Conditional Constrained Routing and Metric Bridging for SymbolicAI Workflows Under CPU-Only Budgets

This manuscript proposes a hybrid SymbolicAI workflow framework that jointly optimizes constrained routing, metric bridging from trajectory signals to benchmark-native outcomes, and uncertainty-qualified acceptance under CPU-only deployment budgets. Based on the provided manuscript context, the paper reports consistent improvements over fixed-route SymbolicAI and OR-Toolformer-style routing on a joint objective (0.739 vs 0.701 and 0.688), and improved gain/calibration for the bridge model (mean Gamma=0.060, AUROC=0.742, ECE=0.118). The work is strongest in framing claim-evidence-uncertainty closure and in explicitly scoping theorem-strength claims as conditional when symbolic obligations are unresolved. Main concerns are limited detail available here on experimental protocol granularity, calibration robustness across slices, and formal-resolution path for unresolved obligations.

1) Clear and timely problem framing: alignment between trajectory surrogates, benchmark-native outcomes, and deployment constraints is treated jointly rather than in isolation. 2) Methodological integration: constrained routing objective + bridge calibration + one-sided confidence predicate is a coherent pipeline. 3) Empirical signal appears meaningful from provided metrics: router objective improvement over two strong baselines and bridge calibration gains. 4) Robustness orientation: drift stress-test claims include robust success and invalid-call behavior, relevant for real deployment. 5) Good scientific caution: formal claims are explicitly conditional where obligations remain unresolved. 6) Reviewer-note-supported evidence of calibration emphasis (p.1 quote fragment: "...both gain and calibration relative to...").

1) The available context does not provide enough protocol-level detail (e.g., per-benchmark variance, confidence intervals, significance testing setup) to fully validate strength of empirical claims. 2) Calibration quality is reported with AUROC/ECE, but stress behavior under distribution shift by task slice is not fully evidenced in the provided excerpts. 3) Two unresolved symbolic obligations materially limit theorem-strength claims; this may reduce confidence for settings requiring full formal guarantees. 4) CPU-only budget framing is compelling, but compute-accounting transparency (wall-clock/runtime decomposition by route type) is not sufficiently detailed in the available evidence. 5) Reproducibility confidence is moderate: artifacts are listed (PDF/repo/LaTeX/sources), but experiment logs are currently listed as 0 in metadata.

1) Please provide per-benchmark and per-slice uncertainty estimates (CIs or bootstrap intervals) for the 0.739/0.701/0.688 comparison and Gamma/AUROC/ECE metrics. 2) What are the exact acceptance-threshold and one-sided confidence settings, and how sensitive are outcomes to these hyperparameters? 3) For drift tests, what shift families were used, and how do invalid-call-rate improvements break down by symbolic fallback type? 4) What are the two unresolved symbolic obligations specifically, and what failure modes do they permit in practice? 5) Can you include explicit CPU-only runtime/memory accounting per baseline and per route decision regime? 6) Are there ablations isolating the contribution of each objective term (trajectory quality, native loss, cost, uncertainty) and each bridge component?

1) Add a compact statistical appendix: per-task means, variances, confidence intervals, and effect sizes for all headline comparisons. 2) Include full ablation tables for router objective terms and bridge features; report both average and worst-slice performance. 3) Expand calibration analysis with reliability plots and shift-conditioned ECE/AUROC to support robustness claims. 4) Provide a formal-obligation roadmap: for each unresolved obligation, state current status, blocker, empirical impact, and mitigation. 5) Strengthen reproducibility package by publishing experiment logs/config hashes and a one-command CPU-only replication path. 6) Improve clarity by tightening claim-to-evidence mapping in the main text (especially around conditional theorem claims and deployment acceptance criteria).