Search papers, labs, and topics across Lattice.
16 papers published across 0 labs.
Strict safety guarantees in RL are now achievable without performance trade-offs, even with neural approximations.
A single filter can reduce unsafe motion generation by 90% by grounding safety rules in the generator's own reference trajectories, without retraining.
Standard Shapley values are brittle; interval-valued Shapley values reveal their true robustness landscape in tree models.
Distillation inference can reliably detect unauthorized LLM distillation by comparing suspect model outputs against teacher reasoning traces via shadow models.
Releasing LLM watermark detectors might be safe: a split-key approach allows public detection of forgeries while keeping full forensic capabilities private.
Strict safety guarantees in RL are now achievable without performance trade-offs, even with neural approximations.
A single filter can reduce unsafe motion generation by 90% by grounding safety rules in the generator's own reference trajectories, without retraining.
Standard Shapley values are brittle; interval-valued Shapley values reveal their true robustness landscape in tree models.
Distillation inference can reliably detect unauthorized LLM distillation by comparing suspect model outputs against teacher reasoning traces via shadow models.
Releasing LLM watermark detectors might be safe: a split-key approach allows public detection of forgeries while keeping full forensic capabilities private.
Adversarial outliers can be effectively handled in linear dynamical system identification by leveraging group-sparsity and robust estimation techniques, achieving provable guarantees.
Probes can now detect subtle, unverbalized LLM deception with near-perfect accuracy, even when the model's true intentions are hidden from external observation.
LLM test generation quality is overestimated by 30% when evaluated against single references, with a new metric revealing widespread missed behaviors and faulty assertions.
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.
LLMs exhibit "Thinking Inertia," stubbornly continuing to show explicit reasoning even when explicitly instructed not to, particularly in open-ended tasks.
A single adversarial auditor can't fool Auditopus, a decentralized LLM fairness auditing system that achieves up to 78% error reduction against fairwashing attacks.
A single-word instruction change can tank VLA performance by 98%, but a simple LLM-based rephrasing pipeline boosts success by up to 27% without retraining.
Planning policy updates offline can significantly reduce the risk of performance regression while maximizing long-term gains, even with noisy improvement signals.
Intrusion detectors confidently misclassify unseen attacks, but PairAudit's relational graph tokens uncover these overlooked errors, boosting human review accuracy by 30% on novel threats.
LLM safety patches can appear effective on benchmarks but cause severe collateral damage to benign behaviors, a critical flaw missed by aggregate metrics.
