HSA-Net: Hierarchical and Structure-Aware Framework for Efficient and Scalable Molecular Language Modeling

Lei Wang; Li Liu; Wencai Ye; Wentao Lei; Zihang Shao

REVIEW 3 major objections 4 minor 26 references

HSA-Net: Hierarchical and Structure-Aware Framework for Efficient and Scalable Molecular Language Modeling

T0 review · 3 major / 4 minor · reviewed 2026-08-05 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read The paper claims that a per-layer mixture of cross-attention and Graph-Mamba projection, fused by a sparse expert network, outperforms existing graph-conditioned LLMs on molecule description, IUPAC naming, and property prediction.

desk verdict Plausible architecture with a load-bearing training gap: the hard routing in Eqs. (3) and (5) has no gradient path, so the router cannot learn as described, and the SOTA claims are unsupported. read the letter →

arxiv 2508.08334 v1 pith:KJ33UV2G submitted 2025-08-10 cs.LG cs.AIq-bio.QM

Zihang Shao , Wentao Lei , Lei Wang , Wencai Ye , Li Liu This is my paper

classification cs.LGcs.AIq-bio.QM

keywords molecularrepresentationlearningover-smoothingcross-attentionGraph-Mambamixture-of-expertsmoleculecaptioningIUPACpredictionproperty

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

This paper sets out to show that the over-smoothing problem in GNN-based molecular encoders is best handled by choosing a feature projector separately for each GNN layer rather than applying one projector to all layers. Its evidence is that cross-attention projections preserve fine-grained local information in shallow layers but collapse in deep layers, while a Graph-Mamba state-space projection keeps global topology in deep layers but is less discriminative in shallow layers. The proposed HSA-Net learns this layer-dependent choice and then fuses the projected features with a sparse mixture-of-experts module. If the reported results hold, HSA-Net is the current best graph-LLM approach on the tested benchmarks: molecule-description BLEU 43.5 vs. 37.9, IUPAC BLEU 65.4 vs. 61.1, and property MAE 0.0049 vs. 0.0061 compared with the prior state of the art.

What carries the argument

The load-bearing mechanism is the hierarchical adaptive projector (HAP), a mixture-of-experts module whose two experts are a learnable-query cross-attention projector and a structure-aware Graph-Mamba projector (atom sequences ordered by fragment and degree, then processed by a graph state-space model). A per-layer linear gating network selects one expert per GNN layer, and the source-aware fusion (SAF) module applies token-level top-2 sparse routing over MLP experts. This pair converts the observed global-local trade-off into an explicit design: shallow layers get cross-attention, deep layers get Graph-Mamba, and the fusion decides how to combine the results.

What would settle it

Retrain the generalist model with the HAP router frozen to a fixed layer schedule (cross-attention on the first two layers, Graph-Mamba on the rest) and SAF replaced by a simple MLP fusion. If BLEU and METEOR stay near 43.5 and 72.1, adaptive gating and sparse fusion are not the operative cause. More directly, inspect the gradient norms of the gating networks after the alignment stage: if they are zero, the argmax selection and unweighted top-2 sum cannot have trained the router as written.

Watch

Extended reading notes

Core claim

The central discovery is a complementary failure mode: cross-attention and Graph-Mamba projectors degrade in opposite directions as the GNN deepens, and HSA-Net exploits that complementarity. The HAP module uses a per-layer gating network to pick between the two projectors, with the learned gating ratio shifting toward Graph-Mamba on deeper layers. The SAF module routes each feature token to the top two of several MLP experts, producing an adaptive mixture of hierarchical structural features and motif features. The paper argues this design avoids deep-feature collapse, separates molecule classes more cleanly, and beats existing models on all six datasets.

Load-bearing premise

The paper assumes the gating and routing are trainable even though the projector is chosen by argmax and the fusion sums top-2 expert outputs without probability weighting, and no gradient estimator or auxiliary routing loss is described; if no gradients reach the router, the learned layer-dependent switching cannot have been produced by training.

Editorial extensions

If this is right

HSA-Net becomes the strongest reported graph-conditioned LLM on molecule captioning, IUPAC naming, and property prediction across the generalist and specialist settings tested.
The learned gating evidence—low Graph-Mamba ratio on layers 1-2, increasing with depth—would confirm that shallow and deep GNN layers genuinely carry different kinds of information worth different projectors.
The method's advantage grows with molecular size, suggesting hierarchical projection is a viable route to modeling long-range structure in larger molecules.
The SAF sparse-fusion design is a reusable recipe for any graph-LLM pipeline that needs to merge heterogeneous feature sources.
A roughly 20% reduction in property-prediction MAE over LLaMo implies that representation quality, not just text generation, improves from the hierarchical projection.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

A natural test would be to replace HAP's argmax selection and SAF's unweighted top-2 sum with soft, probability-weighted combinations; if performance holds, the discrete routing is not the active ingredient.
The size-by-size analysis points to macromolecular graphs as the next target: if the gain really grows with atom count, protein and polymer benchmarks should show even larger margins.
SAF is arguably independent of HAP, so it could be dropped into existing graph-conditioned LLMs; that would tell whether adaptive fusion or the two-projector pairing contributes more.
A fixed depth schedule (cross-attention below layer 3, Graph-Mamba above) would test whether the learned gating is necessary or merely a convenient way to discover the regime.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Colleague,

Short version: the paper has a decent empirical observation and a modular design, but the routing mechanism cannot be trained as written. The non-differentiability is not a minor detail; it is the core of the claimed contribution.

