| --- |
| license: mit |
| language: |
| - en |
| pretty_name: LongHaul-Bench |
| size_categories: |
| - 1K<n<10K |
| task_categories: |
| - text-generation |
| - question-answering |
| tags: |
| - agents |
| - llm-agents |
| - agent-memory |
| - benchmark |
| - long-horizon |
| - industrial |
| - fault-diagnosis |
| - edge-ai |
| - synthetic |
| configs: |
| - config_name: v0-standard |
| default: true |
| data_files: |
| - split: test |
| path: longhaul-v0-standard/episodes.jsonl |
| - config_name: v0-hard |
| data_files: |
| - split: test |
| path: longhaul-v0-hard/episodes.jsonl |
| - config_name: v1-standard |
| data_files: |
| - split: test |
| path: longhaul-v1-standard/episodes.jsonl |
| --- |
| |
| # LongHaul-Bench |
|
|
| **Does a self-improving LLM agent get better or slowly rot over ~1,000 sequential tasks?** |
| LongHaul-Bench is a seed-reproducible synthetic industrial fault-diagnosis environment for measuring the |
| long-horizon reliability of agents that learn from their own experience, under edge constraints |
| (quantized small model, hard memory budget, offline). |
|
|
| - **Paper (preprint):** [doi:10.5281/zenodo.23624799](https://doi.org/10.5281/zenodo.23624799) |
| - **Code, harness, run logs, analysis scripts:** [github.com/vahit19/longhaul-bench](https://github.com/vahit19/longhaul-bench) |
|
|
| ## Dataset structure |
|
|
| Each release is one synthetic plant (five machine types, 15 component types, 16 failure modes) plus a stream |
| of 1,000 diagnostic episodes, generated from seed 42. |
|
|
| | Config | Episodes | Difficulty | Notes | |
| |---|---|---|---| |
| | `v0-standard` (default) | 1,000 | log dropout 0.3, symptom dropout 0.3 | all paper results use this tier | |
| | `v0-hard` | 1,000 | log dropout 0.7, symptom dropout 0.5 | | |
| | `v1-standard` | 1,000 | standard knobs | fault frequencies calibrated to the AI4I 2020 dataset | |
|
|
| An LLM-free 91-line heuristic scores 99.9% / 86.3% / 73.3% exact accuracy on the easy / standard / hard tiers. |
|
|
| **`episodes.jsonl`** (loaded by the configs above), one episode per line: |
|
|
| | Field | Description | |
| |---|---| |
| | `episode_id` | e.g. `E00001`; episodes are meant to be run **in order** | |
| | `machine_id` | machine the fault occurs on | |
| | `alarms` | list of `{code, message}` | |
| | `log_excerpt` | HMI/PLC-style log lines (subject to dropout) | |
| | `operator_note` | free-text note | |
| | `ground_truth` | `{component, failure_mode, repair}`: **for scoring only, never show it to the agent** | |
|
|
| **`world.json`** (per release, not loaded by the configs): the agent's tool corpus, with the alarm-code table, |
| per-machine troubleshooting manual excerpts (the retrieval corpus), and maintenance history. |
| The three releases share the same world file. |
|
|
| ```python |
| from datasets import load_dataset |
| from huggingface_hub import hf_hub_download |
| import json |
| |
| episodes = load_dataset("renderfy/longhaul-bench", "v0-standard", split="test") |
| world = json.load(open(hf_hub_download("renderfy/longhaul-bench", "longhaul-v0-standard/world.json", |
| repo_type="dataset"))) |
| ``` |
|
|
| File integrity: `SHA256SUMS` matches the canonical files in the GitHub repository. |
|
|
| ## Protocol |
|
|
| The agent sees alarms, the log excerpt, and the operator note; it may call `alarm_lookup`, `manual_search`, |
| and `maintenance_history` over `world.json`, and must output a JSON diagnosis (component + failure mode), |
| scored by exact match. After each episode the environment returns outcome feedback that an improvement |
| operator may write into a bounded experience memory (100 records in the paper). Feedback can be corrupted |
| with plausible wrong diagnoses to test knowledge rot. The full harness is in the GitHub repository. |
|
|
| ## Reference results (paper, Qwen2.5-3B-Instruct Q4_K_M, five worlds) |
|
|
| | Arm | Exact accuracy (mean over 5 worlds) | |
| |---|---| |
| | frozen (no learning) | 58.2% | |
| | append memory | 62.0% | |
| | reflect memory | 61.3% | |
| | reflect + 40% plausible corrupted feedback | 55.3% | |
| | same, with manual-consistency gates | 61.3% | |
| | manual oracle (correct manual row given) | 76.8% | |
|
|
| Memory helps only when recall surfaces the correct precedent often enough: in the paper's recall-precision |
| decomposition the break-even recall precision is about 0.7, and plausible corruption causes rot mainly by |
| lowering that precision (0.81 → 0.72 → 0.65 at 0/20/40% corruption). |
|
|
| ## Limitations |
|
|
| The environment is template-generated: fault frequencies can be calibrated to public data (v1), but symptom |
| and log text are synthetic, and on the standard tier the LLM-free heuristic floor exceeds the 3B agent's |
| oracle ceiling. Results characterize memory dynamics, not absolute diagnostic competence. No proprietary or |
| employer data is used. |
|
|
| ## Citation |
|
|
| ```bibtex |
| @misc{feryad2026longhaul, |
| title = {LongHaul-Bench: A Reproducible Benchmark for Long-Horizon Reliability of Self-Improving LLM Agents Under Industrial Edge Constraints}, |
| author = {Feryad, Vahit}, |
| year = {2026}, |
| publisher = {Zenodo}, |
| doi = {10.5281/zenodo.23624799}, |
| url = {https://doi.org/10.5281/zenodo.23624799} |
| } |
| ``` |
|
|
