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REVIEW 3 major objections 6 minor 1 cited by

Fact-Augmented Lookahead Planning for LLM Agents

T0 review · 3 major / 6 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read Fact-augmented lookahead planning lets a frozen LLM agent improve at text tasks purely through in-context learning, without any weight updates.

desk verdict A solid, incremental contribution with an honest appendix and real unnormalized gains, but the missing search-without-facts control leaves the central fact-conditioning claim underdetermined. read the letter →

arxiv 2506.09171 v2 pith:YKPOPSNE submitted 2025-06-10 cs.LG cs.AIcs.CL

Samuel Holt , Max Ruiz Luyten , Thomas Pouplin , Mihaela van der Schaar This is my paper
classification cs.LGcs.AIcs.CL
keywords atomicfactsin-contextlearningLLMagentslookaheadplanninglatentworldmodelstateabstractionDyna-styletextenvironments
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper claims that a frozen LLM can become a markedly better agent in long-horizon interactive text environments simply by learning a compact set of "atomic facts" from its own past episodes and feeding those facts to a recursive lookahead search. After each episode the agent extracts minimal textual facts (e.g., "hole at (2,3)", "object X is in receptacle Y"), optionally compresses them, and uses the resulting fact set to condition three LLM components: action proposal, one-step latent world-model simulation, and state-value estimation. Candidate actions are scored by simulated lookahead Q-values $Q(o_t, a_i) = r' - \lambda_{\mathrm{step}} + \gamma \hat{V}(o')$, so experience reshapes behavior entirely through prompts. If the claim holds, it offers a cheap, gradient-free route to online adaptation for LLM agents: accumulate experience as text instead of fine-tuning weights. The authors support it with an abstraction-style bound that ties performance to fact quality and simulation fidelity, and with experiments on TextFrozenLake, CrafterMini, and ALFWorld where LWM-Planner outperforms ReAct, Reflexion, and a fact-augmented ReAct ablation.

What carries the argument

The load-bearing objects are the atomic fact set $F_t$ and the three-LLM lookahead loop. A post-episode reflection prompt turns each trajectory into minimal new facts ("(1,0) is a hole", "(2,3) is the goal"), which are unioned into a fact memory and optionally compressed by a redundancy-removal LLM; the fact set then augments the prompts of an action proposer, a single-step latent world-model simulator $simulate\_step(o, a) \to (o', r', \mathit{done})$, and a leaf value estimator $estimate\_value(o')$ — all called at temperature zero. Search proceeds recursively to depth $d$ with branching $b$, and every action is scored by the identity $Q(o_t, a_i) = r' - \lambda_{\mathrm{step}} + \gamma \hat{V}(o')$, with the root action chosen by max $Q$. The theory wraps this in a fact-based abstraction: the fact extractor defines an abstract MDP over fact sets, bisimulation error $\epsilon_{\mathrm{sim}}$ bounds abstraction loss, $\epsilon_{\mathrm{plan}}$ bounds planning sub-optimality, and $\delta_{\mathrm{model}}$ bounds the LLM simulator's one-step error, combining into the value-loss bound stated above.

What would settle it

Run LWM-Planner on the paper's three environments while logging every simulated (next observation, reward, done) from simulate_step against the environment's actual response: if fact-conditioned next-state prediction agrees with the real transition no better than chance yet the agent still outperforms its no-lookahead ablation, the stated mechanism is not what drives the gains. Likewise, if stripping the facts out of the simulator (keeping them only for value estimation) leaves performance unchanged, the "fact-augmented world model" claim fails.

