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Interleaves chain-of-thought reasoning with tool-using actions in one LLM loop: the model plans, queries a source like Wikipedia, then revises from results. Cuts hallucination versus reasoning-only prompting and beats trained agents on interactive tasks.
Treats the interface between an LM agent and a computer as a design variable. A custom agent-computer interface (ACI) with concise file-edit, repo-navigation, and test commands plus compact feedback reaches 12.5% pass@1 on SWE-bench, 87.7% on HumanEvalFix.
Grounds language-based robot planning in learned skill value functions, selecting actions that are both useful and feasible. PaLM-SayCan reports 84% planning and 74% execution success across 101 real-world instructions.
Turns language commands into executable robot policies that combine perception, control APIs, loops, and geometry libraries. Hierarchical code generation recursively fills in missing functions to support new behaviors without policy retraining.
Teaches a language model when and how to call external APIs using a few demonstrations and self-generated training data. Calls are retained only when their results improve prediction of subsequent text.
Tests whether browser agents complete 812 tasks across realistic shopping, forum, development, and content-management sites. Evaluates final outcomes rather than matching a reference sequence of clicks.
Lets language models move information between their context window and external storage. Memory editing, retrieval, and function chaining support conversations and document analysis beyond a single prompt's capacity.
Tests whether web agents can use visual evidence to finish 910 tasks across shopping, classifieds, and social forums. Execution-based checks distinguish successful actions from plausible explanations.
Lets language-model agents compose tools through executable Python and revise actions from interpreter feedback. Evaluates 17 models and releases 7,000 interaction trajectories for training agents that can debug their own code.
Explains when language models benefit from external programs, comparing tool selection, use, and creation. Evaluates performance gains against compute costs and identifies gaps in tool-use benchmarks.
Tests whether agents can finish 369 tasks in real desktop applications, using executable checks of the final state. The original study found a wide gap between agent and human success rates.
Extracts reusable workflows from past task trajectories to guide future web actions. Supports offline demonstrations and online experience, improving long-horizon navigation without manually written routines.