File size: 31,568 Bytes
31ce914
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
0e01057
31ce914
 
 
0e01057
31ce914
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
0e01057
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
31ce914
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
0e01057
 
 
31ce914
 
 
 
 
 
 
 
 
 
 
 
 
0e01057
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
31ce914
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
<!doctype html>
<html lang="en">
<head>
<meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1">
<title>JEV-CPU — Running Semantic-If Decisions on a CPU (Technical Report)</title>
<meta name="description" content="A technical report on JEV-CPU: running SemIf-style semantic-if decisions on a CPU by reading option logits from a small open model, with latency, scaling, and input-length measurements.">
<style>
  :root{
    --bg:#ffffff; --fg:#1a1f26; --muted:#5b6672; --line:#e4e8ee;
    --accent:#2b6cff; --accent2:#1f8a4c; --warn:#c2410c; --code:#f4f6fa; --chip:#eef3ff;
  }
  @media (prefers-color-scheme: dark){
    :root{--bg:#0e1116;--fg:#e6edf3;--muted:#93a1b0;--line:#232b35;--accent:#6ea8ff;
          --accent2:#57d977;--warn:#ff9d5c;--code:#161b22;--chip:#16233a;}
  }
  *{box-sizing:border-box}
  html{scroll-behavior:smooth}
  body{margin:0;background:var(--bg);color:var(--fg);
    font:16px/1.65 -apple-system,BlinkMacSystemFont,"Segoe UI",Roboto,Helvetica,Arial,"Noto Sans KR",sans-serif;}
  .wrap{max-width:820px;margin:0 auto;padding:40px 22px 90px}
  header.title{border-bottom:1px solid var(--line);padding-bottom:26px;margin-bottom:34px}
  .kicker{color:var(--accent);font-weight:700;letter-spacing:.06em;text-transform:uppercase;font-size:12.5px}
  h1{font-size:30px;line-height:1.25;margin:.35em 0 .3em}
  .authors{color:var(--muted);font-size:15px;margin:.2em 0}
  .badges{margin-top:14px;display:flex;flex-wrap:wrap;gap:8px}
  .badges a{font-size:12.5px;text-decoration:none;color:var(--fg);background:var(--chip);
    border:1px solid var(--line);border-radius:20px;padding:4px 12px}
  .badges a:hover{border-color:var(--accent)}
  h2{font-size:21px;margin:2.1em 0 .5em;padding-top:.4em}
  h3{font-size:17px;margin:1.5em 0 .4em}
  p,li{color:var(--fg)}
  a{color:var(--accent)}
  .muted{color:var(--muted)}
  .abstract{background:var(--code);border:1px solid var(--line);border-radius:12px;padding:18px 20px}
  .abstract p{margin:.3em 0}
  figure{margin:24px 0;text-align:center}
  figure img{max-width:100%;height:auto;border:1px solid var(--line);border-radius:10px}
  figcaption{color:var(--muted);font-size:13.5px;margin-top:8px}
  code{background:var(--code);border:1px solid var(--line);border-radius:5px;padding:.5px 5px;font-size:13.5px;
    font-family:ui-monospace,SFMono-Regular,Menlo,Consolas,monospace}
  pre{background:var(--code);border:1px solid var(--line);border-radius:10px;padding:14px 16px;overflow:auto}
  pre code{background:none;border:none;padding:0;font-size:13px;line-height:1.55}
  .tablewrap{overflow-x:auto;margin:18px 0}
  table{border-collapse:collapse;width:100%;font-size:14.5px}
  th,td{border:1px solid var(--line);padding:8px 11px;text-align:left}
  th{background:var(--code);font-weight:650}
  td.n,th.n{text-align:right;font-variant-numeric:tabular-nums}
  .ok{color:var(--accent2);font-weight:700}
  .bad{color:var(--warn);font-weight:700}
  .note{border-left:3px solid var(--accent);background:var(--chip);padding:10px 16px;border-radius:0 8px 8px 0;margin:18px 0}
  .warnbox{border-left:3px solid var(--warn);background:color-mix(in srgb,var(--warn) 10%,transparent);
    padding:10px 16px;border-radius:0 8px 8px 0;margin:18px 0}
  ol.refs{padding-left:22px} ol.refs li{margin:.4em 0;font-size:14.5px;color:var(--muted)}
  ol.refs a{word-break:break-word}
  hr{border:none;border-top:1px solid var(--line);margin:40px 0}
  .toc{font-size:14.5px;columns:2;column-gap:30px}
  .toc a{display:block;color:var(--muted);text-decoration:none;padding:2px 0}
  .toc a:hover{color:var(--accent)}
  footer{margin-top:50px;border-top:1px solid var(--line);padding-top:18px;color:var(--muted);font-size:13.5px}
  @media(max-width:560px){.toc{columns:1}h1{font-size:25px}}
</style>
</head>
<body>
<div class="wrap">