Watch

Extended reading notes

Core claim

The central claim is that an LLM agent's planning in partially observable, long-horizon text environments can be improved purely in-context — no parameter updates — by maintaining a dynamically growing set of task-critical atomic facts extracted from completed trajectories and by conditioning action proposal, single-step latent world-model simulation, and state-value estimation on those facts inside a recursive, depth-limited lookahead search. The fact set functions as a learned state abstraction: well-chosen facts remove aliasing between states with different values or optimal actions (lowering the abstraction error $\epsilon_{\mathrm{sim}}$), and fact-conditioned simulation lowers the one-step model error $\delta_{\mathrm{model}}$ of the LLM acting as an implicit abstract world model. The theory decomposes the agent's value loss as $\lesssim \frac{2\epsilon_{\mathrm{sim}}}{1-\gamma} + \epsilon_{\mathrm{plan}} + (C_1 + C_2)\frac{\delta_{\mathrm{model}}}{(1-\gamma)^2}$, and the practical recipe is that compact, experience-derived facts let a general LLM approximate this idealized fact-based agent. Empirically, on TextFrozenLake variants (including a $4\times4$ board with 90% holes where the agent learns all hole locations and reaches the goal in the optimal six steps), CrafterMini, and ALFWorld, LWM-Planner achieves higher normalized cumulative return than ReAct, Reflexion, and the ReAct+FEC ablation, and degrades less than baselines as the state-action space grows.

Load-bearing premise

The method assumes the LLM, prompted with the learned atomic facts, simulates transitions and estimates state values accurately enough that lookahead Q-values rank actions better than acting without search; the paper never measures simulated against real transitions.

Editorial extensions

If this is right

  • A frozen LLM agent improves with accumulated experience without any gradient updates, so the same deployed model can adapt to a new environment by growing its fact set rather than being re-trained.
  • Test-time search pays off mainly when grounded by compact, experience-derived facts: the ReAct+FEC ablation shows facts alone help, and the full lookahead adds further returns, while search-free and reflection-based baselines lag.
  • As environments grow (larger TextFrozenLake grids), baselines degrade sharply while LWM-Planner continues to solve them, indicating that fact-based abstraction scales better than long raw histories or high-level verbal lessons.
  • The agent converges to near-optimal behavior within a handful of episodes in simple domains, as illustrated by the $4\times4$ FrozenLake trace where repeated failures are converted into hole-location facts and later episodes take the optimal six steps.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • If fact granularity is the true source of the gains, then a variant of Reflexion that stores object-level state lessons instead of strategic advice should close most of the gap to LWM-Planner on ALFWorld; this is directly testable and the paper does not run it.
  • The theory's steep $2\epsilon_{\mathrm{sim}}/(1-\gamma)$ penalty for abstraction error implies that investing in fact quality (e.g., verifying each extracted fact against the environment) should dominate increasing search depth — a priority ordering the paper states only implicitly.
  • Because facts are plain text, the machinery should transfer to multi-modal or partially observable settings whenever the missing knowledge can be verbalized, suggesting a general recipe for memory in LLM agents beyond the three text benchmarks studied.
  • The framework never measures how often the LLM simulator's predicted next observation matches the real environment; adding such a measurement would let practitioners decide when lookahead is trustworthy versus when it injects noise.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 6 minor

Summary. The paper proposes LWM-Planner, an LLM agent that improves planning purely through in-context learning by extracting task-critical atomic facts from past trajectories and using them to condition action proposal, single-step latent world-model simulation, and state-value estimation within a recursive depth-limited lookahead search. The authors provide an abstraction-style theoretical motivation connecting fact quality (epsilon_sim), simulation fidelity (delta_model), and planning suboptimality (epsilon_plan), and evaluate the method on TextFrozenLake variants, CrafterMini, and ALFWorld against ReAct, Reflexion, ReAct+FEC, and Random policies. The raw unnormalized results in Appendix J.3 support the main directional claim on most environments, but the principal attribution claim—that lookahead search is most useful when grounded by experience-derived facts—is not directly tested because no search-without-facts baseline is included.

