Chen

RETRACE achieves a 7% boost in patch verification accuracy by independently reconstructing and reconciling the problem and solution, ensuring coding agents generate reliable fixes.

Role-aware summaries can boost bug localization effectiveness by 40% while being 10.4 to 20.9 times more efficient than raw source code.

Existing benchmarks fail to reveal the true performance capabilities of LLMs, with only 6.11% showing significant speed advantages over traditional implementations.

Automated program repair still struggles in real-world CI environments, succeeding in less than 20% of cases, even with the best LLMs.

Effective GenAI-mediated language learning hinges on *when* feedback is given, not just *what* feedback is given: high-progress learners benefited from corrective prompts immediately following their responses.

Incomplete schedule search can lead to permanently suboptimal silicon, irrecoverable by software tuning, highlighting the critical need for co-design in 3D-stacked AI accelerators.