Rubric-based rewards are increasingly used for open-ended language model post-training, but criterion-level scores are commonly aggregated as independent utilities. This flat scalarization ignores semantic dependencies among criteria, allowing downstream credit or penalties to be counted even when the conditions that license them are absent. We call this structural failure False Credit Propagation (FCP).

We propose GEAR (Graphical Event Aggregation for Rubric rewards), a dependency-aware aggregation framework for rubric rewards. GEAR represents rubric criteria as latent events in a typed dependency graph, uses monotone retention factors to infer dependency-adjusted criterion marginals, and computes a normalized expected-utility reward.

This preserves compatibility with standard rubric-based reinforcement learning while adding only linear-time aggregation over criteria and edges. Experiments on HealthBench, WritingBench, and PLawBench with two policy backbones show that GEAR consistently improves over flat aggregation and hard gating, both in controlled aggregation-only settings and as a plug-in module for existing rubric-guided RL pipelines.

FCP diagnostics further show that GEAR substantially reduces invalid downstream utility while avoiding the over-suppression of deterministic gating.

LLM agents are increasingly expected to operate across heterogeneous task regimes that require distinct execution paradigms. This challenges fixed agent systems and motivates system-level meta-adaptation beyond isolated component updates. While existing works have adapted external harness or trained underlying reasoning policies, full-system adaptation remains insufficiently characterized. The adaptation space between structure and execution is rarely made explicit, and the compatibility between the external harness and the internal reasoner is not optimized jointly. We propose HarnessForge, a meta-adaptive framework for evolving LLM agent systems. HarnessForge formulates an agent system as a harness–policy pair, defining a stable adaptation space that separates harness-level execution structure from policy-level reasoning behavior. It then performs harness–policy co-evolution through fault-guided harness tailoring and harness-conditioned policy alignment. Experiments across five benchmarks from diverse domains show that HarnessForge consistently improves both Qwen3-4B and Qwen3-8B backbones, outperforming harness-only and policy-only baselines with gains of up to 12.0% over the strongest baseline and achieving favorable rollout-efficiency tradeoffs, demonstrating that harness–policy co-evolution is effective, and that executable compatibility between the harness and reasoning policy is essential for agent-system adaptation.