FINAL-Bench
/

ZTC-Judge-4B

@@ -1,305 +1,312 @@
----
-license: apache-2.0
-language:
-- en
-- ko
-tags:
-- ztc
-- answer-verification
-- hallucination-detection
-- zero-token
-- confidence-estimation
-library_name: transformers
-pipeline_tag: text-classification
----
-# ZTC-Judge-4B
-**Answer verification from a single forward pass, with zero generated tokens — at 4B.**
-ZTC-Judge-4B takes a question and an answer written by *any* model and scores whether that
-answer can be trusted. It is the bottom rung of a four-point size ladder measured under one
-identical protocol, and it is published so the shape of that ladder can be checked rather than
-asserted.
-> **ZTC** — Zero-Token Confidence · **Judge** — it evaluates *someone else's* answer, not its own
----
-## Read this before deploying
-This model is **not** the strongest member of the family, and the card says so with numbers.
-| Model | Leaderboard AUC |
-|---|---|
-| Darwin-397B-ZTC | 0.7364 |
-| ZTC-Judge-27B | 0.7282 |
-| **ZTC-Judge-9B** | **0.6506** |
-| **ZTC-Judge-4B** | **0.6360** |
-| *Answer length and formatting only* | *0.6223* |
-**The ladder does not decline smoothly — it steps.** Between 9B and 27B the score moves 0.078,
-while between 4B and 9B it moves 0.015. Whatever carries verification quality is largely absent
-below 27B on this axis.
-**Where this model is worth deploying is one specific place**, and it is a real one:
-| Domain | Surface baseline | **4B** | Margin |
-|---|---|---|---|
-| **Professional exams (law · math · biology)** | 0.7138 | **0.7787** | ****+0.0649**** |
-| Scientific reasoning | 0.7272 | 0.5576 | 🔴 -0.1696 |
-| Biology & medicine | 0.5908 | 0.6223 | **+0.0315** |
-| Disaster & safety procedures | 0.5949 | 0.5842 | 🔴 -0.0107 |
-| General multi-step reasoning | 0.5420 | 0.5738 | **+0.0318** |
-| **Size-weighted mean** | **0.6223** | **0.6360** | ****+0.0137**** |
-🔴 **Do not use this model for disaster and safety content.** In that domain it does not clear the
-surface baseline, which means it is reading answer shape rather than correctness there.
-✅ **Professional-exam style content is where it earns its size.** It runs on a laptop, on CPU, and
-inside networks that never reach the internet — places a hosted API cannot go.
-## How it works
-```
-[question + answer]  →  one forward pass
-                     →  final-layer hidden state at the last position (2560-d)
-                     →  probe
-                     →  score
-```
-**Generated tokens: 0.** No access to the answering model's weights or logits is required; the text
-of the answer is the only input. Latency is one forward pass, and batching converts directly into
-throughput.
-## Usage
-```python
-import json
-import numpy as np, torch
-from huggingface_hub import hf_hub_download, snapshot_download
-from transformers import AutoModel, AutoTokenizer
-REPO = "FINAL-Bench/ZTC-Judge-4B"
-cfg  = json.load(open(hf_hub_download(REPO, "ztc_config.json"), encoding="utf-8"))
-path = snapshot_download(REPO)
-tok   = AutoTokenizer.from_pretrained(path)
-model = AutoModel.from_pretrained(path, dtype=torch.bfloat16, low_cpu_mem_usage=True).eval()
-def hidden(question, answer):
-    b = tok([cfg["template"] % (question, answer)], return_tensors="pt",
-            truncation=True, max_length=cfg["max_length"])
-    dev = next(model.parameters()).device
-    with torch.no_grad():
-        h = model(input_ids=b["input_ids"].to(dev),
-                  attention_mask=b["attention_mask"].to(dev)).last_hidden_state
-    return h[0, int(b["attention_mask"].sum()) - 1].float().numpy().astype(np.float64)
-# linear probe — one dot product
-p = np.load(hf_hub_download(REPO, "ztc_probe.npz"))
-v = hidden("Which defensive chemical does an insect release?", "C. Allomone")
-print(float(((v - p["mu"]) / p["sd"]) @ p["w"]))
-```
-The score is an unbounded real number; higher means more likely correct. It is a **ranking signal**,
-not a calibrated probability — choose a threshold from your own review budget.
-## Two probes ship with this model
-| File | Produces |
-|---|---|
-| `ztc_probe.npz` | linear readout — one dot product |
-| `ztc_curve_probe.npz` | **the reported figure 0.6360** — 256 anchors, RBF kernel |
-Both read the same input. The curved probe is the one to use when the number matters.
