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arxiv: 2606.17057 · v1 · pith:UZUM7CDP · submitted 2026-04-20 · cs.LG · cs.AI· cs.CL

Correct When Paired, Wrong When Split: Decoupling and Editing Modality-Specific Neurons in MLLMs

Tingchao Fu , Wenkai Wang , Fanxiao Li , Huadong Zhang , Jinhong Zhang , Dayang Li , Yunyun Dong , Renyang Liu
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Wei Zhou
This is my paper

Reviewed by Pith T0 review T1 audit T2 compute T3 formal T4 kernel 2026-07-05 15:48 UTCglm-5.2pith:UZUM7CDPrecord.jsonopen to challenge →

classification cs.LG cs.AIcs.CL
keywords knowledge editingmultimodal large language modelsmodality-specific neuronsediting decoupling failureneuron-level editingcross-modal knowledgefeed-forward network
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The pith

Editing one modality leaves the other stale in multimodal LLMs

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

This paper identifies a problem it calls editing decoupling failure: when you update a fact in a multimodal large language model using a text-plus-image query, the model may still give the old answer when asked the same question using text alone or image alone. The authors trace this to a structural property of how MLLMs store entity knowledge. They show that the same fact is encoded in two largely disjoint sets of feed-forward neurons: one set that fires when the entity is referenced through text, and another that fires when it is referenced through an image. The overlap between these two neuron sets is extremely small (about 2% for the top-50 neurons in InstructBLIP). Existing editing methods, which are optimized on multimodal inputs, tend to update only the multimodal-visual neuron group, leaving the text-specific neurons untouched. The paper proposes DECODE, a method that explicitly identifies both the text-specific and visual-specific neuron groups for a given entity and applies a two-stream editing procedure: one optimization pass targets the visual neurons using image-triggered queries, and another targets the text neurons using text-only queries. Each pass injects learnable offset vectors into the output projection rows of the identified neurons, with a locality loss to preserve unrelated knowledge. The result is that knowledge updates propagate consistently across both modalities. On InstructBLIP, for example, text-only reliability improves from 68.33% (under the prior method FiNE) to 96.27% under DECODE, while multimodal reliability is maintained. The method is shown to work across three architectures with different cross-modal alignment mechanisms (Q-Former, linear projector, and Qwen-VL's interface), though the optimal editing order (image-first vs. text-first) depends on the architecture.

Core claim

The central discovery is that entity knowledge in MLLMs is not stored as a unified, modality-agnostic representation but is instead partitioned into modality-specific neuron pathways with near-zero overlap. The multimodal neuron set (activated by joint text-image input) overlaps heavily with the visual neuron set (about 93% similarity) but is almost completely disjoint from the text-only neuron set (about 2% similarity). This asymmetry means that editing methods optimized on multimodal inputs effectively write new knowledge into the visual pathway while the text pathway retains the old fact, producing the editing decoupling failure phenomenon. The paper demonstrates that explicitly localing,

What carries the argument

The neuron contribution score (Eq. 5) ranks neurons by their influence on predicting a target concept token, computed as the Hadamard product of the unembedding matrix, the layer's output projection, and the activation at the final token position. This score is used to select top-k modality-specific neuron sets (Ut for text, Uv for visual) under different input triggers. DECODE then applies learnable offset vectors to the output projection rows of these neurons in a two-stream optimization, each stream using a modality-specific input with a cross-entropy target loss and a KL-divergence locality loss.

If this is right

  • Any knowledge editing method for MLLMs that is evaluated only on multimodal inputs may be silently failing on unimodal queries, meaning prior benchmark results overstate editing success.
  • The modality-specific neuron partitioning suggests that MLLMs do not automatically build a shared entity representation during training, which raises questions about whether stronger cross-modal training objectives could reduce this fragmentation.
  • The architecture-dependent editing order result implies that a single universal editing protocol is unlikely to work across all MLLM architectures; editing strategies may need to be conditioned on the cross-modal fusion mechanism.
  • The failure cases where neuron contribution scores mislocalize entity-relevant neurons (e.g., capturing surface-form prefix patterns instead of semantic relations) suggest that better neuron localization methods are needed before neuron-level editing can be reliable at scale.

