happens to be right. |
|
|
| Final-step evaluation collapses the entire denoising trajectory into a single bit. `traj-eval` records and scores every denoising step (`0` to `255`) across open masked diffusion language models (`Dream-org/Dream-v0-Instruct-7B`, `GSAI-ML/LLaDA-8B-Instruct`, and `GSAI-ML/LLaDA-1.5`) on 64-item **ARC-Challenge** (`direct` and canonical `reasoning` conditions) and **GSM8K** (`numeric`) suites. |
|
|
| ## Trajectory JSONL Schema |
|
|
| Each `.jsonl` file in `results/` contains one JSON trajectory object per prompt (`n = 64` trajectories per file, `256` denoising steps per trajectory): |
|
|
| ```json |
| { |
| "id": "Mercury_LBS10610", |
| "prompt": "Question: The morning temperature in a city is 41°F...", |
| "gold_answer": "C", |
| "steps": [ |
| { |
| "step": 0, |
| "prediction": null, |
| "reasoning_text": "", |
| "confidence": null, |
| "logits": null, |
| "answer_committed": false, |
| "committed_reasoning_fraction": 0.0 |
| } |
| ] |
| } |
| ``` |
|
|
| Per-step fields: |
| - `step` (`int`): 0-indexed denoising step (`0` to `255`). |
| - `prediction` (`str | null`): extracted verdict at that step (`"A"` to `"D"` on ARC-Challenge, normalized numeric string on GSM8K, or `null` when no verdict is decodable yet). Under the null rule, `null` means no verdict yet and is never counted as a verdict flip. |
| - `reasoning_text` (`str`): mask-stripped decoded generation region at that step. |
| - `answer_committed` (`bool | null`): whether the final answer token span has irreversibly left the mask state and remained equal to its final value through the last step (`T_commit`). |
| - `committed_reasoning_fraction` (`float | null`): fraction of rationale positions committed by that step (`1 - U` at `T_stab`). |
|
|
| ## Results (64-item suites only) |
|
|
| Exact verified measurements from `results/baselines.md` and the scored 64-item JSON artifacts on `master`. Five-item validation smoke runs are pipeline checks only and are excluded from this table and from this dataset bundle. |
|
|
| | Checkpoint | Task | Condition | n | Accuracy | Coverage | Conditional Accuracy | Mean Convergence Step | Notes | Source File | |
| |---|---|---|---:|---:|---:|---:|---:|---|---| |
| | `Dream-v0-Instruct-7B` | `ARC-C` | `direct` | 64 | `0.8906` | `1.0000` | `0.8906` | `3.8` | 64 of 64 decided | `results/dream_arcc_smoke64_direct_results.json` | |
| | `Dream-v0-Instruct-7B` | `ARC-C` | `reasoning` | 64 | `0.8281` | `0.9844` | `0.8413` | `189.5` | One undecided (`Mercury_412681`, truncated during option analysis) | `results/dream_arcc_smoke64_reasoning_results.json` | |
| | `LLaDA-8B-Instruct` | `ARC-C` | `direct` | 64 | `0.8750` | `1.0000` | `0.8750` | `4.5` | 64 of 64 decided | `results/llada_instruct_arcc_smoke64_direct_results.json` | |
| | `LLaDA-8B-Instruct` | `ARC-C` | `reasoning` | 64 | `0.9062` | `1.0000` | `0.9062` | `32.1` | 64 of 64 decided | `results/llada_instruct_arcc_smoke64_reasoning_results.json` | |
| | `LLaDA-1.5` | `ARC-C` | `direct` | 64 | `0.8750` | `1.0000` | `0.8750` | `7.4` | 64 of 64 decided | `results/llada_15_arcc_smoke64_direct_results.json` | |
| | `LLaDA-1.5` | `ARC-C` | `reasoning` | 64 | `0.9219` | `1.0000` | `0.9219` | `21.7` | 64 of 64 decided | `results/llada_15_arcc_smoke64_reasoning_results.json` | |
| | `Dream-v0-Instruct-7B` | `GSM8K` | `numeric` | 64 | `0.7656` | `0.9688` | `0.7903` | `161.3` | 62 of 64 decided | `results/dream_gsm8k_smoke64_results.json` | |