<header class="title">
  <div class="kicker">Technical Report · Independent Project</div>
  <h1>JEV-CPU: Running Semantic-If Decisions on a CPU</h1>
  <div class="authors">leesk212 · <span class="muted">Meanblock</span></div>
  <div class="authors muted">September 19, 2026 · v1</div>
  <div class="badges">
    <a href="https://github.com/leesk212/JEV-CPU">GitHub</a>
    <a href="https://huggingface.co/Meanblock/JEV-CPU">Hugging Face</a>
    <a href="https://github.com/TheoLeeCJ/SemIf">Upstream: SemIf</a>
  </div>
</header>

<div class="abstract">
  <p><strong>Abstract.</strong> <em>Semantic-if</em> decisions — <em>route this</em>, <em>is this a policy
  violation</em>, <em>what severity is this incident</em> — are usually answered by having a chat model
  generate text that software then parses back into a branch. SemIf (an open reproduction of TypeSafe's
  Jev pattern) instead reads a decision directly from a model's <em>option logits</em> in a single
  forward pass, with no text generated. SemIf targets a GPU holding a 4B model. This report describes
  <strong>JEV-CPU</strong>, a small adaptation that runs the <em>same</em> engine on a commodity CPU with
  a 0.6B open model, and reports measurements from an 8&nbsp;GB, GPU-less machine: qualitative decisions
  across eight domains, a latency-versus-input-length curve, and the resulting practical input ceiling.
  We make no claim of methodological novelty — the contribution is engineering and empirical: showing the
  method is device-agnostic, quantifying where CPU latency (not memory or the model) becomes the limit,
  and documenting how accuracy scales with model size. All code, weights pointer, and demos are public.</p>
</div>

<h2 id="toc">Contents</h2>
<nav class="toc">
  <a href="#intro">1. Introduction</a>
  <a href="#background">2. Background</a>
  <a href="#method">3. Method: reading a decision from logits</a>
  <a href="#system">4. System: the CPU port</a>
  <a href="#domains">5. Qualitative results across domains</a>
  <a href="#latency">6. Latency and input-length limits</a>
  <a href="#scaling">7. Accuracy scaling with model size</a>
  <a href="#outlook">7.1 Outlook: GPU serving &amp; a production JEV</a>
  <a href="#limitations">8. Limitations</a>
  <a href="#conclusion">9. Conclusion</a>
  <a href="#repro">10. Reproducibility</a>
  <a href="#appendix">Appendix A. Code-level walkthrough</a>
  <a href="#refs">References</a>
</nav>

<figure>
  <img src="assets/jev-cpu-demo.gif" alt="JEV-CPU deciding live across eight domains on CPU">
  <figcaption>Figure 1. JEV-CPU running live on a CPU across eight domains: the state is typed in, criteria
  are added, and each decision is read from <code>Qwen3-0.6B</code>'s option logits in ~1&nbsp;s — no text generated.</figcaption>
</figure>

<h2 id="intro">1. Introduction</h2>
<p>Most decisions inside software agents are small and typed: choose a queue, pick a severity, decide
whether evidence supports a claim. A chat model can answer them, but generating an answer sentence and
parsing it back into an <code>if</code> is slow and brittle. A <em>decision-native</em> alternative is to
present the options as tokens and read the model's probability over exactly those tokens in one forward
pass. TypeSafe's closed <em>Jev</em> service popularized this interface; <a href="https://github.com/TheoLeeCJ/SemIf">SemIf</a>
reproduces the interface pattern with open models on a GPU.</p>
<p>This report asks a narrow, practical question: <strong>does the method still work with no GPU and a
tiny model, and where does it break down?</strong> We port SemIf's scoring to CPU (<a href="#system">§4</a>),
run it across eight domains (<a href="#domains">§5</a>), and measure the latency wall that determines the
usable input size (<a href="#latency">§6</a>).</p>

