Counterfactual Ablation for Memory-Utility Evaluation: A Pre-Registered Case Study in Specialist Re-ranking

Context allocation across time — not context length — is the central memory problem for retrieval-augmented language-model agents. The paper's methodological contribution is counterfactual ablation as a per-memory utility signal: remove each retrieved memory in turn and label it by the resulting change in answerer correctness. The construction is non-circular by three structural arguments, with Spearman correlations from $-0.024$ to $+0.161$ across four large-scale runs — three within-pipeline on MemoryAgentBench and LoCoMo, one substrate-independent on LoCoMo Multi-Hop whose CI spans zero and which we treat as binding. We exercise the signal on one operationalization of the hypothesis that context-allocation requires per-memory utility distinct from cosine, and report a documented dissolution as the case study. A 1.5B-parameter LoRA specialist trained on these labels produced point-estimate gains of $+8/+7/+4/+5$ substring-exact-match over vanilla retrieval at $K=5$ on MAB. Five rigor layers tighten this result. Paired-bootstrap $95%$ CIs leave two strictly significant cells. K-normalization to the published comparator depth leaves $1/4$ datasets within ± 2pp, on partial data. BM25 sparse retrieval beats the specialist by $+13$ to $+22$pp on three of four datasets, reframing the K=5 gains as "less suboptimal than BGE cosine alone" rather than competitive. Cross-substrate transfer to LoCoMo Multi-Hop returns F1 $17.0%$ against a published $45.85%$ (Xu et al., 2025), but a prompt-control shows the specialist contributes $+13$pp over vanilla cosine on the same prompt — the residual gap is pipeline-attributable, not total cross-substrate failure. Learning-pattern probes score memory-equals-query at $100%$ above zero and fail label discrimination on a held-out validation sample. What survives: counterfactual ablation as a non-circular outcome signal and the rigor-dissolution discipline with pre-registered ADRs anchored to public git history. The broader hypothesis remains untested under operationalizations we did not run.