mlsanityData to 14 Oct 2026

Constraint-Anchored Attribution: Feasibility-Certified Counterfactuals and Bonferroni-PAC Sufficient Subsets for Neural CO Policies

arXiv 2605.25235 ↗First posted 24 May 2026cs.LG cs.AI math.OCcited by 0

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Abstract (arXiv)

We give an attribution method for neural combinatorial-optimisation (CO) policies that (i) decomposes a decision by constraint families via LP-relaxation duals, (ii) certifies counterfactuals through a combinatorial feasibility model (implemented as a CSP feasibility-decision model), and (iii) bounds the size of a PAC-sufficient explanation with a Bonferroni-corrected Hoeffding sufficient-subset test along a greedy ordering. Across three CO problems and three seeds, our LP-anchored Lambda-attribution matches the CF-derived signal at 96.5% on CVRPTW (ncert=344) and 77.2% on the Orienteering Problem (ncert=281) vs 75.0% and 35.2% for proxy gradient (paired diffs +0.215 and +0.420; McNemar exact p ≤ 10-14). In the rank-aligned regime of the Flexible Job-Shop Scheduling Problem, both backends agree on every CSP-certified flip (ncert=59), confirming the no-gain prediction. Bonferroni-PAC subsets average 5.0 nodes per step (M=70, varepsilon=δ=0.2, kmax=25). Reference implementation: https://github.com/sohaibafifi/neuro-co-cax

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 8 of its 18 references that are in the map. First posted on arXiv 24 May 2026.

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References · 8

  1. 2017Axiomatic Attribution for Deep Networkscited by 3,384
  2. 2017Learning Important Features Through Propagating Activation Differencescited by 1,458
  3. 2017Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPRcited by 1,089
  4. 2020POMO: Policy Optimization with Multiple Optima for Reinforcement Learningcited by 348
  5. 2018Abduction-Based Explanations for Machine Learning Modelscited by 160
  6. 2023RL4CO: an Extensive Reinforcement Learning for Combinatorial Optimization Benchmarkcited by 71
  7. 2021Counterfactual Explanations in Sequential Decision Making Under Uncertaintycited by 37
  8. 2024Learning Model Agnostic Explanations via Constraint Programmingcited by 2

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