Search papers, labs, and topics across Lattice.
Shanghai Artificial Intelligence Laboratory
3
0
6
LLM-based agents can now autonomously enhance their own harnesses, leading to performance boosts of up to 18% without human intervention.
Current open-world semi-supervised learning methods fall short in practical applications because they fail to extract latent semantic information, but SECOS overcomes this by directly predicting textual labels from a candidate set, achieving state-of-the-art results.
Structured composition unlocks significantly better agent performance compared to flat skill invocation, even with the same skill set.