Significance. If the central claim holds, LWM-Planner offers a practical recipe for improving frozen LLM agents in text environments without weight updates, using a compact symbolic memory to guide model-based lookahead. The paper has several strengths: the unnormalized Table 8 shows LWM-Planner outperforming ReAct and Reflexion on TextFrozenLake and ALFWorld, with comparable performance on CrafterMini; the algorithm and prompt appendices are detailed enough to support reproduction; and the limitations section (Appendix K) candidly acknowledges that LLM simulation can be imperfect and that epsilon_sim/delta_model are not directly measurable. These strengths make the empirical direction credible, but the missing search-only control and the test-set hyperparameter selection leave the headline attribution claim underdetermined.

major comments (3)
  1. [Section 5.1, Table 1; Appendix J.3, Table 8; Abstract] The abstract claims improvement over 'search-only baselines,' and the paper's central attribution is that additional test-time search is most useful when grounded by facts. However, no algorithm that performs recursive lookahead search without the extracted atomic facts appears anywhere in Tables 1-8. The experimental design is effectively a 2x2 with one missing cell: ReAct is (no facts, no search), ReAct+FEC is (facts, no search), LWM-Planner is (facts, search), and no method is (no facts, search). Therefore the gains of LWM-Planner over ReAct+FEC in Table 8 (e.g., ALFWorld-A 21.33 vs 4.70) could be due to lookahead alone, with the facts contributing little; conversely, search without facts might collapse, which would support but not isolate the interaction claim. The authors should add a search-without-facts baseline (for example, the same recursive lookahead planner with an empty fact memory) or explicitly rephrase the claim to avoid attributing the benefit to the fact-search interaction.
  2. [Section 5.1, Table 1; Appendix H] The headline normalized results in Table 1 are self-referential: the normalization sets the best observed method to 100, and since LWM-Planner is the best method by construction, its score of 100 is forced by the metric. The caption states that '100 corresponds to the expert, or here the highest on average being LWM-Planner,' which means the table cannot be read as showing LWM-Planner achieving expert-level performance. The unnormalized Table 8 is the informative comparison and should be promoted to the main text, with the normalized table either removed or renormalized to a fixed, external expert (e.g., an oracle or optimal policy) so that 100 has an absolute meaning.
  3. [Appendix J.2, Table 7] The choice of search depth d=3 and branching factor b=4 is validated on the same environments used for the main results. Table 7 reports ablations on grid_4_h_9_s_0, crafter_mini_5_s_0, and alfworld_task_3/5/90, which are exactly the environments in the main tables. Tuning hyperparameters on the test environments can overstate the method's performance and is not a fair comparison to baselines with fixed defaults. The authors should select hyperparameters on separate validation environments (or report results across a range of d and b for all methods), and clearly state that no test-set tuning occurred.
minor comments (6)
  1. [Section 3.3, Eq. (4); Appendix E.1.1] Equation (4) is described as 'rigorously connects' the error terms, but it is only a '≲' bound with unspecified constants C1 and C2, and the proof of Theorem 3.4 invokes a value-difference bound for arbitrary policies under approximate bisimulation from [Ravindran and Barto, 2004] without deriving it. Since the paper explicitly frames this as motivation rather than a formal guarantee, the word 'rigorously' should be softened and the cited properties stated more precisely.
  2. [Section 5.2, Table 2] The column headers 'TextFrozenLake (6×6;h=9)' and 'TextFrozenLake (8×8;h=5)' appear to contain typos: the hole density should likely be h=0.9 and h=0.5 to be consistent with the h=0.9 used for the 4×4 environment. Please correct the decimal points.
  3. [Appendix J.3, Table 8] In Table 8, the LWM-Planner method is labeled 'Wolrd_parallel,' which appears to be a typo for 'World_parallel' or similar; please rename it consistently with the rest of the paper.
  4. [Appendix J.2, Table 7] Several entries in Table 7 report '±nan' for the 95% confidence interval (e.g., grid_4_h_9_s_0 for d=3,b=4 and d=2,b=4). The paper should explain why the confidence interval is undefined for these runs, or report standard deviations over non-degenerate seeds.
  5. [Appendix J.1, Table 3] The caption of Table 3 says 'single-seed runs, 95% CIs,' which is contradictory: a single seed cannot produce a confidence interval, and the per-task Tables 4-6 indeed show no CIs. Please clarify whether the aggregate table uses multiple seeds or whether the CIs are computed across tasks.
  6. [Section 4, Figure 1; Appendix G.5] The sign convention for the step penalty is inconsistent: the main text says a small step penalty is incorporated, and the Q-value formula uses Q = r' − λstep + γ V(o'), but Appendix G.5 gives the example λstep = -0.01, which would make the term r' − λstep a reward bonus rather than a penalty. Please clarify whether λstep is defined as a positive penalty or as a negative offset.

