Search papers, labs, and topics across Lattice.
26 papers published across 4 labs.
Current package managers are surprisingly vulnerable: a single misconfiguration can silently allow attackers to inject malicious dependencies, a problem solved by this paper's cryptographically enforced provenance system.
Graph models can now generalize to entirely new datasets with different input features, thanks to a simple projection into a shared random space.
Synthetic data augmentation and per-language threshold tuning can significantly boost the performance of LLMs on multilingual tasks, outperforming alternative architectures that showed promise on the development set.
Teachers can now scalably provide high-quality, personalized feedback to students by leveraging a multi-LLM system that synthesizes rubric data and qualitative observations, while retaining control through a teacher-in-the-loop workflow.
A judge-orchestrated ensemble of diverse LLMs trounces single models in multi-turn response generation, proving that strategic model selection beats brute force scaling.
Synthetic data augmentation and per-language threshold tuning can significantly boost the performance of LLMs on multilingual tasks, outperforming alternative architectures that showed promise on the development set.
Graph models can now generalize to entirely new datasets with different input features, thanks to a simple projection into a shared random space.
Teachers can now scalably provide high-quality, personalized feedback to students by leveraging a multi-LLM system that synthesizes rubric data and qualitative observations, while retaining control through a teacher-in-the-loop workflow.
A judge-orchestrated ensemble of diverse LLMs trounces single models in multi-turn response generation, proving that strategic model selection beats brute force scaling.
Open-source image editing models can match or beat fine-tuned models on visual understanding tasks *without any task-specific training*.
Dissimilarity, not just similarity, unlocks better language generalization for low-resource varieties.
Unlock Tajik NLP: a new open-source toolkit delivers a comprehensive pipeline for processing Cyrillic-script Tajik text, complete with datasets and pre-trained embeddings.
Forget retraining: NeWTral instantly restores safety to your LLM after adding a risky LoRA, slashing attack success rates from 70% to 13% without sacrificing expertise.
Exponent bits are the Achilles' heel of floating-point arithmetic, as corrupting them in RISC-V vector processors leads to the most severe silent data corruption.
Forget complex assembly: this 3D printing technique lets you pop out functional, self-folding robots with integrated sensors and actuators directly from a flat sheet.
Forget full fine-tuning: LoRA lets you adapt Geospatial Foundation Models for wildfire mapping with comparable accuracy while only tweaking 1% of the parameters.
Forget resource-intensive pipelines: a purely academic team achieves SOTA search agent performance with just 10.6k SFT data points, outperforming models trained with CPT+SFT+RL.
Forget massive models: small, locally-deployable language models can achieve surprisingly strong performance on privacy-sensitive clinical information extraction tasks with self-prompting and preference-based optimization.
Despite impressive multilingual capabilities, today's LLMs still can't reliably translate between English and Ghanaian languages at scale.
LLMs exhibit a surprising "False Illegitimation bias," systematically misclassifying legitimate battles as violence against civilians, highlighting a critical flaw for conflict monitoring applications.
LLM benchmarks are increasingly measuring the capabilities of yesterday's models, not today's frontier, creating a widening gap that misrepresents the state of AI.
Finally, a zero-knowledge data valuation system that scales: ZK-Value proves Shapley values in seconds to minutes, beating specialized ZK baselines by over an order of magnitude.
Current package managers are surprisingly vulnerable: a single misconfiguration can silently allow attackers to inject malicious dependencies, a problem solved by this paper's cryptographically enforced provenance system.
An open-source alternative to expensive, proprietary digital human modeling software could democratize ergonomic analysis and workplace design.
Public antiviral drug discovery datasets are riddled with errors that can be fixed with careful polyprotein splitting, unlocking significant performance gains in binding affinity prediction.
Open-sourcing a 0.1B-scale speech-native omni model lets you directly inspect the complete interaction loop and reveals critical design choices for building effective small multimodal models.
Sustainable scientific software isn't just about the code; it's about consistent testing and clear links between code quality and tests, a pattern often missing in unsustainable projects.
Open-sourcing a VLA model that beats closed-source giants on embodied reasoning tasks could finally make real-world robot deployment practical.
Autonomous agents can produce plausible-sounding research that's subtly wrong, so ARIS uses adversarial collaboration between different LLMs to catch these errors.
Synthetic data closes the Indic ASR gap where commercial and open-source systems fail, boosting entity recognition by up to 22x.
Meta's risk assessment of its Code World Model (CWM) gives it a clean bill of health, concluding it poses no *new* catastrophic risks beyond those already present in the AI landscape.
