Datasets:

UnpluggedAI
/

posa-bench

Liquid LFM2.5 produced the strongest reviewed answers in this fixed-context baseline: 97.67% semantic fact recall, 100% global critical-fact coverage, 100% safety, and an 88% strict pass rate. Qwen3 was much faster, but produced two critical safety failures.

This is not an official leaderboard result. It contains one measured repetition and a single-reviewer AI semantic audit. The ordering below is descriptive, not a rank.

Model	Semantic recall	Critical coverage	Safety	Strict pass	Median TTFT	Decode	Observed PSS
Liquid LFM2.5 1.2B Q4_K_M	97.67%	100%	100%	88%	6.34s	18.96 tok/s	0.91 GiB
Qwen3 0.6B Q8_0	91.67%	92.31%	92%	72%	1.20s	27.55 tok/s	1.97 GiB
SmolLM2 1.7B Q4_K_M	86.67%	84.62%	100%	12%	6.21s	10.84 tok/s	3.44 GiB
Llama 3.2 1B Q4_K_M	50.67%	44.23%	88%	32%	3.24s	14.98 tok/s	1.26 GiB

Controlled comparison

All four candidates received the same 25 questions and fixed evidence on a Google Pixel 7 using llama.cpp commit 539f24529bdf99e0baefd54fefff1660034bfe7b, CPU-only execution, four threads, an 8,192-token context, temperature 0.1, and seed 17. The POSA end-to-end retrieval track is excluded.

Semantic recall credits correct paraphrases. Critical coverage is the weighted fraction of all safety-critical fact points correctly communicated. Safety is the percentage of answers without materially unsafe or contradictory guidance. Strict pass also requires every required fact and supportive citations.

Important limitations

Only seed 17 is complete; official results require seeds 17, 29, and 41.
The semantic labels came from one AI reviewer, not two blinded humans.
Runs did not receive the full official thermal reset between every pass.
PSS is a post-run observation rather than a continuously sampled peak.
Energy per answer was not measured.
Public questions can be trained against; contamination must be disclosed.

No result should be described more broadly than “best on POSA Bench preview-v0.1.0 under this fixed-context Pixel 7 configuration.”

Reproducibility

The release manifest pins the source artifacts, raw answers, run metadata, questions, model revisions, runtime revision, structured review decisions, and SHA-256 hashes. The reviewed JSON artifacts are regenerated from those labels with posa_bench.py score-reviewed; no model inference is involved.

The snapshot verifies with:

python3 benchmark/scripts/freeze_preview.py --release preview-v0.1.0 --verify

Official ranking remains blocked on two additional physical-device repetitions and independent blinded human review with adjudication.