<h2 id="background">2. Background</h2>
<p>Reading a categorical decision from an LM's next-token distribution — rather than sampling text — is a
well-established idea (verbalizer-style zero-shot classification, answer-token scoring, NLI cross-encoders).
We claim <strong>no novelty</strong> for the mechanism. JEV-CPU is a CPU adaptation and empirical study of
SemIf, which is itself an independent reproduction of the Jev pattern; Jev and TypeSafe are the property of
their owners and are not affiliated with this work.</p>

<h2 id="method">3. Method: reading a decision from logits</h2>
<p>A decision is one record: a <code>state</code> (evidence), a <code>question</code> (criterion), and
2–16 typed <code>options</code>. The <code>direct</code> scoring path is unchanged from SemIf:</p>
<ol>
  <li><strong>Letter-choice prompt.</strong> Each option is labeled <code>A</code>, <code>B</code>,
  <code>C</code>… in a chat turn whose system instruction asks for a single uppercase letter and nothing
  else. With the generation prompt applied, the next token the model would emit is the answer letter.</li>
  <li><strong>Single-token pinning.</strong> Each letter is verified to encode to exactly one token that
  round-trips and does not perturb the prompt's tokenization, so every option maps to one clean, comparable
  vocabulary slot.</li>
  <li><strong>One forward pass.</strong> The prompt is run through the model once; we keep the logits at the
  final position — the distribution over the next token. No sampling, no decode loop, no JSON to repair.</li>
  <li><strong>Softmax over slots.</strong> From that full-vocabulary logit vector we gather only the option
  letters' token ids and softmax over those, giving a probability per option conditional on the declared set.</li>
</ol>
<pre><code>logits   = model(**inputs, use_cache=False).logits[:, -1, :]   # (vocab,)
selected = logits[slot_ids]        # logits at tokens A, B, C, ...
probs    = softmax(selected)       # distribution over the declared options
winner   = options[argmax(probs)]</code></pre>
<p>Because it is one forward pass reading fixed positions, latency is dominated by prompt <em>prefill</em>,
not by generation length — the property JEV-CPU relies on to stay usable on a CPU.</p>

<h2 id="system">4. System: the CPU port</h2>
<p>SemIf forces a GPU in exactly one place — its model loader checks for a single CUDA device and loads with
<code>device_map={"":"cuda:0"}</code>, <code>bfloat16</code>. Everything downstream is device-agnostic: the
scoring code follows <code>device = next(model.parameters()).device</code> and the only CUDA-specific call,
<code>torch.cuda.synchronize()</code>, is guarded by <code>if device.type == "cuda"</code> (a no-op on CPU).</p>
<p>JEV-CPU therefore changes <strong>only the loader</strong>: a CPU / <code>float32</code> loader that reuses
SemIf's original, unmodified scoring functions. A small standard-library web server exposes the engine with a
three-pane UI (state, criteria, results). Swapping the model is a one-line change (<code>MODEL = …</code>);
the engine and UI are model-agnostic.</p>
<figure>
  <img src="assets/jev-cpu-ui.png" alt="JEV-CPU three-pane web UI">
  <figcaption>Figure 2. The web UI: state (top-left), criteria (bottom-left), and per-option probability
  bars with the chosen option (right). The footer names the method (JEV-CPU engine) and the brain model.</figcaption>
</figure>

