Latent Agents
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Multi-agent debate makes models reason better. It also burns tokens generating long transcripts before any answer comes out. Latent Agents distills the entire debate into a single LLM through a two-stage fine-tuning pipeline: the model first learns debate structure, then internalizes it through dynamic reward scheduling and length clipping. The internalized model matches or beats explicit multi-agent debate while using up to 93% fewer tokens, which makes debate-quality reasoning practical at production scale.
Two-stage internalization pipeline: Stage one teaches the structure of debate (turn taking, critique, revision) through supervised fine-tuning on transcript data. Stage two uses dynamic reward scheduling and length clipping to compress that structure into single-pass reasoning without losing the gains from the multi-agent setup.
Up to 93% token savings: The internalized model matches or beats explicit debate accuracy while drastically reducing inference cost. For teams running reasoning workloads at scale, this is the kind of efficiency win that turns a research idea into a deployment default.
Activation steering reveals agent subspaces: The "agents" survive distillation as identifiable circuits in activation space. Probing finds interpretable directions corresponding to different agent perspectives, which means the internal structure persists even when the external transcript is gone.
A safety angle worth noting: When malicious agents are deliberately embedded via distillation, negative steering suppresses them more cleanly than steering a base model would, with smaller hits to general performance. Internalized debate may turn out to be a useful interpretability and alignment substrate, not just a token-saver.
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