12.1 Research themes
Model efficiency. Investigate ways to improve quality per unit of compute, memory, and latency. Tool reliability. Study how models select tools, handle malformed results, recover from errors, and avoid repeated unsafe actions. Context management. Explore retrieval, summarization, project memory, and methods for reducing irrelevant context. Evaluation. Develop task-specific test sets and repeatable evaluation pipelines, including failure analysis rather than only aggregate scores. Multimodal systems. If pursued, test how models combine text with images, audio, video, or spatial information, and document the limitations of each modality. Human-AI interaction. Evaluate whether people can understand, correct, and supervise the system effectively. Robotics and embodied AI. Explore simulation-first methods and tightly constrained physical experiments only when the required expertise and safety infrastructure are available.12.2 Experiment discipline
Each experiment should record a question, hypothesis, baseline, configuration, data version, metrics, results, and interpretation. Negative results are useful when they eliminate a weak approach or reveal a hidden failure mode. Research artifacts should have versioned configurations and, where licensing and privacy permit, enough detail for another engineer to reproduce the result. A demo video is useful communication, but it is not a reproducibility package.12.3 Research gates
Before a research result becomes a product feature, it should pass relevant gates:- The behavior is repeatable under defined conditions.
- The expected benefit is measurable.
- Failure modes are understood well enough for the proposed use.
- Security and privacy implications have been reviewed.
- User-facing claims match the evaluation evidence.
- Monitoring and rollback plans exist for production deployment.