Effectiveness and Auditability of Latent Agentic Reasoning

Design learning objectives, interpretability probes, and evaluation benchmarks to make latent-space planning, decision-making, and collaboration in large language model-based agentic systems both effective and auditable.

Background

Latent agentic reasoning performs planning and decision-making in internal latent spaces, improving efficiency and scalability but reducing interpretability and controllability. Aligning such latent processes with external objectives, tools, agents, and memory systems is nontrivial.

Diagnosing failures becomes difficult when intermediate reasoning is not externally observable. Therefore, objectives, probes, and benchmarks are needed to ensure latent reasoning remains both powerful and accountable.