Public articles linked to the same research event.
arXiv The work introduces SEDIMA, a persistent hierarchical insight memory for LLM-driven evolutionary search agents that distills raw traces into natural-language insights, clusters them by semantic similarity using attention-weighted centroids, and retrieves relevant guidance to condition future mutations, accumulating transferable knowledge across runs and problems; as a drop-in module that leaves search operators unmodified, it improves average final performance by 5.5% on AlgoTune and 6.6% on ALE-Bench LITE under a fixed budget of 100 evaluated candidates, and under OpenEvolve requires 32.3% fewer iterations on average to reach baseline-best performance across the five evaluated backbones.
The work introduces SEDIMA, a persistent hierarchical insight memory for LLM-driven evolutionary search agents that distills raw traces into natural-language insights, clusters them by semantic similarity using attention-weighted centroids, and retrieves relevant guidance to condition future mutations, accumulating transferable knowledge across runs and problems; as a drop-in module that leaves search operators unmodified, it improves average final performance by 5.5% on AlgoTune and 6.6% on ALE-Bench LITE under a fixed budget of 100 evaluated candidates, and under OpenEvolve requires 32.3% fewer iterations on average to reach baseline-best performance across the five evaluated backbones.
The work introduces SEDIMA, a persistent hierarchical insight memory for LLM-driven evolutionary search agents that distills raw traces into natural-language insights, clusters them by semantic similarity using attention-weighted centroids, and retrieves relevant guidance to condition future mutations, accumulating transferable knowledge across runs and problems; as a drop-in module that leaves search operators unmodified, it improves average final performance by 5.5% on AlgoTune and 6.6% on ALE-Bench LITE under a fixed budget of 100 evaluated candidates, and under OpenEvolve requires 32.3% fewer iterations on average to reach baseline-best performance across the five evaluated backbones.
The work introduces SEDIMA, a persistent hierarchical insight memory for LLM-driven evolutionary search agents that distills raw traces into natural-language insights, clusters them by semantic similarity using attention-weighted centroids, and retrieves relevant guidance to condition future mutations, accumulating transferable knowledge across runs and problems; as a drop-in module that leaves search operators unmodified, it improves average final performance by 5.5% on AlgoTune and 6.6% on ALE-Bench LITE under a fixed budget of 100 evaluated candidates, and under OpenEvolve requires 32.3% fewer iterations on average to reach baseline-best performance across the five evaluated backbones.