activity
20152026
most citedRecommendations as Treatments: Debiasing Learning and Evaluation

138 citations · 494 across the 31 of their papers we have counts for

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7 papers · 1 filter

cs.AI2026

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…

cs.AI2026

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…

cs.AI2025

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…

cs.AI2024★ 2 cited

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…

cs.AI2024★ 1 cited

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…

cs.AI2023

LLF-Bench: Benchmark for Interactive Learning from Language Feedback

Ching-An Cheng, Andrey Kolobov, Dipendra Misra +2

We introduce a new benchmark, LLF-Bench (Learning from Language Feedback Benchmark; pronounced as "elf-bench"), to evaluate the ability of AI agents to interactively learn from nat…

cs.AI2019★ 29 cited

NAIL: A General Interactive Fiction Agent

Matthew Hausknecht, Ricky Loynd, Greg Yang +2

Interactive Fiction (IF) games are complex textual decision making problems. This paper introduces NAIL, an autonomous agent for general parser-based IF games. NAIL won the 2018 Te…