| Convergence Epochs<\/td> | 150<\/td> | 120<\/td> | N\/A<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n Include actual \u03b8\u2013f heatmaps as promised. Discuss figure trends: e.g., “KL divergence stabilizes post-100 epochs, indicating policy robustness.”<\/p>\n\n\n\n Discussion<\/h4>\n\n\n\n\n- Issues<\/strong>: Strong on advantages (e.g., real-time feedback beats simulations), but interpretations are qualitative. Policy convergence claim (“after ~200 epochs”) cites JS but ignores entropy-return scatter (Fig. 4)\u2014does low entropy correlate with high returns? Safety discussion is vague: How is SAR enforced (hard constraint or soft penalty)? Limitations are honest but brief; e.g., no phase measurement limits interference patterns\u2014quantify impact.<\/li>\n\n\n\n
- Suggestions<\/strong>: Tie back to results explicitly (e.g., “Fig. 4’s negative entropy-return slope validates exploration benefits”). Add a paragraph on clinical translation (e.g., FDA SAR limits). Balance with a “Broader Impacts” subsection.<\/li>\n<\/ul>\n\n\n\n
Conclusion<\/h4>\n\n\n\n\n- Issues<\/strong>: Repetitive of abstract; no new synthesis.<\/li>\n\n\n\n
- Suggestions<\/strong>: End with a call-to-action, e.g., “This framework paves the way for RL-driven TMS devices.”<\/li>\n<\/ul>\n\n\n\n
Figures and General Presentation<\/h4>\n\n\n\n\n- Issues<\/strong>: Figures are referenced but not embedded in the provided text (assuming PDF has them). Captions are descriptive but lack scales\/units (e.g., y-axis “Entropy (bits)”\u2014what’s the max possible?). Typos abound: “Visitation\u0019Policy” (likely “Visitation-Policy”), “learns conditions” (probably “learns optimal”), garbled chars in PDF extraction. “No Collaborators” is quirky\u2014remove or explain.<\/li>\n\n\n\n
- Suggestions<\/strong>: Ensure figures are high-res and self-contained. Use consistent notation (e.g., \u03b8_0 vs. \u03b8). Proofread for LaTeX artifacts.<\/li>\n<\/ul>\n\n\n\n
Final Recommendations<\/h3>\n\n\n\nThis has strong potential as a workshop paper (e.g., NeurIPS ML4H) but needs ~50% more content for broader venues: prioritize results with data, methods with details, and citations (aim for 15\u201320 refs). Run ablation studies (e.g., w\/o camera feedback) to bolster claims. Total score: 6\/10\u2014innovative idea, but execution lags. Revise iteratively, perhaps sharing drafts on arXiv for feedback. Great start\u2014keep pushing the neural-EM intersection!<\/p>\n\n\n\n Potential Synergies Between TTA for Quantized NNs and Neural MIMO Beam Steering<\/h3>\n\n\n\nYour Neural MIMO beam steering paper (from the prior critique) focuses on RL-driven adaptation for precise, safe electromagnetic targeting in neuromodulation, using a camera-in-the-loop setup with PPO and epsilon-greedy methods. It’s innovative for handling dynamic anatomy but, as noted, lacks depth in results, efficiency for real-time hardware, and handling of quantization-induced errors\u2014common in edge-deployed systems like wearable neuromod devices. The new paper on Test-Time Model Adaptation for Quantized Neural Networks (TTA for QNNs) introduces Zeroth-Order Adaptation (ZOA), a forward-pass-only framework for adapting low-bit models (e.g., W6A6 ViT) to domain shifts without backpropagation. This is highly relevant, as neuromodulation hardware often quantizes models for power\/latency constraints (e.g., on FPGAs or MCUs), amplifying sensitivity to shifts like tissue variations or interference.<\/p>\n\n\n\n Here’s how this TTA work could help strengthen your paper<\/strong>, structured by key areas: conceptual integration, methodological enhancements, and empirical extensions. These suggestions address prior weaknesses (e.g., irreproducibility, limited results) while boosting novelty for venues like NeurIPS or EMBC.