<h2 id="domains">5. Qualitative results across domains</h2>
<p>Using <code>Qwen/Qwen3-0.6B</code> in <code>float32</code> on CPU, we ran the same engine across eight
domains (Figure 1). Each decision took ≈1&nbsp;s.</p>
<div class="tablewrap">
<table>
  <thead><tr><th>Domain</th><th>Criterion</th><th>Decision</th><th class="n">Forward</th></tr></thead>
  <tbody>
    <tr><td>Customer support</td><td>Sentiment</td><td><span class="ok">negative — 97.4%</span></td><td class="n">~1.1 s</td></tr>
    <tr><td>Customer support</td><td>Route to team</td><td><span class="ok">billing — 100%</span></td><td class="n">~1.0 s</td></tr>
    <tr><td>Content moderation</td><td>Policy violation?</td><td><span class="ok">violation — 99.3%</span></td><td class="n">~1.1 s</td></tr>
    <tr><td>Content moderation</td><td>Recommended action</td><td><span class="ok">warn — 69.2%</span></td><td class="n">~1.0 s</td></tr>
    <tr><td>Code-review triage</td><td>Merge risk</td><td><span class="ok">high — 99.8%</span></td><td class="n">~1.2 s</td></tr>
    <tr><td>Code-review triage</td><td>PR disposition</td><td><span class="ok">block — 94.9%</span></td><td class="n">~1.1 s</td></tr>
    <tr><td>Incident / DevOps</td><td>Severity</td><td><span class="ok">sev1 — 100%</span></td><td class="n">~1.2 s</td></tr>
    <tr><td>Incident / DevOps</td><td>Page on-call now?</td><td><span class="ok">page_now — 100%</span></td><td class="n">~1.1 s</td></tr>
    <tr><td>Email intent</td><td>Primary intent</td><td><span class="ok">sales — 100%</span></td><td class="n">~1.2 s</td></tr>
    <tr><td>Compliance gate</td><td>Change ticket required?</td><td><span class="ok">required — 100%</span></td><td class="n">~1.1 s</td></tr>
    <tr><td>Loan / credit risk</td><td>Credit risk</td><td><span class="ok">high — 100%</span></td><td class="n">~1.2 s</td></tr>
    <tr><td>Loan / credit risk</td><td>Recommended decision</td><td><span class="bad">approve — 82.8% ⚠︎</span></td><td class="n">~1.1 s</td></tr>
    <tr><td>Support prioritization</td><td>Priority</td><td><span class="ok">p1 — 100%</span></td><td class="n">~1.2 s</td></tr>
  </tbody>
</table>
</div>
<p class="muted">These are single illustrative runs, not a benchmark; they show the interface and the
qualitative behavior. The loan <em>decision</em> row is a deliberate example of a small-model slip
(see <a href="#limitations">§8</a>).</p>

<h2 id="latency">6. Latency and input-length limits</h2>
<p>Two numbers are often conflated. The model's context window is <strong>40,960 tokens</strong> — large even
at 0.6B, since context length comes from positional encoding, independent of parameter count. SemIf's engine
applies a default cap of <strong>4,096 tokens per decision</strong> as a guard (over-long prompts raise instead
of being silently truncated); it is configurable. Neither is the real constraint on CPU.</p>
<p>We measured one decision with a growing state on the 8&nbsp;GB CPU box (peak RSS via <code>getrusage</code>):</p>
<div class="tablewrap">
<table>
  <thead><tr><th class="n">Input tokens</th><th class="n">Forward (CPU)</th><th class="n">Peak RAM</th></tr></thead>
  <tbody>
    <tr><td class="n">254</td><td class="n">2.5 s</td><td class="n">~3.5 GB</td></tr>
    <tr><td class="n">731</td><td class="n">5.6 s</td><td class="n">~3.5 GB</td></tr>
    <tr><td class="n">1,363</td><td class="n">11.9 s</td><td class="n">~3.5 GB</td></tr>
    <tr><td class="n">2,629</td><td class="n">26.0 s</td><td class="n">~3.5 GB</td></tr>
    <tr><td class="n">5,165</td><td class="n">66.1 s</td><td class="n">~3.5 GB</td></tr>
    <tr><td class="n">7,697</td><td class="n">117.4 s</td><td class="n">~3.5 GB</td></tr>
  </tbody>
</table>
</div>
<p>RAM stayed flat at ~3.5&nbsp;GB with no OOM even at 7,697 tokens — well past the 4,096 default — so the
ceiling on this box is <strong>prefill latency (roughly quadratic in length)</strong>, not memory or the model.</p>
<div class="warnbox">
  <strong>Practical input ceiling.</strong> We treat <strong>≈7,700 tokens (~117&nbsp;s)</strong> as the usable
  maximum on this CPU: beyond it a single decision crosses <strong>~120&nbsp;s</strong>, which is no longer useful,
  so larger inputs are considered unsupported on this hardware. Interactive ~1&nbsp;s decisions want short states
  (≲~300 tokens). A GPU removes this wall — inputs up to the model's 40,960 become usable again.
</div>

