{"id":"50d7895b-ac16-46c2-9e3c-88773981ec3d","arxiv_id":"2508.20279","paper_version":1,"verdict":"CONDITIONAL","confidence":"HIGH","novelty_score":6.0,"correctness_risk":"medium","formal_verification":"none","parameter_count":3,"one_line_summary":"Probing layer-wise embeddings with three prompt-variant families reveals a consistent grounding-reasoning-decoding structure in LLaVA-1.5, LLaVA-Next, and Qwen2-VL, with base LLM architecture shifting layer allocation.","lead":"This paper uses linear probes to show that multimodal language models process image questions in distinct layer-wise phases: early visual grounding, middle reasoning, and late answer formatting. It offers a lightweight, model-agnostic method for locating where these phases happen inside a vision-language model.","discovery_kind":"new_method","skeptic_critique":{"model":"deepseek-v4-flash","headline":"Causal attribution to base-LLM architecture is confounded: neither model-pair comparison isolates architecture from tokenization, tuning data, or pretraining corpus.","rationale":"Reader's weakest assumption concerns probe-transfer miscalibration; that is a legitimate threat to all layer-boundary claims. I do not make it the headline because the output-format variant is a partial control: it holds the semantic decision fixed and still shows a mid-layer plateau followed by a late-layer drop, so the qualitative boundary between reasoning and formatting is not purely an artifact of feature shift. The architecture attribution, by contrast, has no comparable control. It is the part of the strongest claim that the experiments cannot support as stated. The LLaVA-1.5 vs. LLaVA-Next-Llama-3 comparison is used to conclude invariance to tokenization/data, but it also changes the base LLM; the LLaVA vs. Qwen2-VL comparison is used to conclude architecture controls allocation, but it also changes vision encoder, tokenizer, data, and training. This confound is load-bearing because the abstract elevates the architecture attribution to a headline finding. The proposed 2x2 comparison uses publicly available checkpoints and requires no retraining, so the confound is easily settled. If the controlled comparison reproduces the predicted pattern, the claim stands; otherwise the paper should be revised to a descriptive claim about the specific models examined, and the determinants conclusions should be softened. For these reasons I recommend CONDITIONAL, consistent with the reader's verdict but for a different, more specific reason.","tokens_in":11684,"tokens_out":11025,"duration_ms":107977,"concrete_test":"Run the probing framework on LLaVA-Next-Vicuna-7B in addition to LLaVA-1.5-Vicuna-7B and LLaVA-Next-Llama-3-8B, yielding a 2x2 factor contrast: LLaVA-1.5-Vicuna vs. LLaVA-Next-Vicuna changes tokenization and instruction data while holding the base LLM architecture (Vicuna) fixed; LLaVA-Next-Vicuna vs. LLaVA-Next-Llama-3 changes only the base LLM while holding tokenization and instruction data roughly fixed. If the first pair shows different stage boundaries, the invariance claim fails; if the second pair shows identical boundaries, the architecture-control claim fails. If both show the predicted pattern, the causal attribution is supported. Use the same anchor, variants, and filtering protocol as in Section 2, and report layer boundaries quantitatively (e.g., change-point detection) with error bars over probe seeds.","verdict_should_be":"CONDITIONAL","load_bearing_attack":"The paper's strongest claim goes beyond describing a four-stage structure: Section 3.3 and the abstract assert that visual tokenization, instruction-tuning data, and pretraining corpus do not affect the stage structure, while the base-LLM architecture controls layer allocation. The evidence is two two-model comparisons, each varying several factors at once. LLaVA-1.5-Vicuna-7B vs. LLaVA-Next-Llama-3-8B changes visual tokenization (576 fixed tokens vs. 4x multi-resolution), instruction-tuning data, and the base LLM (Vicuna/LLaMA-2 vs. LLaMA-3) simultaneously; similar probing curves cannot be uniquely attributed to invariance of tokenization or data, because architecture also changed. Qwen2-VL-7B vs. LLaVA-1.5 changes base architecture, vision encoder, projector, tokenizer, instruction data, and pretraining corpus together; the observed reallocation cannot be uniquely attributed to the base LLM. The text describes LLaMA-3 as sharing architecture with Vicuna, but LLaMA-3 has a different tokenizer, attention configuration, and pretraining, so it is not an architecture