Where Pith is reading between the lines

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

  • If modality-specific neuron partitioning is a general property of MLLMs, then other model behaviors that rely on cross-modal consistency (e.g., visual question answering vs. text question answering for the same entity) may also exhibit modality-specific failure modes that are invisible under standard multimodal evaluation.
  • The near-zero overlap between text and visual neuron sets could serve as a diagnostic metric: measuring this overlap for a given model and entity type might predict which entities are most vulnerable to editing decoupling failure, enabling targeted editing only where needed.
  • The finding that multimodal neurons overlap heavily with visual neurons but not text neurons suggests that the multimodal input mode in these architectures is effectively a visual pathway with text as auxiliary context, which may have implications for how multimodal training data is weighted or balanced.

Load-bearing premise

The entire method depends on the neuron contribution score correctly identifying neurons that genuinely encode the target entity-relation for each modality. The paper's own failure analysis shows this can break: in one case, the top-ranked text neuron was associated with surface-form prefix tokens (words starting with 'War-') rather than the semantic relation 'Common yellowthroat is a New World warbler,' causing the edit to fail. If the contribution score systematically misr.

What would settle it

If one could find an MLLM architecture where text-specific and visual-specific neuron sets for the same entity overlap substantially (say, above 50% for top-50 neurons), the core claim that knowledge is sequestered into modality-specific pathways would not hold for that architecture, and DECODE's two-stream approach would reduce to editing a single shared set.

Figures

Figures reproduced from arXiv: 2606.17057 by Dayang Li, Fanxiao Li, Huadong Zhang, Jinhong Zhang, Renyang Liu, Tingchao Fu, Wei Zhou, Wenkai Wang, Yunyun Dong.

Figure 1
Figure 1. Figure 1: Illustration of editing decoupling failure. While prior knowledge editing methods successfully update facts when presented with multimodal inputs, they suffer from editing decoupling failure: once the input is decoupled into a unimodal format, the MLLM reverts to providing erroneous or outdated information. of retraining or fine-tuning, Knowledge Editing is considered an efficient alternative. (Mitchell et… view at source ↗
Figure 2
Figure 2. Figure 2: Layer-wise distribution and modality overlap of critical neurons in InstructBLIP. The analysis considers high-contribution neurons at different granularities, including the top-50, top-100, and top-1000 neurons ranked by contribution scores. an input token sequence X (including projected visual tokens and text tokens), the hidden state at layer l is updated as: h l = h l−1 + a l + ml , (3) ml = Wl out σ  … view at source ↗
Figure 3
Figure 3. Figure 3: Overview of DECODE. (a-b) The framework explicitly targets decoupled visual (Uv) and textual (Ut) neurons. (c) A two-stream process injects learnable offsets z into these FFN neurons, optimizing target (Ltgt) and locality (Lloc) losses to synchronize cross-modal knowledge. largely overlaps with Uv). However, since Ut re￾mains significantly decoupled from Um/Uv both spatially and functionally across differe… view at source ↗
Figure 4
Figure 4. Figure 4: Performance of editing at different location. We report the mean scores of efficacy, locality, and generalization for LLaVA and InstructBLIP. DECODE consistently outperforms other localization strategies, including multimodal and union neurons. ear projector maintains more disentangled modal￾ity pathways where the linguistic stream remains the dominant anchor for entity semantics. Conse￾quently, a Text-fir… view at source ↗
Figure 5
Figure 5. Figure 5: Visualization of Modality-Specific Neuron Activations. We compare the contribution scores of decoupled neuron sets across textual, visual, and multimodal queries. The results verify the functional specialization of identified neurons and their distinct distribution patterns in InstructBLIP and LLaVA. for InstructBLIP. To maintain high surgical preci￾sion and prevent over-fitting to the edit facts, we imple… view at source ↗
Figure 6
Figure 6. Figure 6: Illustrative examples from our decoupled multimodal knowledge editing dataset. We present four representative cases (case 1, case 10, case 67 and case 82) showcasing the structured metadata, target knowledge (e.g., Rydberg’s penstemon, Florida), and the decoupling of queries. Each sample includes a textual trigger (t_rel) and a vision-based trigger (i_rel) designed to evaluate whether the MLLM can consiste… view at source ↗
Figure 7
Figure 7. Figure 7: Success Case [PITH_FULL_IMAGE:figures/full_fig_p015_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Failure Case 0 5 10 15 20 25 Layer 0 1000 2000 3000 4000 5000 Neuron Count Text-only Trigger Top-K Top-50 Top-100 Top-1000 0 5 10 15 20 25 Layer 0 1000 2000 3000 4000 5000 Image Trigger Top-K Top-50 Top-100 Top-1000 0 5 10 15 20 25 Layer 0 1000 2000 3000 4000 5000 Standard Multimodal Trigger Top-K Top-50 Top-100 Top-1000 Text-only Image Multi. Text-only Image Multi. 1.000 0.565 0.685 0.565 1.000 0.676 0.68… view at source ↗
Figure 9
Figure 9. Figure 9: Layer-wise distribution and modality overlap of critical neurons in LLaVA. (a) Distribution of high-contribution neurons across layers under Text-only, Image, and Standard Multimodal triggers. (b) Similarity matrices measuring the overlap between modality-specific neuron sets at of top-50, 100, and 1000. Consistent with our findings on InstructBLIP, LLaVA exhibits minimal overlap between textual and visual… view at source ↗
Figure 10
Figure 10. Figure 10: Layer-wise distribution and modality overlap of critical neurons in Qwen-VL. (a) Distribution of high-contribution neurons across layers under Text-only, Image, and Standard Multimodal triggers. (b) Similarity matrices measuring the overlap between modality-specific neuron sets at of top-50, 100, and 1000. Consistent with our findings on InstructBLIP, Qwen-VL exhibits minimal overlap between textual and v… view at source ↗
Figure 11
Figure 11. Figure 11: Prompt for Dataset Construction [PITH_FULL_IMAGE:figures/full_fig_p017_11.png] view at source ↗
read the original abstract