Circularity Check

1 steps flagged · score 6.0 of 10

Headline normalized-return metric is partly definitional: LWM-Planner's 100 is forced by choosing LWM-Planner as the 'expert' anchor; unnormalized Table 8 provides independent support.

  1. self definitional [Section 5, Evaluation paragraph; Table 1 caption; Appendix H (Normalized Cumulative Return)]
    "Moreover, we normalize the cumulative return following the standard RL normalization [Yu et al., 2020]—normalized to be between 0 and 100, where a score 0 is given by the random policy and a score of 100 by an expert, which is the highest benchmark method here, being LWM-Planner."

    The 'expert' anchor of the normalization is not an independent expert policy; it is defined as the highest benchmark method, which is LWM-Planner. Consequently, LWM-Planner's cumulative return in Table 1 is 100.00 +/- CI by construction in every environment, regardless of behavior. The paper then cites this definitional 100 as evidence that 'LWM-Planner performs the best across all environments' and that it learns 'near-optimal behaviour'. Appendix H states the same: the expert score is 'the average cumulative return achieved by the highest-performing benchmark method observed in our experiments.' Because the anchor is fitted to LWM-Planner's own raw return, the normalized 100 is a relabeling of the input rather than an empirical prediction.

full rationale

The core methodology—extracting atomic facts and using them to condition lookahead search—is not itself circular: the fact extractor, simulator, and value estimator are distinct LLM calls, and the raw unnormalized results in Table 8 provide independent evidence that LWM-Planner outperforms ReAct, Reflexion, and ReAct+FEC on most environments. No load-bearing self-citation was found; the paper's self-citations in Related Work and Appendix A (G-Sim, hybrid digital twins, ODE discovery, RAT) are contextual rather than used to justify the central claim. The theoretical bound in Eq. (4) is assembled from cited results (Ferns et al., Strehl et al., Jiang et al., Kearns and Singh) and is explicitly framed as motivation, not a formal guarantee; the paper itself acknowledges in Appendix K.4 that eps_sim and delta_model are difficult to measure. This is a limitation, not a circular reduction. The concrete circular step is the headline normalized cumulative return: defining the expert score as LWM-Planner makes LWM-Planner's 100.00 a tautology. The missing search-only baseline and the unmeasured fidelity of LLM simulation are experimental gaps rather than circularity, but they further weaken the attribution of the gain to fact augmentation. Overall, the central empirical claim retains independent raw-score support, so the circularity is partial rather than total.

Assumptions & free parameters 6 free parameters · 5 assumptions · 1 invented entities

The theoretical motivation imports standard abstraction and MBRL bounds; the load-bearing assumptions are that LLM fact extraction produces value-preserving facts and that LLM simulation and value estimation are accurate enough. Neither is measured. Main hyperparameters d=3 and b=4 were selected on a test environment.

