SOLUTION.md

Track A Solution

Competition Context

Track A is the wireless network troubleshooting and optimization track of the Telco Troubleshooting Agent challenge. The task is to solve 5G drive-test troubleshooting scenarios by selecting the best optimization action from a list of candidate choices.

For each scenario, the solution must produce one final prediction and record the generated reasoning/completion trace. In the final evaluation phase, the organizer provides the private test scenarios and deploys the sandbox tool server locally. The submitted code is expected to run without manual intervention and write the required output files under result/.

The expected final output shape is:

result/
  traces.json
  results.csv
  runtime.json

results.csv must contain:

scenario_id,prediction

Packaged Files

This submission directory is designed to be zipped as code.zip.

README.md
SOLUTION.md
run.py
main.py
train.py
requirements.txt
data/
  Phase_1/
    train.json
src/
  __init__.py
  model_core.py
models/
  deploy.sh
  model_v4_bundle.pkl
result/
  results.csv
  traces.json
  runtime.json

The result/ directory contains the currently staged outputs copied from the existing Track A run. The evaluator can regenerate the same required filenames by running run.py.

Solution Overview

The solution combines a trained auxiliary candidate ranker with an agentic reasoning layer.

The main components are:

• models/model_v4_bundle.pkl: trained Track A auxiliary model bundle.
• data/Phase_1/train.json: labelled Phase 1 training data used to reproduce the auxiliary model.
• train.py: training entrypoint. It cross-validates the template classifier and candidate selector, trains final models on all labelled data, and writes the final bundle to models/model_v4_bundle.pkl.
• src/model_core.py: shared feature extraction, option parsing, model scoring, server hydration, and output writing utilities.
• main.py: original Track A inference entrypoint. It loads scenarios, optionally hydrates placeholder telemetry from the Track A tool server, loads the model bundle, produces predictions, and records traces.
• run.py: submission wrapper. It calls main.py, then converts the raw Track A output into the Phase 3-required result/results.csv, result/traces.json, and result/runtime.json.
• models/deploy.sh: vLLM deployment helper for the Qwen3.5-35B-A3B base model.

Architecture

The code is organized as a small, reproducible training and inference stack:

Training path:

data/Phase_1/train.json
  -> train.py
  -> cross-validation metrics and experiment artifacts
  -> final template classifier and candidate selector
  -> models/model_v4_bundle.pkl

Inference path:

private test.json
  -> run.py
  -> main.py
  -> optional sandbox hydration
  -> feature extraction and option parsing
  -> template classifier
  -> candidate option selector
  -> bounded agentic reranker
  -> raw result.csv, debug.json, traces.json
  -> run.py output normalizer
  -> result/results.csv, result/traces.json, result/runtime.json

Submission Wrapper

run.py is the competition-facing entrypoint. It keeps the package output in the required Phase 3 shape while preserving the existing Track A inference code. It:

• Resolves paths relative to the submission directory.
• Calls main.py in a subprocess.
• Writes raw intermediate files under result/_raw_track_a/.
• Converts the raw Track A ID,Track A,Track B CSV into scenario_id,prediction.
• Normalizes traces.json so every trace has scenario_id, completion_id, and completion.
• Creates runtime.json from the per-scenario execution_time_seconds in the traces.
• Deletes only the temporary _raw_track_a/ folder after conversion.

Inference Controller

main.py owns the batch inference loop. It:

• Loads the test scenarios.
• Detects whether scenario data contains placeholder text instructing the code to use the API.
• Hydrates missing telemetry from the Track A sandbox when needed.
• Loads models/model_v4_bundle.pkl.
• Builds an OpenAI-compatible client if an API key is available.
• Solves each scenario with run_agent_with_trained_model.
• Records ordered results, debug rows, and trace rows.
• Checkpoints after each scenario by default.

The per-scenario solve_one function is also where runtime is measured with time.perf_counter(). That timing is written into each trace as execution_time_seconds, and run.py later converts it into runtime.json.

