HiMA-Ecom and HiMA-R1 show how vertical-domain agent teams can be trained jointly, remembered selectively, and evaluated more honestly than ordinary chatbot benchmarks allow.
A mechanism-first analysis of Outlier-Safe Pre-Training and why quantization-friendly LLMs may need to be designed before deployment, not repaired afterwards.
A study of GPT-4o lesson generation shows that decomposition helps educational content design, but only when the workflow preserves coherence and keeps humans in the review loop.
Smart-LLaMA-DPO shows that the next useful leap in AI security tooling may come from expert preference training, not larger generic models.
A case-first analysis of what TRIZ Agents really shows: useful structured ideation support, not autonomous engineering invention.
A mechanism-first reading of OmniAvatar, showing how audio conditioning, LoRA adaptation, and long-video inference turn avatar generation from lip-sync into controllable body animation.
A new proof benchmark shows that strong reasoning models can still produce confident, invalid arguments—and that enterprise AI needs verification, not longer monologues.
AdvGameBench shows why enterprise AI evaluation should inspect planning, revision, and constraint discipline—not just final answers.
Why agentic AI performance depends less on adding more agents and more on designing the right information, feedback, roles, and collaboration loops.
A mechanism-first analysis of Superego Agents, showing how external constitutional oversight could turn AI governance into runtime infrastructure.