How Can LLMs Negotiate, Support, and Plan More Safely? Three New Papers on Practical Agent Design
How Can LLMs Negotiate, Support, and Plan More Safely? Three New Papers on Practical Agent Design Three recent papers look at a similar question from very different work settings: how to make LLM agents more useful when they must act in ongoing, real-world tasks rather than just answer a prompt once. One paper studies on-call customer support in large cloud platforms and proposes a proactive agent with continuous self-improvement. Another argues that standard chain-of-thought is too limited for embodied planning and introduces an object-oriented, programmatic world model. The third explores whether reinforcement learning with verifiable rewards can teach LLMs to negotiate in bilateral price settings. Together, they show that practical agent design often depends on changing the structure around the model, not only the model itself. [S1][S2][S12] [S1] [S2] [S12] Paper overview: what problem is each one trying to solve? "Help Without Being Asked: A Deployed Proactive Agent System ...