Sandbox Data Hydration

The code treats API calls as deterministic data gathering, not as free-form agent actions. If a scenario contains placeholder telemetry, hydrate_scenario_from_server fetches:

• User-plane drive-test data.
• Network configuration data.
• KPI or traffic data.
• MR data.
• Signaling-plane event logs when required.

Those calls are appended to the trace in compact form. This keeps the LLM from inventing telemetry and makes the agent operate over structured evidence.

Model And Candidate-Ranker Design

The auxiliary model bundle contains two fitted scikit-learn style pipelines:

1. A template classifier.
2. A selector model.

Template Classifier

The template classifier predicts the likely action pattern for a scenario. A template is an ordered action summary such as:

dec_power|tilt_down
inc_power|azimuth
neighbor
dec_power|tilt_down|inc_a3|inc_a3

The action vocabulary is derived from the candidate option text and includes:

server
insufficient
pdcch
inc_power
dec_power
tilt_down
tilt_up
azimuth
inc_a3
dec_a3
a2a5
neighbor

This gives the system a compact prior over "what kind of fix is likely needed" before choosing the exact candidate option ID.

Selector Model

The selector scores every candidate option, conditioned on a template. It is not limited to options that exactly match the top template. At prediction time the code evaluates the top five templates and uses the selector to score candidate options under those templates.

The selector features combine:

• Scenario-level telemetry features.
• Whether the task is single-answer or multiple-answer.
• The candidate option action type.
• The candidate option target cell.
• Parsed option amount and unit, such as dB, dBm, degrees, or SYM.
• A2 versus A5 threshold subtype.
• PDCCH symbol count.
• Target-cell serving and neighbor evidence.
• Target-cell configuration values such as power, downtilt, thresholds, and PDCCH symbols.
• Geometry features such as azimuth error and desired downtilt.
• Proposed-change features, such as post-change power margin or azimuth amount error.

The final ML prior is selected by combining the selector rank score with a small template probability bonus:

candidate_score = selector_rank_score + 0.05 * template_probability

For single-answer tasks, the top ranked candidate is selected. For multiple-answer tasks, the selector tries to satisfy the predicted action counts while preserving diversity across action, target cell, and threshold subtype.

Training And Reproducibility Flow

The submission includes the labelled Phase 1 training set:

data/Phase_1/train.json

The training entrypoint is:

train.py

To rebuild the auxiliary model from scratch, run this command from the submission directory:

python train.py \
  --train_path data/Phase_1/train.json \
  --out models/model_v4_bundle.pkl \
  --experiment_name lgbm_v4 \
  --n_jobs -1

The training script performs the following steps:

1. Loads the labelled Phase 1 training scenarios.
2. Builds scenario-level features for the template classifier.
3. Builds all-option selector rows so every candidate option can be scored.
4. Cross-validates the template and selector models.
5. Writes fold metrics, overall metrics, OOF predictions, split manifests, split JSON files, logs, and metadata under results/experiments/.
6. Trains final models on all labelled training data.
7. Saves the final experiment bundle under the experiment directory.
8. Copies the final trained model bundle to models/model_v4_bundle.pkl.

That final models/model_v4_bundle.pkl file is the model artifact loaded by main.py during inference.

Agent Design Choices

The agent is intentionally narrow and bounded. The design goal is to let the LLM inspect compact evidence and correct obvious candidate-choice mistakes without giving it enough freedom to destabilize the run.

One Tool Only

The only exposed agent tool is:

run_trained_ml_model

That tool returns:

• The ML prior prediction.
• The selected labels.
• The predicted template and top templates.
• The original options.
• Compact evidence for each option.
• Ambiguity groups where similar options differ by amount, target, subtype, or PDCCH symbol count.

The LLM does not call raw sandbox endpoints. Data collection happens before the agent loop, and the agent sees curated evidence.