holdout. No comparison holds architecture fixed while varying tokenization/data, and no comparison holds tokenization/data fixed while varying architecture. The causal conclusion is therefore underdetermined by the presented experiments.","agreement_with_reader":"partial"},"referee_report":{"model":"deepseek-v4-flash","summary":"The paper proposes a layer-wise linear probing framework for multimodal LLMs. The authors extract last-token embeddings at each decoder layer for a fixed 'anchor' prompt and a fine-grained dog-breed classification task, then test fixed probes under three prompt-variation families: lexical rewrites, semantic negation, and output-format changes. On LLaVA-1.5 they report a four-stage structure (visual grounding layers 1-4, lexical integration 5-13, semantic reasoning 12-15, answer formatting 16+), observe a similar structure in LLaVA-Next-LLaMA-3, and a shift in stage allocation in Qwen2-VL. The paper concludes that visual tokenization, instruction-tuning data, and pretraining corpus do not change the stage-wise structure, while the base-LLM architecture controls layer allocation across stages.","tokens_in":11916,"tokens_out":5825,"duration_ms":55552,"significance":"If the descriptive stage-wise pattern is correct, the paper offers a lightweight, model-agnostic interpretability tool and a useful cross-model comparison of a substantive phenomenon: that MLLMs appear to process visual grounding, lexical integration, reasoning, and output formatting in distinct depth bands. The strength of the paper is its controlled prompt-variation design and the honesty of its Limitations section. However, the central causal claims about invariance to tokenization/data and sensitivity to base-LLM architecture are underdetermined by the present experiments, and the quantitative evidence lacks error bars or statistical controls. The descriptive claim is plausible and worth publishing after revision; the causal claim requires either substantial new experiments or explicit weakening.","major_comments":[{"comment":"The causal attribution in Section 3.3 is not supported by the model comparisons. The LLaVA-1.5 vs. LLaVA-Next-LLaMA-3 pair varies visual tokenization (576 tokens vs. multi-resolution 4x tokens), instruction-tuning data, and the base LLM simultaneously; LLaMA-3 is not architecturally identical to Vicuna/LLaMA-2, as it uses a different tokenizer, grouped-query attention, and pretraining objective/corpus. The statement in §3.3.1 that LLaMA-3 \"shares architecture with LLaVA-1.5's Vicuna\" is inaccurate. Similarly, the LLaVA-1.5 vs. Qwen2-VL comparison changes the vision encoder, projector, tokenization, instruction data, and base architecture all at once. The abstract's claim that tokenization, tuning data, and pretraining corpus do not affect the stage structure, while architecture controls layer allocation, therefore goes beyond what the two two-model comparisons can identify. Please either run controlled ablations with one factor varied at a time, or rephrase the conclusion as a hypothesis with the confounds explicitly stated.","section":"§3.3"},{"comment":"The core inference from a probe accuracy drop to a layer's \"sensitivity\" or \"loss of encoding\" conflates distribution shift with loss of task-relevant information. A probe trained on anchor-prompt embeddings can drop in accuracy on variant-prompt embeddings merely because the variant changes the feature distribution, even if the same linear decision boundary still exists in the variant space. The paper does not provide a control such as training new probes on each variant's embeddings and showing whether accuracy recovers, nor a null-prompt condition unrelated to the task. Without such controls, the stage boundaries (e.g., layers 5-13 for lexical integration, layers 16+ for decoding) are not uniquely determined. Please add at least a variant-trained-probe control and a null-variant calibration.","section":"§2.2, Figures 3-6"},{"comment":"All probing curves are single runs with no error bars, no multiple seeds, and no significance tests. The paper's specific layer boundaries (1-4, 5-13, 12-15, 16+) are read off qualitative curve shapes, and small differences between models (e.g., Qwen2-VL's \"extended reasoning\" range) are asserted without any uncertainty estimate. Please report multiple training seeds or bootstrapped confidence intervals, and ideally a statistical test comparing accuracy across layer groups. The output-format variant split between reasoning and decoding also rests on a single prompt pair (\"yes/no\" vs. \"1/0\"); multiple format manipulations would strengthen this central distinction.","section":"§3.1-§3.2, Figures 3-6"},{"comment":"The model-compliance filtering introduces a free threshold (\"confident yes\") that is never specified, and the paper does not report how many images remain per class per model after filtering. Since each model filters a different subset of ImageNet, the evaluation sets differ across models, which can bias the cross-model comparisons in Section 3.3. Additionally, moving 300 images per class from the ImageNet training split to the validation set based on model behavior creates a non-standard, model-dependent split. Please report the compliance threshold, the final dataset sizes, the overlap of retained images across models, and include an ablation showing that the curves are stable under different filtering thresholds.","section":"§2.1"}],"minor_comments":[{"comment":"The running title on page 1 contains a typo: \"T asks\" should be \"Tasks.\"","section":"Title/header"},{"comment":"\"LLaV A-Next\" and \"QWen2-VL\" have inconsistent spacing and capitalization; please standardize to \"LLaVA-Next\" and \"Qwen2-VL.\"","section":"§3.3.1"},{"comment":"The conclusion states a \"three-stage hierarchy\" (grounding, integration, decoding) while Section 3.2's summary and Figure 1 describe four stages including a separate semantic-reasoning stage. Please reconcile the numbering.","section":"Conclusion, §5"},{"comment":"The anchor prompt \"The answer must be always yes or no\" is syntactically ambiguous; if the intended meaning is that the answer must always be \"yes\" for the dog images, rephrase the prompt and the compliance filter description to match.","section":"§2.1, anchor prompt"},{"comment":"The layer-depth comparison between LLaVA-1.5 (32 layers), LLaVA-Next-LLaMA-3 (32 layers), and Qwen2-VL (28 layers) is made in absolute layer indices; a normalized depth analysis would help separate architectural depth allocation from differences in total layer count.","section":"§3.3"}],"recommendation":"major_revision","confidential_remarks":"The paper is already published at COLM 2025, and I am reviewing the arXiv version. I believe the descriptive stage-wise claim is plausible and the probing framework is of interest, but the current version overclaims the causal invariance and architecture-attribution results in the abstract and Section 3.3. The missing error bars, absent probe-recovery controls, and undefined filtering threshold are fixable with additional experiments and careful rephrasing, so I recommend major revision rather than rejection. I would also encourage the editor to ensure the authors address the confounded model comparisons directly, as the current text in §3.3.1 contains a factual inaccuracy about LLaMA-3 sharing Vicuna's architecture."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"Short version: this is a decent paper with one genuinely clever piece—the output-format variant (switching yes/no to 1/0) is a simple way to separate when a model is reasoning from when it is formatting tokens. That device alone is worth keeping in mind. The layer-wise probing across three MLLMs is systematic, and the qualitative four-stage pattern (grounding, lexical integration, reasoning, formatting) is visible in the curves and consistent across models. If you want a cheap, model-agnostic way to compare where different MLLMs commit to answers, this gives you a starting point.\n\nThe main soft spot is the causal attribution in Section 3.3. The paper claims tokenization, instruction-tuning data, and pretraining corpus don't affect the stage structure, and that base-LLM architecture controls layer allocation. The two model comparisons each vary several factors at once. LLaVA-Next-LLaMA-3 vs LLaVA-1.5 changes tokenization, data, and the base LLM; Qwen2-VL vs LLaVA-1.5 changes architecture, vision encoder, tokenizer, and training data together. LLaMA-3 is not an architecture-only replacement for Vicuna either—different tokenizer, attention config, and pretraining. So the claimed invariance and the architecture-shift result are both underdetermined. That's not a reason to dismiss the paper, but it should be phrased as a hypothesis, not a conclusion.\n\nThe usual probing caveats apply: no error bars or seeds, probe accuracy drops on variants could reflect distribution shift rather than loss of encoding, and the per-variant filtering changes the test population. The reasoning/formatting split also rests on a single format pair. These make the fine boundaries fragile, though the broad pattern likely holds. I don't see a circularity problem—the stage labels are post-hoc names for observed curves.