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A chronological view of open research problems, organized by quarter. Track progress from inception to resolution, and see what challenges the lab is targeting next.
Achieve competitive accuracy with 10x reduction in FLOPs for on-device inference through novel quantization and pruning strategies.
Extend fairness metrics to handle intersectional protected groups without exponential sample size requirements.
Build a system that discovers new adversarial attack categories autonomously, beyond known jailbreak taxonomies.
Prove convergence bounds for asynchronous distributed training with heterogeneous hardware and variable network latency.
Develop methods to identify and characterize circuits in models with >100B parameters without prohibitive computational cost.
Measure how biases in high-resource training languages transfer to low-resource language outputs during multilingual fine-tuning.
Design a classifier that detects adversarial prompt injections in real-time with <5ms added latency per request.
Propose and validate human-aligned metrics for scoring poetry, fiction, and visual art generation beyond BLEU/FID.
Apply formal methods to prove properties of reward models used in RLHF, ensuring they preserve human intent under distribution shift.
Extend activation patching techniques to MoE models where expert routing introduces non-trivial causal structure.
Develop calibrated confidence intervals for transformer outputs that are human-interpretable and computationally tractable.
Create privacy-preserving protocols for cross-institutional fairness evaluation without sharing raw data.
Design safety mechanisms that auto-detect and recover from guardrail failures in multi-step agentic systems.
These are research directions the lab plans to formally open. They represent emerging priorities identified through community input and internal review.
Develop calibrated confidence intervals for transformer outputs that are human-interpretable and computationally tractable.
Create privacy-preserving protocols for cross-institutional fairness evaluation without sharing raw data.
Design safety mechanisms that auto-detect and recover from guardrail failures in multi-step agentic systems.
See a gap in the roadmap? Suggest a new research direction for the lab to pursue. Community-proposed targets are reviewed quarterly.
Propose a Direction