diff --git a/Complexity.lean b/Complexity.lean
index cff1298ef..fa699579c 100644
--- a/Complexity.lean
+++ b/Complexity.lean
@@ -2372,6 +2372,7 @@ public import Complexity.Foundations.Analysis.RootError
 public import Complexity.Foundations.Analysis.RowCompression
 public import Complexity.Foundations.Analysis.RowCompression.Floors
 public import Complexity.Foundations.Analysis.RowCompression.Iteration
+public import Complexity.Foundations.Analysis.RowCompression.Preloss
 public import Complexity.Foundations.Analysis.SpecialFunctions.Arccos
 public import Complexity.Foundations.Analysis.SpecialFunctions.PolynomialSwitch
 public import Complexity.Foundations.Analysis.SpecialFunctions.SmoothSwitch
diff --git a/Complexity/Foundations/Analysis/RowCompression/Preloss.lean b/Complexity/Foundations/Analysis/RowCompression/Preloss.lean
new file mode 100644
index 000000000..6be76e8a3
--- /dev/null
+++ b/Complexity/Foundations/Analysis/RowCompression/Preloss.lean
@@ -0,0 +1,184 @@
+/-
+Copyright (c) 2026 Samuel Schlesinger. All rights reserved.
+Released under Apache 2.0 license as described in the file LICENSE.
+Authors: Samuel Schlesinger
+-/
+
+module
+-- upstream: candidate
+
+public import Complexity.Foundations.Analysis.RowCompression.Floors
+import Mathlib.Tactic.FieldSimp
+import Mathlib.Tactic.Linarith
+import Mathlib.Tactic.Positivity
+import Mathlib.Tactic.Ring
+
+/-!
+# Weighted loss before a compression overflow
+
+The preloss estimate of [OpenAI2026LogSpace, Lemma 8.2, (8.9)] bounds the error introduced by
+compression in terms of the errors of the uncompressed prefixes. At an endpoint with true first
+coordinate `d ≥ η > 0`, a compression change is possible only when the current entry is small.
+If the preceding compression loss is at most the current floor `ρ` and `8 * ρ ≤ η`, the raw
+prefix is then at most half of `d`. Its clipped relative error is bounded below by
+`η / (2 * (1 + η))`. Combining this with the local change bound `6 * ρ` charges the change to
+the raw prefix error, with the explicit constant `12 * (1 + η) / η`.
+
+The main theorem applies this argument to the actual recursion `Cslib.RowCompression.iterate`.
+It assumes that each earlier threshold is at most its floor and that
+`6 * ∑ k < i, ρ k ≤ ρ i`. These hypotheses are weaker than the corresponding small-floor
+conditions in (8.8). A corollary discharges both numerical hypotheses for the concrete scaled
+dyadic floors constructed in `Cslib.RowCompression.dyadicFloor`. The clipped
+error is (8.3), with channel indices `0, 1` and group indices starting at zero, so group `i`
+uses the uncompressed prefix at `i + 1`. The argument is independent of the moment exponent.
+
+This module proves the weighted preloss assertion of Lemma 8.2. It does not assert preservation
+of row functions by the stationary reference, stability after overflow, or a machine-space bound.
+
+## Main definitions
+
+* `Cslib.RowCompression.clippedError`: the maximum of the clipped relative first-channel error
+  and the clipped absolute second-channel error.
+
+## Main results
+
+* `Cslib.RowCompression.abs_update_sub_le_mul_clippedError`: the local weighted change bound.
+* `Cslib.RowCompression.abs_iterate_sub_uncompressed_le_weighted`: the weighted preloss estimate
+  for the concrete increment-and-compression recursion.
+* `Cslib.RowCompression.abs_iterate_sub_uncompressed_le_weighted_dyadic`: the same estimate with
+  the numerical floor conditions discharged by the explicit dyadic schedule.