free parameters (6)
  • search_depth D_s = 3
    Appendix J.2 reports that d=3 performs best on TextFrozenLake and 'validates our initial choice of parameters'; this value is used for all main results.
  • branching_factor k_B = 4
    Selected together with search depth on TextFrozenLake in Appendix J.2 and used for all main results.
  • step_penalty lambda_step = 0.01 subtracted in Q-value
    Chosen by hand in Appendix G.5; affects the trade-off between speed and reward in Q-value computation.
  • discount_factor gamma = 0.99
    Chosen by hand in Appendix G.5 and used in Q-value accumulation.
  • fact_memory_capacity = 200 facts
    Deque max length chosen by hand in Appendices G.4 and G.5 to manage context window usage.
  • history_length H_L = 51 interactions
    Short-term history deque length chosen by hand in Appendix G.5.
assumptions (5)
  • domain assumption The environment is an MDP with state s_t recoverable from observation o_t.
    Section 3.1 assumes full observability or a sufficient structured representation; ALFWorld is partially observable, and the paper defers POMDP treatment to Appendix E.2.3.
  • ad hoc to paper There exists a fact-based abstraction Psi that is an epsilon_sim-approximate bisimulation.
    Definition 3.2 assumes the LLM fact extractor can approximate such an abstraction, but no verification that extracted facts satisfy bisimulation conditions is provided.
  • domain assumption LLM simulation and value estimation errors are small enough that delta_model and epsilon_plan stay low.
    Section 3.4 states this informally; Appendix K.2 admits LLM simulations can be imperfect. This is load-bearing for lookahead value estimates.
  • standard math Prior MBRL bounds by Strehl et al., Jiang et al., Kearns and Singh, and Ferns et al. apply as stated to the constructed abstract MDP.
    Eq. (4) and Theorem 3.4 borrow these bounds; no new proof of their applicability is given.
  • domain assumption Facts extracted after each episode are correct and relevant, and do not mislead planning.
    The paper has no fact validation mechanism; Appendix K.1 admits there is no formal guarantee of atomicity or correctness.
invented entities (1)
  • Atomic fact memory M_t
    purpose: A textual set of distilled environment properties used to condition action proposal, simulation, and value estimation prompts.
    The paper postulates that LLM-generated textual facts form a value-preserving abstraction, but it never measures whether facts satisfy the bisimulation or Information Bottleneck conditions; no external falsifiable handle is provided.

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Cite this review

Pith. "Pith review of Fact-Augmented Lookahead Planning for LLM Agents." pith.science (2026). https://pith.science/paper/YKPOPSNE

@misc{pith2026250609171,
  author       = {Pith},
  title        = {Pith review of: Fact-Augmented Lookahead Planning for LLM Agents},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/YKPOPSNE}},
  note         = {Machine review of arXiv:2506.09171}
}
abstract

Large Language Models (LLMs) are increasingly capable, but LLM agents still struggle to plan effectively in interactive, partially observable, long-horizon environments when search is unguided or recent history is insufficient. We introduce LWM-Planner, a fact-augmented lookahead planning framework that improves agent behavior purely through in-context learning. After each episode, the agent extracts task-critical atomic facts from its trajectories, validates candidates with a lightweight predictive-consistency filter (and optionally compresses them), and uses the resulting fact set to condition action proposal, single-step latent world-model simulation, and state-value estimation. Planning then proceeds via recursive, depth-limited lookahead over candidate trajectories conditioned on the accumulated facts and recent history, enabling online improvement without parameter updates. We provide abstraction-style motivation: treating facts as reducing state aliasing (proxy $\epsilon_{\mathrm{sim}}$) and fact-conditioned simulation as lowering one-step error (proxy $\delta_{\mathrm{model}}$), without claiming formal guarantees. Empirically, on text FrozenLake variants, CrafterMini, and ALFWorld, the approach improves cumulative return over ReAct/Reflexion and search-only baselines, suggesting that additional test-time search is most useful when grounded by compact, experience-derived facts.

Figures

Figures reproduced from arXiv: 2506.09171 by the authors.

