Search papers, labs, and topics across Lattice.
3
0
5
6
LLM agents can enhance training-data strategies in over half of their attempts, but their inconsistency reveals critical limitations in recursive self-improvement.
Forget painstakingly curating datasets – STELLAR-E auto-generates high-quality, domain-specific LLM benchmarks, rivaling real-world data in evaluation quality.
LLM agents struggle to maintain performance in multi-day collaborative tasks, dropping significantly after just one environmental update, revealing a critical gap in adaptation to evolving real-world conditions.
