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DreamPhase: Offline Imagination and Uncertainty-Guided Planning for Large-Language-Model Agents

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 6.7Download Report PDF
LLMAutonomous Agents
Abstract:

Autonomous agents capable of perceiving complex environments, understanding instructions, and performing multi-step tasks hold transformative potential across domains such as robotics, scientific discovery, and web automation. While large language models (LLMs) provide a powerful foundation, they struggle with closed-loop decision-making due to static pretraining and limited temporal grounding. Prior approaches either rely on expensive, real-time environment interactions or brittle imitation policies, both with safety and efficiency trade-offs. We introduce DreamPhase, a modular framework that plans through offline imagination. A learned latent world model simulates multi-step futures in latent space; imagined branches are scored with an uncertainty-aware value and filtered by a safety gate. The best branch is distilled into a short natural-language reflection that conditions the next policy query, improving behavior without modifying the LLM. Crucially, DreamPhase attains its performance with substantially fewer real interactions: on WebShop, average API calls per episode drop from ∼\sim40 with ARMAP-M (token-level search) to <10<10 with DreamPhase, a 4×4\times reduction that lowers latency and reduces executed irreversible actions by ∼5×\sim 5\times on WebShop (4.9×\times on ALFWorld) per incident logs. Across web, science, and embodied tasks, DreamPhase improves sample efficiency, safety, and cost over search-based and reward-based baselines. This offers a scalable path toward safe, high-performance autonomous agents via imagination-driven planning. Code: \url{https://anonymous.4open.science/r/DreamPhase-A8AD/README.md}.

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This report is AI-GENERATED using Large Language Models and WisPaper (A scholar search engine). It analyzes academic papers' tasks and contributions against retrieved prior work. While this system identifies POTENTIAL overlaps and novel directions, ITS COVERAGE IS NOT EXHAUSTIVE AND JUDGMENTS ARE APPROXIMATE. These results are intended to assist human reviewers and SHOULD NOT be relied upon as a definitive verdict on novelty.
NOTE that some papers exist in multiple, slightly different versions (e.g., with different titles or URLs). The system may retrieve several versions of the same underlying work. The current automated pipeline does not reliably align or distinguish these cases, so human reviewers will need to disambiguate them manually.
If you have any questions, please contact: mingzhang23@m.fudan.edu.cn

Overview

Overall Novelty Assessment

DreamPhase proposes a modular framework combining offline imagination through a learned latent world model, uncertainty-aware value scoring, and safety-gated filtering to guide LLM-based agents. The paper occupies its own singleton leaf ('DreamPhase and Core Framework') within the taxonomy, indicating it is the sole representative of this specific integration approach. This isolated position suggests the work synthesizes elements from multiple established branches—world model learning, uncertainty quantification, and LLM-based planning—into a novel architectural combination not directly replicated by other surveyed methods.

The taxonomy reveals substantial activity in neighboring branches: 'World Model Learning and Latent Dynamics' contains four leaves with methods like Dreamwalker and Dreamernav focusing on forward dynamics for embodied tasks, while 'Uncertainty Quantification in Model-Based RL' includes ensemble-based and penalty-driven approaches (e.g., MOReL, MOPO). 'LLM-Based Planning and Reasoning' encompasses five leaves addressing navigation, dialogue, and search-augmented planning. DreamPhase diverges by integrating latent-space imagination with uncertainty-aware filtering specifically for LLM agents, whereas sibling branches typically address these components in isolation or apply them to non-LLM settings.

Among 26 candidates examined, the framework-level contribution (Contribution A) shows no clear refutation across 10 candidates, suggesting the specific architectural integration is relatively unexplored. However, uncertainty-aware value estimation (Contribution B) encounters three refutable candidates among six examined, indicating substantial prior work on uncertainty quantification in model-based RL. The language reflection mechanism (Contribution C) finds one refutable candidate among ten, pointing to some overlap with existing LLM prompting or self-refinement techniques. The limited search scope (26 candidates, not exhaustive) means these findings reflect top-K semantic matches rather than comprehensive field coverage.

Given the constrained literature search, DreamPhase appears to occupy a relatively sparse intersection—combining offline imagination, uncertainty filtering, and LLM control—though individual components draw on well-established techniques. The singleton taxonomy position and low refutation rate for the framework contribution suggest architectural novelty, while higher overlap for uncertainty and reflection mechanisms indicates these elements build incrementally on prior uncertainty quantification and LLM prompting research. The analysis captures top-30 semantic neighbors, leaving open the possibility of additional related work beyond this scope.

