Hybrid Reasoning Agents paper:

hybrid reasoning agents. paper looks dense

Thinking in vectors and only switching to words when a tool is listening is basically how I get through meetings.

what decides when the agent switches between fast and slow thinking here? a learned router, or a fixed rule based on task difficulty?

The useful part is that it doesn't commit to one mode. It reasons in latent space and drops back to text only when it needs a tool, which is where the token and cost savings come from.

这思路让我想起脑子里默默盘算但开口只说重点的时候

Hybrid reasoning. So it thinks hard when it has to and coasts the rest of the time. Finally an AI that works like me 💯

show me this on a really long tool chain

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how does hybridrl actually decide when to switch between latent and text? wondering if the reward is purely outcome-based or if you also penalize latent steps that leak garbage text wrappers right before a tool call. also assuming the continuous working memory means you still eat the full kv cache cost for those latent tokens even if they never get decoded?

hybrid reasoning is the interesting direction — knowing when to think fast vs when to actually burn tokens.

the hybrid part is what matters. pure end-to-end agents still break on long tasks, so splitting planning and execution might be the only thing that actually holds up.

Knowing when to think long vs answer fast is most of the cost problem for agents.

if it thinks in latent space and only acts through text, what do you actually read when it breaks on step 7 of a tool chain?