Forced First Tool Call

On the first step, run_agent_with_trained_model sets tool_choice to force run_trained_ml_model. This ensures every agent answer is grounded in the model prior and the structured evidence object.

After that first tool call, the LLM may return strict JSON:

{"final_answer":"C13","used_tool":true,"reason":"short"}

Conservative Reranking

The system prompt tells the LLM to treat the trained model as a strong prior, not as an unchangeable answer. The LLM is allowed to rerank only when the returned option evidence indicates a better:

• Target cell.
• Action subtype, such as A2 versus A5.
• Numerical magnitude, such as azimuth degrees or threshold delta.
• PDCCH symbol value.

This mirrors the candidate-ranker augmentation idea: Phase 2-style problems often require choosing among options that share the same broad action but differ in target, amount, or subtype. The selector exposes those distinctions, and the agent acts as a final evidence-aware sanity check.

Validation And Fallbacks

The agent loop is guarded by:

• max_steps.
• max_tool_calls.
• Per-question timeout.
• Duplicate tool-call detection.
• Strict JSON response validation.
• Valid candidate ID filtering.
• Cardinality checks for single-answer and multiple-answer tasks.

If the LLM is unavailable, times out, returns invalid answers, calls duplicate tools, or raises an exception, the code falls back to the direct ML prior. This means every scenario can still produce a deterministic prediction.

Solution Architecture And Data Flow

The following diagram shows the end-to-end data flow implemented by run.py, main.py, and run_agent_with_trained_model.

+-----------------+
| Start: scenario |
+-----------------+
        |
        v
+-----------------------------+
| Load and normalize scenario |
+-----------------------------+
        |
        v
+------------------------+
| Placeholder telemetry? |
+------------------------+
    | yes            | no
    v                v
+-----------------------------+     +--------------------------+
| Hydrate from Track A sandbox | --> | Build prediction context |
+-----------------------------+     +--------------------------+
                                            |
                                            v
                       +-----------------------------------------------+
                       | Extract scenario, cell, geometry, and option  |
                       | features                                      |
                       +-----------------------------------------------+
                                            |
                                            v
                       +-----------------------------------------------+
                       | Template classifier: top action templates     |
                       +-----------------------------------------------+
                                            |
                                            v
                       +-----------------------------------------------+
                       | Selector model: score options for top         |
                       | templates                                     |
                       +-----------------------------------------------+
                                            |
                                            v
                       +-----------------------------------------------+
                       | ML prior labels and option evidence           |
                       +-----------------------------------------------+
                                            |
                                            v
                                  +-----------------------+
                                  | LLM client available? |
                                  +-----------------------+
                                      | yes           | no
                                      v               v
             +--------------------------------------+  +--------------------+
             | Agent: forced run_trained_ml_model   |  | Direct ML fallback |
             | tool call                            |  +--------------------+
             +--------------------------------------+            |
                                      |                          |
                                      v                          |
             +--------------------------------------+            |
             | Tool result: prior, top templates,   |            |
             | options, ambiguity groups            |            |
             +--------------------------------------+            |
                                      |                          |
                                      v                          |
             +--------------------------------------+            |
             | Agent reranks and returns strict JSON|            |
             +--------------------------------------+            |
                                      |                          |
                                      v                          |
                           +-------------------------+           |
                           | Valid IDs and cardinality?          |
                           +-------------------------+           |
                              | yes               | no           |
                              v                   v              |
                       +--------------+      +--------------+    |
                       | Final labels |      | Budget left? |    |
                       +--------------+      +--------------+    |
                              ^               | yes      | no    |
                              |               v          v       |
                              |      +----------------+   |       |
                              |      | Reprompt for   |   |       |
                              |      | strict JSON    |   |       |
                              |      +----------------+   |       |
                              |               |           |       |
                              +---------------+-----------+-------+
                                              |
                                              v
                     +------------------------------------------------+
                     | main.py writes raw result, debug, and traces   |
                     +------------------------------------------------+
                                              |
                                              v
                     +------------------------------------------------+
                     | run.py normalizes required output files        |
                     +------------------------------------------------+
                                              |
                                              v
                     +------------------------------------------------+
                     | End: result/results.csv, traces.json,          |
                     | runtime.json                                   |
                     +------------------------------------------------+

