University of Limerick

AutoAdapt is a modular instruction-tuning framework that first discovers latent domains with three methods (K-means, BERTopic, MNLI), then trains one LoRA adapter per domain independently and in parallel, and routes at inference with a parameter-free centroid mechanism; across 14 domain-specific benchmarks and GPT-4o pairwise judgements it reaches parity with a single LoRA adapter trained on all domains, while adding or updating a domain requires training only one adapter instead of full-model retraining.

AutoAdapt is a modular instruction-tuning framework that first discovers latent domains with three methods (K-means, BERTopic, MNLI), then trains one LoRA adapter per domain independently and in parallel, and routes at inference with a parameter-free centroid mechanism; across 14 domain-specific benchmarks and GPT-4o pairwise judgements it reaches parity with a single LoRA adapter trained on all domains, while adding or updating a domain requires training only one adapter instead of full-model retraining.

DreamTest adapts a recurrent state-space model into a testing world model that learns agent behaviour and environment dynamics from a DRL agent's training log, then scores a candidate configuration by generating imagined rollouts in parallel and combining termination-weighted failure predictions, guiding search without executing the simulator; on Parking, Humanoid and DonkeyCar its mean AUPRC exceeds the strongest baseline by about 97%, 12% and 39%, gains on five out-of-distribution sets reach 145%, 29% and 44%, and under the same simulator-validation budget the best DreamTest-plus-search combinations find 29%, 22% and 79% more novel failures on average.

DreamTest adapts a recurrent state-space model into a testing world model that learns agent behaviour and environment dynamics from a DRL agent's training log, then scores a candidate configuration by generating imagined rollouts in parallel and combining termination-weighted failure predictions, guiding search without executing the simulator; on Parking, Humanoid and DonkeyCar its mean AUPRC exceeds the strongest baseline by about 97%, 12% and 39%, gains on five out-of-distribution sets reach 145%, 29% and 44%, and under the same simulator-validation budget the best DreamTest-plus-search combinations find 29%, 22% and 79% more novel failures on average.