Abstract
Deep-learning track reconstruction methods such as hepattn and HEPTv2 perform strongly on the TrackML benchmark, but each represents detector hits from a single, architecturally bounded viewpoint. This work asks whether a channel-boosted multi-agent architecture can recover more particle tracks than either expert model achieves alone. The proposed system, CB-HMAS (Channel-Boosted Hierarchical MultiAgent System), combines a newly designed, physics-informed Relational Anchor Agent with two frozen expert peer channels (hepattn and HEPTv2) through a hyper-attention Fusion agent. A reinforcement-learning Topology Controller adjusts each peer channel’s contribution, and a meta-learning optimizer provides per-event adaptation. We evaluated the system on 210 held-out TrackML events (IDs 3000–3210, drawn from a separate data folder with zero overlap with the 1000–2300 training range). Across nearly the full range of similarity thresholds tested, the fused system exceeds both individual peer channels; at threshold 0.9 it reaches 76.0% efficiency, against 42.0% for hepattn alone and 12.2% for HEPTv2 alone. An embedding-capacity ablation identified representational capacity as a limiting factor: increasing the shared Anchor/Fusion embedding dimension from 32 to 64 raised peak efficiency from 45.7% to 76.0% under an otherwise identical configuration. Across five independent training runs with different random seeds, efficiency at threshold 0.9 is stable at 75.6% ± 2.4 percentage points, and the fused system outperforms both individual peer channels for every seed tested. These held-out results indicate that channel boosting recovers track structure that neither expert channel captures alone, and that the advantage is consistent rather than the product of a single favorable run.