| | `LLaDA-8B-Instruct` | `GSM8K` | `numeric` | 64 | `0.6562` | `1.0000` | `0.6562` | `78.3` | 64 of 64 decided | `results/llada_instruct_gsm8k_smoke64_results.json` | |
| | `LLaDA-1.5` | `GSM8K` | `numeric` | 64 | `0.6094` | `1.0000` | `0.6094` | `72.2` | 64 of 64 decided | `results/llada_15_gsm8k_smoke64_results.json` | |
|
|
| **Opposite prompt effects across model families:** On `Dream-v0-Instruct-7B`, direct beats reasoning on accuracy (`0.8906` vs `0.8281`). On `LLaDA-8B-Instruct`, reasoning beats direct (`0.9062` vs `0.8750`), and on `LLaDA-1.5`, reasoning also beats direct (`0.9219` vs `0.8750`). Both directions are reported as measured without smoothing over or explaining away the difference. |
|
|
| ### Rationalization components (`Dream-v0-Instruct-7B` only, ARC-C 64-item) |
|
|
| Side-by-side decomposition from `results/dream_arcc_smoke64_direct_vs_reasoning.json`: |
|
|
| | Component | Direct (`direct_64`) | Reasoning (`reasoning_64`) | |
| |---|---:|---:| |
| | `early_factor` | `0.9852` | `0.2559` | |
| | `post_change_rate` | `0.0001` | `0.1597` | |
| | `length_growth` | `0.0035` | `0.0203` | |
| | `gated` | `0.0012` | `0.0125` | |
|
|
| ## Loading with Hugging Face `datasets` |
|
|
| This repository provides two structured configurations in the Hugging Face Dataset Viewer: |
| 1. `per_trajectory_metrics` (default): per-question scored trajectory metrics (`convergence_step`, `flip_rate`, `consistency`, `answer_commitment_step`, `stability_commitment_lag`, `unwritten_reasoning_at_convergence`, `order_gap`, etc.) across all 9 64-item splits. |
| 2. `trajectories`: full 256-step denoising trajectories (`steps[0..255]`) across all 9 64-item splits. |
|
|
| ```python |
| from datasets import load_dataset |
| |
| # Load per-trajectory scored metrics for a 64-item split |
| metrics_ds = load_dataset( |
| "arrdel/traj-eval-benchmarks", |
| name="per_trajectory_metrics", |
| split="dream_arcc_reasoning_64", |
| ) |
| |
| # Load full 256-step denoising trajectories for a 64-item split |
| trajs_ds = load_dataset( |
| "arrdel/traj-eval-benchmarks", |
| name="trajectories", |
| split="dream_arcc_reasoning_64", |
| ) |
| ``` |
|
|
| ## Quickstart with `traj-eval` |
|
|
| Requires Python 3.10 or newer and `numpy` only (no model weights, no `torch`, and no `transformers` needed): |
|
|
| ```bash |
| pip install -e . |
| python -m traj_eval demo |
| python -m traj_eval run --input results/dream_arcc_smoke64_reasoning.jsonl --output results.json |
| ``` |
|
|
| Python API: |
|
|
| ```python |
| from traj_eval.io import load_trajectories |
| from traj_eval.metrics import evaluate_trajectories |
| |
| trajs = load_trajectories("results/dream_arcc_smoke64_reasoning.jsonl") |
| results = evaluate_trajectories(trajs) |
| print(results["aggregate"]) |
| ``` |
|
|
| ## Caveats |
|
|
| - **Descriptive, not causal proof:** `traj-eval` measures when verdicts and rationale tokens appear and change along the denoising trajectory. It does not prove internal causal mechanisms. |
| - **Operational definition of rationalization:** A high rationalization score means a trajectory looks like rationalization by this operational definition (early verdict stabilization followed by continued text edits or length growth), not that the model is proved to be rationalizing. |
| - **Five-item runs are pipeline checks only:** Five-item smoke runs are used solely as pipeline and extraction sanity gates and are **not** included in the results table above or in this 64-item benchmark bundle. |
| - **Holdout checkpoint status:** `MMaDA-8B-MixCoT` has not been run yet. |
|
|