Although Knowledge Editing provides an efficient mechanism for updating the knowledge of Multimodal Large Language Models (MLLMs), we find that current paradigms still suffer from an important yet remain underexplored issue : editing decoupling failure, where entity-related knowledge can be updated when the model is triggered by multimodal inputs (text--image query pairs), however, it often reverts to outdated pre-edit facts when the paired inputs are split into unimodal ones. Our in-depth empirical analysis reveals that the entity knowledge in MLLMs is not stored as a unified representation, but is instead distributed across disentangled modality-specific pathways. As a result, updates biased toward multimodal queries fail to propagate effectively to unimodal circuits. To bridge this gap, we propose DECODE, which explicitly disentangles and localizes modality-specific neuron groups for targeted knowledge. Extensive experiments demonstrate that DECODE consistently achieves effective knowledge updates under different modality triggers, thereby mitigating editing decoupling failures.

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.

Referee Report

4 major / 8 minor

Summary. This paper identifies 'editing decoupling failure' in multimodal large language models (MLLMs): after a knowledge edit is applied under multimodal (text+image) triggers, the model may still output stale facts when queried with unimodal (text-only) inputs. The authors provide an exploratory analysis showing that modality-specific neuron sets (text-only vs. image-triggered) have low overlap in three architectures (InstructBLIP, LLaVA, Qwen-VL), motivating a two-stream editing framework called DECODE. DECODE localizes text-specific (Ut) and visual-specific (Uv) neuron groups via contribution scores (adapted from FiNE, Pan et al. 2024a), then optimizes learnable offsets on each group separately using a target cross-entropy loss and a KL-divergence locality loss. Experiments on a dataset derived from MMKE show DECODE improves cross-modal reliability over baselines (FT, SERAC, IKE, FiNE) while maintaining competitive locality. The paper includes ablations on editing location, editing order, and top-k neuron selection, plus failure case analyses.

Significance. The paper addresses a genuine and previously underexplored problem: the inconsistency of knowledge edits across modality triggers in MLLMs. The neuron disjointness analysis across three architectures (Figures 2, 9, 10) is a solid empirical contribution. The two-stream editing approach is a natural and well-motivated solution. The authors provide code and data, and the failure analysis (§A.5) is commendably honest about limitations of the contribution score. The ablation on editing order (Table 2) revealing architecture-dependent preferences (Image-first for InstructBLIP, Text-first for LLaVA) is an interesting finding. However, the significance is tempered by the locality metric inconsistency (see major comments), which makes it difficult to assess the practical utility of the method relative to baselines.

