Search papers, labs, and topics across Lattice.
5
0
5
3
Fine-grained credit assignment in LLM agents can be unreliable due to downstream randomness, but AdaStep's optimal shrinkage coefficient precisely balances local and global rewards.
TRACE transforms high-performing LLMs into consistently reliable agents, achieving a remarkable 34.6-point boost in task consistency.
MemFuse achieves superior performance in multi-source memory tasks, revealing that effective memory fusion can significantly enhance an agent's reasoning capabilities.
Mi-Memory achieves over 93% accuracy in preserving user context across diverse AI interactions, redefining how memory can govern personal AI experiences.
HarnessX reveals that evolving agent harnesses through execution feedback can outperform traditional model scaling, achieving up to 44% performance gains in specific tasks.
