--- license: cc-by-4.0 language: - en pretty_name: "RANIntent v1" size_categories: - 1K **Intent Interpretation at RIC Timescales: Jev Decision Models versus Large Language Models in 6G Open RAN** > Delong Li, Xu Wang, Haochen Gong, Rui Lang, and Guangsheng Yu. University of Technology Sydney. > arXiv: [2609.23136](https://arxiv.org/abs/2609.23136) Code, evaluation harness, and reproduction instructions: **https://github.com/OniReimu/6G-JEV** This dataset repository holds the benchmark, the analysis results that the paper's numbers, figures, and tables are computed from, and the raw run records the results are computed from. Apart from `runs/`, the directory layout is identical to the GitHub repository, so the files can be dropped into a clone of the code. ## The task An interpreter reads the issuer, the intent text, and the telemetry table, and returns one policy of A1 policy type 20001 with 9 fields, each set to one allowed value (`data/ranbench/ranintent-v1/schemas/ranintent_v1_policy_type.json`): | Field | Values | |---|---| | `action` | `prioritise`, `deprioritise`, `guarantee_share`, `cap_share`, `restrict_edge`, `relax_edge`, `set_latency_target`, `revert_default`, `unsupported` | | `class` | `emergency_video`, `mission_voice`, `factory_control`, `xr_gaming`, `video_streaming`, `iot_metering`, `analytics_offload`, `best_effort` | | `scope` | `all_cells`, `north_cluster`, `south_cluster`, `stadium`, `hospital_zone`, `most_loaded_cells`, `worst_edge_cells`, `unspecified` | | `target_cluster` | `north_cluster`, `south_cluster`, `stadium`, `hospital_zone`, `none` | | `priority` | `critical`, `high`, `normal`, `low`, `unspecified` | | `prb_share` | `10`, `20`, `30`, `50`, `70`, `unspecified` | | `edge_site` | `on_site`, `metro_edge`, `regional_cloud`, `any`, `unspecified` | | `latency_target` | `5`, `10`, `20`, `50`, `100`, `unspecified` | | `duration` | `until_revoked`, `15min`, `1h`, `event_end`, `unspecified` | The reading rules given to every interpreter and to the verifier are in `data/ranbench/ranintent-v1/schemas/ranintent_v1_reading_rules.md`. In a state-dependent case the scope is `most_loaded_cells` or `worst_edge_cells`, the text names no cluster, and `target_cluster` is the cluster that contains the extreme cell of the telemetry. The rules for stale, noisy, and contradictory telemetry decide which rows count. The paper scores accuracy on `target_cluster`, the match of the actuated fields (`action`, `class`, `scope`, `priority`), full-policy match, schema validity, and unsafe policies. ## Conditions There are 16 conditions (one dataset config each). They share one set of label tuples and one intent text per tuple, and differ only in the attached telemetry: 3, 7, 21, 57 cells, each fresh, stale, noisy, contradictory. Each condition has a `test` split and a `dev` split. The last column is the median `cl100k_base` token count of the telemetry table in the test split. | Config | Cells | Telemetry | Test | Dev | Telemetry tokens (median) | |---|---|---|---|---|---| | `c3_fresh` | 3 | fresh | 300 | 60 | 117 | | `c3_stale` | 3 | stale | 300 | 60 | 273 | | `c3_noisy` | 3 | noisy | 300 | 60 | 157 | | `c3_contradictory` | 3 | contradictory | 300 | 60 | 208 | | `c7_fresh` | 7 | fresh | 300 | 60 | 208 | | `c7_stale` | 7 | stale | 300 | 60 | 567 | | `c7_noisy` | 7 | noisy | 300 | 60 | 248 | | `c7_contradictory` | 7 | contradictory | 300 | 60 | 418 | | `c21_fresh` | 21 | fresh | 300 | 60 | 527 | | `c21_stale` | 21 | stale | 300 | 60 | 1597 | | `c21_noisy` | 21 | noisy | 300 | 60 | 567 | | `c21_contradictory` | 21 | contradictory | 300 | 60 | 1154 | | `c57_fresh` | 57 | fresh | 300 | 60 | 1346 | | `c57_stale` | 57 | stale | 300 | 60 | 4243 | | `c57_noisy` | 57 | noisy | 300 | 60 | 1386 | | `c57_contradictory` | 57 | contradictory | 300 | 60 | 3044 | In every condition, 150 test cases are state-dependent and 150 name their cluster (30 and 30 in dev). Development cases are meant only for prompt checks. ```python from datasets import load_dataset ds = load_dataset("OniReimu/6G-JEV", "c21_fresh") # splits: test, dev ``` ## Case format The first test case of `c57_fresh`, with the telemetry table truncated and the issuer and meta fields shortened: ```json { "case_id": "ranintent_v1_test_0000", "condition": "c57_fresh", "n_cells": 57, "quality": "fresh", "split": "test", "half": "state_dependent", "issuer": { "id": "operator", "kind": "operator" }, "text": "Trouble Ticket: Configure