Experiments¶

The first Bayesian-Agent prototype was validated inside GenericAgent with deepseek-v4-flash. These experiments demonstrate two advantages at once: Bayesian-Agent can run a full self-evolving loop from scratch, and it can also act as an incremental repair layer for an existing agent.

Baseline¶

Benchmark	Agent	Model	Accuracy	Input Tokens	Output Tokens	Total Tokens	Efficiency
SOP-Bench	GA	deepseek-v4-flash	80%	1.34M	57k	1.39M	11.47
Lifelong AgentBench	GA	deepseek-v4-flash	90%	649k	42k	690k	26.07

Full Self-Evolving Mode¶

Benchmark	Agent	Model	Accuracy	Input Tokens	Output Tokens	Total Tokens	Efficiency
SOP-Bench	GA+Bayesian	deepseek-v4-flash	100%	1.07M	52k	1.12M	17.86
Lifelong AgentBench	GA+Bayesian	deepseek-v4-flash	95%	666k	44k	710k	26.77

Incremental Repair Mode¶

Bayesian-Agent read the GA baseline traces and reran only failed tasks.

Benchmark	Agent	Model	Final Accuracy	Incremental Input	Incremental Output	Incremental Total	Incremental Efficiency
SOP-Bench	GA+BayesianIncremental	deepseek-v4-flash	100%	216k	10k	226k	17.73
Lifelong AgentBench	GA+BayesianIncremental	deepseek-v4-flash	100%	71k	7k	78k	25.57

Interpretation¶

The full mode result shows that Bayesian Skill Evolution can improve SOP-Bench accuracy while reducing token usage.

The incremental mode result is the more practical path: Bayesian-Agent can attach to a baseline agent, inspect failed tasks, and spend only a small amount of additional inference to reach 100% final accuracy on the reported runs.

The cross-harness implication is the larger product direction. GenericAgent is the current experimental harness, but the Bayesian-Agent core only requires verified trajectory evidence and an adapter, so the same method can be applied to other agent frameworks.

Artifacts¶

Result artifacts are stored in:

artifacts/
  ga_deepseek_baseline/
  bayesian_full/
  bayesian_incremental/

These files are included so users can inspect result formats and reproduce summary calculations before wiring a live benchmark runner.