Tag
ACTS (Agentic Chain-of-Thought Steering) formulates LLM reasoning control as a Markov decision process where a controller agent adaptively steers a frozen reasoner during inference using reasoning strategies and steering phrases. The approach achieves comparable accuracy to full-thinking models with significant token savings, enabling controllable accuracy-efficiency trade-offs.
This research introduces a technique to loop frozen, off-the-shelf transformer checkpoints at inference time by using damped Runge-Kutta substeps, treating transformer layers as Euler steps in a residual ODE. This allows extra latent compute without fine-tuning, architecture changes, or new weights, showing gains on knowledge tasks like MMLU-Pro, GPQA, and ARC.
OptiLLM is an open-source proxy that boosts any LLM's accuracy 2-10x by adding extra compute at inference time, using techniques like multi-agent cross-verification and Monte Carlo tree search.
OpenAI presents evidence that reasoning models like o1 become more robust to adversarial attacks when given more inference-time compute to think longer. The research demonstrates that increased computation reduces attack success rates across multiple task types including mathematics, factuality, and adversarial images, though significant exceptions remain.