an end-to-end latency threshold of 10 ms for sensor and meter reporting data on cells where boundary user throughput is minimal.", "telemetry": "KPM telemetry snapshot at 2026-10-03T18:36:39Z; 57 cells\ncell_id,cluster,prb_util_pct,dl_thr_mbps,p95_delay_ms,edge_ue_thr_mbps\ncell-100,hospital_zone,67.5,470. [truncated]", "truth": { "action": "set_latency_target", "class": "iot_metering", "scope": "worst_edge_cells", "target_cluster": "hospital_zone", "priority": "unspecified", "prb_share": "unspecified", "edge_site": "unspecified", "latency_target": "10", "duration": "unspecified" }, "meta": { "wording_family": "operator ticket" } } ``` - `issuer`: the operator, or a tenant with the traffic classes it owns and whether it may set `critical` priority. - `telemetry`: the KPM table, one or more rows per radio cell, each row naming its cell's cluster. - `truth`: the policy, one value per field. - `meta`: wording family, generator, verifier, and generation details. ## How the cases were made 1. Label tuples were sampled first, with balanced marginals per field (300 test and 60 development tuples, seed 20260925). 2. A generator model (Gemini-3.8-Flash) wrote one intent text per tuple in one of 6 wording families (NOC chat message, email, exposure API request note, formal SLA clause, operator ticket, voice-assistant utterance). 3. A blind verifier (Claude-Opus-5.5, in a separate session) labelled each text with its telemetry table, without seeing the tuple, using the same reading rules the interpreters get. A case was kept only when its labels matched the tuple on every field. Lint checks rejected texts that name a cluster in a state-dependent case or contain field names or option identifiers. Of 365 generated texts, 360 passed the lints and 360 were accepted. 296 of the 300 test tuples were accepted with their first text. 4. The telemetry tables of all conditions were built programmatically around two extreme cells per tuple, so the target cluster is the same at every telemetry size. The operator issues 175 of the 300 test intents and 40 of the 60 development intents. 4 tenants issue the rest (test: civic_safety 38, lumen_media 38, axis_robotics 32, datacrest 17. Dev: civic_safety 9, datacrest 4, lumen_media 4, axis_robotics 3). The generator and verifier of every case are in `meta.generator` and `meta.verifier` and in `manifest.json`. Neither model is among the interpreters the paper evaluates. ## Other files | Path | Content | |---|---| | `data/ranbench/ranintent-v1/RQ4/` | The 16 conditions, `test.jsonl` and `dev.jsonl` each | | `data/ranbench/ranintent-v1/tuples/`, `_tuples/` | The label tuples of both splits, and the sampled tuples before text generation | | `data/ranbench/ranintent-v1/pool/c2c3_pool.jsonl` | The 150 named-scope test intents of `c21_fresh` that the radio simulation and the real stack draw from | | `data/ranbench/ranintent-v1/schemas/` | The A1 policy type, the truth schema, and the reading rules | | `data/ranbench/ranintent-v1/config.json`, `manifest.json`, `provenance.json`, `stats.json`, `README.md` | Generation settings, generator and verifier with yields and file hashes, assembly record, telemetry statistics, and a description | | `experiments/c1-interpretation/results/` | Interpretation layer: accuracy, validity, latency, fees, and energy per interpreter and condition, near-RT feasibility, hypothesis H3, the per-call ledger fields of the latency distribution, and the RQ3 load traces | | `experiments/c2-closed-loop/results/` | Closed-loop radio simulation: controls, latency-only contrasts and hypotheses H1 and H2, radio KPIs, the RQ3 and RQ5 results, the run matrix, and the per-run selection | | `experiments/c3-real-stack/results/` | Real A1 and E2 control path: path decomposition and per-intent records | | `runs/` | Raw run records (see below and `runs/README.md`) | | `LICENSE` | CC BY 4.0 | | `.gitattributes` | Git LFS patterns | ## Raw run records `runs/` holds 8,943 record files (19.8 GB), the raw per-call and per-run records that `experiments/*/results/` is computed from. In a clone of the GitHub repository with the Quick start environment, the commands below rerun the C1, RQ3, C3, and C2 analyses without API keys, GPUs, or a simulator, from the repository root. The latency summaries and path samples next to them are rebuilt by `scripts/paper_assets.py`, as in the Quick start: | Folder | Content | Files | Size | |---|---|---|---| | `runs/c1/` | The 11 