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Generative compilation enables AI models to receive compiler feedback during code generation, drastically reducing errors and improving code quality in real-time.
Modern embedding models excel in general IR tasks but falter in complex mathematical domains, revealing a critical gap in current evaluation benchmarks.
TIGER achieves unprecedented reconstruction quality in federated learning settings, even under differential privacy constraints, by directly optimizing token embeddings instead of relying on brittle token tests.
LLM-powered honeypots can trick even frontier models into longer interactions than rule-based systems, all while costing less to run.
LLM benchmark translations can be dramatically improved by test-time compute scaling, revealing a surprisingly cheap way to get more reliable multilingual evaluations.
Context files like AGENTS.md, intended to guide coding agents, often *hurt* performance and increase costs, challenging the common practice of using them.