-## API — drop-in for an existing JEV integration
-The endpoint takes the same request shape and returns the same response shape, so switching an
-existing integration is a URL change.
-```bash
-POST /v1/evaluate
-Authorization: Bearer <token>
-{"model": "vidraft/ztc",
- "state": {"question": "...", "answer": "..."},
- "questions": {"correct": {"type": "boolean",
-                           "instructions": "Is the ANSWER factually correct?"}}}
-```
-```json
-{"model": "vidraft/ztc-judge-4b",
- "answers": {"correct": {
-    "probability": 0.1043,
-    "verdict": "review",
-    "score": -0.72,
-    "position": 0.268,
-    "band": "low",
-    "action": "hold_or_escalate",
-    "measured": {
-      "band_accuracy": 0.485,
-      "base_accuracy": 0.748,
-      "if_lowest_20pct_dropped": 0.814,
-      "escalate_gain_at_20pct_budget": 0.0134,
-      "do_not": "resample_same_model",
-      "why_not": "measured: fixes 6.7% of wrong answers, breaks 13.1% of right ones"}}},
- "usage": {"generated_tokens": 0}}
-```
-`type` accepts `boolean` and `noul`. Existing clients read `answers.<key>.probability` and ignore
-the rest; the additional fields are there for clients that want to act on the score rather than
-merely record it. **0.19 s per call, zero generated tokens.**
-### What `probability` means
-The raw score is unbounded. The shipped calibration maps it to P(answer is correct), fitted
-**leave-one-domain-out** — the mapping never sees the domain it is applied to.
-| | Expected calibration error |
-|---|---|
-| **ZTC-Judge-27B (after calibration)** | **0.0245** |
-| JEV, as shipped | 0.0381 |
-| JEV, after the same calibration | 0.0261 |
-| Laya-Multilingual, as shipped | 0.4985 |
-| Laya-Typed-Decisions, as shipped | 0.2641 |
-Measured on the same 2,018 items. **ZTC and JEV are effectively tied on calibration; the difference
-of 0.0016 is not meaningful.** Figures published elsewhere for these systems were measured on other
-test sets and do not reproduce here.
-🔴 Calibration is uneven across domains: 0.0225 on biology & medicine, but 0.2941 on scientific
-reasoning and 0.2381 on general reasoning. Treat the probability as reliable in the first case and
-as a ranking signal only in the other two.
-## What to do when the score is low
-The score alone is not actionable, so the response carries the measured consequence of each choice.
-| Band | Share | **Actual accuracy of answers in this band** | Recommended |
-|---|---|---|---|
-| `low` | 20% | **48.5%** | hold, or escalate to a stronger model |
-| `mid` | 40% | 73.2% | escalate if budget allows |
-| `high` | 40% | **89.6%** | accept |
-**Three things that work, measured on 2,018 items:**
-| Action | Effect |
-|---|---|
-| **Drop the lowest-scoring 20%** | accuracy of what remains: **74.8% → 81.4%** |
-| **Escalate the lowest 20% to a stronger model** | **+1.34 pp** end-to-end |
-| **Send the lowest 20% to human review** | catches **47.2%** of all errors — 2.4× random |
-### Generate several candidates and let the verifier pick
-When the score is low, the most effective next step is not to escalate — it is to **produce more
-candidate answers and select between them**. Measured on 346 questions with five candidates each
-(1,730 candidates), all scored through this endpoint:
-| Policy | Accuracy | Output tokens | vs. one attempt |
-|---|---|---|---|
-| One attempt | 49.13% | 72 | — |
-| Majority vote over 3 | 46.82% | 212 | **−2.31 pp** |
-| Majority vote over 5 | 46.53% | 353 | **−2.60 pp** |
-| **Pick best of 5 with this model** | **51.45%** | 353 | **+2.31 pp** |
-| Pick best of 5 with JEV | 53.47% | 353 | +4.34 pp |
-| Pick one of 5 at random | 46.82% | 353 | −2.31 pp |
-| *Oracle — any correct candidate counts* | *63.87%* | *353* | *+14.74 pp* |
-**The same five candidates swing by 5 points depending on how one is chosen.** Majority voting is
-worse than not resampling at all: when a model prefers a wrong answer, more samples make that wrong
-consensus more certain. A verifier that ranks the candidates is what turns extra samples into
-accuracy.