What is genuinely good: the authors identify a plausible complementarity between cross-attention and Graph-Mamba across GNN layers, and their Figure 2 and ablation study support the intuition that both projectors contribute. The architecture is clearly explained, the two-stage training pipeline is sensible, and the reported gains over LLaMo are substantial. If the routing worked, this would be a useful contribution to molecular graph-LLM integration.

The soft spots are serious. Equations (3) and (5) select projectors and experts by hard argmax and top-2 with no probability weighting, no straight-through estimator, no Gumbel-Softmax, no REINFORCE, and no auxiliary routing loss. Under standard backpropagation, the gating networks G_h and G_s receive zero gradient almost everywhere, so the router cannot learn the layer-dependent behavior displayed in Figure 5. The missing Appendix A.1 might have described a workaround, but there is no appendix. Without a documented gradient path, the main empirical claim—that the model learns to switch projectors adaptively—is unsupported. The selected experts can still learn, so the model might train with effectively fixed routing; but then the SOTA gains would not validate the stated contribution.

I agree with the stress-test note that this is load-bearing. The lack of code and error bars compounds the problem: the benchmark numbers cannot be checked, and even the variance across runs is unknown. The t-SNE motivation is mildly circular since it uses the same benchmark data, but that is not decisive.

Serious thinking is visible in the design and analysis, but the empirical case collapses until the routing is made trainable or the claim of learned switching is retracted. This is not a desk-reject because the idea is trivial; it is a desk-reject because the submitted manuscript, as it stands, does not support its central assertion.

Would I engage with this? Yes, as a cautionary example for a reading group, but I would not cite it. If the authors add a gradient estimator and release code, it might become citable.

Recommendation: do not send to peer review in this form. Tell the authors to fix the gradient path, provide the missing appendix and code, and then resubmit.

Referee Report

3 major / 4 minor

Summary. The paper proposes HSA-Net, a molecular graph-LLM framework with two novel modules: a Hierarchical Adaptive Projector (HAP) that routes each GNN layer's features to either a cross-attention projector or a structure-aware Graph-Mamba projector, and a Source-Aware Fusion (SAF) module that uses sparse Top-2 expert routing to fuse the resulting multi-level features before feeding the LLM. The authors report state-of-the-art results on molecule description, IUPAC prediction, and property prediction across the Mol-Instructions and ChEBI-20 datasets, and they provide ablations, t-SNE visualizations, and a gating-decision analysis to support the claim that HSA-Net resolves a global-local trade-off in molecular feature projection.

Significance. If the reported results were reproducible, HSA-Net would be a meaningful contribution: the identified trade-off between cross-attention and Mamba projectors is plausible, the architecture is well motivated, and the comparisons include strong baselines such as LLaMo and MolCA. The paper also includes an ablation study and qualitative analyses. However, the central training mechanism as written is internally inconsistent: hard-argmax routing without any gradient estimator means the gating networks cannot learn, so the adaptive behavior that is the paper's core contribution cannot be produced by the described training pipeline. The empirical claims are therefore not supported in their present form.

major comments (3)

[Methodology, 'Dynamic Gating and Projector Selection', Eq. (3); also Eqs. (4)-(5)] The routing in Eq. (3) selects a projector by k = arg max(p^(l)), and Eqs. (4)-(5) select Top-2 experts and sum their outputs without probability weighting. These are discrete functions of the gating parameters in G_h and G_s, so under standard backpropagation the loss is piecewise constant with respect to those parameters and the gradient is zero almost everywhere. The manuscript describes no straight-through estimator, Gumbel-Softmax, REINFORCE, or auxiliary routing/load-balancing loss. The only pointer to further implementation details, 'Appendix A.1', is absent from the manuscript. The gating networks therefore cannot learn from the described two-stage pipeline; only the selected projectors receive gradients. This undermines the central claim of adaptive hierarchical projection, since the reported gains could in principle arise from fixed or random routing combined with the projector
[Section 'Analysis of HAP Gating Decisions', Figure 5] The text states that the model 'learns' to assign low ratios to the Mamba projector in shallow layers and higher ratios in deep layers, citing Figure 5 as direct evidence. Because no gradient reaches G_h, this learned behavior cannot be produced by the training procedure described in 'Implementation Details'. Furthermore, the caption of Figure 5 describes t-SNE feature distributions, not per-layer Mamba selection ratios, so the figure does not actually display the claimed evidence. This analysis therefore does not support the model's central mechanism.
[Tables 1-3, text 'Comparison with SOTA Methods'] All quantitative results are reported as single values without error bars, confidence intervals, or significance tests. For example, the property-prediction MAE difference (0.0049 vs 0.0061, Table 1) is described as a nearly 20% improvement, but with no variance information it is impossible to assess whether this gap is meaningful. No code is released, and the implementation-details appendix is missing. The empirical claims are therefore not independently verifiable, compounding the training-mechanism problem.

