// LLMs Improving LLMs //

Interesting progress the past of couple of weeks around self-improving AI agents.

If autoresearch was interesting， you will like this read.

（bookmark it）

We've been hand-tuning test-time scaling for a year. This work asks what happens when you let an LLM search the space instead.

The paper introduces AutoTTS， a framework that reframes the human role： instead of designing branching， pruning， and stopping heuristics directly， you construct a discovery environment where TTS strategies can be searched automatically. They formulate width-depth TTS as controller synthesis over pre-collected reasoning trajectories and probe signals， so candidate controllers can be evaluated cheaply without repeated LLM calls.

Two design choices carry the search. Beta parameterization makes the control space tractable. Fine-grained execution-trace feedback tells the explorer LLM why a candidate failed， not just that it did.

On math reasoning benchmarks， the discovered controllers beat strong hand-designed baselines on the accuracy-cost Pareto frontier and generalize zero-shot to held-out benchmarks and model scales.

Entire discovery cost： $39.9 and 160 minutes.

Why it matters：

The era of researchers hand-crafting CoT， best-of-N， and self-consistency recipes is on a clock. Once the search loop is cheap enough， TTS becomes another thing LLMs do for themselves.

Paper： https：//arxiv.org/abs/2605.08083

Learn to build effective AI agents in our academy： https：//academy.dair.ai/