Planning in the LLM Era: Building for Reliability and Efficiency
Michael Katz, Harsha Kokel, Kavitha Srinivas, Shirin Sohrabi
Abstract
Growing attention to intelligent agents has put a spotlight on one of their central capabilities: planning. Early attempts to leverage large language models (LLMs) for planning relied on single-shot plan generation, followed by hybrid approaches that coupled LLMs with limited external search. These methods, unsound and incomplete by their very nature, often require substantial resources without yielding better solutions on unseen problems. As the limitations of LLMs become clearer, recent work has shifted toward using them at solution construction time -- generating symbolic solvers for a family of problems that can be verified and then used efficiently at inference time. This trend reflects the growing need for agents that are both reliable and resource-efficient. It also offers a path towards generating maintainable planners with minimal dependence on language models at inference time. In this paper, we argue that this shift reflects a broader realignment of the planning field in the LLM era. We examine three major categories of planner-generation methods, discuss their current limitations, and outline research steps towards a more reliable and efficient LLM-based generation of planners.
AI Impact Assessments
(1 models)Scientific Impact Assessment
Core Contribution
This is a position paper that argues for a paradigm shift in LLM-based planning: from using LLMs at inference time (generating plans directly or guiding search) to using them at construction time (generating reusable planners, PDDL models, or policy code). The paper organizes the landscape into three categories — NL2Search, NL2PDDL, and NL2Policy — and discusses current limitations and future research directions for each.
The central thesis is conceptually clean: LLMs are expensive, unsound, and incomplete as planners, but they can be effective as *generators* of domain-specific planning artifacts that can be verified, maintained, and deployed efficiently. This is not a novel technical contribution but rather a synthesis and framing of an emerging trend, primarily anchored in the authors' own prior work (Thought of Search, AutoToS) and adjacent recent literature.
Methodological Rigor
As a position paper, there are no experiments or formal proofs to evaluate. The rigor here lies in the quality of argumentation and the comprehensiveness of the literature survey. On this front, the paper is reasonably thorough but has notable gaps:
Potential Impact
The paper's framing could have moderate influence on how the community thinks about LLM-planning integration. The construction-time vs. inference-time distinction is valuable and could help researchers avoid dead-end approaches that simply throw more LLM calls at planning problems.
However, the practical impact is limited by several factors:
1. No new technical contributions: The paper synthesizes existing work rather than introducing new methods, benchmarks, or theoretical results.
2. Limited engagement with real-world complexity: While the paper mentions AppWorld and WebArena as motivating applications, the actual discussed methods operate almost exclusively in classical PDDL planning domains. The gap between these toy settings and real agentic applications is enormous and underexplored.
3. Missing quantitative analysis: There is no systematic comparison of the three approaches along dimensions like coverage, plan quality, computational cost, or development effort. Such analysis would significantly strengthen the position.
The identification of key challenges — state representation learning, handling partial observability, moving beyond linear refinement, and determining appropriate abstraction levels — could guide productive research, though these challenges are individually well-known.
Timeliness & Relevance
The paper is well-timed. There is genuine confusion in the community about how LLMs should be used for planning, and the rapid pace of new methods makes a organizing framework valuable. The emphasis on reliability and efficiency resonates with growing concerns about LLM computational costs and the need for verifiable AI systems.
The argument that LLM-based plan generation is fundamentally limited (unsound, incomplete, expensive) is increasingly supported by empirical evidence, including recent evaluations of frontier models like GPT-5. The paper correctly identifies that even when frontier models approach classical planner coverage, they do so at vastly higher computational cost and with fragility under problem reformulation.
Strengths
Limitations
Overall Assessment
This is a competent position paper that provides a useful organizing framework for an active research area. Its central argument — that LLMs should be used to generate planners rather than serve as planners — is sound and increasingly well-supported. However, the paper's impact is constrained by the lack of new technical contributions, limited empirical grounding, and relatively shallow analysis of cross-category synergies. It reads more as a research group's roadmap than a transformative position piece that will reshape community thinking.
Generated May 22, 2026
Comparison History (15)
TerminalWorld introduces a novel, scalable benchmark with concrete data (1,530 tasks from 80,870 recordings), open-source resources, and empirical findings showing current agents achieve only 62.5% on real terminal tasks. It fills a clear gap in agent evaluation with a reproducible methodology. Paper 2 is a position/survey paper arguing for a shift in LLM-based planning approaches, which, while insightful, offers less concrete novelty and empirical contribution. Benchmarks tend to have outsized impact by enabling standardized evaluation across the community.
Paper 1 addresses a fundamental challenge in AI planning with LLMs, proposing a paradigm shift toward generating verified symbolic solvers rather than relying on LLMs at inference time. This has broad implications across AI agents, robotics, and automated reasoning. Its focus on reliability and efficiency addresses critical bottlenecks in deploying LLM-based agents. Paper 2, while introducing a useful benchmark for T2I prompting evaluation, addresses a narrower problem space. Paper 1's conceptual framework for categorizing planner-generation methods and its roadmap for future research are likely to influence a wider community and have longer-lasting impact.
Paper 1 offers a concrete architectural innovation (ResDreamer) with empirical state-of-the-art results in reinforcement learning, directly addressing multi-step error accumulation in world models. Paper 2 is a perspective paper outlining research directions for LLM-based planning. While Paper 2 is highly timely, Paper 1's methodological rigor, self-supervised scalability, and demonstrated improvements in sample and parameter efficiency provide a more tangible and immediate technical impact on the development of autonomous agents.
