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cs.LG2026
How Long, Not How Close: A Learned Temporal Metric for Planning in Latent World Models
Lama Moukheiber, Haotian Xue, Yongxin Chen
Latent world models plan by rolling a frozen predictor forward under candidate action sequences and ranking the candidates by the latent distance between their imagined end state a…
cs.LG2026
LabBook: Harnessing Experimental History for Efficient LLM-Driven Discovery
Bo Yuan, Wenqian Ye, Zelin Zhao +4
Evolutionary approaches to LLM-driven discovery often generate new programs from a small set of selected ancestors. This keeps contexts manageable but can omit useful evidence from…