<h2 id="scaling">7. Accuracy scaling with model size</h2>
<p><code>Qwen3-0.6B</code> is the smallest model on SemIf's ladder, chosen to fit in CPU RAM; it is the
accuracy floor, not the ceiling. From SemIf's own evaluation:</p>
<div class="tablewrap">
<table>
  <thead><tr><th>Brain model</th><th class="n">Size</th><th class="n">Authored balanced acc.</th><th class="n">TypeSafe subset agr.</th></tr></thead>
  <tbody>
    <tr><td>Qwen3-0.6B <span class="muted">(JEV-CPU default)</span></td><td class="n">0.6 B</td><td class="n">0.440</td><td class="n">0.407</td></tr>
    <tr><td>MiniCPM5-2B</td><td class="n">2 B</td><td class="n">0.686</td><td class="n">0.637</td></tr>
    <tr><td>Qwen3.5-4B</td><td class="n">4 B</td><td class="n"><strong>0.813</strong></td><td class="n"><strong>0.845</strong></td></tr>
  </tbody>
</table>
</div>
<p>Because the engine is model-agnostic, moving up the ladder is a one-line change — at the cost of RAM and
compute that exceed this CPU box (a 4B model wants a GPU, SemIf's target). The takeaway: <strong>JEV-CPU shows
the method runs anywhere; accuracy scales with the model you point it at.</strong></p>

<h3 id="outlook">7.1 Outlook — GPU serving and a production JEV</h3>
<p>The two axes of this report — accuracy (§7) and CPU latency (§6) — are usually assumed to trade off, but a
GPU relaxes <em>both at once</em>. On CPU the wall is prefill: a single 7,697-token decision took ~117&nbsp;s
(§6). A GPU changes the regime on three fronts:</p>
<ul>
  <li><strong>Latency collapses.</strong> Prefill is exactly the workload GPUs are built for. SemIf reports a
  4B model on a single RTX&nbsp;3090 returning <strong>21 binary decisions in a median 1.02&nbsp;s</strong>,
  versus 5.3&nbsp;s to generate the equivalent JSON array — the same logit-readout path, just not latency-bound.</li>
  <li><strong>Input size grows.</strong> With the latency wall gone, the practical input rises from our
  ~7,700-token / ~120&nbsp;s CPU ceiling toward the model's full <strong>40,960-token</strong> context — long
  documents, transcripts, or code diffs become decidable in one pass.</li>
  <li><strong>Throughput multiplies.</strong> Because a decision is one forward pass reading fixed positions,
  decisions <em>batch</em> trivially, and shared-state prefix reuse amortizes one long state across many
  criteria. SemIf measures <strong>~20 decisions/s</strong> (777 decisions in 38.8&nbsp;s) with parallel
  suffixes on one 3090.</li>
</ul>
<p>Put together, a larger open-weight model (4B+) served on a GPU is <strong>simultaneously more accurate,
accepts far larger inputs, and answers many decisions per second</strong> — all with typed, auditable outputs
and no generation to parse. That combination is the shape of a <strong>production, potentially commercial,
JEV</strong>: semantic-if offered as a low-latency, high-throughput hosted service, with per-tenant models and
batched shared-state decisions. In that framing, <strong>JEV-CPU is the floor</strong> — proof the method is
portable to any machine — and a <strong>GPU-served larger model is the ceiling</strong> that turns the same
engine into a product.</p>
<div class="note">
  <strong>Scope.</strong> We did not run GPU experiments in this report. The latency and throughput figures
  above are SemIf's published single-GPU (RTX 3090, 4B) measurements together with projections from our own
  CPU curve (§6) — offered as an implication, not as measured results of this work. Quantifying a GPU-served
  JEV (tokens/s, decisions/s, cost per million decisions, batching and concurrency) is the natural next study.
</div>