<\/p>\n\n\n\n1. Addressing Quantization Sensitivity in Dynamic Environments<\/strong><\/h4>\n\n\n\n\n- Relevance<\/strong>: Your paper notes free-space limitations and calls for tissue phantoms in future work. The TTA paper’s Proposition 1 theoretically proves QNNs suffer exponential loss degradation under OOD perturbations (\u0394L \u221d 1\/2^{2n}), empirically shown in Fig. 1 (e.g., 20%+ accuracy drop for W3A3 ViT on ImageNet-C). This mirrors your MIMO challenges: quantized beamforming weights could amplify errors from anatomical shifts, worsening SAR violations or targeting precision.<\/li>\n\n\n\n
- How it Helps<\/strong>:\n
\n- Incorporate Theoretical Motivation<\/strong>: Add a subsection in your Sec. III (Results\/Discussion) adapting their Prop. 1 to beam steering. E.g., model quantization noise in phase weights (Eq. 1) as \u0394w \u221d 1\/2^n, showing how it exacerbates off-target radiation. This substantiates your safety-aware rewards empirically (e.g., via simulated OOD fields).<\/li>\n\n\n\n
- Practical Boost<\/strong>: Quantize your ULA weights (e.g., to 4-8 bits) and demonstrate TTA-like adaptation reduces side-lobe ratios by 10-15% on perturbed datasets (e.g., noisy camera feeds simulating tissue scatter).<\/li>\n<\/ul>\n<\/li>\n\n\n\n
- Impact on Your Paper<\/strong>: Elevates it from descriptive RL to a robustness-focused study, with citations to [42] (FOA, a baseline they beat). Cite arXiv:2508.02180 for the theoretical hook.<\/li>\n<\/ul>\n\n\n\n
2. Efficient, Gradient-Free Adaptation for Real-Time Constraints<\/strong><\/h4>\n\n\n\n\n- Relevance<\/strong>: Your PPO uses policy gradients, which vanish in quantized nets (as TTA notes), and requires many epochs (Figs. 1-3 show ~200 for convergence). PPO’s factorized heads are clever but compute-heavy for edge neuromod (e.g., no BP on low-power arrays). ZOA uses zeroth-order optimization (ZO) with two forward passes per sample<\/em>\u2014one for inference, one for perturbation-based gradient estimation\u2014via continual domain knowledge learning. It reuses historical adaptations with low memory (domain management scheme), cutting interference in long-term streams.<\/li>\n\n\n\n
- How it Helps<\/strong>:\n
\n- Replace\/ Augment RL Backend<\/strong>: Swap PPO’s gradients for ZOA’s two-sided ZO estimator (their Sec. 4). For your bandit\/PPO hybrid, treat steering angle \u03b8_0 and phase offsets as low-dim actions; perturb them forward-only to minimize a TTA objective like entropy on field intensities (from camera feedback). This enables single-sample updates, ideal for real-time (e.g., <10ms per beam adjustment).<\/li>\n\n\n\n
- Domain Knowledge Reuse<\/strong>: Adapt their management scheme to store “domain snapshots” (e.g., \u03b8-f heatmaps per anatomy type). Use learnable coefficients to blend them, reducing your policy entropy drops (Fig. 1) and enabling continual learning across sessions\u2014addressing your exploration-exploitation analysis.<\/li>\n\n\n\n
- Implementation Tip<\/strong>: Their GitHub (https:\/\/github.com\/DengZeshuai\/ZOA) has lightweight ZO scripts; integrate with your “lightweight scripts wired to make” for \u03b8-f viz. Test on quantized PPO heads to show 2x faster convergence vs. epsilon-greedy.<\/li>\n<\/ul>\n<\/li>\n\n\n\n
- Impact on Your Paper<\/strong>: Fixes efficiency critiques\u2014e.g., add ablation in expanded Results: ZOA vs. PPO on 8-bit weights shows 3x fewer passes, 5% better main-lobe gain. Positions your work as “ZO-RL for quantized neuromod,” novel for bio-EM.<\/li>\n<\/ul>\n\n\n\n
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