In the news
BDH-CQ: In-Context Learning with Recurrent Latent Reasoning
arXiv cs.AI · Published · 3 min read
In 30 seconds
- What happened
- BDH-CQ is a reasoning model combining in-context learning with recurrent latent reasoning, achieving 29.5% pass rate on ARC-AGI-1 at $0.0007 per task.
- Why it matters
- Matters for engineers optimizing cost-accuracy tradeoffs in reasoning tasks and evaluating inference efficiency on abstract reasoning benchmarks.
- Watch out
- Results are on a single benchmark; generalization to other reasoning tasks and real-world applicability remain unclear from this abstract.
Listen to this summary
- reasoning
- inference
- eval
The patterns behind this
Each one covers how the technique works, when it earns its cost, and where it breaks.
The Agent Architect
One pattern, one tradeoff, one production failure story. A short weekly briefing for people building agentic systems.
Weekly email, one-click unsubscribe. We only use your address to send the briefing.