Search papers, labs, and topics across Lattice.
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StepGuard slashes attack success rates by over 77% while maintaining nearly all utility, setting a new standard for safety in LLM-based agents.
Task order can dramatically skew the performance of self-improving agents, revealing hidden dependencies that could undermine their reliability in real-world applications.
Covert channels in LLM traffic can leak private information without any malicious intent, revealing a surprising vulnerability in seemingly benign interactions.
The shift from monomer folding to generative modeling could revolutionize our approach to understanding complex protein interactions and design.
Evolving safety harnesses using trajectory data can reduce agent safety risks by over 3x while enhancing overall utility.
Over 1,500 submissions revealed stark differences in model performance across diverse domains, highlighting the challenges of generalizing egocentric video understanding.
Language's role in embodied agents is often overstated, with many claims lacking robust empirical support, revealing a critical gap in our understanding of its contributions.
AgentDoG 1.5 proves you can achieve GPT-5.4-level agent safety with open-source models trained on just 1k samples, slashing deployment overhead by two orders of magnitude.
