AOrchestra shows that the practical edge in multi-agent systems may come less from adding more agents and more from dynamically composing the right instruction, context, tools, and model for each subtask.
A mechanism-first reading of Conformal Thinking, showing how risk-controlled early stopping turns reasoning budgets from guesswork into an operational error-budget decision.
A mechanism-first reading of why multi-agent LLM systems saturate when agents repeat each other, and why useful diversity beats raw agent count.
Search-R2 shows why reliable retrieval agents need local error repair, not just more search calls or larger rollout budgets.
TodyComm shows why multi-agent AI systems need learned communication governance, not just more agents talking more often.
AutoFigure shows why publication-ready scientific diagrams need reasoning-first visual pipelines, not prettier text-to-image prompts.
A mechanism-first reading of conversational inertia: why long context can make agents imitate their own mistakes, and why strategic forgetting may beat bigger memory.
Avenir-Web shows why reliable web agents need procedural experience, hybrid grounding, explicit progress tracking, and compressed memory—not just bigger multimodal models.
SafeGround shows how uncertainty calibration can turn GUI agents from reckless clickers into risk-budgeted automation systems.
A mechanism-first reading of MAPPA, a process-reward method for turning multiagent LLM workflows from prompted collaboration into trainable systems.