Paper 1 likely has higher impact due to demonstrated real-world deployment, clear methodological contribution (multimodal wearable sensing + foundation-model fine-tuning), and direct, high-stakes application in special-education safety (predicting challenging behaviors 10 minutes ahead, AUC 0.78). It advances translational ML/healthcare/education and can influence clinical, HCI, and assistive-tech fields. Paper 2 is timely and broad but appears primarily as a position/survey outlining trends and future steps without presenting a concrete validated method, making near-term measurable scientific and practical impact less certain.
Paper 2 proposes and evaluates a concrete, evidence-grounded system (pArticleMap) for literature mapping and hypothesis generation in nanomedicine, with retrospective benchmarks and blinded human assessment—supporting methodological rigor and near-term real-world utility for accelerating discovery. Its approach is timely (agentic LLM workflows with auditing/grounding) and could generalize to other scientific domains with fragmented literatures, broadening impact beyond nanomedicine. Paper 1 is mainly a perspective/survey outlining trends and research steps; valuable conceptually, but likely less immediately impactful than a validated end-to-end system.
Paper 1 presents a novel, concrete methodology (Cognitive User Simulator and Asymmetric-View Policy Optimization) addressing a specific and important problem in proactive dialogue systems. It introduces new training paradigms combining privileged information distillation with RL, offering both theoretical insights (why passive LLMs fail) and practical techniques. Paper 2 is a survey/position paper arguing for a shift toward planner generation, which is valuable but primarily synthesizes existing trends rather than introducing new methods. Paper 1's technical contributions—novel simulator design, asymmetric training, and state-transition refinement—are more likely to spawn follow-up research and applications.
Paper 1 introduces a concrete, novel hierarchical RL framework to extract and transfer shared safety skills from crowd preferences, with empirical validation across safe RL settings and an LLM-style task. This combination of methodological contribution plus demonstrated performance without explicit safety rewards suggests strong real-world applicability and timely relevance to alignment/safe RL. Paper 2 is primarily a position/survey-style argument about trends in LLM-based planning; while broad and timely, it offers fewer directly testable new methods, so its incremental scientific contribution and near-term measurable impact are likely lower.
Paper 2 offers a broader paradigm shift by outlining a new direction for LLM-based planning. While Paper 1 provides a valuable technical contribution to safety alignment, perspective papers that successfully identify limitations in current hot fields (like LLM agents) and propose new, reliable, and efficient research trajectories typically have a higher and more widespread scientific impact by shaping future research agendas.
Paper 2 outlines a critical paradigm shift in AI agent planning, advocating for verifiable symbolic solvers over unreliable single-shot LLM generation. As a perspective paper guiding future research across the broad and active field of intelligent agents, it has greater potential for foundational impact and widespread citations compared to Paper 1, which offers a narrower, albeit efficient, algorithmic optimization for Video LLMs.
Paper 1 proposes a concrete, novel training signal (latent user concerns) plus a new simulator and optimization method to elicit proactivity in task-oriented dialogue, with clear downstream applications (sales/support) and likely reusable techniques (asymmetric-view distillation, state-transition refinement). Its contribution is methodological and implementable, potentially influencing TOD, RLHF/RLAIF, user simulation, and agent persuasion. Paper 2 is a high-level positioning/survey-style argument about planner generation trends; timely and broad, but offers fewer new methods or empirical advances, so its incremental scientific impact is likely lower.
Paper 2 likely has higher impact due to broader scope and cross-field relevance: it frames a timely shift in LLM-based planning toward verifiable, efficient solver generation, influencing AI agents, formal methods, and planning communities. Its emphasis on reliability/efficiency addresses a central bottleneck for real-world deployment and may shape research agendas. Paper 1 is a useful, practical, training-free technique for Video LLM token compression, but its impact is narrower (video/multimodal models) and more incremental relative to existing pooling/token reduction lines.
Paper 2 addresses a fundamental challenge in AI planning—reliability and efficiency of LLM-based agents—which is a core research problem with broad implications across robotics, autonomous systems, and AI agent development. Its systematic categorization of planner-generation methods and identification of a paradigm shift (from direct LLM planning to LLM-generated symbolic solvers) provides a conceptual framework likely to influence a wide research community. Paper 1, while innovative as a pedagogical contribution with a useful benchmark, has narrower impact primarily in AI education, and its benchmark is domain-specific with limited scalability beyond the classroom context.
Paper 1 addresses a critical, widespread bottleneck in modern AI—the reliability and efficiency of LLM-based planning. By advocating a paradigm shift towards generating symbolic solvers, its insights have broad implications across robotics, automation, and general AI agent design. Paper 2 offers an innovative approach, but its impact is more narrowly confined to negotiation research and behavioral sciences, giving Paper 1 a larger footprint in the foundational advancement of AI.
Paper 2 outlines a critical paradigm shift in LLM-based planning from unreliable inference-time generation to verifiable construction-time solver generation. By synthesizing current limitations and proposing a broader research agenda for reliable AI agents, it has the potential to steer future research directions across the broader AI community, likely yielding wider scientific impact than the specific evaluation methodology proposed in Paper 1.
Paper 2 is likely to have higher impact: it introduces a concrete, timely benchmark for end-to-end LLM-agent workflows in a high-stakes domain (finance), with a multidimensional evaluation taxonomy that can standardize future research and enable measurable progress. Its real-world applicability is direct, and benchmarks often become widely adopted across industry and academia. Paper 1 appears more like a perspective/survey outlining trends and research directions in LLM-based planning; valuable, but typically less impactful than a widely used dataset/benchmark unless it introduces a fundamentally new method.