5 papers · 1 filter
Reasoning about Reasoning: BAPO Bounds on Chain-of-Thought Token Complexity in LLMs
Kiran Tomlinson, Tobias Schnabel, Adith Swaminathan +1
Inference-time scaling via chain-of-thought (CoT) reasoning is a major driver of state-of-the-art LLM performance, but it comes with substantial latency and compute costs. We addre…
The Context Gathering Decision Process: A POMDP Framework for Agentic Search
Chinmaya Kausik, Adith Swaminathan, Nathan Kallus
Large Language Model (LLM) agents are deployed in complex environments -- such as massive codebases, enterprise databases, and conversational histories -- where the relevant state…
Lost in Transmission: When and Why LLMs Fail to Reason Globally
Tobias Schnabel, Kiran Tomlinson, Adith Swaminathan +1
Despite their many successes, transformer-based large language models (LLMs) continue to struggle with tasks that require complex reasoning over large parts of their input. We argu…
Trace is the Next AutoDiff: Generative Optimization with Rich Feedback, Execution Traces, and LLMs
Ching-An Cheng, Allen Nie, Adith Swaminathan
We study a class of optimization problems motivated by automating the design and update of AI systems like coding assistants, robots, and copilots. AutoDiff frameworks, like PyTorc…
The Importance of Directional Feedback for LLM-based Optimizers
Allen Nie, Ching-An Cheng, Andrey Kolobov +1
We study the potential of using large language models (LLMs) as an interactive optimizer for solving maximization problems in a text space using natural language and numerical feed…