+
+## References
+
+* [OpenAI, *Exact Derandomization of Logarithmic Space: L = RL = BPL*][OpenAI2026LogSpace],
+  Section 8.3, Lemma 8.2, weighted preloss assertion (8.9)
+  (23 September 2026, revision `7ef5c0cc`, not peer reviewed).
+-/
+
+public section
+
+namespace Cslib.RowCompression
+
+open Finset
+
+variable {ι : Type*}
+
+/-- The clipped endpoint error of [OpenAI2026LogSpace, (8.3)]. The first true coordinate is
+nonnegative in applications; the second coordinate is measured by absolute error. -/
+@[expose] noncomputable def clippedError (d v : ℝ) (w : Fin 2 → ℝ) : ℝ :=
+  max (min 1 (|w 0 - d| / (1 + d))) (min 1 |w 1 - v|)
+
+theorem clippedError_nonneg (d v : ℝ) (w : Fin 2 → ℝ) : 0 ≤ clippedError d v w :=
+  (le_min (by norm_num) (abs_nonneg _)).trans (le_max_right _ _)
+
+theorem clippedError_le_one (d v : ℝ) (w : Fin 2 → ℝ) : clippedError d v w ≤ 1 :=
+  max_le (min_le_left _ _) (min_le_left _ _)
+
+@[simp]
+theorem clippedError_self (d v : ℝ) (w : Fin 2 → ℝ) (hd : w 0 = d) (hv : w 1 = v) :
+    clippedError d v w = 0 := by
+  simp [clippedError, hd, hv]
+
+private theorem le_clipped_relative_error {η d r ρ : ℝ} (hη : 0 < η) (hd : η ≤ d)
+    (hsmall : 8 * ρ ≤ η) (hr : r ≤ 4 * ρ) :
+    η / (2 * (1 + η)) ≤ min 1 (|r - d| / (1 + d)) := by
+  have hden : 0 < 1 + d := by linarith
+  have hηden : 0 < 2 * (1 + η) := by positivity
+  have hdiff : d ≤ 2 * |r - d| := by linarith [neg_le_abs (r - d)]
+  refine le_min ?_ ?_
+  · apply (div_le_iff₀ hηden).2
+    linarith
+  · apply (div_le_div_iff₀ hηden hden).2
+    nlinarith [mul_nonneg (show 0 ≤ 1 + η by positivity)
+      (show 0 ≤ 2 * |r - d| - d by linarith)]
+
+/-- Before overflow, a change at a genuinely large target can be charged to the raw prefix's
+clipped error. `raw` is the uncompressed prefix; `w` also includes preceding compression loss.
+Only the first coordinate of that preceding loss is needed. -/
+theorem abs_update_sub_le_mul_clippedError {s : Finset ι} {n K : ℕ} {ρ z η d v : ℝ}
+    {w : ι → Fin 2 → ℝ} {raw : Fin 2 → ℝ} (hη : 0 < η) (hd : η ≤ d)
+    (hno : threshold s n ρ z w ≤ ρ) (hsmall : 8 * ρ ≤ η) {y : ι}
+    (hprev : |w y 0 - raw 0| ≤ ρ) (ν : Fin 2) :
+    |update s n K ρ z w y ν - w y ν| ≤
+      (12 * (1 + η) / η) * ρ * clippedError d v raw := by
+  have hρ : 0 ≤ ρ := threshold_nonneg.trans hno
+  have hC : 0 ≤ 12 * (1 + η) / η := by positivity
+  by_cases hlarge : 3 * threshold s n ρ z w ≤ entrySize (w y)
+  · rw [update_eq_self_of_three_mul_le hlarge, sub_self, abs_zero]
+    exact mul_nonneg (mul_nonneg hC hρ) (clippedError_nonneg _ _ _)
+  have hsize : entrySize (w y) < 3 * threshold s n ρ z w := lt_of_not_ge hlarge
+  have hraw : raw 0 ≤ 4 * ρ := by
+    have hw := (le_abs_self (w y 0)).trans (abs_apply_le_entrySize (w y) 0)
+    have hp := (abs_le.mp hprev).1
+    linarith
+  have he : η / (2 * (1 + η)) ≤ clippedError d v raw :=
+    (le_clipped_relative_error hη hd hsmall hraw).trans (le_max_left _ _)
+  have hcancel : (12 * (1 + η) / η) * ρ * (η / (2 * (1 + η))) = 6 * ρ := by
+    field_simp
+    ring
+  calc |update s n K ρ z w y ν - w y ν| ≤ 6 * ρ :=
+      (abs_update_sub_le_six_mul y ν).trans (mul_le_mul_of_nonneg_left hno (by norm_num))
+    _ = (12 * (1 + η) / η) * ρ * (η / (2 * (1 + η))) := hcancel.symm
+    _ ≤ (12 * (1 + η) / η) * ρ * clippedError d v raw :=
+      mul_le_mul_of_nonneg_left he (mul_nonneg hC hρ)
+
+/-- The weighted preloss estimate [OpenAI2026LogSpace, Lemma 8.2, (8.9)] for actual iterates.
