Search papers, labs, and topics across Lattice.
40 papers published across 4 labs.
Purely synthetic training data outperforms native real-world supervision on satellite stereo benchmarks, showing that pristine geometric labels and zero temporal noise beat noisy real-world data collection.
Synthetic pre-pretraining boosts LLM efficiency at scale, but not by teaching grammar; it's the retrieval skills that matter.
StreamMAE unlocks self-supervised learning on continuous video streams, matching i.i.d. MAE performance by tackling intra-batch similarity with novel regularization.
Forecast errors, not drift alarms, directly signal when to retrain time series models, achieving state-of-the-art performance with a plug-in module.
LLM agents can improve their decision-making by learning from their own diagnosed failures, with first-proposal corrections boosting success rates by over 30 percentage points.
Synthetic pre-pretraining boosts LLM efficiency at scale, but not by teaching grammar; it's the retrieval skills that matter.
StreamMAE unlocks self-supervised learning on continuous video streams, matching i.i.d. MAE performance by tackling intra-batch similarity with novel regularization.
Forecast errors, not drift alarms, directly signal when to retrain time series models, achieving state-of-the-art performance with a plug-in module.
LLM agents can improve their decision-making by learning from their own diagnosed failures, with first-proposal corrections boosting success rates by over 30 percentage points.
Wild AI-generated text can actively harm LLM performance on human text, with its value flipping from beneficial to detrimental as token ratios increase.
LLM agents trained solely in rule-generated fictional worlds achieve state-of-the-art transfer to real-world search tasks, outperforming agents trained on actual data.
Language models can learn new knowledge without forgetting old by dynamically prioritizing replay based on observed forgetting.
Human password generation exhibits persistent linguistic regularities that can be exploited for a 16% boost in password recovery by adapting pre-trained language models.
State-of-the-art precipitation downscaling models struggle to generalize across continents, revealing a critical gap in current ML-based climate forecasting.
Finite-sample performance guarantees for label-noise transition matrix estimation are now achievable by bypassing class-posterior estimation through one-sided selective classification.
Zero-shot in-context inference trained strictly on synthetic causal graphs can outperform fully tuned AutoML pipelines across diverse real-world tabular benchmarks.
Non-generative "System One" models can beat closed-source baselines in Chinese decision tasks with a 20x speedup and 15 ms latency, proving encoder-only architectures remain radically underutilized for structured reasoning.
Mixing auxiliary data doesn't just offer more tokens—under the right spectral alignment and dataset growth ratio, it provably accelerates the downstream power-law scaling exponent itself.
Collapsing pre-, mid-, and post-training into a single fully synthetic run matches open-weight model performance while slashing required training tokens by up to 140×.
Training data attribution on 32B-parameter models no longer requires intractable gradient storage: extreme 1-bit eigenbasis projection achieves competitive influence estimation accuracy at over 100× lower per-example footprint than standard baselines.
Cross-lingual data pooling acts as a powerful low-resource jumpstart—boosting macro-F1 by up to 0.43 at 25 labels—but predictably degenerates into negative transfer once local annotations surpass 800 examples.
Standard refusal detectors miss up to 99.6% of noncompliant instruction-tuning data, but multi-pass consensus from a 1.6T-parameter judge successfully isolates millions of latent refusals and evasions at 86.7% precision.
Standard next-token adaptation fails on financial time series unless fine-tuning objectives explicitly penalize rollouts for distorting higher-order temporal structures like volatility clustering.
Synthesizing historical street views is now possible with 17% better realism and consistency by treating it as a reward-guided editing problem.
Collecting more errors doesn't guarantee better models: targeting only the high-impact failure modes signaled by a network's own internal representations outperforms standard rarity-based active learning while saving annotation budget.
Static problem pools cause LLM reasoning post-training to stall as models saturate the dataset, but dynamically synthesizing frontier tasks via regret-guided procedural generation keeps learning signals saturated for sustained gains.
Even when provided with only target column metadata, LLMs outperform specialized AutoML heuristics like AutoGluon at inferring machine learning task types (0.98 vs. 0.93 macro F1), unlocking fully autonomous pipeline configuration.
Contemporary frontier speech models still catastrophically fail on living Arabic dialects, with top systems like GPT-4o achieving an error rate no better than 35.0% WER across 17 regional vernaculars.
Jev’s apparent miscalibration on binary choices is an artifact of forced binarization: recovering a hidden, unexpressed "I don't know" probability mass catapults the model's calibration accuracy from 0.771 to 0.978.
Verifying LLM span extractions no longer requires full manual re-annotation: character-level policy reconciliation isolates genuine disagreements, slashing human review to just 5.4 focused conflicts per document.
Industrial anomaly detection usually breaks down where data is sparsest, but baking reaction kinetics directly into conditional diffusion enables high-fidelity trajectory synthesis of catastrophic failure states far outside the training distribution.
Model collapse in generative self-training can be weaponized: intentionally amplifying a generator's Jacobian-derived geometric distortions creates a far more potent negative guidance signal than standard synthetic outputs.
High-fidelity 3D texture generation is now achievable without any 3D assets, by cleverly transforming 2D images into effective training data.
Frontier LLMs struggle to replicate expert consensus in complex, longitudinal cancer treatment discussions, scoring below expert equivalence on a new real-world benchmark.
Enterprise AI agents can now generate valid, high-fidelity tabular data for training and evaluation without needing access to sensitive schemas or real systems, bypassing critical industry bottlenecks.
Synthetic-to-real pretraining bridges the chronic scarcity of longitudinal clinical imaging, enabling transformers to reliably forecast up to 15 years of spinal disease progression.
Purely synthetic training data outperforms native real-world supervision on satellite stereo benchmarks, showing that pristine geometric labels and zero temporal noise beat noisy real-world data collection.
