That looks interesting but it seems inefficient to put an LLM directly into the compilation pipeline, not to mention that it introduces nondeterministic behavior.
It has different limitations but inefficiency doesn't seem likely to be one of them. Did you read the Experimental Results section?
> Figure 2 shows the experimental results, and GenDB outperforms all baselines on every query in both benchmarks. On TPC-H, GenDB achieves a total execution time of 214 ms across five representative queries.
> This result is 2.8× faster than DuckDB (594 ms) and Umbra (590 ms), which are the two fastest baselines, and 11.2× faster than ClickHouse.
> On SEC-EDGAR, GenDB achieves 328 ms, which is 5.0× faster than DuckDB and 3.9× faster than Umbra.
> The performance gap increases with query complexity. For example, on TPC-H Q9, which is a five-way join with a LIKE filter, GenDB completes in 38 ms, which is 6.1× faster than DuckDB. GenDB uses iterative optimization with early stopping criteria.
> On TPC-H, Q6 reaches a near-optimal time of 17 ms at iteration 0 with zone-map pruning and a branchless scan, and does not require further optimization. In contrast, Q18 starts at 12,147 ms and decreases to 74 ms by iteration 1, which is a 163× improvement. This gain comes from replacing a cache-thrashing hash aggregation with an index-aware sequential scan.
> On SEC-EDGAR, Q4 decreases from 1,410 ms to 106 ms over three iterations, which is a 13.3× improvement, and Q6 decreases from 1,121 ms to 88 ms over four iterations, which is a 12.7× improvement. In Q6, the optimizer gradually fuses scan, compact, and merge operations into a single OpenMP parallel region, which removes three thread-spawn overheads. By iteration 1, GenDB already outperforms all baselines
That's all great, but sadly impractical.
I looked at one of the first statements:
> GenDB is an LLM-powered agentic system that decomposes the
complex end-to-end query processing and optimization task into
a sequence of smaller and well-defined steps, where each step is
handled by a dedicated LLM agent.
And knowing typical LLM latency, it's outside of the realm of OLTP and probably even OLAP. You can't wait tens of seconds to minutes until LLM generates you some optimal code that you then compile and execute.
Considering it's just s single Phd student who does this work, I don't believe such a task can be realistically accomplished, even as a PoC / research.