Agentic-Planning

TL;DR for operators When a research agent fails on a difficult proof, the next question is how much more inference to spend: another independent attempt, verifier feedback, decomposition into intermediate claims, search across alternative proof branches, or a larger revisable plan. TCSAlgBench makes that allocation problem measurable on research-level theoretical computer science. ...

TL;DR for operators Most LLM agents still behave like overconfident interns with a browser: observe, guess the next action, click, apologise, repeat. SiRA proposes a more serious pattern. Before acting, the agent writes down a belief state, proposes several high-level candidate actions, simulates likely future states with an LLM-based world model, scores those futures against the goal, and only then converts the selected intent into an executable browser action.1 ...