Figure 1
Figure 1. Illustration of LWM-Planner’s fact-augmented lookahead search. The agent begins from a current environment observation o0 (left panel). Leveraging previously extracted Atomic Facts (e.g., Hole@(3,0)), LWM-Planner executes a recursive lookahead search (right panel) to determine the optimal action. This search involves: (i) an LLM, acting as a Latent World Model, simulating action sequences (ai) to predict subsequent … view at source ↗

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Forward citations

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Reference graph

Works this paper leans on

32 extracted references · 32 canonical work pages · cited by 1 Pith paper

  1. [1]

    Environment Description ({{ env_description_str }} ): A comprehensive description of the environment G, including its rules, objectives, action space A, and the nature of observations o ∈ O

    work page

  2. [2]

    Current Fact Memory ({{ current_facts_list_str }} ): The set of atomic facts, Mt, that the agent currently holds, passed as a list of strings

    work page

  3. [3]

    • LLM Task Specification (Instructions guiding the LLM to generate arguments for the fact_extraction function):

    Episode Trajectory Summary ({{ episode_trajectory_summary_str }} ): A string summarizing the completed episode τe, including the outcome (e.g., success/failure), total reward, and a formatted sequence of observations, actions, rewards, and next observations. • LLM Task Specification (Instructions guiding the LLM to generate arguments for the fact_extracti...

    work page

  4. [4]

    Analyze the provided {{ episode_trajectory_summary_str }} in conjunction with the {{ current_facts_list_str }} and {{ env_description_str }}

    work page

  5. [5]

    minimal new atomic facts

    Identify “minimal new atomic facts” ( ∆Fe) that are evidenced by or can be reliably inferred from the trajectory and are not already present or directly implied by the {{ current_facts_list_str }} or {{ env_description_str }}

    work page

  6. [6]

    Prioritize facts crucial for explaining significant trajectory events (e.g., unexpected rewards, state transitions leading to success or failure, particularly those that would improve the prediction of state values or rewards if known beforehand)

    work page

  7. [7]

    Ensure facts are concise, atomic, and adhere to any implicitly defined predi- cate vocabulary illustrated by examples (e.g., hole_at(x,y) for TextFrozenLake, object_X_is_in_receptacle_Y for ALFWorld)

    work page

  8. [8]

    • Qualitative Goal: The LLM engages in a form of abductive reasoning to hypothesize underlying environmental properties or dynamics

    The LLM should structure its output to call the fact_extraction function, providing its internal reasoning as the thought argument and the identified new facts as a list of strings for the new_facts argument. • Qualitative Goal: The LLM engages in a form of abductive reasoning to hypothesize underlying environmental properties or dynamics. These hypothese...

    work page

Show all 32 references
  1. [10]

    • LLM Task Specification (Instructions guiding the LLM to generate arguments for the fact_redundancy_remover function):

    Augmented Fact Set ({{ current_facts_list_for_compression_str }} ): The union of the previous Fact Memory and newly extracted facts, Mt ∪ ∆Fe, passed as a list of strings. • LLM Task Specification (Instructions guiding the LLM to generate arguments for the fact_redundancy_remo...

  2. [11]

    Review the entire provided set of facts for semantic overlap, direct redundancy (e.g., facts identical to or trivially inferable from the {{ env_description_str }} ), or subsumption by more general facts within the set

  3. [12]

    Generate a revised and refined fact set, Mt+1, by removing or merging facts to enhance conciseness while preserving all critical, distinct pieces of information essential for optimal decision-making and world model accuracy

  4. [13]

    • Qualitative Goal: This process aims to manage the complexity of the abstract state representation |ZF |

    The LLM should structure its output to call the fact_redundancy_remover function, pro- viding its reasoning as the thought argument and the complete, refined list of facts as the all_facts argument. • Qualitative Goal: This process aims to manage the complexity of the abstract...

  5. [15]

    Current Atomic Facts ({{ current_facts_list_str }} ): The agent’s Fact Memory, Mt

  6. [16]

    Current Observation({{ current_observation_at_node_k_str }} ): The observation ok from which actions are to be proposed

  7. [17]

    Obs:

    Recent Trajectory History ({{ recent_history_for_prompt_str }} ): An excerpt of the (simulated or real) trajectory within the current lookahead search (or agent history) leading to ok. This is typically a list of "Obs: ..." and "Act: ..." strings

  8. [18]

    Available Actions({{ available_actions_list_str }} ): The set of legally permissible actions A(sk) from the underlying ground state sk corresponding to ok (or the full action set A)

  9. [19]

    Branching Factor ({{ branch_factor_k_B_int }} ): The maximum number of actions to propose. • LLM Task Specification (Instructions for the propose_actions function): Given ok, Mt, and {{ recent_history_for_prompt_str }} , propose up to {{ branch_factor_k_B_int }} distinct actio...

