Agentic Orchestration of Heterogeneous Predictors for RNA Binding Pocket Discovery

Protein surfaces that engage RNA are targeted today by four disjoint families of tools: end to end complex structure prediction, surface pocket detection, binding residue classification, and docking. Each encodes a different inductive bias and is validated under its own protocol, so a practitioner facing a new target has no principled basis for choosing among them. On a structurally disjoint benchmark curated from 1,809 experimental complexes, no single tool is universally best, per target performance spreads more widely *within* a family than *between* families, and the winner is partly predictable from target properties. Per target tool choice, rather than another monolithic model, is therefore the operative design variable. RiboSeer acts on this by separating planning from arithmetic: a language model profiles each pair and selects from a 15 tool library subject to a mandatory core covering all four paradigms, a learned gradient boosted fusion combines per residue scores, cross tool agreement, neighborhood context, and the agent profile into a calibrated binding probability, and an exponential moving average over per category reliability closes the loop across samples. On 107 held out targets separated from training by TM score, RiboSeer reaches Pearson $r$ of $0.593$ against $0.439$ for the best single tool and $0.515$ for a weighted mean over the same tools, with AUROC $0.899$. The margin holds across four splits, four contact cutoffs, and strata by RNA length, domain family, and difficulty. Ablations attribute the gain: learned fusion over a paradigm diverse pool supplies most of it, while the language model contributes $+0.039$ Pearson $r$ over a bandit only selector.