minor comments (4)

[Methodology, Eq. (2)] Eq. (2) calls p^(l) a probability, but no softmax or normalization is specified. Clarify whether G_h outputs logits that are softmax-normalized before the arg max.
[Title and Abstract] The title promises 'Efficient and Scalable' modeling, but the paper reports no runtime, parameter-count, or scaling experiments. Consider either adding such measurements or tempering the wording.
[Figure 2 and Introduction] The t-SNE motivation in Figure 2 is performed on the same benchmark datasets later used for evaluation. This is an exploratory justificatory observation, not a controlled experiment, and should be explicitly labeled as such.
[Throughout] Typos and formatting issues: 'down-stream' should be 'downstream'; the Related Work heading 'Mambas for Molecule Learning' should be 'Mamba'; in Table 2 the HSA-Net row lacks spacing between entries. The missing Appendix A.1 should also be added or the reference removed.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity; reported gains are benchmark-derived and independent of the paper's own prior results.

full rationale

The central claim is that HSA-Net outperforms SOTA on three benchmark tasks (Tables 1-2). These numbers come from training/evaluation on public datasets (Mol-Instructions, PubChem324k, ChEBI-20, IUPAC), not from a fitted parameter relabeled as a prediction. HAP/SAF are introduced as new modules with equations (1)-(6); nothing in those equations defines the reported BLEU/METEOR/MAE in terms of the module inputs. The gating decisions in Eqs. (2)-(5) are discrete (arg max and Top-2 without probability weights), so the paper's training description is incomplete — no gradient estimator or auxiliary routing loss is given — but this is a correctness/implementation risk, not a circular reduction: the predictions are not forced by construction. No load-bearing self-citations appear; references to LLaMo, MolCA, Mol-Instructions, Mamba, etc. are external works. The t-SNE motivation in Figures 2 and 4 is qualitative and uses the same benchmark domain, which is a mild justification concern but does not make the empirical result equivalent to its inputs. The unsupported pointer to Appendix A.1 is a verification gap, not circularity. Therefore the derivation chain is self-contained: the architecture is motivated by an observation, implemented, and tested against fixed baselines, with outcomes determined by the data and training dynamics rather than by definition.

Assumptions & free parameters 4 free parameters · 4 assumptions · 0 invented entities

The ledger lists architecture hyperparameters chosen by hand and domain assumptions about molecular structure. No new physical entities are introduced. The most significant ad hoc item is the assumed trainability of the hard routing, which is not supported by any described training mechanism.

free parameters (4)

Number of GNN layers L = 6 (implied by Layer 1 and Layer 6 in Fig. 1/2)
Depth of hierarchical features; chosen by hand, not justified by a search.
Top-2 expert selection k in SAF = 2
Sparse MoE routing hyperparameter, chosen by hand.
Number of SAF experts N = not specified
MoE capacity hyperparameter, omitted from main text and Appendix A.1.
Query token count in cross-attention projector = not specified
Projector capacity hyperparameter, not stated.

assumptions (4)

domain assumption GNN encodings at different layers capture local-to-global structural information (Eq. 1).
The hierarchical view of over-smoothing and shallow/deep feature differences relies on this.
domain assumption Molecule-specific atom ordering by fragments and node degrees yields a meaningful sequence for Mamba.
The Graph-Mamba projector depends on this serialization; structural validity is assumed.
domain assumption Retrosynthesis-based motif extraction produces chemically meaningful functional groups that aid downstream tasks.
Motif features are added as inputs without validation of the vocabulary.
ad hoc to paper Backpropagation through the hard routing choices is possible (or a gradient estimator exists).
None stated; the equations imply non-differentiable selection.

