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REVIEW 4 major objections 4 minor 23 references

Reasoning Capabilities of Large Language Models on Dynamic Tasks

T0 review · 4 major / 4 minor · reviewed 2026-08-15 · deepseek-v4-flash

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Pith's one-line read This paper claims that dynamic benchmarks expose a knowing-doing gap in LLM agents: models can recite optimal strategies yet fail to execute them under planning and spatial-coordination demands.

desk verdict A useful four-model comparison of prompting strategies on SmartPlay with honest variance reporting, but the text-only interface may be doing more of the work than the reasoning claims acknowledge. read the letter →

arxiv 2505.10543 v2 pith:6JH2K3KU submitted 2025-05-15 cs.AI cs.CL

Annie Wong , Thomas Bäck , Aske Plaat , Niki van Stein , Anna V. Kononova This is my paper · ORCID
classification cs.AIcs.CL
keywords largelanguagemodelsdynamicenvironmentsin-contextlearningself-reflectionplanningspatialreasoningSmartPlayemergent
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

The paper asks whether in-context prompting alone can turn large language models into self-learning agents in dynamic environments. The authors test four open-source models on four SmartPlay games, comparing a base agent with three augmentations: self-reflection, heuristic mutation, and forward planning. They find that prompting can close the gap between small and large models on complex tasks, but that it also raises variance, sometimes sharply lowering scores. Against human baselines, they see little evidence of emergent reasoning: models can recite optimal solutions yet fail to execute them, a pattern they call a knowing-doing gap, alongside a language-embodiment gap in which text understanding does not produce grounded spatial planning. If right, static question-answering and math benchmarks overstate LLM reasoning, and dynamic environments should become a standard test bed.

What carries the argument

The load-bearing object is a text-only agent loop on SmartPlay, a suite of text-based games in which an agent must act from natural-language observations. At each timestep the model receives a game manual, the episode history as (state, action, reward, next state) tuples, a current observation, and a list of legal actions, and must choose one. Three modules alter that prompt: Reflection appends retrospective feedback after each step; Oracle mutates a candidate set of textual heuristics between episodes and keeps a mutation only when it improves cumulative reward; Planner simulates futures up to three steps ahead and recommends the action with the highest predicted reward. Comparing these variants, along with reward-shaping and state-encoding adjustments, against the base agent and human baselines is what carries the paper's conclusion that prompting gains are task-specific, unstable, and do not amount to emergent reasoning.

What would settle it

Run the same four models on three-disk Tower of Hanoi and Messenger with a structured state encoding (for example, explicit disk-stack arrays and grid coordinates) under identical prompting strategies and many seeds: if goal-completion rises to near human baseline, the failures are interface artifacts and 'little evidence for emergent reasoning' is falsified; if scores stay near zero, the deficit is in reasoning and planning.

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Extended reading notes

Core claim

On the paper's own terms, the central finding is that advanced prompting does not make LLMs reliable dynamic reasoners. In the Tower of Hanoi, for example, the largest model can state the optimal seven-move sequence when prompted, yet in the game it averages about 30 moves without completing the puzzle and repeatedly makes illegal moves; in the unmodified three-disk setting, a uniform random policy can beat every prompted agent on goal completion and move legality. In Messenger, models confuse object identities and fail to navigate even when they understand the synonym task, and the Reflection + Planner module can collapse a strong model's score while helping smaller ones. The authors attribute these patterns to excessive context diluting signal, overthinking, sparse reward signals, and deficits in stateful planning and spatial coordination. They conclude that there is little evidence for emergent reasoning, framing the results as a knowing-doing gap and a language-embodiment gap that prompt engineering alone does not close.

Load-bearing premise

The load-bearing premise is that the SmartPlay setup—four games, a 10-step Messenger horizon, a 20-episode cap, three runs per cell, and text-only state descriptions—measures LLM reasoning neutrally rather than penalizing the interface; if a cleaner state representation, longer episode budget, or more runs lets prompted agents solve the tasks, the 'little evidence for emergent reasoning' conclusion weakens.

