# phase7 — Benchmark plan for sharky-0.5B (kev/jev-referenced)

**Status: PLANNED, no runs executed.** Drafted 2026-09-24; **revision 2, same day** — Jev API access
is unavailable, so the direct Jev head-to-head is **deferred** and its protocol preserved in
appendix form. All comparisons now run against locally served, open-weights kev-family models.
Plan of record for benchmarking
[luispoveda93/sharky-0.5B](https://huggingface.co/luispoveda93/sharky-0.5B). Every run that
executes from this plan must append its result to `phase7/` with a suite manifest, per kev
discipline.

## Why this phase

The model card's current evaluation (phase6) is entirely self-referential: sharky is measured on
splits of the dataset it was trained against. There is no baseline from the kev/jev family, and no
kev/Jev model has ever published pcap numbers. phase7 adds comparative baselines, controls, and the
kev eval-protocol guarantees the card currently lacks (read-once locked tests, paired significance).

## Reference baselines (all locally served — no hosted API required)

| Baseline | What it is | Published reference numbers | How we compare |
|---|---|---|---|
| **kev-0.5b** ([jaredpalmer/kev-0.5b](https://huggingface.co/jaredpalmer/kev-0.5b)) | Open repro of Jev's inferred architecture: Qwen2.5-0.5B + LoRA r16 α32 + pointer head, `/v1/systemone` contract | In-dist acc **0.799 / ECE 0.065**; agnews 0.940, banking77 0.860, mnli 0.747, sst5 0.533, boolq 0.753 | Served locally via `kev.serve`, queried with sharky's exact battery and states |
| **kev-0.6b** (research preview) | Same recipe at 0.6B; decision-v4 with none-of-the-above minimal pairs | In-dist **0.805 / OOD 0.598**, measured head-to-head against **Jev: 0.845 / 0.855** on the same frozen sets | Served locally; its published Jev deltas give sharky an *indirect Jev reference* without API access |
| **kev-4b** | 4B tier, kevBench hard/transfer suites | hard-v1 **0.803**, devtools-v1 0.756, transfer-v4 locked **0.838**; gate **34% automated @ 5% error budget** → 48% | Head-to-head on pcap battery + gate-curve shape reference |
| **Laya** ([`convaiinnovations/laya`](https://huggingface.co/convaiinnovations/laya)) — *optional* | Third-party open-weights decision model (Apache-2.0, 421M params), same question format | Numbers published by its authors, not by kev | Same protocol if included; labeled as out-of-family control |
| **Controls** | (a) MiniCPM4-0.5B backbone + sharky pointer head *without* triage LoRA training; (b) majority-class; (c) option-order permutation | — | Isolate what the LoRA + pointer head actually contribute |

Architecture lineage references: Archer Hume, *"Jev's Architecture Unmasked"* (archerhume.com);
kev code at `github.com/jaredpalmer/kev` (`kev/train.py`, `kev/serve.py`, `runs/leaderboard.md`).

**On Jev itself:** TypeSafe's hosted model is unavailable for this phase (waitlist access). Its
self-run consensus figure (67.8%) is directional only — frontier-model consensus labels, no public
harness, not reproducible. The indirect anchor is kev-0.6b's published head-to-head with Jev (see
table); when API access lands, the deferred B3 protocol (appendix below) runs unchanged.

## P0 — Protocol hardening (no GPU required)

1. **Paired significance.** Add record-clustered paired bootstrap (10k resamples, 95% CI) to
   `phase4/eval_4.py`-derived reporting. Any sharky-vs-baseline delta quoted on the model card must
   carry a CI; if it straddles zero, say so.
2. **Suite manifests.** Every `result.json` records: dataset revision SHA, code hash, git commit,
   model adapter SHA — copying kev's `result.json` discipline verbatim.
3. **Read-once discipline.** The locked split (2,446 states) is read at most once per promoted
   candidate. Selection happens on dev partitions only.
4. **Calibration re-audit.** Refit temperature on the 8,420 held-out calibration records; report
   drift from T=1.7411 and resulting ECE change before any new numbers are generated.