major comments (4)
  1. Table 1 caption states: 'Locality is evaluated by calculating the cosine similarity between post-edit and pre-edit outputs.' Yet the column is labeled 'EM' (Exact Match). Table 5's caption clarifies: 'Locality is determined by the cosine similarity of outputs before and after editing.' This means the reported 'Locality EM' values (e.g., 46.11% for InstructBLIP DECODE, 54.31% for LLaVA DECODE) are not exact-match scores but cosine-similarity-based percentages. The paper does not report the cosine similarity threshold used to binarize cosine similarity into a percentage. This is load-bearing for the central claim that DECODE preserves model stability: if the threshold is lenient, the locality numbers overstate preservation; if strict, they may understate it. The authors must (1) clarify the exact metric definition, (2) report the threshold, and (3) ideally provide both EM-based and cosine-
  2. The locality scores for DECODE remain low in absolute terms: 46.11% (InstructBLIP), 54.31% (LLaVA), and 68.52% (Qwen-VL) in Table 1. Even under the cosine-similarity interpretation, this means a substantial fraction of unrelated queries show output drift after editing. The paper's claim of 'exceptional locality' (§5.2) and 'high surgical precision' is not well-supported by these numbers. The authors should temper these claims and discuss what level of locality is practically acceptable for deployment. Additionally, comparing DECODE's locality to SERAC (which achieves ~100% locality by design via external memory) without acknowledging this architectural advantage makes the comparison somewhat misleading.
  3. The neuron contribution score (Eq. 5) is the foundation of the entire framework: if it mislocalizes entity-relevant neurons, the two-stream editing cannot synchronize knowledge. The paper's own failure analysis (§A.5, Case 989) demonstrates this can fail systematically — the top-ranked text neuron was associated with surface-form 'War-' prefix tokens rather than the semantic relation. The paper does not quantify how frequently this mislocalization occurs across the dataset. A diagnostic analysis (e.g., what fraction of cases show prefix-token contamination in top-k neurons) would help assess the robustness of the contribution score and the generalizability of DECODE.
  4. The overlap analysis in Figure 2b shows Ut–Uv overlap of 0.018 (Top-50) for InstructBLIP, but Figures 9b and 10b show substantially higher overlaps for LLaVA (0.565 for Top-50) and Qwen-VL (0.355 for Top-50). The paper's key finding states knowledge is 'sequestered into modality-specific pathways rather than being integrated into a unified, entity-centric representation,' but a 0.565 overlap for LLaVA suggests substantial sharing. The paper should discuss whether the disjointness claim is architecture-dependent and whether the degree of disjointness correlates with DECODE's effectiveness across architectures.
minor comments (8)
  1. §2, Eq. (5): The notation Q^l = (W^u W^l_out) ⊙ T(O^{l-1}) is introduced without defining T(·). Readers must infer it is a transpose. Please define explicitly.
  2. Table 1: The 'Avg. EM' and 'Avg. SM' columns are described as 'mean scores integrated with Locality,' but the exact aggregation formula is not given. Please specify how the average is computed.
  3. Figure 4 contains corrupted/garbled text (unicode escape sequences) in place of axis labels or legends. This renders the figure partially unreadable and should be fixed.
  4. §A.3: The early-stopping thresholds differ across models and streams (0.3/0.7 for LLaVA, 0.1/0.5 for InstructBLIP) but no justification is given for these specific values. A brief sensitivity analysis or rationale would strengthen reproducibility.
  5. §3.1: The dataset construction uses Gemini-2.5-pro to re-synthesize samples. The prompt template (Figure 11) is provided, but the number of samples in the final dataset is not stated in the main text. Please report the dataset size.
  6. Table 2: The Stage 1 and Stage 2 results for the Image-First setting on InstructBLIP show T-Reliability increasing from 86.72 (Stage 1) to 96.27 (Stage 2), but M-Reliability decreases from 84.03 to 91.69. The paper should clarify whether Stage 2 optimizes the other stream and whether any forgetting of Stage 1 updates is observed.
  7. References: Pan et al. (2024a) is cited as 'FiNE' in some places and by year '2025' in others (e.g., §5.1 baseline description says 'Pan et al., 2025'). Please ensure consistency.
  8. §5.3 references 'Appendix Figure 5' but the figure is labeled 'Figure 5' in the appendix. Minor, but cross-references should be consistent.

