3 papers
cs.LG2026
Refining the Information Bottleneck via Adversarial Information Separation
Shuai Ning, Zhenpeng Wang, Lin Wang +5
Generalizing from limited data is particularly critical for models in domains such as material science, where task-relevant features in experimental datasets are often heavily conf…
stat.ML2026
Uncertainty-Aware Multimodal Learning via Conformal Shapley Intervals
Mathew Chandy, Michael Johnson, Judong Shen +4
Multimodal learning combines information from multiple data modalities to improve predictive performance. However, modalities often contribute unequally and in a data dependent way…
cs.LG2026
ALIGN: Aligned Delegation with Performance Guarantees for Multi-Agent LLM Reasoning
Tong Zhu, Baiting Chen, Jin Zhou +3
LLMs often underperform on complex reasoning tasks when relying on a single generation-and-selection pipeline. Inference-time ensemble methods can improve performance by sampling d…