Search papers, labs, and topics across Lattice.
8 papers published across 1 lab.
Quantum chemistry's density matrix approach reveals interpretable early warning signals of phase transitions in deep learning, from grokking to emergent misalignment.
LLMs spontaneously organize into brain-like functional units where the whole is greater than the sum of its parts, and destroying these synergistic cores cripples reasoning.
Training language models on individual children's language reveals that distributional and interactional linguistic features, not just dataset size, are key to efficient learning, mirroring factors that drive child language acquisition.
Forget full automation – the sweet spot for AI deployment is often partial automation, where humans and AI collaborate to minimize costs.
Forget painstaking hyperparameter tuning: this hypersphere parameterization lets you transfer a single learning rate across model sizes, depths, and even MoE architectures, slashing compute costs by 1.58x.
Quantum chemistry's density matrix approach reveals interpretable early warning signals of phase transitions in deep learning, from grokking to emergent misalignment.
LLMs spontaneously organize into brain-like functional units where the whole is greater than the sum of its parts, and destroying these synergistic cores cripples reasoning.
Training language models on individual children's language reveals that distributional and interactional linguistic features, not just dataset size, are key to efficient learning, mirroring factors that drive child language acquisition.
Forget full automation – the sweet spot for AI deployment is often partial automation, where humans and AI collaborate to minimize costs.
Forget painstaking hyperparameter tuning: this hypersphere parameterization lets you transfer a single learning rate across model sizes, depths, and even MoE architectures, slashing compute costs by 1.58x.
Scaling laws work so well because they capture the essence of computation, not the specifics of implementation, leading to a persistent efficiency arms race.
Scientific reasoning gains from prompt engineering are often mirages, driven by model-specific hacks that don't generalize.
LLMs exhibit polarity illusions without rational inference, suggesting that "good enough" processing and partial grammaticalization may suffice to explain these phenomena in both machines and humans.
