EvolveNet: Collaborative Harness Evolution for Agent Self-Improvement

TL;DR - EvolveNet is a federated-style paradigm for evolving an LLM agent's harness (the program that builds context, calls tools, verifies, and recovers) across isolated deployments, sharing learned program adaptations instead of raw execution data. It matters because it lets agents inherit operational experience from workloads that cannot be centrally pooled, with no model weight updates.

• Frames agent capability as harness-dependent: a shared harness is broadcast to data-local deployments, each evolves it on its own workload, and only the resulting program adaptations are composed back into an updated shared harness.
• Moves the aggregation boundary from raw workloads to learned adaptations, keeping data local and enabling concurrent evolutionary searches with reduced serial depth versus single-optimizer sequential evolution.
• Since modified programs can't be parameter-averaged and may conflict, it introduces scope-typed, evidence-guided program aggregation to compose adaptations.
• Reported improvements in all five evaluated settings (text-to-SQL, data-science coding, competitive programming, software engineering, agentic workflows), largest under heterogeneous workloads; ablations credit composition of adaptations rather than selection among them.