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LLM-generated quantum tests are executable but fail to reliably distinguish correct from faulty code, with the best models achieving only 71% and 34% test quality on separate benchmarks.
Instead of stuffing raw context with dialogue history or brittle retrieval vectors, treating user personalization as particle-filtered hypothesis tracing over natural language resolves the tension between fleeting intent and long-term preferences.
Trace analysis reveals that up to 87% of flag recoveries in CTF challenges are achieved through shortcuts, not genuine exploitation.
LLMs can generate hardware designs that are functionally correct but often lag in timing performance, revealing a complex trade-off in financial FPGA design.
General-purpose LLMs can't hack it in quantum coding, but a RAG system armed with a code-aware embedding strategy can boost performance by 28% on real-world quantum programming challenges.
Automating vulnerability analysis with LLMs is now more practical: RAVEN shows how to generate structured, high-quality reports using RAG, even achieving a 54% quality score on real-world vulnerabilities.
