Anmol Goel

Machine translation benchmarks have functionally saturated, but pairing human-authored failure cases with deterministic verification rules reveals critical multimodal blind spots that automated metrics consistently miss.

A staggering 73% of evaluated computer-use agents leak sensitive information across contexts, underscoring a critical gap in privacy safeguards.

Framework choice in multi-agent systems matters just as much as the LLM itself, a fact obscured by existing model-centric benchmarks.