Search papers, labs, and topics across Lattice.
4
0
8
16
Data mixing, especially with instruction-heavy data, emerges as the crucial factor for optimizing VLM training, challenging traditional filtering approaches.
Today's best LLMs fail spectacularly at long-horizon reasoning, achieving under 10% accuracy on a new benchmark designed to isolate this critical capability.
Forget generic pre-training: Speculative decoding gets a serious speed boost when your draft model is a specialist trained on data matching the target task.
LLMs still struggle to generalize quantum code generation across frameworks like Qiskit, PennyLane, and Cirq, even with feedback-based repair pushing performance to 83.3%, 76.2%, and 66.7% respectively.