+The right-hand errors use raw prefixes, not compressed rows. No target upper bound is needed. -/
+theorem abs_iterate_sub_uncompressed_le_weighted {s : Finset ι} {n K : ℕ → ℕ}
+    {ρ : ℕ → ℝ} {Δ : ℕ → ι → Fin 2 → ℝ} {a : State ι} {η d v : ℝ}
+    (hη : 0 < η) (hd : η ≤ d) (j : ℕ)
+    (hno : ∀ i < j, stepThreshold s n K ρ Δ a i ≤ ρ i)
+    (hsmall : ∀ i < j, 8 * ρ i ≤ η)
+    (hprior : ∀ i < j, 6 * ∑ k ∈ range i, ρ k ≤ ρ i) (y : ι) (ν : Fin 2) :
+    |(iterate s n K ρ Δ a j).row y ν - uncompressedRow Δ a.row j y ν| ≤
+      (12 * (1 + η) / η) * ∑ i ∈ range j,
+        ρ i * clippedError d v (uncompressedRow Δ a.row (i + 1) y) := by
+  induction j with
+  | zero => simp
+  | succ j ih =>
+    have hj := Nat.lt_succ_self j
+    have hno' : ∀ i < j, stepThreshold s n K ρ Δ a i ≤ ρ i :=
+      fun i hi => hno i (Nat.lt_succ_of_lt hi)
+    have hprev : |inputRow s n K ρ Δ a j y 0 -
+        uncompressedRow Δ a.row (j + 1) y 0| ≤ ρ j := by
+      rw [uncompressedRow_succ]
+      simp only [inputRow, Pi.add_apply, add_sub_add_right_eq_sub]
+      exact (abs_iterate_sub_uncompressed_le_of_no_overflow j hno' y 0).trans (hprior j hj)
+    have hstep := abs_update_sub_le_mul_clippedError (K := K j) (v := v) hη hd
+      (hno j hj) (hsmall j hj) hprev ν
+    have hbefore := ih hno' (fun i hi => hsmall i (Nat.lt_succ_of_lt hi))
+      (fun i hi => hprior i (Nat.lt_succ_of_lt hi))
+    have hdiff : |inputRow s n K ρ Δ a j y ν - uncompressedRow Δ a.row (j + 1) y ν| =
+        |(iterate s n K ρ Δ a j).row y ν - uncompressedRow Δ a.row j y ν| := by
+      rw [uncompressedRow_succ]
+      simp only [inputRow, Pi.add_apply, add_sub_add_right_eq_sub]
+    calc _ ≤ |(iterate s n K ρ Δ a (j + 1)).row y ν - inputRow s n K ρ Δ a j y ν| +
+          |inputRow s n K ρ Δ a j y ν - uncompressedRow Δ a.row (j + 1) y ν| :=
+        abs_sub_le _ _ _
+      _ ≤ (12 * (1 + η) / η) * ρ j *
+            clippedError d v (uncompressedRow Δ a.row (j + 1) y) +
+          (12 * (1 + η) / η) * ∑ i ∈ range j,
+            ρ i * clippedError d v (uncompressedRow Δ a.row (i + 1) y) := by
+        rw [hdiff]
+        exact add_le_add hstep hbefore
+      _ = _ := by rw [sum_range_succ]; ring
+
+/-- Weighted preloss for the concrete dyadic floors. The numerical small-floor hypotheses are
+proved by the schedule; only the absence of overflow before the selected prefix remains assumed.
