Preventing Error Cascades in Long-Horizon Multimodal Agents with Edge-Reliability Graph Memory

Long-horizon tool-augmented agents suffer sharp degradation as trajectories grow: small tool errors are stored, repeatedly reused, and amplified into cascading failures. Existing approaches rely on post-hoc verification, item-level memory scoring, or executive context management, but do not directly control how unreliable evidence propagates through reuse. We propose \textbf{EPOCH} (Edge-Pathway Outcome-supervised Cascade-Halting memory), which mitigates cascades by learning an outcome-correlated reliability proxy on reuse pathways connecting observations over time, instantiated as a sparse memory graph with quality-weighted edges, a Temporal Graph Transformer over edge reliabilities, and a learned mid-context correction. \textbf{We prove that quality-weighted and uniform retrieval are asymptotically separated in horizon length:} the ratio of cumulative cascade error diverges unboundedly as $T \to \infty$ whenever the calibration parameter is positive, a result that does not require distributional regularity. We validate this prediction empirically at trajectory lengths up to $T=500$ and additionally establish a quantitative edge-vs-node separation in terms of an empirically measurable cross-context error variance. Because outcome supervision is correlational by construction, we do not claim the learned scores recover causal pathway reliability; we validate them on two complementary axes (counterfactual edge ablation: $\rho=0.71$, $n=21{,}743$; human annotation: ROC-AUC~$=0.94$, ECE~$=0.041$, $n=6{,}000$). Across 11 multi-modal benchmarks spanning native long-horizon search, wrapped knowledge-seeking tasks, and clean perception controls, EPOCH consistently outperforms the strongest reliability-aware and memory-augmented baselines in noisy long-horizon regimes while remaining on par on clean perception controls---quality-aware filtering does not collapse on clean data without claiming it helps there.