Von 1.3

Von is an open-source, non-autoregressive System One decision model: it picks an option, judges a condition or rates a level in one bidirectional forward pass, with no token generation. This card is about the weights. Install, CLI, wire protocol and examples are in the repo: https://github.com/wfzyx/von

• Version: 1.3 (von-1.3.0; engine release, weights unchanged from 1.2)
• Size: 395M (ModernBERT-large backbone + Option-Marker scoring head), 1.5 GB fp32
• Context: 8192 tokens
• License: Apache-2.0
• Weights SHA: 5df8185a4f2327ad0a7cd117cc4f701ac557b9ae

pip install "von-sdk>=1.3.5"

Previously published as wfzyx/von-1.0; the old id redirects.

Files

File	Purpose
model.safetensors	ModernBERT-large backbone (fine-tuned)
option_marker.pt	Option-Marker scoring head
marker_calibration.json	Calibration map, zero-shot Noul prior, and independent_options: true — the SDK reads this flag to run the order-invariant attention mode these weights were trained under. Do not drop it.
config.json, tokenizer*	Standard HF config and tokenizer

Architecture

The state, the question and every candidate option are packed into one sequence. Each option gets a [MASK] marker whose final hidden state is scored by a small head; a softmax over the marker logits is the answer distribution. Cost is one encoder pass regardless of option count. Noul is a two-option Choice over "condition holds / is false" descriptions (or the caller's criteria); Score is a Choice over level descriptions with the expectation taken over the distribution.

Order-invariant scoring (since 1.2). Inside the encoder an option's tokens attend only to the shared premise and to that option's own tokens, and every option's position ids restart at the end of the premise; ModernBERT's sliding-window attention is computed from those ids. Each option's logit is therefore a function of (premise, that option) alone: permuting the options permutes the logits and changes nothing else. Measured on JevBench hard, 111 items × 4 orderings: 1.1 flipped 49.5 % of answers, 1.2 flips 0.

Training data

Base: ModernBERT-large (2T tokens general web, technical text and code). Fine-tuning is a listwise softmax cross-entropy + Brier objective over the option markers, with independent-options masking and digit-split tokenisation, on a ~290k-item balanced decision corpus, then continue-trained on synthetic two-hop and numeric sets. Every row is the same shape the model serves: state, question, described options, gold (or a soft target distribution where the source gives one).

Cluster	Share	Sources and tasks
Operational & enterprise workflow	~25 %	IT ticket triage, customer intent routing (Banking77), billing/refund policy, warranty checks, order exceptions
Security, DevOps & compliance	~20 %	Secret-leak and SQL-injection screening, phishing, commit intent, on-call routing, PII
Safety, policy & moderation	~15 %	Ad policy, Fair Housing, travel-expense limits, ToS gating
Linguistic & content semantics	~15 %	Formality, grammar error taxonomy, emotion/sentiment (dair-ai/emotion), reading level
Triage & services	~10 %	Symptom and veterinary urgency, 311 routing, dietary/allergen checks
Adversarial reasoning anchor	~15 %	ANLI R1–3 + WANLI as Noul entailment and 3-way Choice, kept to anchor general logic

No JevBench item, public or sealed, is used for gradient training. The calibration map was fitted on the 231 public JevBench items (see Calibration); jabr v2, Decision Index and the coding-agent probes below are fully out of sample.

Corpus builders are in the repo under training/ (prepare_universal_dataset.py, prepare_decision_dataset.py, generate_synthetic_decisions.py, prepare_judge_diversity.py for the judge-shaped and label-diverse public mix used in 1.4 experiments).

Calibration

Confidence comes from an input-conditioned temperature stored in marker_calibration.json: T = bias + w_H·H_norm + w_len·log10(state_tokens)/4 + w_K·(K/8), clamped to [0.3, 12], where H_norm is the normalised entropy of the raw option distribution. Temperature is monotonic, so it never changes an answer, only how sure the answer claims to be. Noul additionally passes through the band decision rule (p' = 0.8 + 0.1·(p−0.5), mirrored below 0.5) so every yes/no commits outside the 0.2–0.8 abstention band; --noul-decision raw returns the calibrated posterior.

The shipped map is in-sample on JevBench public (Calibration axis 77.4 in-sample, 67.6 split-half) and does not carry to other distributions — out of domain Von can be confidently wrong. von calibrate labels.jsonl refits it on your labels with frozen weights on CPU in minutes; on a 38-label coding-agent probe set a scalar T = 2.15 took ECE from 0.21 to 0.11.