Taxonomy

Core-task Taxonomy Papers
46
3
Claimed Contributions
26
Contribution Candidate Papers Compared
4
Refutable Paper

Research Landscape Overview

Core task: offline imagination and uncertainty-guided planning for language model agents. The field structure reflects a convergence of model-based reinforcement learning and large language model capabilities, organized into several complementary branches. World Model Learning and Latent Dynamics encompasses methods that build predictive models of environment transitions, often through learned representations or generative processes (e.g., Dreamwalker[1], Dreamernav[10]). Uncertainty Quantification in Model-Based RL addresses the challenge of estimating epistemic and aleatoric uncertainty to guide exploration and avoid overconfident predictions in learned dynamics (e.g., MOReL[8], Uncertainty Driven Imagination[5]). LLM-Based Planning and Reasoning leverages the reasoning and knowledge capabilities of language models for sequential decision-making (e.g., Interactive Planning LLM[6], NavCoT[7]), while Policy Learning and Value Estimation focuses on deriving effective control policies from imagined or real trajectories. Multi-Agent and Safety-Critical Domains and Specialized Planning branches handle coordination, safety constraints, and domain-specific adaptations, ensuring robustness in complex or high-stakes settings. A particularly active tension lies between purely model-free LLM planning approaches and hybrid methods that combine learned world models with uncertainty-aware rollouts. Works like Kalm[3] and Knowledgeable Agents[2] explore how language models can internalize domain knowledge for planning, yet they often lack explicit uncertainty estimates that guard against compounding errors in long-horizon imagination. DreamPhase[0] sits at the intersection of these themes, residing in the DreamPhase and Core Framework branch. It emphasizes offline imagination—generating hypothetical trajectories without environment interaction—paired with uncertainty quantification to prune unreliable rollouts, bridging the gap between classical model-based RL (e.g., MOReL[8]) and modern LLM reasoning. Compared to Kalm[3], which focuses on knowledge integration, DreamPhase[0] prioritizes uncertainty-driven selectivity in imagined futures, offering a principled mechanism to balance exploration breadth with epistemic caution in language-driven agents.

Claimed Contributions

DreamPhase framework for offline imagination-based planning

The authors propose DreamPhase, a modular agent framework that uses a learned latent world model to simulate multiple future trajectories offline in latent space. These imagined branches are evaluated using uncertainty-aware value estimates and filtered through a safety gate before execution, enabling planning without real environment interactions.

10 retrieved papers
Uncertainty-aware value estimation with theoretical regret bound

The authors develop a risk-aware branch selection mechanism that scores imagined trajectories using value estimates penalized by uncertainty measures. They provide a theoretical regret bound that relates cumulative regret to model approximation error and mis-gating rate, offering formal guarantees on decision quality.

6 retrieved papers
Can Refute
Language reflection mechanism for zero-tuning policy control

The authors introduce a mechanism that distills the best imagined trajectory into concise natural-language reflections and summaries. These are injected into the policy LLM prompt to guide action selection without fine-tuning the model, enabling interpretable behavior improvement while keeping the LLM frozen.

10 retrieved papers
Can Refute

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Within the taxonomy built over the current TopK core-task papers, the original paper is assigned to a leaf with no direct siblings and no cousin branches under the same grandparent topic. In this retrieved landscape, it appears structurally isolated, which is one partial signal of novelty, but still constrained by search coverage and taxonomy granularity.

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

DreamPhase framework for offline imagination-based planning

The authors propose DreamPhase, a modular agent framework that uses a learned latent world model to simulate multiple future trajectories offline in latent space. These imagined branches are evaluated using uncertainty-aware value estimates and filtered through a safety gate before execution, enabling planning without real environment interactions.

Contribution

Uncertainty-aware value estimation with theoretical regret bound

The authors develop a risk-aware branch selection mechanism that scores imagined trajectories using value estimates penalized by uncertainty measures. They provide a theoretical regret bound that relates cumulative regret to model approximation error and mis-gating rate, offering formal guarantees on decision quality.

Contribution

Language reflection mechanism for zero-tuning policy control

The authors introduce a mechanism that distills the best imagined trajectory into concise natural-language reflections and summaries. These are injected into the policy LLM prompt to guide action selection without fine-tuning the model, enabling interpretable behavior improvement while keeping the LLM frozen.

Table of Contents