#llm-agents

Just-in-Time Memory curates raw agent traces when a new task arrives

DAIR.AI describes a Salesforce AI Research paper, Just-in-Time Memory, that keeps raw trajectories and writes a short memory payload only after the next task arrives. It reports gains of 16.2, 16.3 and 3.9 success-rate points over the strongest baseline on ALFWorld, WebShop and tau2-bench.

SkillLift paper reports 40–70% lower token cost for evolving agent skills

DAIR.AI highlighted a SkillLift paper that trains a learned rubric to rank agent skill-prompt candidates instead of scoring every revision with a full rollout. It reports beating SkillOpt and CoEvoSkills on SkillsBench and WildClawBench, reaching target performance with 40 to 70% fewer tokens.