## P1 — pcap head-to-heads (core)

All on sharky's fixed battery (`verdict` / `exploit_evidence` / `recon_only` / `severity`) over the
same state strings, scored identically:

- **B1 — kev-0.5b zero-shot on pcap.** Serve kev-0.5b locally, query with the sharky battery on the
  locked split (2,446) and DAPT-2020 hold (3,674). Expect weak zero-shot accuracy — the measurement
  is *transfer* to the pcap state format, and it is the only apples-to-apples 0.5B comparison in the
  family. Est. 1–2 h on a10g-small.
- **B2 — untrained-head control.** Same protocol on the MiniCPM4-0.5B backbone with the pointer head
  but no triage LoRA. Bounds how much of the card's 0.98+ is architecture vs training.
- **B3 — kev-0.6b and kev-4b on the same protocol.** Served locally via `kev.serve`, identical
  battery and states. kev-0.6b additionally carries the indirect Jev anchor (its published
  head-to-head with Jev), letting sharky be positioned on the kev-family scale without API access.
  kev-4b needs ~8 GB fp16 for serving — still within a10g-small. Est. +1–2 h.
- **B4 (optional) — Laya control.** Same protocol on `convaiinnovations/laya`; run only if its
  served output format maps cleanly to the battery. Out-of-family, labeled as such.
- **Metrics (all arms):** accuracy, macro-F1, per-class F1 (report the suspicious↔malicious
  confusion separately — the card's dominant error mode), ECE, Brier, NLL, option-order permutation
  flips (k=300), gate coverage@precision.

## P2 — Shared public suites (generality probe, secondary)

Render sst2, ag_news and banking77 into kev-style state+question records per the jevbench
classifier protocol and evaluate sharky's pointer head zero-shot. Compare against kev-0.5b's
published per-suite numbers. This measures generality of the decision architecture, not triage
skill — it must never be presented on the card as a security benchmark.

## P3 — Gate benchmark

Sweep the auto-close threshold (benign p≥τ) and escalate threshold (malicious p≥τ) on the locked
split and produce coverage@precision curves. Validate the card's operating claims (benign auto-close
40% @ 99.9% precision; malicious escalate ~32% @ 99.1%) and compare curve shape to kev-4b's
gate discipline. Publish the chosen operating points alongside the curves.

## Cost & hardware sketch (indicative, no budget committed)

| Arm | Hardware | Est. wall time | Est. cost |
|---|---|---|---|
| P1 B1–B3 (kev-0.5b/0.6b/4b + control) | a10g-small, prefill-only | 3–4 h | ≈ $3.50–5 |
| P2 | same | 1 h | ≈ $1.20 |
| P0 | none (CPU) | — | $0 |

## Deliverables on completion

- `phase7/manifests/` — frozen suite manifests (revisions, hashes).
- `phase7/eval_pcap_headtohead.json` — B1–B4 results with CIs.
- `phase7/eval_public_suites.json` — P2 results.
- `phase7/gate_curves.json` + `phase7/leaderboard.md` — P3 + summary table.
- Model card: replace the static Evaluation table with a comparison table (sharky vs controls vs
  the kev tiers measured), each row carrying suite hash + n + CI.

## Explicitly out of scope

- TypeSafe's consensus-benchmark figures as a comparable number (not reproducible).
- Re-training; phase7 measures the current checkpoint only.
- New external pcap corpora (CIC-IDS2017 etc.) — candidate for a later phase if the kev-family
  comparisons leave open questions about cross-dataset generalization.

## Appendix — deferred: direct Jev head-to-head (moved out of scope this phase)

Jev API access is unavailable (waitlist). The protocol below is kept verbatim so it runs unchanged
when access lands; it is **not** part of phase7's deliverables until then.

- **B3-JEV — Jev head-to-head (API).** Same states, same battery, via `typesafe-sdk` against
  `api.typesafe.ai/v1/systemone`, model route `jev-latest`. Volume: ~2,446–6,120 states × ~500
  tokens ≈ 1.2–3M input tokens ≈ **$0.05–0.13** at $0.042/M. Report accuracy, macro-F1, ECE, gate
  coverage@precision; note that Jev's claimed `/v1/systemone` behavior (one prefill pass, no
  generation) must be verified from the response shape before any number is trusted.