-**Spend the budget only where it is needed.** Generating extra candidates only for low-scoring
-first attempts captures most of the gain at a fraction of the cost:
-| Triggered on | Accuracy | Output tokens | vs. one attempt |
-|---|---|---|---|
-| 10% of items | 49.71% | 80 | +0.58 pp |
-| **30% of items** | **50.87%** | **132** | **+1.73 pp** |
-| 100% of items | 51.45% | 353 | +2.31 pp |
-**At a 30% trigger rate you get three quarters of the benefit for 1.8× the tokens**, where always
-generating costs 4.9× for 1.3× the benefit.
-*Scope: one generator (GPT-4o-mini), one item set, five candidates. The oracle row shows the
-headroom that remains — a correct candidate is present far more often than any policy recovers it.*
-**One thing that does not work:**
-🔴 **Do not take a majority vote over resamples.** Measured: five resamples with majority voting score **46.53%** where a single attempt scores **49.13%**. More candidates make a wrong consensus more certain unless something picks between them — see the table above.
-*Escalation pays for itself through precision, not recall. Re-answering repairs about 38% of wrong
-answers and damages about 30% of right ones, so a gate is only worth its budget if it mostly calls
-answers that are actually wrong.*
----
-## Protocol
-| | |
-|---|---|
-| Items | 2,018 · 508 incorrect · 5 domains · answers written by 4 different models |
-| Metric | AUC — how well wrong answers sort to the bottom. Threshold-free. 0.5 = coin flip |
-| Selection | **Leave-one-domain-out.** Every figure comes from a domain the probe never saw; hyper-parameters are chosen inside the training domains only |
-| Aggregation | Per domain, then size-weighted. Pooling all items into one AUC inflates the result |
-The same protocol, item set and grading code are applied to every rung of the ladder and to the
-other systems on the independent leaderboard:
-<https://huggingface.co/spaces/mayafree/typed-decision-leaderboard>
-## Out of scope
-- **Not a grounding checker.** It does not take a source document and decide whether the answer
-  follows from it.
-- **Not a safety, toxicity or policy classifier.**
-- **Not a calibrated probability.** Use it to rank and threshold.
-- **Not a general-purpose verifier at this size.** See the domain table above.
-## Limitations
-- **Domain coverage.** Scores are meaningful only for the five domains listed. Outside them nothing
-  has been measured and no guarantee is published.
-- **Below the surface baseline on disaster and safety.** Stated in the table rather than omitted.
-- **Sensitive to which model wrote the answer.** The probe is fitted on answers from four models.
-  Adding 1,772 answers from a single additional model shifted the mixture and **lowered** the
-  size-weighted score from 0.7278 to 0.7177 — professional exams rose to 0.8575 while every other
-  domain fell. Treat "works on any model's output" as a design goal, not a measured guarantee: if
-  your generator differs sharply from the training mixture, measure before relying on the number.
-- **Revision lock.** The probe is fitted to one specific revision of the base model. Running it on a
-  different revision produces **no error and silently wrong scores**; this repository ships the
-  matching weights so that failure mode cannot occur.
-- **It reports the verifier's judgement**, which is not the answering model's own confidence — that
-  quantity measures 0.5000 on this set.
-## Lineage
-| | |
-|---|---|
-| Base model | `Qwen/Qwen3.5-4B`, Apache-2.0 |
-| Modification to base weights | none — the probes are separate files |
-| Added by FINAL-Bench | probes, inference code, evaluation protocol and tables |
-## What this repository contains
-| | |
-|---|---|
-| Included | Base weights · tokenizer · linear probe · curved probe · configuration |
-| Not included | Training corpus · hidden-state matrices · fitting pipeline |
-## The rest of the ladder
-[ZTC-Judge-27B](https://huggingface.co/FINAL-Bench/ZTC-Judge-27B) ·
-[Darwin-397B-ZTC](https://huggingface.co/FINAL-Bench/Darwin-397B-ZTC) ·
-[ZTC-Judge-9B](https://huggingface.co/FINAL-Bench/ZTC-Judge-9B) ·
-[ZTC-Judge-4B](https://huggingface.co/FINAL-Bench/ZTC-Judge-4B)
-## License
-The base model is Apache-2.0 and redistributable. The probes, the inference code and the evaluation
-tables are assets of FINAL-Bench / VIDRAFT.

