REVIEW 7 cited by
ADaPT: As-Needed Decomposition and Planning with Language Models
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
Large Language Models (LLMs) are increasingly being used for interactive decision-making tasks requiring planning and adapting to the environment. Recent works employ LLMs-as-agents in broadly two ways: iteratively determining the next action (iterative executors) or generating plans and executing sub-tasks using LLMs (plan-and-execute). However, these methods struggle with task complexity, as the inability to execute any sub-task may lead to task failure. To address these shortcomings, we introduce As-Needed Decomposition and Planning for complex Tasks (ADaPT), an approach that explicitly plans and decomposes complex sub-tasks as-needed, i.e., when the LLM is unable to execute them. ADaPT recursively decomposes sub-tasks to adapt to both task complexity and LLM capability. Our results demonstrate that ADaPT substantially outperforms established strong baselines, achieving success rates up to 28.3% higher in ALFWorld, 27% in WebShop, and 33% in TextCraft -- a novel compositional dataset that we introduce. Through extensive analysis, we illustrate the importance of multilevel decomposition and establish that ADaPT dynamically adjusts to the capabilities of the executor LLM as well as to task complexity.
Forward citations
Cited by 7 Pith papers
-
Solver-Aware Decompositions for Programming-by-Example: When Dividing Requires Knowing how to Conquer
Solver-aware training of a PBE decomposer with a frozen synthesizer's loss outperforms supervised imitation of ground-truth subgoals, solving tasks that a ground-truth decomposition oracle fails.
-
FinGAIA: A Chinese Benchmark for AI Agents in Real-World Financial Domain
FinGAIA is a 407-task Chinese financial agent benchmark where the best agent, ChatGPT DeepResearch, scores 48.9%, far below financial experts at 84.7%.
-
Build the web for agents, not agents for the web
The paper proposes a paradigm shift: design a standardized Agentic Web Interface for AI agents, rather than adapting agents to human-facing websites.
-
Cognitive Duality for Adaptive Web Agents
A web agent called CogniWeb reportedly reaches 43.96% success on WebArena with 75% fewer tokens by switching between fast and slow reasoning, but the supplied body is a different paper and the claims could not be checked.
-
We Should Identify and Mitigate Third-Party Safety Risks in MCP-Powered Agent Systems
MCP-powered LLM agents are vulnerable to prompt injection from third-party services, and simple detection or filtering defenses do not reliably stop these attacks.
-
Rhetorical Text-to-Image Generation via Two-layer Diffusion Policy Optimization
Rhet2Pix combines staged LLM prompt decomposition with a discounted PPO fine-tuning scheme for Stable Diffusion, claiming strong rhetorical text-to-image generation, but the quantitative evidence is circular and undefined.
-
Large Language Models for Planning: A Comprehensive and Systematic Survey
A structured survey of LLM planning methods, benchmarks, and interpretability work, organized around a three-way taxonomy.
Discussion (0). Continue with ORCID to comment.