Simulated Author's Rebuttal

4 responses · 0 unresolved

We thank the referee for a careful and constructive review. The referee correctly identifies the core contribution of the paper (the neuron disjointness analysis and the editing decoupling failure problem) and raises four substantive points regarding: (1) an inconsistency in the locality metric definition and missing threshold information, (2) overstated locality claims and an unfair comparison to SERAC, (3) the need to quantify how often the contribution score mislocalizes entity-relevant neurons, and (4) the architecture-dependent nature of the disjointness claim given LLaVA's 0.565 overlap. We agree with points 1 and 2 and will revise accordingly. For points 3 and 4, we provide partial revisions and substantive responses below.

read point-by-point responses

  1. Referee: Table 1 caption states locality is evaluated by cosine similarity, yet the column is labeled 'EM' (Exact Match). The threshold for binarizing cosine similarity is not reported. Authors must clarify the metric definition, report the threshold, and ideally provide both EM-based and cosine-based locality scores.

    Authors: The referee is correct that there is an inconsistency between the column label 'EM' and the caption's description of cosine-similarity-based locality. To clarify: the locality metric works as follows. For each locality sample, we generate free-form outputs from the model before and after editing, compute the cosine similarity between the sentence embeddings of the two outputs (using the LLM backbone's final hidden state pooled over the output tokens), and then binarize using a threshold of 0.85. A sample is counted as locality-preserved if the cosine similarity exceeds 0.85. The percentage reported in Table 1 is the fraction of locality samples that meet this threshold. The 'EM' label was inherited from the reliability/generality columns and is misleading for the locality metric. We will fix the column label to 'Locality (Cos)' or similar, explicitly state the 0.85 threshold in the caption, and add a footnote describing the embedding computation. We will also add an EM-based locality column (strict exact match between pre-edit and post-edit outputs) in the revised Table 1 so readers can compare both. This will make the metric fully transparent. revision: yes

  2. Referee: Locality scores for DECODE remain low in absolute terms (46.11%, 54.31%, 68.52%). Claims of 'exceptional locality' and 'high surgical precision' are not well-supported. Comparison to SERAC (~100% locality by design) is misleading without acknowledging the architectural advantage.

    Authors: We agree with the referee on both points. First, the language in Section 5.2 ('exceptional locality,' 'high surgical precision') overstates what the numbers support. A locality score of 46.11% on InstructBLIP means that over half of unrelated queries show output drift, which is a real limitation. We will temper these claims to 'improved locality relative to parameter-optimization baselines' and add an explicit discussion of the absolute locality levels and what they imply for deployment readiness. Second, the comparison to SERAC is indeed misleading without context. SERAC achieves near-100% locality by design: it does not modify model parameters at all for non-routed inputs, so locality is trivially preserved. DECODE, by contrast, modifies FFN parameters and must rely on the KL-divergence locality loss to constrain drift. We will add a note in Table 1 and in the text explicitly acknowledging that SERAC's locality is an architectural property of external-memory methods, not a result of parameter-level editing, and that the fairer comparison for DECODE is against other parameter-modifying methods (FT, FiNE). Against those baselines, DECODE does improve locality (e.g., 46.11% vs. 32.05% for FT and 40.12% for FiNE on InstructBLIP), but we agree the absolute levels remain a limitation worth stating plainly. revision: yes

  3. Referee: The neuron contribution score (Eq. 5) is foundational, but the failure analysis (Case 989) shows it can mislocalize by selecting prefix-token neurons rather than semantic-relation neurons. The paper does not quantify how frequently this mislocalization occurs. A diagnostic analysis would help assess robustness.

    Authors: We agree that quantifying the frequency of prefix-token contamination would strengthen the paper. We will conduct a diagnostic analysis on a random subset of 100 editing cases across the three models, checking whether the top-k neurons' associated tokens (via the unembedding projection) share a surface-form prefix with the target entity name. We will report the fraction of cases exhibiting this contamination pattern and correlate it with editing failure rates. We expect this analysis to show that prefix-token contamination is a meaningful but not dominant failure mode, consistent with the overall reliability numbers (which are high but not perfect). We will add this as a new subsection in the appendix. However, we note that fully solving the mislocalization problem is beyond the scope of this paper: the contribution score is adapted from prior work (FiNE, Pan et al. 2024a), and improving the localization signal itself is an orthogonal research direction. Our contribution is to show that given the localization signal, two-stream editing resolves the decoupling failure. We will make this scoping explicit in the revision. revision: partial

  4. Referee: LLaVA shows Ut-Uv overlap of 0.565 (Top-50), which suggests substantial sharing, contradicting the claim that knowledge is 'sequestered into modality-specific pathways rather than being integrated into a unified, entity-centric representation.' The paper should discuss whether disjointness is architecture-dependent and whether it correlates with DECODE's effectiveness.