+The parameter choice against the later estimator's noise/capacity constants is separate. -/
+theorem abs_iterate_sub_uncompressed_le_weighted_dyadic {s : Finset ι} {n K : ℕ → ℕ}
+    {Δ : ℕ → ι → Fin 2 → ℝ} {a : State ι} {η d v : ℝ}
+    (hη : 0 < η) (hd : η ≤ d) (m j : ℕ) (hj : j ≤ m)
+    (hno : ∀ i < j, stepThreshold s n K (dyadicFloor η m) Δ a i ≤ dyadicFloor η m i)
+    (y : ι) (ν : Fin 2) :
+    |(iterate s n K (dyadicFloor η m) Δ a j).row y ν - uncompressedRow Δ a.row j y ν| ≤
+      (12 * (1 + η) / η) * ∑ i ∈ range j,
+        dyadicFloor η m i * clippedError d v (uncompressedRow Δ a.row (i + 1) y) := by
+  apply abs_iterate_sub_uncompressed_le_weighted hη hd j hno
+  · exact fun i _ => eight_mul_dyadicFloor_le hη.le m i
+  · intro i hi
+    have hsum := six_sum_range_dyadicFloor_le hη.le (hi.trans_le hj)
+    have hfloor := dyadicFloor_nonneg hη.le m i
+    linarith
+
+end Cslib.RowCompression
diff --git a/ComplexityTests/RowCompressionPreloss.lean b/ComplexityTests/RowCompressionPreloss.lean
new file mode 100644
index 000000000..7b86bd280
--- /dev/null
+++ b/ComplexityTests/RowCompressionPreloss.lean
@@ -0,0 +1,86 @@
+/-
+Copyright (c) 2026 Samuel Schlesinger. All rights reserved.
+Released under Apache 2.0 license as described in the file LICENSE.
+Authors: Samuel Schlesinger
+-/
+
+import Complexity.Foundations.Analysis.RowCompression.Preloss
+import Mathlib.LinearAlgebra.Matrix.Notation
+import Mathlib.Tactic.FinCases
+import Mathlib.Tactic.IntervalCases
+import Mathlib.Tactic.NormNum
+
+/-!
+# Weighted compression preloss examples
+
+A two-group calculation has actual nonzero compression loss, satisfies the small-floor and
+no-overflow hypotheses, and invokes the weighted theorem. Other examples check saturation,
+negative raw first coordinates, and exact targets with zero error.
+-/
+
+namespace ComplexityTests.RowCompressionPreloss
+
+open Finset Cslib.RowCompression
+
+private def zeroState : State (Fin 1) := ⟨0, fun _ => ![0, 0]⟩
+
+private noncomputable def floors (i : ℕ) : ℝ := if i = 0 then 1 / 100 else 1 / 10
+
+private theorem no_overflow (i : ℕ) (hi : i < 2) :
+    stepThreshold univ (fun _ => 1) (fun _ => 1) floors (fun _ => 0) zeroState i ≤
+      floors i := by
+  interval_cases i <;>
+    norm_num [stepThreshold, inputRow, iterate, advance, zeroState, floors, threshold,
+      newDefault, update, updateEntry, radius, entrySize, defaultEntry, kthLargest, Fin.univ_succ]
+
+-- Two genuine compression changes: the raw prefix stays zero while the stored row becomes 0.1.