<h2 id="limitations">8. Limitations</h2>
<ul>
  <li><strong>No new method.</strong> The logit-readout mechanism is prior art; this is a port and measurement.</li>
  <li><strong>Small-model errors.</strong> At 0.6B, secondary decisions can be inconsistent — e.g. the loan
  case flags <em>high risk</em> correctly yet still leans <em>approve</em>. Larger models resolve this (§7).</li>
  <li><strong>Not a benchmark.</strong> §5 shows single runs for illustration; we do not report accuracy on a
  held-out labeled set here. SemIf's repository contains the quantitative evaluation we cite in §7.</li>
  <li><strong>Single-machine measurements.</strong> Latencies (§6) are from one 8&nbsp;GB CPU box and will vary
  with hardware, threads, and BLAS backend.</li>
  <li><strong>Probabilities are conditional</strong> on the declared options and are uncalibrated as confidence.</li>
</ul>

<h2 id="conclusion">9. Conclusion</h2>
<p>Decision-native, logit-readout inference is not tied to a GPU or a large model. With only a loader change,
SemIf's engine runs on a commodity CPU with a 0.6B model, decides across many domains at ~1&nbsp;s each, and
stays memory-stable well past its default token cap. On this hardware the honest limit is latency: inputs above
~7,700 tokens cross the ~120&nbsp;s mark and are impractical, and small-model accuracy — not context or memory —
is what improves by scaling the model up. JEV-CPU is offered as a reproducible, minimal demonstration of that
floor; the same engine, given a larger open-weight model on a GPU, points toward the ceiling (§7.1) — an
accurate, high-throughput, low-latency semantic-if service, i.e. a production JEV.</p>

<h2 id="repro">10. Reproducibility</h2>
<p>Code, the web UI, the exact scripts behind §5–§6, and all demo GIFs are public:</p>
<ul>
  <li>GitHub — <a href="https://github.com/leesk212/JEV-CPU">github.com/leesk212/JEV-CPU</a></li>
  <li>Hugging Face — <a href="https://huggingface.co/Meanblock/JEV-CPU">huggingface.co/Meanblock/JEV-CPU</a></li>
</ul>
<pre><code>python3 -m venv .venv &amp;&amp; source .venv/bin/activate
pip install --index-url https://download.pytorch.org/whl/cpu torch
pip install transformers accelerate
python semif_cpu.py      # CLI: typed option probabilities
python server.py         # web UI on http://localhost:8080</code></pre>

<hr>

<h2 id="appendix">Appendix A. Code-level walkthrough: turning an open-weight model into a decision engine</h2>
<p>A "JEV" / decision engine is <strong>not a fine-tune and not new weights</strong> — it is a way of
<em>calling</em> an ordinary open-weight causal language model so that a typed decision falls out of a single
forward pass. This appendix walks the full pipeline function by function, as it runs in JEV-CPU. The scoring
code is <a href="https://github.com/TheoLeeCJ/SemIf">SemIf</a>'s, reproduced here verbatim for the report;
JEV-CPU changes only the model <em>loader</em> (<a href="#a7">A.7</a>). All snippets are from
<code>src/semif_phase1/{core,direct}.py</code>.</p>

<h3 id="a1">A.1 — The record and its validation</h3>
<p>A decision is one plain record: a <code>state</code> (evidence, string / JSON object / array), a
<code>question</code> (criterion), and 2–16 <code>options</code>, each with a stable <code>id</code> and a
human <code>description</code>. Validation is strict so nothing is silently coerced:</p>
<pre><code>def validate_row(row):
    required = {"id", "state", "question", "options"}
    if not required &lt;= row.keys(): raise ValueError(...)
    # state must be a nonempty, finite, JSON-serializable string/object/array
    json.dumps(state, ensure_ascii=False, allow_nan=False)
    # 2..16 options, each {id: str, description: str}, ids unique
    if not isinstance(options, list) or not 2 &lt;= len(options) &lt;= len(LETTERS): raise ValueError(...)</code></pre>

<h3 id="a2">A.2 — Building a letter-choice prompt</h3>
<p>Each option is assigned an uppercase letter and the record is serialized into a two-turn chat. The system
turn constrains the model to answer with a single letter and nothing else — this is what makes the <em>next</em>
token the entire decision:</p>
<pre><code>LETTERS = "ABCDEFGHIJKLMNOP"
DIRECT_SYSTEM = ("Apply the supplied criterion to the supplied evidence. "
                 "Choose exactly one listed option. Respond with only its "
                 "uppercase letter, with no explanation or reasoning.")

