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LATS: Monte Carlo Tree Search for LLM Agent Decisions
LATS brings Monte Carlo Tree Search, the algorithm behind AlphaGo, to language agents. Reasoning, acting, and planning become a single tree that the agent navigates by balancing exploration against exploitation. Accuracy climbs, cost climbs with it.
Reflexion: Verbal Self-Critique After Failure for LLM Agents
When an agent fails a task, a separate critique call can write down what went wrong in words, store the critique as a memory, and let the next attempt condition on it. No gradient updates. A model that learns from text about its own past.
Graph-of-Thought: Non-Linear Reasoning with Merge and Refine
Trees branch and backtrack but never merge or loop. Graph-of-Thought extends the search to a directed graph, adding aggregation of parallel branches and refinement of earlier thoughts. The payoff is on problems that need synthesis rather than pure search.
Tree-of-Thought: Branching Reasoning with Search for LLMs
Chain-of-thought commits to one path. Tree-of-thought explores several, evaluates them, and backtracks. On problems that need exploration rather than sequential derivation, the difference is dramatic.
Chain-of-Thought Prompting for LLM Reasoning
Asking a large language model to reason step by step before answering reliably improves performance on multi-step problems. The technique is small in the prompt and large in effect, but only on models above a certain scale.
ReAct: Reasoning and Acting in One LLM Loop
The foundational agent pattern from Yao et al. interleaves a short reasoning trace with a single tool call, then folds the observation back into the next step. Simple in shape, strong on grounded reasoning benchmarks.