AVO: Agentic Variation Operators for Autonomous Evolutionary Search
Authors: Terry Chen, Zhifan Ye, Bing Xu, Zihao Ye, Timmy Liu, Ali Hassani, Tianqi Chen, Andrew Kerr, Haicheng Wu, Yang Xu, Yu-Jung Chen, Hanfeng Chen, Aditya Kane, Ronny Krashinsky, Ming-Yu Liu, Vinod Grover, Luis Ceze, Roger Bringmann, John Tran, Wei Liu, Fung Xie, Michael Lightstone, Humphrey Shi
Agentic Variation Operators (AVO) are a new family of evolutionary variation operators that replace the fixed mutation, crossover, and hand-designed heuristics of classical evolutionary search with autonomous coding agents. Rather than confining a language model to candidate generation within a prescribed pipeline, AVO instantiates variation as a self-directed agent loop that can consult the current lineage, a domain-specific knowledge base, and execution feedback to propose, repair, critique, and verify implementation edits. We evaluate AVO on attention, among the most aggressively optimized kernel targets in AI, on NVIDIA Blackwell (B200) GPUs. Over 7 days of continuous autonomous evolution on multi-head attention, AVO discovers kernels that outperform cuDNN by up to 3.5% and FlashAttention-4 by up to 10.5% across the evaluated configurations. The discovered optimizations transfer readily to grouped-query attention, requiring only 30 minutes of additional autonomous adaptation and yielding gains of up to 7.0% over cuDNN and 9.3% over FlashAttention-4. Together, these results show that agentic variation operators move beyond prior LLM-in-the-loop evolutionary pipelines by elevating the agent from candidate generator to variation operator, and can discover performance-critical micro-architectural optimizations that produce kernels surpassing state-of-the-art expert-engineered attention implementations on today's most advanced GPU hardware.
Figures & tables
Figure 1: EVO vs AVO : Comparison between prior evolutionary search frameworks (e.g. FunSearch, AlphaEvolve, and related LLM-augmented evolutionary approaches) and the proposed Agentic Variation Operator. Left: Prior approaches follow a fixed pipeline where the LLM is confined to a single-turn generation step or a predefined workflow, with sampling and evaluation controlled by the framework. Right: AVO replaces this pipeline with an autonomous AI agent that iteratively plans, implements, tests, and debugs across long-running sessions, with direct access to previous solutions, evaluation utilities, tools, and persistent memory.
Figure 2: Illustration of the Agentic Variation Operator (AVO).
Figure 3: Multi-head attention forward-pass prefilling throughput (TFLOPS) on NVIDIA B200 with head dimension 128, 16 heads, and BF16 precision. Batch size and sequence length are varied with a fixed total of 32k tokens.
Figure 4: Grouped-query attention forward-pass prefilling throughput (TFLOPS) on NVIDIA B200 with 32 query heads, head dimension 128 and BF16 precision. Results are shown for two GQA configurations (group sizes 8 and 4) under both causal and non-causal masking. The GQA kernel was produced by prompting the AVO agent to adapt the evolved MHA kernel, requiring approximately 30 minutes of autonomous effort.
Figure 5: Evolution trajectory of AVO across 40 kernel versions over 7 days on causal MHA. The solid green line tracks the running-best geometric mean throughput across all configurations; green circles mark versions that set a new best. Dashed colored lines show per-configuration throughput (seq_len = 4k, 8k, 16k, 32k). Horizontal dashed lines indicate the geometric mean throughput of cuDNN and FA4.
Figure 6: Evolution trajectory of AVO across 40 kernel versions over 7 days on non-causal MHA. The solid green line tracks the running-best geometric mean throughput across all configurations; green circles mark versions that set a new best. Dashed colored lines show per-configuration throughput (seq_len = 4k, 8k, 16k, 32k). Horizontal dashed lines indicate the geometric mean throughput of cuDNN and FA4.
Optimization
Versions
Non-causal
Causal
Branchless accumulator rescaling
v19 → v20
+8.1%
+1.6%
Correction/MMA pipeline overlap
v29 → v30
+1.1%
+0.4%
Register rebalancing across warp groups
v32 → v33
+2.1%
∼0%
Table 1: Summary of agent-discovered optimizations and their measured ablation gains (geomean TFLOPS improvement over the preceding version, across all benchmark configurations).
Appendix figures & tables1 asset
Supplementary material from the paper’s appendix.
Appendix
Figure 7: Multi-head attention forward-pass throughput (TFLOPS) on NVIDIA B200, comparing AVO (measured on our hardware) against cuDNN and FA4 baseline numbers as reported in the FA4 paper [ 10 ] . Head dimension 128, 16 heads, BF16. Left: non-causal. Right: causal.