def direct_messages(row):
    payload = {
        "evidence":  row["state"],
        "criterion": row["question"],
        "options":   [{"letter": LETTERS[i], "description": o["description"]}
                      for i, o in enumerate(row["options"])],
    }
    return [{"role": "system", "content": DIRECT_SYSTEM},
            {"role": "user",   "content": json.dumps(payload, ensure_ascii=False)}]</code></pre>

<h3 id="a3">A.3 — Pinning each option to exactly one token</h3>
<p>The readout compares the model's probability of each answer letter, so every letter must map to a
<strong>single, clean vocabulary slot</strong>. Each letter is checked to encode to exactly one token that
round-trips, with no collisions between options:</p>
<pre><code>def _slot_ids(tokenizer, count):
    result = []
    for letter in LETTERS[:count]:
        encoded = tokenizer.encode(letter, add_special_tokens=False)
        if len(encoded) != 1 or tokenizer.decode(encoded) != letter:
            raise ValueError(f"Answer slot {letter!r} is not one exact round-trip token")
        result.append(encoded[0])
    if len(result) != len(set(result)):
        raise ValueError("Answer-slot tokens collide")
    return result            # e.g. token ids for "A", "B", "C"</code></pre>

<h3 id="a4">A.4 — Encoding and verifying the answer boundary</h3>
<p>The chat template is applied with the generation prompt on and thinking disabled, then two invariants are
asserted: the prompt fits the token budget (no silent truncation), and appending any answer letter extends the
tokenization by exactly that one slot token — i.e. the boundary between prompt and answer is stable:</p>
<pre><code>def encode_prompt(tokenizer, row, max_tokens):
    prompt = tokenizer.apply_chat_template(
        direct_messages(row), tokenize=False,
        add_generation_prompt=True, enable_thinking=False)
    ids = tokenizer.encode(prompt, add_special_tokens=False)
    if not ids or len(ids) &gt; max_tokens:
        raise ValueError("input tokens exceed limit; no truncation allowed")
    slots = _slot_ids(tokenizer, len(row["options"]))
    for letter, token in zip(LETTERS, slots):
        if tokenizer.encode(prompt + letter, add_special_tokens=False) != ids + [token]:
            raise ValueError(f"Answer boundary changes tokenization for slot {letter}")
    return ids, slots, digest(prompt)   # digest = sha256 for auditability</code></pre>

<h3 id="a5">A.5 — One forward pass, last-position logits</h3>
<p>The whole model is run <strong>once</strong>. We keep only the final position's logits — the distribution
over the next token. <code>logits_to_keep=1</code> (when the model supports it) tells the model to compute
just that row, avoiding a full-sequence logit tensor. There is no sampling and no decode loop:</p>
<pre><code>def _forward(model, inputs):
    params = inspect.signature(model.forward).parameters
    kwargs = dict(inputs, use_cache=False, return_dict=True)
    if "logits_to_keep" in params:
        kwargs["logits_to_keep"] = 1
    return model(**kwargs).logits[:, -1, :]     # shape (batch, vocab)</code></pre>

<h3 id="a6">A.6 — Gather the slots, softmax, decide</h3>
<p>From the full-vocabulary logit vector we index the option-letter tokens and softmax over <em>only</em>
those, giving a probability per option conditional on the declared set. Note the two device lines: the code
reads whatever device the model is on and only synchronizes under CUDA — the property that makes it run
unchanged on CPU:</p>
<pre><code>def score(model, tokenizer, row, metadata, max_tokens=4096):
    import torch
    ids, slots, prompt_hash = encode_prompt(tokenizer, row, max_tokens)
    device = next(model.parameters()).device          # &lt;- follow the model
    inputs = {"input_ids":      torch.tensor([ids], dtype=torch.long, device=device),
              "attention_mask": torch.ones((1, len(ids)), dtype=torch.long, device=device)}
    if device.type == "cuda": torch.cuda.synchronize(device)   # &lt;- no-op on CPU
    with torch.inference_mode():
        vocabulary = _forward(model, inputs)[0].float()        # (vocab,)
    selected = vocabulary[slots].cpu().tolist()   # logits at A, B, C, ...
    return {
        "option_ids":   [o["id"] for o in row["options"]],
        "option_logits": selected,
        "probabilities": softmax(selected),        # winner = argmax
        "input_tokens":  len(ids),
        "readout": "native full-vocabulary last-position logits restricted to declared answer slots",
        "probability_status": "conditional option score; uncalibrated as decision confidence",
        ...
    }</code></pre>
<p>That is the entire "JEV-ification": a stock <code>AutoModelForCausalLM</code> is never asked to generate;
it is asked once for its next-token logits, and the decision is the arg-max over the option slots.</p>

