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<title>faeval.git/protocol/examples/verify_pitfalls.py, branch master</title>
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<entry>
<title>Add reproducers for pitfalls 1-3 in CHECKLIST.md</title>
<updated>2026-04-08T03:52:41+00:00</updated>
<author>
<name>YurenHao0426</name>
<email>Blackhao0426@gmail.com</email>
</author>
<published>2026-04-08T03:52:41+00:00</published>
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<content type='text'>
Each bug from the catalog has a synthetic reproducer that runs in &lt;1 sec
without GPU:

  Bug 1: x.norm(-1) on a 2x2 tensor returns 1.143 (L_{-1} of whole tensor)
         instead of [5, 10] (per-row L_2 along dim=-1).
  Bug 2: F.cosine_similarity(a, b) with ||b||=5e-10 returns +0.000905
         instead of the true +0.018101. The clamp (eps=1e-8) underestimates
         the divisor 20x.
  Bug 3: 5e-10 in fp16 -&gt; 0 (underflows smallest subnormal ~6e-8).
         Downstream F.cosine_similarity returns NaN. bf16 works because it
         shares fp32's exponent range.

Bugs 4-6 (Bs reproducibility, aggregation, layer-0 dominance) require a
trained network and are demonstrated inside audit_table and
ablation_decision_utility.
</content>
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<pre>
Each bug from the catalog has a synthetic reproducer that runs in &lt;1 sec
without GPU:

  Bug 1: x.norm(-1) on a 2x2 tensor returns 1.143 (L_{-1} of whole tensor)
         instead of [5, 10] (per-row L_2 along dim=-1).
  Bug 2: F.cosine_similarity(a, b) with ||b||=5e-10 returns +0.000905
         instead of the true +0.018101. The clamp (eps=1e-8) underestimates
         the divisor 20x.
  Bug 3: 5e-10 in fp16 -&gt; 0 (underflows smallest subnormal ~6e-8).
         Downstream F.cosine_similarity returns NaN. bf16 works because it
         shares fp32's exponent range.

Bugs 4-6 (Bs reproducibility, aggregation, layer-0 dominance) require a
trained network and are demonstrated inside audit_table and
ablation_decision_utility.
</pre>
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</content>
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