Search papers, labs, and topics across Lattice.
44 papers published across 1 lab.
AI's role in medical education has exploded post-2021, with generative AI and learner-facing applications dominating a field now heavily concentrated in the US and a few elite institutions.
Multi-head attention applied to network depth unlocks substantial performance gains and compute efficiency in large MoE models.
Human GUI interactions can be precisely captured and translated into reusable agent skills, dramatically boosting performance on unseen software and workflows.
Direct 2D spatial composition with four binding types unlocks unprecedented controllability in poster generation, outperforming prompt-based methods.
Unifying density ratio estimation and distribution transport, this work reveals how a single displacement field can simultaneously capture statistical shifts and dynamical transformations between probability distributions.
AI's role in medical education has exploded post-2021, with generative AI and learner-facing applications dominating a field now heavily concentrated in the US and a few elite institutions.
Multi-head attention applied to network depth unlocks substantial performance gains and compute efficiency in large MoE models.
Human GUI interactions can be precisely captured and translated into reusable agent skills, dramatically boosting performance on unseen software and workflows.
Direct 2D spatial composition with four binding types unlocks unprecedented controllability in poster generation, outperforming prompt-based methods.
Unifying density ratio estimation and distribution transport, this work reveals how a single displacement field can simultaneously capture statistical shifts and dynamical transformations between probability distributions.
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.
Conformal prediction can now achieve subgroup reliability without explicit group labels or sacrificing efficiency by learning a stochastic grouping map that pools calibration information locally.
Weisfeiler-Lehman graph kernels unlock superior structural features for algorithm selection, outperforming traditional methods on challenging constraint satisfaction problems.
Optimal regret in adversarial MDPs with stochastic hard constraints is finally achieved, bridging the gap between existing upper and lower bounds by achieving $\widetilde{\mathcal{O}}(\sqrt{T}/\rho + 1/(d\rho))$ regret.
Manual assembly instruction writing is now obsolete: LLMs can generate accurate, CAD-grounded procedures, preserving engineering details and passing automated checks.
Research reports can now be continuously updated with over 60% fewer search calls and 33% less token consumption, maintaining high quality over time.
LLMs can now generate executable feature programs from long text, enabling efficient structured data prediction without real-time LLM inference.
A residual memory mechanism allows LLMs to retain crucial context for long-horizon tasks, achieving near full-context performance with only 5.2% of input positions.
Directly predicting conditional marginals with a single Transformer pass unlocks efficient decision-making under uncertainty, outperforming generative models and classical methods even with full model knowledge.
Synergistic multimodal information, previously elusive, is unlocked by modeling higher-order statistical dependencies via tensor decomposition.
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.
Unconditional KSDs are lifted to the conditional setting using operator-valued kernels, enabling goodness-of-fit testing for conditional models without knowing covariate distributions.
Deterministic anomaly detection with RIFT precisely matches Isolation Forest's theoretical limit in 1D and generalizes robustly to higher dimensions, outperforming IF's variance in ensemble settings.
Chronologically consistent language models trained on scientific literature and patents outperform general domain models and even predecessors on temporal science and technology tasks, even with limited historical data.
Self-supervised speech embeddings unlock substantial gains in cross-speaker dysarthria detection during awake craniotomy, outperforming handcrafted features by up to 26.1% AUC.
A single forecasting backbone unifies multivariate and multimodal time series, outperforming 16 SOTA models by disentangling media identity and mastering temporal scales.
Most AI agent skills copied on GitHub originate from a few hidden hubs, rendering security patches ineffective for the vast majority of users.
Most clinical LLM benchmarks fail to capture the dominant documentation and knowledge retrieval tasks physicians actually use them for, rendering their scores largely irrelevant to real-world performance.
A novel contrastive feedback mechanism allows LLM agents to learn skill proposals that are aligned with the current actor's behavior without requiring expensive skill evaluations.
LLM agents can now reliably track and fulfill complex deliverable requirements across numerous files without losing context, thanks to an environment-side "tab" that acts as a persistent, shared task ledger.
Agnostic smoothed online learning is now achievable without knowing the base measure or smoothing parameter, matching statistical learning's flexibility with online learning's robustness.
Fine-grained emotion control in TTS is now achievable at inference time with minimal speaker or content degradation, bypassing the need for expensive model retraining.
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.
The optimal strategy for the cow-path problem is provably cyclic, meaning no search algorithm can outperform a fixed path traversal order.
Machine learning agents discover new exact values for notoriously difficult-to-compute link invariants like slice genus and unknotting number.
Small Language Models can parse ML pipeline structures, but their insights into data science practices diverge significantly from other classifiers, highlighting the critical impact of taxonomy definition.
Generative orthogonal learning unlocks rate double robustness and quasi-oracle efficiency for estimating distributional treatment effects under complex time-varying confounding.
Inertial activity recognition models can now learn rich, global motion structure from unlabeled data by discovering and grouping motion primitives, achieving state-of-the-art performance with orders of magnitude less data.
Spatially dependent data breaks standard conformal prediction; this method achieves exact coverage and oracle efficiency by sequentially whitening residuals, even with nonrandom sampling.
Provably private tabular classification is now achievable with a single forward pass, outperforming baselines and drastically cutting fitting time.
Sim racing drivers' braking and strategy behaviors are not car-interchangeable, challenging existing profiling assumptions.
Adversarial bandits with $m$-set choices can suffer drastically slower learning rates than previously thought, with regret scaling quadratically with $m$ in some settings.
Black-box text-to-music generators can be steered to precisely match complex user intent by an agent that iteratively refines prompts based on rubric-based audio verification.
English skeletons offer a minor, inconsistent boost in multilingual math reasoning, with optimal language choice proving highly context-dependent.
Key contribution not extracted.
Interactive video generation systems can now robustly handle latent dependencies by transforming unstructured inputs into executable constraints, achieving a 10.3-point average improvement.