+example : (iterate univ (fun _ => 1) (fun _ => 1) floors (fun _ => 0) zeroState 2).row 0 0 =
+    1 / 10 := by
+  norm_num [iterate, advance, zeroState, floors, threshold, newDefault, update, updateEntry,
+    radius, entrySize, defaultEntry, kthLargest, Fin.univ_succ]
+
+-- The theorem applies to this concrete schedule; its weighted right side is 24·(0.01+0.1)/2.
+example (ν : Fin 2) :
+    |(iterate univ (fun _ => 1) (fun _ => 1) floors (fun _ => 0) zeroState 2).row 0 ν -
+      uncompressedRow (fun _ => 0) zeroState.row 2 0 ν| ≤ 33 / 25 := by
+  have h := abs_iterate_sub_uncompressed_le_weighted (s := univ) (n := fun _ => 1)
+    (K := fun _ => 1) (ρ := floors) (Δ := fun _ => 0) (a := zeroState)
+    (η := 1) (d := 1) (v := 0) (by norm_num) le_rfl 2 no_overflow
+    (by intro i hi; interval_cases i <;> norm_num [floors])
+    (by intro i hi; interval_cases i <;> norm_num [floors, sum_range_succ]) 0 ν
+  norm_num [floors, uncompressedRow, zeroState, clippedError, sum_range_succ] at h
+  simpa [uncompressedRow, zeroState] using h
+
+-- Relative error saturates at one, and either channel can attain the maximum.
+example : clippedError 1 0 (![-4, 0] : Fin 2 → ℝ) = 1 ∧
+    clippedError 1 0 (![1, 2] : Fin 2 → ℝ) = 1 := by
+  norm_num [clippedError]
+
+-- Exact targets above the sensitivity floor force zero compression change in both coordinates.
+example (d v : ℝ) (hd : 1 ≤ d) (w : Fin 1 → Fin 2 → ℝ)
+    (hw : w 0 = ![d, v]) (hno : threshold univ 2 (1 / 10) 0 w ≤ 1 / 10) (ν : Fin 2) :
+    update univ 2 1 (1 / 10) 0 w 0 ν = w 0 ν := by
+  have h := abs_update_sub_le_mul_clippedError (K := 1) (η := 1) (d := d) (v := v)
+    (raw := ![d, v]) (by norm_num) hd hno (by norm_num)
+    (by rw [hw]; norm_num) ν
+  have he : clippedError d v (![d, v] : Fin 2 → ℝ) = 0 := by simp [clippedError]
+  rw [he, mul_zero] at h
+  exact sub_eq_zero.mp (abs_nonpos_iff.mp h)
+
+-- The explicit dyadic schedule discharges all small-floor conditions, even with a real change.
+example :
+    |(iterate univ (fun _ => 1) (fun _ => 1) (dyadicFloor 1 1)
+        (fun _ => 0) zeroState 1).row 0 0 -
+      uncompressedRow (fun _ => 0) zeroState.row 1 0 0| ≤ 3 / 256 := by
+  have h := abs_iterate_sub_uncompressed_le_weighted_dyadic (s := univ)
+    (n := fun _ => 1) (K := fun _ => 1) (Δ := fun _ => 0) (a := zeroState)
+    (η := 1) (d := 1) (v := 0) (by norm_num) le_rfl 1 1 le_rfl
+    (by
+      intro i hi
+      interval_cases i
+      norm_num [stepThreshold, inputRow, iterate, zeroState, dyadicFloor, dyadicSlack,
+        threshold, kthLargest, entrySize, Fin.univ_succ]) 0 0
+  norm_num [dyadicFloor, dyadicSlack, uncompressedRow, zeroState, clippedError] at h ⊢
+  exact h
+
+end ComplexityTests.RowCompressionPreloss