A mechanism-first reading of HERA, a training-free multi-agent RAG framework that turns past execution experience into orchestration policy, prompt evolution, and practical lessons for enterprise AI systems.
A closer look at how Omni-SimpleMem shows that autonomous research pipelines can improve agent memory by finding the boring system failures humans usually miss.
A mechanism-first reading of PsychAgent and what its experience-driven learning loop implies for enterprise AI systems beyond psychological counseling.
A mechanism-first reading of new evidence that reasoning models may encode tool-use decisions before visible chain-of-thought begins.
A mechanism-first reading of AMST, a multi-round framework for testing whether LLM safety survives accumulated adversarial pressure rather than merely passing isolated prompts.
HippoCamp shows why personal AI agents fail less at finding files than at proving they understand the life those files describe.
A mechanism-first reading of how activation-level monitoring can detect hidden coordination among AI agents before surface behavior reveals the strategy.
A mechanism-first reading of MONA’s Camera Dropbox extension, showing why learned approval can suppress reward hacking without recovering useful capability.
A decision-theoretic reading of why useful AI agents need to price information, latency, congestion, and uncertainty before they ask one more question.
A mechanism-first reading of why structured intent frameworks improve AI alignment, where the evidence is strongest, and where too much structure becomes its own tax.