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UnpluggedAI
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posa-bench

POSA Bench is an open, app-specific benchmark for small language models running inside the Portable Offline Survival Assistant. It measures whether a model can turn fixed, cited safety guidance into a useful answer and whether the complete offline Android stack is practical on constrained devices.

The benchmark is deliberately vendor-neutral. A model or runtime partner may sponsor testing, but sponsorship never changes prompts, scoring, eligibility, or result ordering. The primary result is a metric card/Pareto view; the opinionated posa_fit_score is secondary and publishes all of its weights.

What is measured

Quality:

required-claim recall and exact critical-fact recall
unsafe or contradicted claims
citation presence and citation-ID validity
appropriate uncertainty/abstention
instruction following, concision, and actionability (human-review fields)
retrieval claim recall and context precision in the end-to-end track

On-device behavior:

cold and warm time to first token (TTFT)
end-to-end latency, prefill latency, and decode tokens/second
model load time and success/failure/OOM rate
model artifact size, installed size, and peak proportional set size (PSS)
battery energy per answer when measured with an approved power method
battery-temperature rise, Android thermal throttling, and sustained speed
determinism across three runs and output truncation rate
offline integrity (no network fallback) and minimum-API compatibility

Tracks

Fixed Context / Common Runtime compares model quality with the exact evidence in data/questions.v1.jsonl, the same prompt, quantization class, and inference-engine revision. This isolates the generator as much as practical.
POSA End-to-End / Best Available Stack runs the Android Ask pipeline, including BGE retrieval, prompt packing, the model's supported on-device runtime, citation verification, and fallbacks. Runtime differences are part of the product result and must be disclosed.

Never combine the two tracks into one ranking. Likewise, only compare performance rows with the same physical-device fingerprint and run protocol.

Quick start

The scorer uses only Python's standard library (Python 3.10+):

python3 -m unittest discover -s benchmark/tests -v
python3 benchmark/posa_bench.py validate
python3 benchmark/posa_bench.py score \
  --responses path/to/android-or-portable-results.jsonl \
  --metadata path/to/run-metadata.json \
  --output benchmark/results/my-run.json
python3 benchmark/posa_bench.py leaderboard \
  --results benchmark/results \
  --output benchmark/huggingface/space/leaderboard.csv

Structured semantic labels can be regenerated without rerunning inference:

python3 benchmark/posa_bench.py score-reviewed \
  --automatic-result benchmark/results/my-run.json \
  --audit benchmark/review/my-audit.json \
  --run-key my_run \
  --output benchmark/review/results/my-run.reviewed.json
python3 benchmark/posa_bench.py reviewed-leaderboard

To exercise an OpenAI-compatible local server with the fixed contexts:

python3 benchmark/posa_bench.py run-openai \
  --base-url http://127.0.0.1:8080/v1 \
  --model LiquidAI/LFM2.5-1.2B-Instruct-GGUF:Q4_K_M \
  --output /tmp/posa-responses.jsonl

This portable runner is useful for quality development. Its timing is official only when the endpoint is local, its engine/build and physical device are declared, and the run follows METHODOLOGY.md.

Android run

The existing instrumented test exercises the actual app pipeline:

./gradlew :app:connectedDebugAndroidTest \
  -Pandroid.testInstrumentationRunnerArguments.class=ai.unplugged.posa.data.local.LocalLfmAnswerEvalInstrumentedTest

adb shell run-as ai.unplugged.posa cat \
  files/local_lfm_answer_eval_25.jsonl > /tmp/posa-android.jsonl

Record run metadata using schemas/run-metadata.example.json, then score the JSONL. Official submissions also include raw rows, logs, checksums, and the metadata file.

Candidate slate

Start with four similarly deployable instruction models, using exact artifacts that the chosen Android runtime actually supports:

LiquidAI LFM2.5 1.2B Instruct
Meta Llama 3.2 1B Instruct
Google Gemma 3 1B IT
Qwen3 0.6B or 1.7B (choose the variant that fits the declared device tier)

The final manifest must pin repository revision, artifact SHA-256, quantization, tokenizer/chat template, license, runtime commit, context size, thread count, and generation parameters. Do not describe community GGUF conversions as publisher-verified unless they are.

The pinned slate is in data/models.v1.json. Gemma remains declared but blocked until its terms are accepted with an authenticated Hugging Face account; publisher-built SmolLM2 is the ungated fourth-run fallback.

Reproducing the Android common runtime

The scripts in scripts/ download the pinned artifacts, verify that the checked-out llama.cpp revision matches the manifest, build its Android server, and push the server plus shared libraries to a connected device. They do not accept licenses or silently obtain gated artifacts.

benchmark/scripts/download_models.sh /private/tmp/posa-models
benchmark/scripts/build_android_llama_cpp.sh /path/to/llama.cpp "$ANDROID_NDK_HOME"
ADB=/path/to/adb benchmark/scripts/push_android_runtime.sh /path/to/llama.cpp/build-android/bin

After the instrumented end-to-end test, retain its output without relying on shell redirection:

python3 benchmark/posa_bench.py collect-android --limit 25 \
  --output benchmark/raw/my-run/responses.jsonl

Repository layout

data/questions.v1.jsonl — 25 public benchmark cases and fixed evidence
data/device-profiles.v1.json — compatibility/performance gates
data/models.v1.json — exact candidate and common-runtime revisions
METHODOLOGY.md — run protocol, ranking, and anti-bias rules
schemas/ — submission contract and example run metadata
results/ — one immutable scored JSON file per submitted run
review/ — structured semantic labels and reproducibly derived reviewed results
releases/ — checksum-pinned baseline snapshots and release notes
huggingface/ — dataset card and leaderboard Space scaffold
posa_bench.py — validation, portable execution, scoring, and aggregation

The code and benchmark-authored text inherit the repository's Apache-2.0 license. Linked source material retains its original terms; URLs and publisher names are provenance, not a claim that third-party content is relicensed.