arxiv: 2605.00358 · v1 · submitted 2026-05-01 · 💻 cs.CL · cs.CV

Recognition: unknown

From Backward Spreading to Forward Replay: Revisiting Target Construction in LLM Parameter Editing

Wei Liu , Hongkai Liu , Zhiying Deng , Yee Whye Teh , Wee Sun Lee

Authors on Pith no claims yet

Pith reviewed 2026-05-09 19:39 UTC · model grok-4.3

classification 💻 cs.CL cs.CV

keywords LLM parameter editingtarget constructionbackward spreadingforward propagationanchor pointhidden statesmodel editing

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The pith

Optimizing the anchor at the first editing layer and propagating forward yields more accurate targets for all layers than backward spreading.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

Current LLM parameter editing methods compute an ideal hidden-state target at a chosen anchor layer and then spread adjustments backward to earlier layers. This paper examines the foundations of that backward spreading process and identifies its practical boundaries and failure modes. The authors propose a direct alternative: optimize the anchor point at the first editing layer instead, then propagate the resulting target forward through the remaining layers. This produces layer-wise targets that are both more accurate and mutually compatible while using exactly the same amount of computation. The change requires no modifications to the initial target calculation or to any other part of the editing pipeline.

Core claim

Instead of optimizing the target hidden-state at the last editing layer and spreading it backward, the method optimizes the anchor at the first editing layer and then propagates it forward, automatically generating accurate and mutually compatible target hidden-states for every subsequent layer at the same computational cost.

What carries the argument

Forward propagation of the anchor point optimized at the first editing layer, which replaces backward spreading from the last layer.

If this is right

The method achieves identical computational complexity to existing backward-spreading techniques.
Layer-wise targets are more accurate and mutually compatible across edited layers.
The approach integrates without changing the initial target computation or any other pipeline components.
The same forward-propagation construction can be applied to a wide range of existing LLM parameter editing methods.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

Multi-layer edits may accumulate fewer inconsistencies because targets are generated sequentially from the same starting point rather than adjusted retroactively.
The change could simplify debugging of editing failures by making the relationship between layers more transparent.
Similar forward-construction logic might be tested on sequential models outside the LLM setting where hidden-state targets must be defined across layers.

Load-bearing premise

That optimizing the anchor at the first layer and propagating it forward will automatically produce mutually compatible and accurate targets for all later layers without any further adjustments.

What would settle it

An experiment that measures editing success rates or downstream task performance when using forward-propagated targets versus backward-spread targets on the same models and benchmarks, with forward propagation showing clearly worse results.

Figures

Figures reproduced from arXiv: 2605.00358 by Hongkai Liu, Wee Sun Lee, Wei Liu, Yee Whye Teh, Zhiying Deng.

**Figure 1.** Figure 1: (a) A toy example of the backward spreading of ideal hidden states (i.e., m) in model editing. ① Getting the target hidden state of the final decisive layer by taking it as optimizable parameters and minimizing cross-entropy. ② Getting all the target hidden states of the decisive layers with backward spreading. ③ Using the target hidden states to guide the parameter editing. post hoc. Recent work shows tha… view at source ↗

**Figure 2.** Figure 2: (a) A toy example of our method: forward replay of all hidden states (i.e., m) in model editing. ① Finding m1 with gradient descent. ② Picking up the hidden states stored in the back-propagation path with forward-propagation replay. ③ Editing the model with the target hidden states. a single residual direction (e.g., m3 − h3 in view at source ↗

**Figure 3.** Figure 3: The steering step should be small if θ is large. passive shift induced at the final layer L (i.e., δ l mL ) after applying steering at layer l (i.e., δml ). The results are in view at source ↗

read the original abstract

LLM parameter editing methods commonly rely on computing an ideal target hidden-state at a target layer (referred as anchor point) and distributing the target vector to multiple preceding layers (commonly known as backward spreading) for cooperative editing. Although widely used for a long time, its underlying basis have not been systematically investigated. In this paper, we first conduct a systematic study of its foundations, which helps clarify its capability boundaries, practical considerations, and potential failure modes. Then, we propose a simple and elegant alternative that replaces backward spreading with forward-propagation. Instead of optimizing the target at the last editing layer, we optimize the anchor point at the first editing layer, and then propagate it forward to obtain accurate and mutually compatible target hidden-states for all subsequent editing layers. This approach achieves the same computational complexity as existing methods while producing more accurate layer-wise targets. Our method is simple, without interfering with either the computation of the initial target hidden state or any other components of the subsequent editing pipeline, and thus constituting a benefit for a wide range of LLM parameter editing methods.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

Forward replay from a first-layer anchor is a simple swap for backward spreading in LLM editing, but the compatibility of those targets under joint multi-layer edits is not obviously preserved once the layers change.

read the letter

The punchline is that this paper gives a clean alternative to the usual backward-spreading step in parameter editing: optimize the target hidden state at the earliest edited layer, then run the original forward pass to generate targets for the layers after it. They also supply the first systematic look at what backward spreading actually assumes and where it can fail. That combination is the real addition here. The method stays plug-and-play with existing editing pipelines, keeps the same computational cost, and avoids touching the initial target computation or the rest of the editing machinery. For anyone already using these techniques, that is a low-friction change worth testing. The analysis of backward spreading is also useful on its own; it spells out the practical limits and failure modes without extra machinery. The soft spot is the multi-layer case the stress-test note flags. Once several layers are edited together, the hidden-state trajectory inside the model is no longer the original one, so targets computed by forward-propagating through the untouched model are no longer guaranteed to be the right fixed points for the edited model. The abstract asserts that the new targets are both more accurate and mutually compatible without further correction, but that compatibility is precisely what simultaneous edits can break. No quantitative results or error analysis appear in the abstract, so the accuracy claim rests on the construction itself rather than measured improvement. If the full paper contains controlled experiments that isolate this issue and show the forward targets still outperform backward spreading under joint edits, the concern shrinks; otherwise it stays central. This is for researchers who work on model editing for safety, unlearning, or customization and who already edit multiple layers. A reader in that subfield will get a practical option and some diagnostic language for the old method. It is narrow enough that it does not need to be a top-tier paper, but the concrete change and the foundational check make it worth a serious referee's time. I would send it to review with a request that the referees focus on the joint-edit compatibility experiments and any ablation that compares the two target constructions head-to-head.