<h3 id="a7">A.7 — The only change to run on CPU</h3>
<p>SemIf's loader hard-codes a single CUDA device and <code>bfloat16</code>. JEV-CPU replaces just this
function; every function above is untouched:</p>
<div class="tablewrap">
<table>
  <thead><tr><th>SemIf — <code>core.load_causal_model</code> (GPU)</th><th>JEV-CPU — <code>load_causal_model_cpu</code></th></tr></thead>
  <tbody><tr>
  <td><pre style="margin:0"><code>if not torch.cuda.is_available() \
   or torch.cuda.device_count() != 1:
    raise ValueError("Expose exactly "
        "one CUDA GPU ...")
...
model = cls.from_pretrained(
    source, config=config,
    dtype=torch.bfloat16,
    device_map={"": "cuda:0"},
    low_cpu_mem_usage=True, **common)</code></pre></td>
  <td><pre style="margin:0"><code># no CUDA check, no device_map
model = AutoModelForCausalLM.from_pretrained(
    source, config=config,
    dtype=torch.float32,   # CPU-stable
    low_cpu_mem_usage=True, **common)
model.eval()</code></pre></td>
  </tr></tbody>
</table>
</div>
<p>Because <code>score()</code> and <code>score_shared()</code> derive their device from the model object,
loading on CPU is sufficient — the same scoring code then runs with <code>torch.cuda.synchronize</code> skipped.
Swapping the brain model is the one other knob: change <code>MODEL = "Qwen/Qwen3-0.6B"</code> to any causal
LM whose answer letters are single tokens.</p>

<h3 id="a8">A.8 — Reusing one state across many criteria (shared mode)</h3>
<p>When many criteria judge the same <code>state</code>, <code>shared.py</code> prefills that state once into a
native key–value cache, replicates the cache across branches with <code>cache.reorder_cache(...)</code>, and
evaluates every criterion's answer position in one batched forward using a vector
<code>logits_to_keep</code>. One expensive prefill, many cheap decisions — and, like the direct path, it
synchronizes only under CUDA, so it too runs unchanged on CPU.</p>

<hr>

<h2 id="refs">References</h2>
<ol class="refs">
  <li>T. Lee (TheoLeeCJ). <em>SemIf — Semantic ifs from open models.</em> <a href="https://github.com/TheoLeeCJ/SemIf">github.com/TheoLeeCJ/SemIf</a>. Browser demo: <a href="https://openjev.com/">openjev.com</a>.</li>
  <li>TypeSafe. <em>Jev</em> (closed service for runtime-defined semantic decisions). Names/marks are the property of their owners; this work is independent and unaffiliated.</li>
  <li>Qwen Team. <em>Qwen3-0.6B.</em> <a href="https://huggingface.co/Qwen/Qwen3-0.6B">huggingface.co/Qwen/Qwen3-0.6B</a>.</li>
  <li>A. Liu et&nbsp;al. <em>WANLI: Worker and AI Collaboration for NLI.</em> <a href="https://huggingface.co/datasets/alisawuffles/WANLI">dataset</a> (used in SemIf's evaluation cited in §7).</li>
  <li>This report and JEV-CPU code are released under the MIT License.</li>
</ol>

<footer>
  JEV-CPU Technical Report · v1 · September 19, 2026 · leesk212 (Meanblock).
  An independent CPU port and empirical study of <a href="https://github.com/TheoLeeCJ/SemIf">SemIf</a>.
  Not affiliated with or endorsed by SemIf's author, TypeSafe, or Jev.
</footer>

</div>
</body>
</html>