Search papers, labs, and topics across Lattice.
31 papers published across 3 labs.
Slash LLM evaluation costs by over 270x without sacrificing accuracy, revealing that even top open-weight models struggle to outperform simple baselines on AML transaction fraud detection.
Small models can now learn new skills without forgetting old ones, thanks to a novel distillation technique that leverages a larger model to guide the learning process.
One million parallel environments are insufficient without intelligent sampling: Success Guided Sampling unlocks mega-scale RL for robots by focusing training on the edge of learned capabilities.
Rare concepts in LLM fine-tuning are significantly hindered by "prior barriers" that require targeted supervision, not just more data.
A novel hierarchical decomposition into soft communities allows for scalable attributed graph generation that balances structural fidelity and downstream utility without memorizing the reference graph.
Slash LLM evaluation costs by over 270x without sacrificing accuracy, revealing that even top open-weight models struggle to outperform simple baselines on AML transaction fraud detection.
Small models can now learn new skills without forgetting old ones, thanks to a novel distillation technique that leverages a larger model to guide the learning process.
One million parallel environments are insufficient without intelligent sampling: Success Guided Sampling unlocks mega-scale RL for robots by focusing training on the edge of learned capabilities.
Rare concepts in LLM fine-tuning are significantly hindered by "prior barriers" that require targeted supervision, not just more data.
A novel hierarchical decomposition into soft communities allows for scalable attributed graph generation that balances structural fidelity and downstream utility without memorizing the reference graph.
Human motion alone, when translated into a shared latent space, unlocks zero-shot generalist humanoid control for locomotion and manipulation tasks.
Out-of-distribution data can dramatically boost discrete diffusion model performance under extreme data scarcity by strategically injecting it at low noise levels.
Large observational studies can finally boost the precision of small RCTs for treatment effect estimation without sacrificing unbiasedness or making strong confounding assumptions.
Satellite imagery unlocks fine-grained socioeconomic predictions with calibrated uncertainty, even without direct ground truth.
Frontier LLMs fail to reliably identify high-value training data or accurately predict performance gains, highlighting a critical gap in their self-improvement potential.
Synthetic data alone can match real-world GAD performance, but unlocking further gains requires models that explicitly fuse topological and semantic evidence.
Human tactile gloves costing under $11 can now generate robot-ready tactile data, slashing acquisition costs by over 95% and boosting efficiency 3.5x for dexterous manipulation.
Language models can actively select replay targets to improve their own broad capability preservation, not just retain explicitly trained tasks.
Synthesized software environments unlock a 50% boost in CUA performance on unseen benchmarks, proving scalable supervision is key.
Audio-language models excel at identifying sounds but fail to grasp the crucial context of how speakers react to them.
Forget-only unlearning demands models memorize far more than standard training, challenging the premise of efficient deletion.
A 300K-sample dataset and diagnostic benchmark unlock the challenging task of generating product-preserving, narrative-driven advertisement videos.
Pretraining neural CFD surrogates across diverse domains unlocks substantial gains in zero- and few-shot generalization, drastically reducing data requirements for new applications.
Recurrent non-stationarity unlocks data reuse, but its "safe" application hinges on a bias-variance tradeoff akin to classical settings, with a novel algorithm achieving near-optimal regret.
A unified, open-source framework and a massive 3.3 TB dataset now enable rigorous ML-driven prediction of complex multiphase flows in porous media, accelerating research in critical energy and engineering applications.
Instruction-tuned models can achieve monolithic training performance on new domains via parallel, independent LoRA adapter training and parameter-free routing, eliminating costly full-model retraining.
Differentially private synthetic time-series forecasting utility hinges on forecaster-specific window assembly, not just generator quality.
Unbiased dataset pruning can be framed as a linear program, enabling efficient, label-free subset selection that outperforms geometric methods at small budgets.
Uncoded crash factors like phone use and medical episodes are vastly undercounted in police records, with narratives revealing 2x more injury crashes than coded fields.
Transformers learn translation, rotation, and scale invariance through distinct, identifiable mechanisms, paving the way for structured equivariant function learning.
Smoothness in treatment effects unlocks near-optimal policy learning in high-dimensional, low-signal digital experimentation.
Learned relational structure from dynamics fails to transfer to inverse problems, even when edge accuracy is high, due to uncaptured approximation errors.
Optimizing BO hyperparameters with BO itself reveals configurations that significantly reduce cumulative regret on GP-inferred sample paths.
AI is poised to revolutionize ARPES, moving beyond data analysis to enable fully autonomous, self-driving experimental labs.
Provably private tabular classification is now achievable with a single forward pass, outperforming baselines and drastically cutting fitting time.
A single 500-hour visual-tactile dataset boosts zero-shot contact prediction from 13% to 38% IoU and quadruples robot manipulation success rates, proving scale and consistency are paramount for embodied learning.
