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LLMs can now diagnose diseases with the transparency of formal logic, offering verifiable reasoning chains that clinicians can audit and refine.
Forget red-teaming, POLARIS automatically turns safety policies into attack strategies, finding more LLM vulnerabilities with verifiable traceability.
Reference patches, typically discarded in software-engineering agent training, can be distilled into latent process graphs to guide trajectory curation, leading to more effective and efficient learning.
Stop writing incomplete tests: TestGeneralizer can automatically expand your existing tests to cover 31% more scenarios and catch more bugs.
LLMs can now predict project-wide code edits with significantly improved accuracy and efficiency by intelligently interleaving neural prediction with existing IDE tools.
Stop rewriting security rules for every SIEM platform: ARuleCon automates the process with 15% higher fidelity than existing LLMs.
Code-generating LLMs may ace static benchmarks, but developers are actually *slower* when using them because they disrupt mental flow, highlighting the need for benchmarks that capture the temporal dynamics of coding.
The trustworthiness of LLM-enabled applications hinges not on further model improvements, but on establishing system-level threat monitoring to detect post-deployment anomalies.
Self-evolving LLM agents can be persistently compromised by injecting malicious payloads into their long-term memory, turning them into "zombie agents" that execute unauthorized actions across sessions.
