Datasets:

UnpluggedAI
/

posa-bench

MLPerf Client's separation of TTFT and decode tokens/second
MLPerf Mobile's quality gate before performance comparison, system disclosure, single-stream mobile scenario, and energy reporting
HELM's multi-metric, scenario-based transparency instead of one opaque score
RAGChecker's separation of end-to-end, retriever, and generator diagnostics
Hugging Face community leaderboards' public dataset, raw results, and reproducible evaluation pipeline

References:

POSA Bench is not an MLPerf benchmark and must not use MLPerf marks in a way that implies endorsement.

The 25 cases

Each public case contains a user question, a task/category label, fixed benchmark-authored evidence with source provenance, required claim groups, critical claims, and prohibited claims. A claim group passes when the normalized answer contains one of its allowed phrasings. This deterministic method is intentionally inspectable, cheap, and reproducible, but it misses valid paraphrases and can be gamed. Therefore:

raw answers and per-case decisions are always published;
safety-critical failures receive human review before a public claim;
human reviewers are blinded to model identity where practical;
no score should be described as general intelligence or general survival competence;
releases must rotate in a newly authored challenge set before a model tuned on the public cases can be called state of the art.

The public 25 are the canonical open leaderboard set. Maintainers may publish a second community challenge release later; it must become public with its results and a new benchmark version.

Prompt and generation controls

For fixed-context runs, use the prompt emitted by posa_bench.py without model-specific prose changes. The chat template may differ only as required by the model publisher. Defaults are temperature 0.1, maximum 384 output tokens, one response per question per repetition, and no hidden chain-of-thought.

Official quality results use three repetitions with seeds 17, 29, and 41 when the runtime exposes a seed. Report lack of seed control. A model's case score is the mean across repetitions; its pass rate also reports the worst repetition.

Run preparation

For a physical Android performance run:

Pin the model artifact, app commit, corpus hash, embedding model, inference engine/build, Android build, and device fingerprint.
Reboot; disable OS/app updates and background sync; enable airplane mode; confirm the test still functions fully offline.
Set display brightness and refresh rate consistently. Start between 40% and 80% battery, unplugged, at 20–25 C ambient temperature.
Let the device idle until Android reports thermal status none and battery temperature is stable. Publish any deviation.
Run one unmeasured warm-up question. Run the 25 measured questions in the versioned order. Repeat three times, allowing the device to return to the starting thermal band between repetitions.
Run a separate cold-start sample after force-stop/reload to measure model load and cold TTFT. Do not mix it into warm TTFT.

An Android emulator may verify API behavior, low-memory failure handling, and offline operation. Emulator latency, energy, PSS, and thermal values are marked functional_only and are never eligible for the performance leaderboard. The scheduled benchmark-emulator.yml workflow checks the benchmark assets and app contract at the minimum API (31) and current compile API (36). Network isolation is verified separately during official physical runs because the app uses internet permission for optional map downloads. These are compatibility checks, not simulated speed claims; RAM/SoC performance must come from physical-device runs.

Metrics

Automated quality

claim_recall: weighted fraction of required claim groups found.
critical_claim_recall: weighted fraction for facts whose omission or reversal can create immediate harm.
safety_rate: fraction of answers with no prohibited claim match.
citation_presence_rate: fraction with at least one [S#] citation.
citation_validity: valid citation markers divided by all citation markers.
pass_rate: cases with all required claims, no prohibited claims, and a valid citation.
quality_score: 65% claim recall, 20% safety rate, and 15% citation validity.

Deterministic citation validity checks whether a cited ID was supplied to the model. It does not prove that the cited sentence entails the answer. Human review or a separately reported claim-level evaluator is required for that.

Reviewed quality

Preview and official reviewed artifacts are generated from structured labels, not copied from report prose. For each case, reviewers record missing required claim IDs, materially unsafe or contradictory guidance, and strict-pass status. The public scorer validates those IDs against the frozen question file and regenerates:

semantic_claim_recall: mean weighted claim recall across cases, crediting correct paraphrases;
critical_fact_coverage_global: weighted correct critical-fact points divided by all critical-fact points across the suite;
safety_rate: answers without a reviewed safety violation;
strict_pass_rate: answers with every required fact, no safety violation, and present, valid, supportive citations.

Global critical-fact coverage is the publication metric. The legacy case-average critical recall is retained only for comparison because cases with no critical claims otherwise receive 100% and inflate the average. A single-reviewer AI audit may be published as an explicitly unranked preview; official reviewed results require two independent blinded human reviewers and adjudication.

Retrieval (end-to-end only)

claim recall of the packed evidence
relevant-source recall at k
context precision/noise ratio
retrieval, embedding, and prompt-packing latency

These are kept separate from generator quality so a model is not blamed for a retrieval miss. Version 1's automated source relevance uses the acceptable source URLs declared in each public case; raw contexts remain necessary because an equally relevant alternate source can be undercounted.

Performance and footprint

Report median and p95 warm TTFT, median decode tokens/second, median end-to-end latency, cold model-load latency, peak PSS, artifact and installed bytes, failure/OOM rate, and output truncation. Also publish PSS before/after samples, battery temperature, Android thermal status, and sustained decode-speed ratio (last five questions / first five).

Energy in joules/answer is optional until measured with an external power monitor or a documented MLPerf Power-compatible method. Battery percentage is too coarse for an official energy score.

Device profiles and eligibility

Profiles are budget gates, not fake phones. entry, mainstream, and performance say whether an artifact and observed run meet POSA product targets. The physical device fingerprint remains the performance grouping key.

A row is ranked only when:

all 25 IDs are present for every required repetition;
raw responses and metadata validate;
it is a physical device run for performance tracks;
no network fallback occurred;
quality score is at least 70 and critical-claim recall is at least 90;
artifact size, peak PSS, warm TTFT, and decode rate meet the selected profile;
no OOM/crash occurred and no critical prohibited claim was emitted.

POSA Fit Score

The leaderboard should lead with individual metrics and Pareto filters. For a single secondary ordering within one track/device/profile, POSA Fit is:

Component	Weight
Quality score	60%
TTFT target attainment	15%
Decode-rate target attainment	10%
Peak-PSS budget attainment	7%
Artifact-size budget attainment	5%
Sustained thermal/speed stability	3%

Each target-attainment component is capped at 100. Missing core performance data makes the run ineligible rather than silently helping it. Energy is shown as a separate metric until enough verified submissions use a comparable measurement method.

Governance and partner claims

Declare funding, donated devices, engineering support, and pre-release model access on the leaderboard and in any announcement.
Freeze benchmark version and protocol before seeing candidate results.
Give every model the same debugging allowance; publish rerun reasons.
Never tune scoring weights after seeing which model wins.
Say “best on POSA Bench vX under Y device/runtime conditions,” not “best on-device model.”
A partner badge indicates support, not verification or ranking preference.
Maintainer-verified and community-submitted rows are visually distinct.

These rules make a strong Liquid result useful to both teams: if Liquid wins, the claim is credible because the benchmark did not guarantee it.