Research Directions › Practical Implications for Researchers

Whether you are starting research in this field or trying to direct existing research toward areas of highest impact, the field described above suggests several practical observations about where effort is most likely to produce useful results.

The problems most likely to yield to new ideas are those with clear evaluation criteria and tractable sub-problems that can be studied independently. Hallucination is hard to measure in aggregate but factual accuracy on specific domains is measurable, and improving it on even narrow domains provides useful signal about what interventions work. Systematic generalization is hard to study in large, opaque models but can be studied in controlled settings that isolate the relevant variables. Identifying the tractable sub-problem within a large open problem is often the most important creative act in starting a new research direction.

The problems most likely to require community-scale efforts are those involving data. Improving multilingual representation, constructing long-horizon evaluation environments, building robustness evaluation suites, and creating evaluation infrastructure for low-resource languages all require more data work, annotation work, and infrastructure work than any single research group can accomplish. These are areas where community collaboration, standardization, and sustained infrastructure investment pay disproportionate dividends relative to individual research contributions.

The problems most likely to be solved by scaling alone are narrower than they appear from the outside. Scaling has reliably improved average performance on standard benchmarks, but it has not consistently closed the gap on systematic generalization, calibration, or long-context faithfulness. Researchers who believe that scale is the primary driver of progress should check their intuitions against these specific failure cases, which have persisted across multiple generations of scaling. The persistence of specific failure modes across orders of magnitude of parameter count and compute is often the best evidence that those failure modes have architectural or training causes that are not addressed by scale.

The problems most underinvested relative to their impact, based on the gap between their importance and the research attention they receive, are evaluation methodology, multilingual capability, and mechanistic interpretability. Evaluation methodology is the bottleneck for measuring progress on everything else; without better evaluation, it is impossible to know whether proposed solutions to open problems work. Multilingual capability affects the majority of the world's population and is systematically underserved by current research. Mechanistic interpretability is the most promising path to understanding why models fail when they fail, which is necessary for designing principled improvements rather than empirical patches.