Search papers, labs, and topics across Lattice.
18 papers published across 4 labs.
Strict safety guarantees in RL are now achievable without performance trade-offs, even with neural approximations.
Activations, not just descriptions, unlock the prediction of how LLM alignment generalizes to unseen values, revealing a structured value space.
LLM agents can now be monitored and intervened upon in real-time, saving 18% of compute by detecting failures within the first 8 steps with 83% accuracy.
A single filter can reduce unsafe motion generation by 90% by grounding safety rules in the generator's own reference trajectories, without retraining.
A single feedforward network learns to predict safety values and amortize minimal-intervention filtering, enabling safe RL in high-dimensional systems without analytical safety functions or competing policy objectives.
Strict safety guarantees in RL are now achievable without performance trade-offs, even with neural approximations.
Activations, not just descriptions, unlock the prediction of how LLM alignment generalizes to unseen values, revealing a structured value space.
LLM agents can now be monitored and intervened upon in real-time, saving 18% of compute by detecting failures within the first 8 steps with 83% accuracy.
A single filter can reduce unsafe motion generation by 90% by grounding safety rules in the generator's own reference trajectories, without retraining.
A single feedforward network learns to predict safety values and amortize minimal-intervention filtering, enabling safe RL in high-dimensional systems without analytical safety functions or competing policy objectives.
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.
Releasing LLM watermark detectors might be safe: a split-key approach allows public detection of forgeries while keeping full forensic capabilities private.
LLMs can attribute responsibility and blame, but their reasoning is incomplete and varies with model scale, with specific attribution dimensions demonstrably encoded in their latent spaces.
Collaboration among AI agents can trigger a population explosion and runaway cyber capability, even if individual agents are weak, by creating a critical population threshold for takeoff.
Real-world AI agent breaches at major labs demonstrate that assumed boundaries are insufficient, demanding a shift to continuous, proactive assurance across the entire execution system.
LLM agents can achieve high task accuracy while exhibiting profound epistemic *im*humility, failing to acknowledge uncertainty even when presented with contradictory evidence.
A single adversarial auditor can't fool Auditopus, a decentralized LLM fairness auditing system that achieves up to 78% error reduction against fairwashing attacks.
Planning policy updates offline can significantly reduce the risk of performance regression while maximizing long-term gains, even with noisy improvement signals.
Even perfect AI agents fail to guarantee safety in realistic, time-sensitive, and partially observable environments, fundamentally challenging agent-agnostic alignment approaches.
AI malfunctions, like Clever Hans effects, can be directly traced to specific artifact patterns in the input data using a model-agnostic dictionary of interpretable concepts.
LLM safety patches can appear effective on benchmarks but cause severe collateral damage to benign behaviors, a critical flaw missed by aggregate metrics.
Most alignment model organisms degrade core capabilities and naturalness during training, undermining interpretability findings; a new multi-objective approach preserves these crucial aspects.
Stated-preference economics offers a robust framework for evaluating LLM responses to subjective questions, revealing that even advanced models can fail basic economic validity tests.
