A mechanism-first reading of TED, a framework for evaluating whether AI agents actually complete workflows across different user behaviors, not merely sound helpful while wandering through them.
A human-centered reading of why standard counterfactual-explanation metrics fail as proxies for what users actually judge as good explanations.
A business-focused reading of ALTK, showing why reliable AI agents need lifecycle middleware around tool calls, JSON outputs, silent failures, and final responses—not just a stronger model.
A mechanism-first reading of a proposed artificial psyche architecture, and why its practical value lies less in human-like emotions than in need-aware control for autonomous agents.
OpenSeeker shows why the next moat in deep-search agents may be data synthesis pipelines rather than model size or reinforcement-learning theater.
A mechanism-first reading of why uncertainty verbalization, not magical reflection tokens, helps reasoning models recover from silent divergence.
A mechanism-first reading of why LLM alignment conflicts emerge, how priority hacking exploits them, and what enterprise AI systems should do at runtime.
A mechanism-first reading of AMRO-S, a semantic and ant-colony-inspired routing framework for making multi-agent LLM systems cheaper, faster, and easier to inspect.
CRYSTAL shows why answer-only multimodal AI benchmarks can hide shortcut reasoning, and how step-level evaluation can make enterprise AI diagnosis more credible.
MineEvolve shows why self-improving agents need structured execution feedback, curated skills and remedies, and local plan repair—not just larger memories or longer prompts.