+---
+license: apache-2.0
+language:
+- en
+- ko
+tags:
+- ztc
+- answer-verification
+- hallucination-detection
+- zero-token
+- confidence-estimation
+library_name: transformers
+pipeline_tag: text-classification
+---
+# ZTC-Judge-4B
+**Answer verification from a single forward pass, with zero generated tokens — at 4B.**
+ZTC-Judge-4B takes a question and an answer written by *any* model and scores whether that
+answer can be trusted. It is the bottom rung of a four-point size ladder measured under one
+identical protocol, and it is published so the shape of that ladder can be checked rather than
+asserted.
+> **ZTC** — Zero-Token Confidence · **Judge** — it evaluates *someone else's* answer, not its own
+---
+## Read this before deploying
+This model is **not** the strongest member of the family, and the card says so with numbers.
+| Model | Leaderboard AUC |
+|---|---|
+| Darwin-397B-ZTC | 0.7272 |
+| ZTC-Judge-27B | 0.7289 |
+| **ZTC-Judge-9B** | **0.6506** |
+| **ZTC-Judge-4B** | **0.6360** |
+| *Answer length and formatting only* | *0.6223* |
+> **Revised 2026-09-21.** Earlier revisions of this card reported **0.7364** for Darwin-397B-ZTC
+> and **0.7282** for ZTC-Judge-27B. Those figures were produced by a run whose standardisation
+> statistics were computed over all five domains, including the held-out one, which leaks a small
+> amount of the evaluation domain into every figure. Re-run with the statistics fitted inside the
+> training domains only, the figures are **0.7272** and **0.7289**. Cite the current values.
+**The ladder does not decline smoothly — it steps.** Between 9B and 27B the score moves 0.078,
+while between 4B and 9B it moves 0.015. Whatever carries verification quality is largely absent
+below 27B on this axis.
+**Where this model is worth deploying is one specific place**, and it is a real one:
+| Domain | Surface baseline | **4B** | Margin |
+|---|---|---|---|
+| **Professional exams (law · math · biology)** | 0.7138 | **0.7787** | ****+0.0649**** |
+| Scientific reasoning | 0.7272 | 0.5576 | 🔴 -0.1696 |
+| Biology & medicine | 0.5908 | 0.6223 | **+0.0315** |
+| Disaster & safety procedures | 0.5949 | 0.5842 | 🔴 -0.0107 |
+| General multi-step reasoning | 0.5420 | 0.5738 | **+0.0318** |
+| **Size-weighted mean** | **0.6223** | **0.6360** | ****+0.0137**** |
+🔴 **Do not use this model for disaster and safety content.** In that domain it does not clear the
+surface baseline, which means it is reading answer shape rather than correctness there.
+✅ **Professional-exam style content is where it earns its size.** It runs on a laptop, on CPU, and
+inside networks that never reach the internet — places a hosted API cannot go.
+## How it works
+```
+[question + answer]  →  one forward pass
+                     →  final-layer hidden state at the last position (2560-d)
+                     →  probe
+                     →  score
+```
+**Generated tokens: 0.** No access to the answering model's weights or logits is required; the text
+of the answer is the only input. Latency is one forward pass, and batching converts directly into
+throughput.
+## Usage
+```python
+import json
+import numpy as np, torch
+from huggingface_hub import hf_hub_download, snapshot_download
+from transformers import AutoModel, AutoTokenizer
+REPO = "FINAL-Bench/ZTC-Judge-4B"
+cfg  = json.load(open(hf_hub_download(REPO, "ztc_config.json"), encoding="utf-8"))
+path = snapshot_download(REPO)
+tok   = AutoTokenizer.from_pretrained(path)
+model = AutoModel.from_pretrained(path, dtype=torch.bfloat16, low_cpu_mem_usage=True).eval()
+def hidden(question, answer):
+    b = tok([cfg["template"] % (question, answer)], return_tensors="pt",
+            truncation=True, max_length=cfg["max_length"])
+    dev = next(model.parameters()).device
+    with torch.no_grad():
+        h = model(input_ids=b["input_ids"].to(dev),
+                  attention_mask=b["attention_mask"].to(dev)).last_hidden_state
+    return h[0, int(b["attention_mask"].sum()) - 1].float().numpy().astype(np.float64)
+# linear probe — one dot product
+p = np.load(hf_hub_download(REPO, "ztc_probe.npz"))
+v = hidden("Which defensive chemical does an insect release?", "C. Allomone")
+print(float(((v - p["mu"]) / p["sd"]) @ p["w"]))
+```
+The score is an unbounded real number; higher means more likely correct. It is a **ranking signal**,
+not a calibrated probability — choose a threshold from your own review budget.