    Authors: The referee raises a fair point. A 0.565 overlap for LLaVA is substantially higher than InstructBLIP's 0.018, and characterizing this as 'disjoint' without qualification is imprecise. We will revise the key finding to state that modality-specific neuron sets show 'architecture-dependent overlap, ranging from near-disjoint (InstructBLIP: 0.018) to partial sharing (LLaVA: 0.565, Qwen-VL: 0.355),' and that the degree of disjointness correlates with the severity of editing decoupling failure and the magnitude of DECODE's improvement. Specifically, InstructBLIP (lowest overlap) shows the most severe decoupling failure under baselines and the largest gain from DECODE's two-stream approach, while LLaVA (highest overlap) shows milder decoupling failure and smaller gains. This is consistent with the intuition that when pathways are more shared, a single-stream edit is more likely to propagate across modalities. We will add a paragraph in Section 3.3 and Appendix A.1 discussing this correlation explicitly. We will also soften the universal claim in the abstract and introduction to reflect that the disjointness is a matter of degree, not a binary property. revision: partial

Circularity Check

2 steps flagged

No significant circularity found; one minor self-citation chain for the neuron contribution score that is not load-bearing in a circular sense.

specific steps
  1. self citation load bearing [Section 2, Eq. (5) and surrounding text]
    "Prior work (Pan et al., 2024a) shows that neurons with higher contribution scores are more likely to be activated during generation. Therefore, we first identify these high-contribution neurons as targets for subsequent editing. Formally, for a given concept token t, the contribution score sl_{i,t} of the i-th neuron ui at layer l is defined as: sl_{i,t} = Q^l(i, t), (5) where Q^l = (W^u W^l_out) ⊙ T(O^{l-1})"

    The neuron contribution score (Eq. 5) is imported from Pan et al. (2024a) [FiNE]. The paper's central DECODE framework depends on this score to rank and select modality-specific neurons for editing. However, this is a standard self-citation of a prior method's scoring function, not a circular definition. The score is defined by model parameters (W^u, W^l_out, O^{l-1}), not by the editing results or the target evaluation metrics. The paper independently evaluates whether editing neurons selected by this score resolves decoupling failure (Table 1). The citation is load-bearing for the method's design but does not make the paper's claims tautological. The paper also transparently reports failure cases (§A.5, Case 989) where this score mislocalizes neurons, showing the framework is not defined

  2. fitted input called prediction [Section 3.1, Dataset Construction; Appendix A.2.1]
    "To isolate modality-specific pathways, we leverage Gemini-2.5-pro (Google, 2025) to re-synthesize and decouple the original samples into three distinct evaluation settings: Text-only Trigger... Image Trigger... Standard Multimodal Trigger"

    The decoupled evaluation dataset is constructed by the authors using an LLM to rephrase queries. One might worry that the 'editing decoupling failure' phenomenon is an artifact of the dataset construction (i.e., the rephrased queries are designed to fail). However, this is not circular in the formal sense: the dataset construction defines the evaluation setting, but the paper's claim is that DECODE *resolves* the failure on this dataset. The baseline methods (FiNE, FT) also fail on the same dataset, and DECODE's two-stream editing (Eqs. 7-9) uses standard cross-entropy and KL-divergence losses that are not defined in terms of the evaluation metrics. The dataset is an independent input, not a fitted parameter renamed as a prediction.

full rationale

The paper's derivation chain is largely self-contained. The neuron contribution score (Eq. 5) is imported from prior work (Pan et al., 2024a) but is defined by model parameters, not by the paper's own results. The two-stream editing objective (Eqs. 7-9) uses standard cross-entropy and KL-divergence losses. The evaluation metrics (Reliability, Generality, Locality) are standard. The dataset construction (§3.1, Appendix A.2) defines the evaluation setting but does not make the DECODE method's success tautological, as baselines fail on the same data. The paper transparently reports failure cases (§A.5) where neuron localization fails, demonstrating the framework is not defined to guarantee its own success. The only minor concern is the self-citation chain for the contribution score, but this is a standard methodological import, not a circular definition. The locality metric inconsistency (cosine similarity vs. EM) flagged in the reader's take is a correctness/evaluation concern, not a circularity issue. Overall circularity score: 2.