The state carried through this graph is:

• scenario: original or hydrated scenario record.
• context: extracted scenario features, cell stats, geometry stats, options, actions, and semantics.
• ml_result: model prediction, top templates, selector evidence, and ambiguity groups.
• messages: bounded LLM conversation state.
• tool_trace: compact record of data hydration and trained-model tool calls.
• labels: final normalized C-label prediction.
• debug: template probabilities, fallback reason, timeout status, and whether the agent changed the ML prior.

Runtime Flow

1. The organizer deploys the Qwen3.5-35B-A3B base model with vLLM, or uses models/deploy.sh as a deployment template.
2. The organizer starts the Track A sandbox tool server locally.
3. run.py is called with the private Track A test file.
4. run.py delegates inference to main.py.
5. main.py loads the auxiliary model bundle and solves each scenario.
6. main.py writes raw intermediate output under result/_raw_track_a/ and writes raw traces to result/traces.json.
7. run.py converts the raw result.csv into the required results.csv format.
8. run.py derives runtime.json from the per-scenario execution timings stored in the traces.
9. run.py removes the temporary result/_raw_track_a/ directory.

The final result directory contains only the required competition output files.

Environment

Install dependencies from the submission directory:

pip install -r requirements.txt

The solution expects:

• Python 3.10 or newer.
• A local OpenAI-compatible vLLM endpoint for Qwen3.5-35B-A3B.
• The Track A sandbox tool server at https://localhost:8081/no, unless overridden.
• No token authentication for the Track A sandbox endpoint.

Default model endpoint:

http://localhost:8001/v1

Default served model name:

Qwen3.5-35B-A3B

Model Deployment

The base model weights are not included in the submission. The organizer-provided or locally mounted Qwen3.5-35B-A3B weights should be deployed with vLLM.

Example:

BASE_MODEL_PATH=/path/to/Qwen3.5-35B-A3B bash models/deploy.sh

The deployment script serves the model through an OpenAI-compatible API on port 8001 by default.

Running The Submission

From inside this submission directory:

python run.py --input /path/to/test.json --output result

Useful explicit form:

python run.py \
  --input /path/to/test.json \
  --output result \
  --server_url https://localhost:8081/no \
  --model_url http://localhost:8001/v1 \
  --model_name Qwen3.5-35B-A3B

run.py sets a placeholder API key automatically if no OpenAI-compatible API key environment variable is present, which is sufficient for many local vLLM deployments.

Output Files

result/results.csv

Contains one final Track A prediction per scenario:

scenario_id,prediction

Predictions are normalized choice labels such as:

C13
C5|C9

result/traces.json

Contains the recorded completions and tool-call context for each scenario. Each trace includes at least:

scenario_id
completion_id
completion

Additional fields such as question text, selected prediction, tool calls, and execution time are retained for auditability.

result/runtime.json

Contains per-scenario runtime in seconds:

[
  {
    "scenario_id": "example-scenario-id",
    "runtime_seconds": 12.34
  }
]

Runtime values are derived from the scenario-level timing recorded by the inference code.

Staged Output Verification

The included staged outputs were copied from:

hf_dataset/Track A/results/

The staged output folder has been checked for:

• results.csv present.
• traces.json present.
• runtime.json present.
• 500 predictions in results.csv.
• 500 trace entries.
• 500 runtime entries.
• No blank Track A predictions.
• Matching scenario IDs across results, traces, and runtime files.

Notes

The auxiliary model bundle is packaged as models/model_v4_bundle.pkl. This is the model artifact used by the current Track A pipeline.

The submission wrapper does not train models, download files, or require internet access during inference when the base model, dependencies, and sandbox services are already available locally.