Self-supervised confidence fine-tuning makes reasoning more efficient, reducing generated tokens by up to 25\% at matched accuracy across Gemma, Qwen, Nemotron, and GPT-OSS models on mathematical, scientific, and coding reasoning benchmarks, with efficiency gains comparable to methods that explicitly optimize for short...
Parsa Hosseini, Akasha Tigalappanavara, Sumit Nawathe et al.· 0 citations
This taxonomy shows how verbal reinforcement is reshaping agent development, while also defining the challenges and opportunities for building more capable and aligned agents.
This work introduces Structured Thoughts, a framework that organizes reasoning into alternating blocks and blocks that captures exploratory scratch work, while the distilled conclusion of that step contains the distilled conclusion of that step.
This work introduces AcCoRD, a user-agent collaboration benchmark requiring agents to handle diverse user preference dynamics in two domains: online shopping and travel planning, and evaluates five frontier LLMs under two prompting strategies: vanilla ReAct, and an uncertainty-guided variant that prompts models to iden...