+## Two probes ship with this model
+| File | Produces |
+|---|---|
+| `ztc_probe.npz` | linear readout — one dot product |
+| `ztc_curve_probe.npz` | **the reported figure 0.6360** — 256 anchors, RBF kernel |
+Both read the same input. The curved probe is the one to use when the number matters.
+## API — drop-in for an existing JEV integration
+The endpoint takes the same request shape and returns the same response shape, so switching an
+existing integration is a URL change.
+```bash
+POST /v1/evaluate
+Authorization: Bearer <token>
+{"model": "vidraft/ztc",
+ "state": {"question": "...", "answer": "..."},
+ "questions": {"correct": {"type": "boolean",
+                           "instructions": "Is the ANSWER factually correct?"}}}
+```
+```json
+{"model": "vidraft/ztc-judge-4b",
+ "answers": {"correct": {
+    "probability": 0.1043,
+    "verdict": "review",
+    "score": -0.72,
+    "position": 0.268,
+    "band": "low",
+    "action": "hold_or_escalate",
+    "measured": {
+      "band_accuracy": 0.485,
+      "base_accuracy": 0.748,
+      "if_lowest_20pct_dropped": 0.814,
+      "escalate_gain_at_20pct_budget": 0.0134,
+      "do_not": "resample_same_model",
+      "why_not": "measured: fixes 6.7% of wrong answers, breaks 13.1% of right ones"}}},
+ "usage": {"generated_tokens": 0}}
+```
+`type` accepts `boolean` and `noul`. Existing clients read `answers.<key>.probability` and ignore
+the rest; the additional fields are there for clients that want to act on the score rather than
+merely record it. **0.19 s per call, zero generated tokens.**
+### What `probability` means
+The raw score is unbounded. The shipped calibration maps it to P(answer is correct), fitted
+**leave-one-domain-out** — the mapping never sees the domain it is applied to.
+| | Expected calibration error |
+|---|---|
+| **ZTC-Judge-27B (after calibration)** | **0.0245** |
+| JEV, as shipped | 0.0381 |
+| JEV, after the same calibration | 0.0261 |
+| Laya-Multilingual, as shipped | 0.4985 |
+| Laya-Typed-Decisions, as shipped | 0.2641 |
+Measured on the same 2,018 items. **ZTC and JEV are effectively tied on calibration; the difference
+of 0.0016 is not meaningful.** Figures published elsewhere for these systems were measured on other
+test sets and do not reproduce here.
+🔴 Calibration is uneven across domains: 0.0225 on biology & medicine, but 0.2941 on scientific
+reasoning and 0.2381 on general reasoning. Treat the probability as reliable in the first case and
+as a ranking signal only in the other two.
+## What to do when the score is low
+The score alone is not actionable, so the response carries the measured consequence of each choice.
+| Band | Share | **Actual accuracy of answers in this band** | Recommended |
+|---|---|---|---|
+| `low` | 20% | **48.5%** | hold, or escalate to a stronger model |
+| `mid` | 40% | 73.2% | escalate if budget allows |
+| `high` | 40% | **89.6%** | accept |
+**Three things that work, measured on 2,018 items:**
+| Action | Effect |
+|---|---|
+| **Drop the lowest-scoring 20%** | accuracy of what remains: **74.8% → 81.4%** |
+| **Escalate the lowest 20% to a stronger model** | **+1.34 pp** end-to-end |
+| **Send the lowest 20% to human review** | catches **47.2%** of all errors — 2.4× random |
+### Generate several candidates and let the verifier pick
+When the score is low, the most effective next step is not to escalate — it is to **produce more
+candidate answers and select between them**. Measured on 346 questions with five candidates each
+(1,730 candidates), all scored through this endpoint:
+| Policy | Accuracy | Output tokens | vs. one attempt |
+|---|---|---|---|
+| One attempt | 49.13% | 72 | — |
+| Majority vote over 3 | 46.82% | 212 | **−2.31 pp** |
+| Majority vote over 5 | 46.53% | 353 | **−2.60 pp** |
+| **Pick best of 5 with this model** | **51.45%** | 353 | **+2.31 pp** |
+| Pick best of 5 with JEV | 53.47% | 353 | +4.34 pp |
+| Pick one of 5 at random | 46.82% | 353 | −2.31 pp |
+| *Oracle — any correct candidate counts* | *63.87%* | *353* | *+14.74 pp* |
+**The same five candidates swing by 5 points depending on how one is chosen.** Majority voting is
+worse than not resampling at all: when a model prefers a wrong answer, more samples make that wrong
+consensus more certain. A verifier that ranks the candidates is what turns extra samples into
+accuracy.