  10. [22]

    Current Observation ({{ current_observation_at_node_k_str }} , i.e., ok)

  11. [23]

    Action to Simulate ({{ action_to_simulate_str }} , i.e., aj ∈ A)

  12. [24]

    Recent Trajectory History ({{ recent_history_for_prompt_str }} ) leading to ok. • LLM Task Specification (Instructions for the simulate_step function): Predict the next_observation (o′ j), reward (rj), and done (d′ j) status that would result from taking {{ action_to_simulate_...

  13. [25]

    Environment Description ({{ env_description_str }} )

  14. [26]

    Current Atomic Facts ({{ current_facts_list_str }} , i.e., Mt)

  15. [27]

    Observation to Evaluate ({{ observation_to_evaluate_str }} , i.e., ok)

  16. [28]

    Recent Trajectory History ({{ recent_history_for_prompt_str }} ) leading to ok

  17. [29]

    Act:", a∗ t )); 11 H.append(FormatAsHistoryString(

    Discount Factor ({{ discount_gamma_float }} , i.e., γ). • LLM Task Specification (Instructions for the estimate_value function): Estimate the cumu- lative future discounted reward (value) achievable from {{ observation_to_evaluate_str }}, considering {{ current_facts_list_str ...

  18. [30]

    Term I: Abstraction Error on the Optimal Value Function This term, V ∗ G (s) − V ∗ MΨ∗ (Ψ∗(s)) , represents the difference between the optimal value function in the ground MDP and the optimal value function in the abstract MDP, mapped back to the ground state via Ψ∗. By Defini...

  19. [31]

    Term II: Planning Error in the Abstract MDP This term, V ∗ MΨ∗ (Ψ∗(s)) − V π◦ MΨ∗ MΨ∗ (Ψ∗(s)) , quantifies the sub-optimality of the policy π◦ MΨ∗ computed by the planner within the abstract model MΨ∗. By Definition 3.3, the planner is ϵplan-optimal, which means: V ∗ MΨ∗ (Ψ∗(s...

  20. [32]

    critical

    Term III: Abstraction Error on the Planned Policy’s Value This term, V π◦ MΨ∗ MΨ∗ (Ψ∗(s)) − V πF G (s) , captures the difference between the value of the planned policy π◦ MΨ∗ when evaluated in the abstract MDP MΨ∗ versus its value when executed in the ground MDP G (which is V...

    2004

  21. [33]

    Both types of experiences are distilled into atomic facts

    Learn from Failures and Successes: Initial interactions quickly identify critical hazards (holes), and successful trajectories confirm safe paths and the goal. Both types of experiences are distilled into atomic facts

  22. [34]

    avoid holes

    Improve Planning via Fact Augmentation: The accumulated atomic facts dynamically augment the prompts for the LLM components (proposer, simulator, value estimator). This grounding significantly improves the LLM’s ability to: • Simulate transitions more accurately (reducing δmod...

  23. [35]

    The agent’s performance, in terms of steps to goal, rapidly improves and stabilizes at the optimal 6 steps after a few initial exploratory episodes

    Achieve Consistent Optimal Behavior More Quickly: By building a sufficiently accurate and granular fact-based abstraction ( Ψ) of the environment, LWM-Planner converges to an optimal policy for this TextFrozenLake instance more rapidly and consistently than Reflexion, and vast...

  24. [36]

    minimal new atomic facts

    Leverage In-Context Learning with Structured Knowledge: All learning occurs via prompt augmentation with dynamically generated, structured atomic facts, without any LLM weight updates. This showcases the power of in-context learning when guided by distilled, experience-derived...

    2023

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