C1 run directories the C1 analysis reads (35,331 ledger rows, RTT probes, GPU power traces, AnyJev-L0 case orders) and the gap-fill case lists | 199 | 158 MB | | `runs/rq3-load-traces/` | Every RQ3 load-trace block of every session (55 blocks: trace, ledger, integrity report), including the earlier AnyJev-L0 sessions kept as audit records | 184 | 86 MB | | `runs/c3-real-stack/` | The 7 C3 main runs (records, A1 log, xApp log, iperf3 trace, integrity report, driver log, gzip-compressed gNB log) and the tunnel round-trip times | 52 | 374 MB | | `runs/c2-runs/` | The 316 C2 runs selected in `selection.csv`, the 8 RQ5 references, and the 18 layout replicates of the robustness check, each under its platform root | 6,519 | 19.2 GB | | `runs/c2-inputs/` | The run matrices, input manifests, intent streams, enforcement schedules, and radio configurations of the three C2 input trees | 1,981 | 41 MB | | `runs/c2-records/` | The C1 decisions of the pool intents that the C2 schedules were built from | 8 | 402 kB | ```bash uv pip install huggingface_hub uv run hf download OniReimu/6G-JEV --repo-type dataset --include "runs/*" --local-dir . # C1: rebuild the interpretation-layer analysis and compare it with the released files uv run python scripts/rb_c1_analyze.py --corpus data/ranbench/ranintent-v1 --out out/c1 \ --runs runs/c1/c1_hosted runs/c1/c1_selfhosted runs/c1/c1_rerun \ runs/c1/c1_hosted_qwen_block2 runs/c1/c1_hosted_qwen_block3 runs/c1/c1_hosted_qwen_gapfill1 \ runs/c1/c1_hosted_qwen_gapfill2 runs/c1/c1_hosted_qwen_gapfill3 runs/c1/c1_hosted_qwen_gapfill4 \ runs/c1/c1_hosted_qwen_gapfill5 runs/c1/c1_hosted_qwen_gapfill6 for f in out/c1/*; do cmp "$f" "experiments/c1-interpretation/results/$(basename "$f")"; done # RQ3 and C3: the raw traces and records go where the release keeps the primary ones, and the tables are rebuilt rsync -a runs/rq3-load-traces/ experiments/c1-interpretation/results/rq3-load/traces/ rsync -a runs/c3-real-stack/ experiments/c3-real-stack/results/runs/ uv run python scripts/c3_rq3_analysis.py git diff --stat # prints nothing when the rebuilt tables are byte-identical # C2: select one copy per run as the paper did, then rerun the analysis (it reads the RQ3 traces placed above) ln -s runs/c2-runs c2-runs && ln -s runs/c2-inputs c2-inputs && ln -s runs/c2-records c2-records MATRIX=experiments/c2-closed-loop/results/run-matrix.csv SELECT_ARGS=() while IFS= read -r arg; do SELECT_ARGS+=("$arg"); done < <(uv run python -c ' import csv rows = list(csv.DictReader(open("experiments/c2-closed-loop/results/selection.csv"))) for kind, flag in (("original", "--original-root"), ("rerun", "--rerun-root")): for root in sorted({r["chosen_root"] for r in rows if r["kind"] == kind}): print(flag); print(root) for r in rows: if r["chosen_root"] and "EXCLUDED" in r["other_copies"]: print("--restrict"); print(r["run_id"] + "=" + r["chosen_root"])') uv run python -m src.ranbench.c2.select_runs --matrix "$MATRIX" "${SELECT_ARGS[@]}" --out c2-selected --force uv run python -m src.ranbench.c2.analysis --matrix "$MATRIX" --runs-root c2-selected --out out/c2 \ --rq5-refs-root c2-runs/rq5-refs-m4 sed "s#$(pwd -P)/runs/##" out/c2/runs.csv > out/c2/runs.rel.csv && mv out/c2/runs.rel.csv out/c2/runs.csv diff -r out/c2 experiments/c2-closed-loop/results/analysis # prints nothing ``` Every C1 file, the three RQ3 and C3 tables, and every file of the C2 analysis directory come out byte-identical to the released ones. The one adjustment is the `platform_root` column of `runs.csv`, which the analysis writes as the absolute path of each run directory on the machine that runs it, so the `sed` line removes the local prefix first. `c2-selected/selection.csv` names the same copy, binary, and completion time for every run as `experiments/c2-closed-loop/results/selection.csv`, but its `reason` and `other_copies` columns only see the copies shipped here. `runs/README.md` describes every file. ## Limitations The intent texts were written and verified by language models, so their wording may favor phrasing that such models parse easily. The blind agreement filter and the reading rules shared by the verifier and the interpreters limit this effect. All texts are in English, and the network, tenants, and telemetry are synthetic. ## License [CC BY 4.0](LICENSE). The code in the GitHub repository is under the MIT license, except the simulator patches, which are GPL-2.0-only like the 5G-LENA code they modify.