+**Spend the budget only where it is needed.** Generating extra candidates only for low-scoring
+first attempts captures most of the gain at a fraction of the cost:
+| Triggered on | Accuracy | Output tokens | vs. one attempt |
+|---|---|---|---|
+| 10% of items | 49.71% | 80 | +0.58 pp |
+| **30% of items** | **50.87%** | **132** | **+1.73 pp** |
+| 100% of items | 51.45% | 353 | +2.31 pp |
+**At a 30% trigger rate you get three quarters of the benefit for 1.8× the tokens**, where always
+generating costs 4.9× for 1.3× the benefit.
+*Scope: one generator (GPT-4o-mini), one item set, five candidates. The oracle row shows the
+headroom that remains — a correct candidate is present far more often than any policy recovers it.*
+**One thing that does not work:**
+🔴 **Do not take a majority vote over resamples.** Measured: five resamples with majority voting score **46.53%** where a single attempt scores **49.13%**. More candidates make a wrong consensus more certain unless something picks between them — see the table above.
+*Escalation pays for itself through precision, not recall. Re-answering repairs about 38% of wrong
+answers and damages about 30% of right ones, so a gate is only worth its budget if it mostly calls
+answers that are actually wrong.*
+---
+## Protocol
+| | |
+|---|---|
+| Items | 2,018 · 508 incorrect · 5 domains · answers written by 4 different models |
+| Metric | AUC — how well wrong answers sort to the bottom. Threshold-free. 0.5 = coin flip |
+| Selection | **Leave-one-domain-out.** Every figure comes from a domain the probe never saw; hyper-parameters are chosen inside the training domains only |
+| Aggregation | Per domain, then size-weighted. Pooling all items into one AUC inflates the result |
+The same protocol, item set and grading code are applied to every rung of the ladder and to the
+other systems on the independent leaderboard:
+<https://huggingface.co/spaces/mayafree/typed-decision-leaderboard>
+## Out of scope
+- **Not a grounding checker.** It does not take a source document and decide whether the answer
+  follows from it.
+- **Not a safety, toxicity or policy classifier.**
+- **Not a calibrated probability.** Use it to rank and threshold.
+- **Not a general-purpose verifier at this size.** See the domain table above.
+## Limitations
+- **Domain coverage.** Scores are meaningful only for the five domains listed. Outside them nothing
+  has been measured and no guarantee is published.
+- **Below the surface baseline on disaster and safety.** Stated in the table rather than omitted.
+- **Sensitive to which model wrote the answer.** The probe is fitted on answers from four models.
+  Adding 1,772 answers from a single additional model shifted the mixture and **lowered** the
+  size-weighted score from 0.7278 to 0.7177 — professional exams rose to 0.8575 while every other
+  domain fell. Treat "works on any model's output" as a design goal, not a measured guarantee: if
+  your generator differs sharply from the training mixture, measure before relying on the number.
+- **Revision lock.** The probe is fitted to one specific revision of the base model. Running it on a
+  different revision produces **no error and silently wrong scores**; this repository ships the
+  matching weights so that failure mode cannot occur.
+- **It reports the verifier's judgement**, which is not the answering model's own confidence — that
+  quantity measures 0.5000 on this set.
+## Lineage
+| | |
+|---|---|
+| Base model | `Qwen/Qwen3.5-4B`, Apache-2.0 |
+| Modification to base weights | none — the probes are separate files |
+| Added by FINAL-Bench | probes, inference code, evaluation protocol and tables |
+## What this repository contains
+| | |
+|---|---|
+| Included | Base weights · tokenizer · linear probe · curved probe · configuration |
+| Not included | Training corpus · hidden-state matrices · fitting pipeline |
+## The rest of the ladder
+[ZTC-Judge-27B](https://huggingface.co/FINAL-Bench/ZTC-Judge-27B) ·
+[Darwin-397B-ZTC](https://huggingface.co/FINAL-Bench/Darwin-397B-ZTC) ·
+[ZTC-Judge-9B](https://huggingface.co/FINAL-Bench/ZTC-Judge-9B) ·
+[ZTC-Judge-4B](https://huggingface.co/FINAL-Bench/ZTC-Judge-4B)
+## License
+The base model is Apache-2.0 and redistributable. The probes, the inference code and the evaluation
+tables are assets of FINAL-Bench / VIDRAFT.