Benchmarks

JevBench v1.5.1 (live board, sealed + open halves)

Chance-corrected competence (100 = perfect, 0 = chance) for Von 1.2 weights, per type and tier. Sealed items are never seen by any submitter.

type	half	easy	standard	judge	hard
Choice	open	91.0	37.6	36.7	17.0
Choice	sealed	50.2	30.7	7.2	11.3
Score	open	6.3	56.5	39.9	36.5
Score	sealed	45.0	29.6	29.6	28.0
Noul (1.2, raw posterior)	sealed	−92	−100	−96	−100

Calibration 83.5, Cost 82.7, median latency 0.92 s (board measurement). The Noul row is the v1.5 abstention rule meeting 1.2's hedged posteriors: nearly every Noul answer fell in the 0.2–0.8 band and was scored as an abstention, which zeroes the Intelligence axis for this row and for most calibrated encoders on the board. The band rule in 1.3.3+ fixes the decision without touching the weights (public Noul competence −76.5 → +18.9 on JevBench, −42 → +32 on jabr v2); the board row awaits re-measurement (JevBench #143).

Sealed Choice ties the 151M Verdict 1.4 (17.9) and trails the 194M GLiNER2 (21.0); Von leads the encoder class on sealed Score (30.5) and open Choice (34.4). Von's open→sealed Choice gap (14.7 pp against a field median of 5.2) is the clearest thing on this board and is what 1.4 is aimed at.

JevBench v1.4 (last board with a Von Intelligence score)

system	params	I	C	S	K	composite	easy	standard	hard	sealed	sealed ECE
Jev 1.13 (closed)	—	53.1	76.3	83.3	52.0	63.3	1.000	0.990	0.741	0.367	—
jeff (GLiFormer-large)	~400M	36.8	67.9	63.5	76.6	30.6	1.000	0.760	0.377	0.331	—
Laya (ModernBERT-large)	421M	36.1	63.7	71.1	86.2	30.3	0.944	0.729	0.341	0.308	0.172
Von 1.2	395M	34.5	75.7	70.5	77.8	27.5	0.931	0.688	0.373	0.279	0.107

Speed 70.5 is an unmeasured carry-over; remeasured under the JevBench protocol Von runs 0.096 s raw p50 on 4 vCPU (OpenVINO, Speed 89.0) and 0.023 s on an A10G (94.2), inside the Jev-class latency line. With the 1.3 chain-of-options engine on the same weights, public hard goes 0.378 → 0.441 locally (2 vs 9 discordant, McNemar p = 0.065) with zero discordant pairs on easy, standard and jabr v2.

Out of sample

suite	what	Von
Decision Index v0.2.1	38 benchmarks, ~150k requests, no truncation (323 context-window refusals recorded as unsupported), 0 errors	13.74 (raw 34.11, coverage 99.96 %), p50 32.8 ms on A10G
jabr v2	49 tasks, 869 cases, out-of-domain routing/judgment	72.0 % macro (Choice macro 83.0 %)
ViZDoom Defend-the-Center	8-seed zero-shot control, six text-described actions per frame	9.0 kills/episode at ~18 ms/decision (Jev 1.13: 5.6)
Coding-agent probes (Hagetino, issue #21)	turn tagging / failure triage / note relation / evidence, 38 labels	26/38 Choice; same on 1.2 and 1.3

Every accuracy claim goes through a paired exact McNemar test with a minimum detectable effect; results below the MDE are reported as UNRESOLVABLE.

Limitations

• English only. Other languages get token overlap with unearned confidence.
• Selects and scores; does not write text.
• Weak on long multi-clause policy and multi-hop temporal/numeric composition (hard tier ~0.37–0.44); the chains engine covers the computable subset only.
• judge without criteria is the weakest path: generic "holds / is false" descriptions plus surface cues. Pass criteria or phrase as a described Choice.
• Known training gap: auth failures (401/403), rate limits and tool rejections route to a generic code-error option with high confidence (issue #22).
• Calibration map is JevBench-shaped; refit with von calibrate if you act on the confidence values.

Citation

@software{von2026,
  title  = {Von: An Open-Source System One Decision Model},
  author = {Panisa, Victor Hugo},
  year   = {2026},
  url    = {https://github.com/wfzyx/von}
}