Shafiq Joty

Achieving 4.7x to 8.2x higher throughput for trillion-parameter MoE models could redefine the limits of large-scale model training.

Automatically generated Multi-Agent Systems are not only outperformed by Single-Agent Systems but also exhibit architectural inefficiencies that challenge the very foundations of multi-agent design principles.

OrchRM slashes training costs while boosting orchestration accuracy, proving that self-supervised reward modeling can revolutionize multi-agent coordination.

LVLM judges, despite excelling in English, exhibit surprisingly inconsistent and unreliable behavior when evaluating content in other languages, revealing a critical blind spot in current alignment and evaluation pipelines.

LLMs can generate syntactically correct tests, but their ability to *reason* about code faults is surprisingly poor, hindering autonomous debugging.

SkillOrchestra slashes the learning costs of AI agent orchestration by up to 700x while improving performance by explicitly modeling agent skills and costs, offering a more scalable and interpretable alternative to RL-based methods.

Reference-guided LLM evaluators can boost alignment in non-verifiable domains, enabling self-improvement to rival reward model training.