\n\nWho should read this? Interpretability researchers who want a lightweight mapping tool, and anyone comparing MLLMs for deployment. The paper is honest about limitations and cites the relevant literature. It deserves a serious referee; I'd expect revision to temper the causal claims and add error bars. If you get a chance, read it—the output-format probe might be a technique you reuse.","headline":"A solid probing study with a clever output-format control; the four-stage structure is plausible, but the causal claim about base-LLM architecture is underdetermined by the three model comparisons.","tokens_in":12464,"tokens_out":3581,"would_cite":true,"duration_ms":31499,"reading_group":"yes","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"deepseek-v4-flash","headline":"A linear probe at every layer shows that multimodal LLMs run the same four-stage pipeline, from grounding to answer formatting, and differ only in how deep each stage runs.","keywords":["multimodal large language models","linear probing","layer-wise analysis","visual grounding","semantic reasoning","answer decoding","prompt variation","interpretability"],"falsifier":"Train separate probes on each prompt variant's own embeddings and compare whether the variant-specific probes achieve anchor-level accuracy at the same layers; if they do at all layers, the accuracy drops in this paper measure distribution shift rather than loss of encoding, and the stage boundaries would not be identified by this protocol.","tokens_in":1511,"feed_emoji":"🔍","tokens_out":5491,"duration_ms":87793,"temperature":0.7,"pith_summary":"This paper asks what happens inside a multimodal LLM between the image being shown and the answer being spoken. By training a simple linear classifier at each layer to recognize fine-grained visual classes from the last-token embedding, and then replaying the same images under three kinds of prompt change, the authors map where each stage of computation happens. They claim that LLaVA-1.5, LLaVA-Next-LLaMA-3, and Qwen2-VL all follow the same four-stage pipeline: visual grounding, lexical integration, semantic reasoning, and answer formatting. They further argue that visual tokenization, instruction-tuning data, and pretraining corpus barely move the stage boundaries, whereas the base LLM architecture reallocates depth between stages. A sympathetic reader would care because it offers a single lens for comparing how any multimodal LLM organizes its internal work.","feed_headline":"Probing shows MLLMs solve image tasks in four universal stages","feed_subtitle":"Grounding, reasoning, and formatting line up in the same order; only layer counts shift between models.","key_machinery":"The central machinery is a layer-wise linear probe combined with three controlled prompt perturbations. A linear classifier is trained on the last-token embedding at each decoder layer to predict fine-grained visual classes under a fixed anchor question; at test time the probe is frozen and images are replayed with prompts that change only the surface wording, only the semantic concept, or only the answer format. The drop in probe accuracy relative to the anchor measures a layer's sensitivity to each perturbation, and the pattern of these drops across depth localizes the stage boundaries. The output-format variant is the key isolating device: because it keeps reasoning fixed and changes only answer tokens, the layer at which its accuracy curve departs from the anchor marks the transition from reasoning to decoding.","core_discovery":"The paper's central discovery is that the internal processing of multimodal LLMs is stage-wise and consistent across models. Using a probe trained on an anchor prompt ('Does this image show an animal?'), with dog-breed classification as the readout, the authors measure how probe accuracy changes under lexical variants (e.g., image to picture), semantic negation (animal to plane), and output-format variants (yes/no to 1/0). For LLaVA-1.5 the resulting profile divides into layers 1-4 visual grounding, 5-11 lexical integration, 12-15 semantic reasoning, and 16+ answer formatting; LLaVA-Next-LLaMA-3 shows the same structure. Qwen2-VL also shows the same sequence but with fewer layers on grounding and a longer reasoning window. The authors conclude that the sequence is intrinsic to how these models process multimodal input, while the base LLM's architecture controls how many