theorem
proved
continuous_no_isolated_zero_defect
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IndisputableMonolith.Foundation.DiscretenessForcing on GitHub at line 326.
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323
324 Note: The actual proof requires the intermediate value theorem and connectedness.
325 We use ℝ as the configuration space for concreteness. -/
326theorem continuous_no_isolated_zero_defect :
327 ∀ x : ℝ, 0 < x → defect x = 0 →
328 ∀ ε > 0, ∃ z : ℝ, z ≠ x ∧ |z - x| < ε ∧ defect z < ε := by
329 intro x hx_pos hx ε hε
330 -- Since defect = 0 implies x = 1, we work at x = 1
331 have hx_eq_1 : x = 1 := (defect_zero_iff_one hx_pos).mp hx
332 subst hx_eq_1
333 -- Take z = 1 + min(ε/2, 1/2) to ensure z > 0 and close to 1
334 let t := min (ε / 2) (1 / 2 : ℝ)
335 have ht_pos : t > 0 := by positivity
336 have ht_le_half : t ≤ 1 / 2 := min_le_right _ _
337 use 1 + t
338 constructor
339 · linarith
340 constructor
341 · simp only [add_sub_cancel_left, abs_of_pos ht_pos]
342 calc t ≤ ε / 2 := min_le_left _ _
343 _ < ε := by linarith
344 · -- defect(1 + t) = J(1 + t) = t²/(2(1+t)) for small t > 0
345 -- For t ≤ min(ε/2, 1/2), we show this is less than ε
346 simp only [defect, J]
347 have ht_pos' : 1 + t > 0 := by linarith
348 have hne : 1 + t ≠ 0 := ht_pos'.ne'
349 -- Compute J(1+t) = ((1+t) + (1+t)⁻¹)/2 - 1 = t²/(2(1+t))
350 have key : (1 + t + (1 + t)⁻¹) / 2 - 1 = t^2 / (2 * (1 + t)) := by
351 field_simp
352 ring
353 rw [key]
354 -- Now show t²/(2(1+t)) < ε
355 have h1t_ge1 : 1 + t ≥ 1 := by linarith
356 have h2 : 2 * (1 + t) ≥ 2 := by linarith
papers checked against this theorem (showing 3 of 3)
-
Self-distillation turns feedback into denser RL signals
"SDPO also outperforms baselines in standard RLVR environments that only return scalar feedback by using successful rollouts as implicit feedback for failed attempts."
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Simple pixel Transformers generate competitive ImageNet samples by predicting clean data
"simple, large-patch Transformers on pixels can be strong generative models: using no tokenizer, no pre-training, and no extra loss."
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Tiny changes fool neural networks and transfer across models
"deep neural networks learn input-output mappings that are fairly discontinuous to a significant extend. We can cause the network to misclassify an image by applying a certain imperceptible perturbation, which is found by maximizing the network's prediction error."