Referee Report

2 major / 0 minor

Summary. The manuscript first performs a systematic analysis of the foundations, capability boundaries, and failure modes of backward spreading for constructing target hidden states in LLM parameter editing. It then proposes forward replay as an alternative: the anchor hidden state is optimized at the first editing layer so that the original forward pass reaches the desired output, after which the original layer transformations are applied to generate targets for all subsequent editing layers. The authors claim this construction yields more accurate and mutually compatible layer-wise targets at the same computational complexity as backward spreading, without requiring changes to the initial target computation or other pipeline components.

Significance. The systematic study of backward spreading provides a useful clarification of its practical limits. If the forward-replay targets are indeed more accurate and remain compatible under joint multi-layer edits, the method would offer a lightweight, non-disruptive improvement that could be adopted across many existing LLM editing algorithms, potentially raising their reliability on knowledge-editing and model-update benchmarks without added cost.

major comments (2)

[Abstract] Abstract: the central claim that forward replay 'produces more accurate layer-wise targets' and 'mutually compatible' targets is stated without any quantitative comparison, error analysis, or ablation against backward spreading; the soundness of the improvement therefore rests on an unverified assertion.
[Method section] Method section (forward-replay construction): the proposal optimizes the anchor at the first editing layer and propagates using the original inter-layer maps. When multiple layers are edited simultaneously, however, the edited model's hidden-state trajectory deviates from the original trajectory, so the pre-computed forward targets are no longer guaranteed to be the fixed points required by the joint editing objective. No analysis, proof, or counter-example addressing this compatibility risk is supplied.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive review and recommendation for major revision. We address each major comment below, agreeing that the claims require stronger quantitative support and explicit analysis of multi-layer compatibility. We will incorporate the necessary additions in the revised manuscript.

read point-by-point responses

Referee: [Abstract] Abstract: the central claim that forward replay 'produces more accurate layer-wise targets' and 'mutually compatible' targets is stated without any quantitative comparison, error analysis, or ablation against backward spreading; the soundness of the improvement therefore rests on an unverified assertion.

Authors: We agree that the abstract presents the accuracy and compatibility claims without direct quantitative backing. The manuscript's core contribution is the systematic analysis of backward spreading's foundations, boundaries, and failure modes, which motivates forward replay as an alternative that avoids those issues by construction. To address the concern, we will revise the abstract for precision and add quantitative comparisons (e.g., layer-wise reconstruction error relative to the original forward pass) plus ablation studies on editing benchmarks in a new results subsection. These will directly validate the claims against backward spreading at equivalent cost. revision: yes
Referee: [Method section] Method section (forward-replay construction): the proposal optimizes the anchor at the first editing layer and propagates using the original inter-layer maps. When multiple layers are edited simultaneously, however, the edited model's hidden-state trajectory deviates from the original trajectory, so the pre-computed forward targets are no longer guaranteed to be the fixed points required by the joint editing objective. No analysis, proof, or counter-example addressing this compatibility risk is supplied.

Authors: This highlights a valid subtlety in joint multi-layer editing. Forward replay constructs targets by optimizing the anchor at the first layer and propagating via the original transformations, ensuring consistency with the unmodified forward trajectory from that anchor. While simultaneous edits will alter the actual trajectory, the targets remain mutually compatible as they derive from a single optimized starting point rather than independent backward computations. We will expand the method section with a dedicated discussion of this approximation, including conditions for validity and empirical counter-examples on multi-layer edits, to clarify the risk without altering the core algorithm. revision: yes

Circularity Check

0 steps flagged

No circularity: forward-propagation proposal is an independent construction

full rationale

The paper first analyzes the foundations and failure modes of backward spreading, then introduces forward replay as a direct alternative: optimize the anchor hidden state at the earliest editing layer and propagate it forward using the original layer transformations to obtain targets for later layers. This is explicitly framed as a simple substitution that preserves computational complexity, does not alter the initial target computation or other pipeline components, and yields 'more accurate and mutually compatible' targets by construction of the forward pass. No equations reduce the claimed accuracy or compatibility to a fitted parameter drawn from the same data used for evaluation, nor does any step rename a known result or import a uniqueness theorem via self-citation. The central modeling choice (that original inter-layer maps remain useful for target construction) is a substantive assumption open to empirical test rather than a self-definitional loop. The derivation chain is therefore self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The proposal rests on the unstated premise that forward propagation from an optimized first-layer anchor yields compatible targets; no free parameters, invented entities, or additional axioms are described in the abstract.

axioms (1)

domain assumption Forward propagation from an optimized anchor at the first editing layer produces mutually compatible and more accurate targets for subsequent layers.
This is the core premise required for the claimed accuracy improvement; it is invoked when the abstract states the new method produces better targets.

pith-pipeline@v0.9.0 · 5495 in / 1173 out tokens · 36125 ms · 2026-05-09T19:39:52.728423+00:00 · methodology

discussion (0)

Reference graph

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