layers each stage receives.","pith_inferences":["If the stage map transfers, then causally intervening at the semantic-reasoning layers should change the answer content but not the phrasing, while intervening at the formatting layers should change wording only; this is a directly testable prediction the paper does not run.","The invariance claim covers only LLaVA-style architectures; extending the same three-variant probe to pre-LLM fusion or early-fusion designs would tell whether the four-stage sequence is a property of multimodal transformers generally or of the projector-plus-LLM design family.","The method should be stress-tested on tasks where the answer vocabulary is not binary, since the yes/no anchor and the 1/0 format variant may make the reasoning/decoding split easier to see than it would be with free-form output."],"forward_implications":["Any LLaVA-style model with a frozen vision encoder should reproduce the same four-stage sequence: grounding, lexical integration, semantic reasoning, and answer formatting.","Differences in visual tokenization (576 fixed tokens, multi-resolution chunking, dynamic resolution) and in instruction-tuning or pretraining data do not by themselves change where the stages fall.","The base LLM architecture is the main knob: switching from a LLaMA-family base to a Qwen-family base reallocates depth, giving Qwen2-VL a longer reasoning window and a shorter grounding phase.","The output-format variant acts as a location tool for reasoning: the layers where accuracy tracks the anchor before dropping mark where the model commits to a decision independent of answer tokens.","Probing with this lightweight protocol can compare any MLLM's internal organization without model-specific instrumentation."],"supporting_citations":[{"why":"supplies LLaVA-1.5, the primary 32-layer model whose probing profile yields the four-stage hierarchy.","marker":"Liu et al. 2023"},{"why":"supplies LLaVA-Next-LLaMA-3, whose near-identical probing trends isolate tokenization, tuning data, and pretraining corpus as non-determinants.","marker":"Liu et al. 2024a"},{"why":"supplies Qwen2-VL, whose different layer allocation is the evidence that base LLM architecture shifts stage depths.","marker":"Wang et al. 2024"},{"why":"defines Vicuna, the LLaMA-based base LLM of LLaVA-1.5, fixing the 32-layer target used in the central analysis.","marker":"Chiang et al. 2023"},{"why":"defines Qwen-LLM, the contrasting base architecture used to attribute stage shifts to architecture rather than data.","marker":"Bai et al. 2023"},{"why":"provides the LLaMA-3 base of LLaVA-Next, controlling for how a different pretraining corpus influences the comparison.","marker":"Dubey et al. 2024"}],"fun_headline_variants":["MLLMs show a consistent four-stage pipeline for image tasks","Probing MLLMs: same four stages, model-specific layer counts","Universal stage sequence in MLLMs: layer counts vary","How MLLMs process images: fixed stages, flexible layer allocation"],"cache_read_input_tokens":14592,"weakest_assumption_plain":"The load-bearing premise is that a probe trained on anchor-prompt embeddings, when dropped onto variant-prompt embeddings, produces accuracy losses that faithfully reflect the layer's sensitivity to the perturbed feature rather than distribution shift or miscalibration.","fun_headline_variants_meta":{"raw":{"variants":["MLLMs show a consistent four-stage pipeline for image tasks","Probing MLLMs: same four stages, model-specific layer counts","Universal stage sequence in MLLMs: layer counts vary","How MLLMs process images: fixed stages, flexible layer allocation"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.000361,"raw_usage":{"total_tokens":1990,"prompt_tokens":1022,"completion_tokens":968,"prompt_tokens_details":{"cached_tokens":384},"prompt_cache_hit_tokens":384,"prompt_cache_miss_tokens":638,"completion_tokens_details":{"reasoning_tokens":895}},"tokens_in":638,"tokens_out":968,"duration_ms":8830,"temperature":1.0,"reasoning_tokens":895,"cache_read_input_tokens":384,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-15T16:47:22.951811+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"Train separate probes on each prompt variant's own embeddings and compare whether the variant-specific probes achieve anchor-level accuracy at the same layers; if they do at all layers, the accuracy drops in this paper measure distribution shift rather than loss of encoding, and the stage boundaries would not be identified by this protocol.","supporting_citations":[],"review_version":1}