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Cite this review

Pith. "Pith review of HSA-Net: Hierarchical and Structure-Aware Framework for Efficient and Scalable Molecular Language Modeling." pith.science (2026). https://pith.science/paper/KJ33UV2G

@misc{pith2026250808334,
  author       = {Pith},
  title        = {Pith review of: HSA-Net: Hierarchical and Structure-Aware Framework for Efficient and Scalable Molecular Language Modeling},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/KJ33UV2G}},
  note         = {Machine review of arXiv:2508.08334}
}

read the original abstract

Molecular representation learning, a cornerstone for downstream tasks like molecular captioning and molecular property prediction, heavily relies on Graph Neural Networks (GNN). However, GNN suffers from the over-smoothing problem, where node-level features collapse in deep GNN layers. While existing feature projection methods with cross-attention have been introduced to mitigate this issue, they still perform poorly in deep features. This motivated our exploration of using Mamba as an alternative projector for its ability to handle complex sequences. However, we observe that while Mamba excels at preserving global topological information from deep layers, it neglects fine-grained details in shallow layers. The capabilities of Mamba and cross-attention exhibit a global-local trade-off. To resolve this critical global-local trade-off, we propose Hierarchical and Structure-Aware Network (HSA-Net), a novel framework with two modules that enables a hierarchical feature projection and fusion. Firstly, a Hierarchical Adaptive Projector (HAP) module is introduced to process features from different graph layers. It learns to dynamically switch between a cross-attention projector for shallow layers and a structure-aware Graph-Mamba projector for deep layers, producing high-quality, multi-level features. Secondly, to adaptively merge these multi-level features, we design a Source-Aware Fusion (SAF) module, which flexibly selects fusion experts based on the characteristics of the aggregation features, ensuring a precise and effective final representation fusion. Extensive experiments demonstrate that our HSA-Net framework quantitatively and qualitatively outperforms current state-of-the-art (SOTA) methods.

Figures

Figures reproduced from arXiv: 2508.08334 by the authors.

**Figure 2.** Visualization of molecule feature distributions us [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗

**Figure 3.** The overall architecture of the HSA-Net framework. It consists of a GNN encoder, a HAP module to process multi [PITH_FULL_IMAGE:figures/full_fig_p003_3.png] view at source ↗

Figures from the paper (3 more)

**Figure 4.** Figure 4: t-SNE visualization of final layer molecule-level features for molecules with (red) and without (blue) a benzene ring. [PITH_FULL_IMAGE:figures/full_fig_p006_4.png]

**Figure 5.** Figure 5: Visualization of molecule feature distributions us [PITH_FULL_IMAGE:figures/full_fig_p006_5.png]

**Figure 6.** Figure 6: Analysis of Model Performance by Molecular Size. The left figure: The atom count distribution of our test dataset, showing that the majority of molecules are relatively small (fewer than 60 atoms). The right figure: A comparison of evaluation metrics between our HSA-Ne…

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Pith tools

Reviewed August 5, 2026 · model on record in the stance chip above.

[1] [1]

, " * write output.state after.block = add.period write newline

ENTRY address archivePrefix author booktitle chapter edition editor eid eprint howpublished institution isbn journal key month note number organization pages publisher school series title type volume year label extra.label sort.label short.list INTEGERS output.state before.all mid.sentence after.sentence after.block FUNCTION init.state.consts #0 'before.a...

[2] [2]

write newline

" write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 global.max substring 't := if while FUNCTION word.in bbl.in capitalize " " * FUNCT...

[3] [3]

L.; Almeida, D.; Altenschmidt, J.; Altman, S.; Anadkat, S.; et al

Achiam, J.; Adler, S.; Agarwal, S.; Ahmad, L.; Akkaya, I.; Aleman, F. L.; Almeida, D.; Altenschmidt, J.; Altman, S.; Anadkat, S.; et al. 2023. Gpt-4 technical report. arXiv preprint arXiv:2303.08774

arXiv 2023

[4] [4]

Banerjee, S.; and Lavie, A. 2005. METEOR: An automatic metric for MT evaluation with improved correlation with human judgments. In Proceedings of the acl workshop on intrinsic and extrinsic evaluation measures for machine translation and/or summarization, 65--72

2005

[5] [5]

Christofidellis, D.; Giannone, G.; Born, J.; Winther, O.; Laino, T.; and Manica, M. 2023. Unifying molecular and textual representations via multi-task language modelling. In International Conference on Machine Learning, 6140--6157. PMLR

work page 2023

[6] [6]

Edwards, C.; Lai, T.; Ros, K.; Honke, G.; Cho, K.; and Ji, H. 2022. Translation between molecules and natural language. arXiv preprint arXiv:2204.11817

arXiv 2022

[7] [7]

Fang, Y.; Liang, X.; Zhang, N.; Liu, K.; Huang, R.; Chen, Z.; Fan, X.; and Chen, H. 2023. Mol-instructions: A large-scale biomolecular instruction dataset for large language models. arXiv preprint arXiv:2306.08018

arXiv 2023

[8] [8]

Gu, A.; and Dao, T. 2023. Mamba: Linear-time sequence modeling with selective state spaces. arXiv preprint arXiv:2312.00752

arXiv 2023