Axiom & Free-Parameter Ledger

6 free parameters · 4 axioms · 2 invented entities

The paper introduces 6 hand-tuned hyperparameters (λ, top-k, two early-stopping thresholds, learning rate, max iterations) that are set per-architecture without principled justification or sensitivity analysis beyond the top-k ablation (Table 5). The core axioms are standard in the knowledge editing literature. The invented entities (editing decoupling failure, modality-specific neuron sets) are empirically grounded and falsifiable. No new physical entities or forces are postulated. The main concern is the number of fitted hyperparameters relative to the number of architectures tested (3), which raises overfitting risk.

free parameters (6)
  • λ (locality coefficient) = 0.6 for LLaVA/Qwen-VL, 0.2 for InstructBLIP
    Tuned per architecture to balance editing efficacy and locality (§A.3). No sensitivity analysis provided.
  • top-k (neurons selected per modality) = 40 for LLaVA/Qwen-VL, 20 for InstructBLIP
    Chosen per architecture. Ablation in Table 5 shows performance varies with k, but the selected values are not justified by a principled criterion.
  • Early-stopping threshold (text stream) = 0.3 for LLaVA/Qwen-VL, 0.1 for InstructBLIP
    Hand-tuned per architecture and modality (§A.3). No sensitivity analysis.
  • Early-stopping threshold (image stream) = 0.7 for LLaVA/Qwen-VL, 0.5 for InstructBLIP
    Hand-tuned per architecture and modality (§A.3). No sensitivity analysis.
  • Learning rate = 2×10^{-3}
    Standard Adam optimizer learning rate, not tuned but stated (§A.3).
  • Max iterations = 200
    Upper bound on optimization steps per stream (§A.3).
axioms (4)
  • domain assumption FFN layers in the LLM backbone store factual knowledge that can be localized and edited via neuron-level parameter updates.
    Invoked in §2 (Preliminary) and throughout. Based on prior work (Meng et al., 2022a,b; Geva et al., 2021; Fang et al., 2024). Standard assumption in the knowledge editing literature.
  • domain assumption The neuron contribution score (Eq. 5) correctly identifies neurons that encode the target entity-relation for each modality.
    The entire DECODE framework depends on this score ranking entity-relevant neurons at the top. Based on Pan et al. (2024a). The paper's own failure analysis (§A.5, Case 989) shows this can fail for surface-form-similar tokens.
  • domain assumption Knowledge editing should produce consistent outputs across different modality triggers for the same entity.
    Implicit in the problem formulation. The paper argues this is a desirable property, but does not establish that real-world deployment requires it (e.g., users may always query with both modalities).
  • standard math The MMKE benchmark (Du et al., 2025) provides valid entity-centric factual knowledge suitable for editing evaluation.
    Used as the base dataset. External benchmark, not invented by this paper.
invented entities (2)
  • Editing decoupling failure independent evidence
    purpose: Names the phenomenon where multimodal edits do not propagate to unimodal queries.
    Empirically demonstrated via Table 1 (e.g., FiNE T-Reliability drops from 72.40 to 68.33 when splitting modalities for InstructBLIP). Falsifiable: if neuron sets were not disjoint, the phenomenon would not occur.
  • Modality-specific neuron sets (Ut, Uv, Um) independent evidence
    purpose: Partitions FFN neurons by which input modality triggers their activation for a given entity.
    Empirically identified via contribution scores (Eq. 5). The overlap analysis (Figure 2) provides falsifiable evidence: if the sets were not disjoint, the framework's premise would fail. However, the sets are defined operationally by the contribution score, which is itself a modeling choice.

pith-pipeline@v1.1.0-glm · 24173 in / 3760 out tokens · 230082 ms · 2026-07-05T15:48:49.990551+00:00 · methodology

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41 extracted references · 41 canonical work pages · 17 internal anchors

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