Editorial extensions

If this is right

  • Prompt engineering alone cannot be treated as a reliable way to turn LLMs into autonomous planning agents; dynamic deployments that require spatial coordination need additional grounding, memory, or perception.
  • Smaller models are the main beneficiaries of advanced prompting, so comparisons of models and strategies should be reported per model size and task rather than as a single aggregate rank.
  • Because the same strategy's best and worst runs differ widely, reporting only median scores or accuracies hides instability; future benchmarks should report per-run distributions and failure modes.
  • Dense, task-aligned reward shaping can serve as a simpler substitute for elaborate prompt engineering in complex tasks, improving both two-disk Hanoi completion and Messenger message pickup.
  • Static reasoning benchmarks such as math word problems will overstate general reasoning ability, so dynamic, interactive tasks should be included whenever claims about reasoning are made.

Reading between the lines

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

  • The authors do not test this, but their random-policy result in the unmodified three-disk Tower of Hanoi suggests part of the deficit is caused by the text state representation: a structured encoding of disk stacks might recover much of the lost performance and would re-scope the 'no emergent reasoning' claim from reasoning failure to interface failure.
  • A concrete extension of the language-embodiment gap would be to add a rendered grid or visual map to the prompt; if Messenger navigation improves sharply, the bottleneck is grounding rather than planning.
  • With only three runs per condition, the large min-max spreads may be sampling noise rather than stable properties of a strategy; a many-seed replication would determine whether Reflection + Planner's RPS gains and Bandit collapses are reproducible or stochastic.
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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

4 major / 4 minor

Summary. The paper presents an empirical evaluation of four prompting strategies (Base, Reflection, Reflection+Oracle, Reflection+Planner) applied to four SmartPlay environments (Bandit, Rock-Paper-Scissors, Tower of Hanoi, Messenger) using four open-source LLMs (Llama 3-8B, Mistral-NeMo-12B, DeepSeek-R1-14B, Llama 3.3-70B). The authors report that larger models generally score higher, that prompting can help smaller models close the gap but with high variance, that overly long prompts can hurt small models, and they conclude that there is 'little evidence for emergent reasoning,' alongside a knowing-doing gap and a language-embodiment gap in dynamic tasks. Additional experiments in Section IV-C investigate reward shaping, synonym removal, and valid-action hints, and include random-action baselines.

Significance. The study is a serious, broad empirical comparison of prompting strategies on a dynamic benchmark, with a public code repository, open models, and concrete qualitative examples of failures. The knowing-doing gap is concretely demonstrated (models that can recite the optimal Hanoi sequence fail to execute it), and the inclusion of random baselines and interface modifications in Section IV-C is informative. If the identified measurement concerns are addressed, the findings would be a valuable contribution to the debate on emergent reasoning and on the limitations of static benchmarks for evaluating LLM agents.

major comments (4)
  1. [§IV-C-1, Table II] The random-action baseline in the 3-disk Hanoi 'No adjustments' condition achieves higher goal completion (2.0% vs 0.0%), higher average disks placed (0.9 vs ≤0.7), and fewer invalid moves (69.7% vs ≥74.1%) than every LLM-based strategy. Because the state is rendered as text-only rod stacks and the prompts are long, this result is equally consistent with an interface-representation failure as with a reasoning failure; the conclusion in the same section that this reveals a 'fundamental inability to internalise the 3-disk Tower-of-Hanoi rules' is therefore not justified without an ablation that varies the state encoding or prompt length. This directly affects the paper's central negative claim.
  2. [§IV-B, Table I] All headline results rest on three runs per cell, with no confidence intervals, significance tests, or effect sizes. For example, the claim that prompting 'can close the gap' between Llama 3-8B and Llama 3.3-70B on RPS uses Llama 3-8B Reflection+Oracle median 26.00 with range [12.00–36.00] versus the 70B baseline 22.20 [21.55–25.05]; the ranges overlap substantially, so the conclusion is not statistically supported. The paper should present per-cell variance, confidence intervals, and significance tests (or equivalent), and temper claims that depend on point estimates.
  3. [Table I footnote and §V] The human baselines are obtained under different conditions (4-step horizon, 100 episodes for Messenger, and original SmartPlay settings) while this work uses a 10-step horizon and 20 episodes, and the footnote states they are 'not directly comparable.' Despite this, the abstract and conclusion claim 'compared to human performance, little evidence of true emergent reasoning.' The non-comparability should be stated in the abstract and conclusion, or the human-comparison framing should be removed from the central claims.
  4. [§IV-B-2, Eq. (1)] The weighted aggregation in Eq. (1) uses SmartPlay's challenge weights and min–max scaling of raw scores, but the paper does not report the weights used for each game or how the scaling interacts with the differing score ranges across games (e.g., Hanoi 0–2 vs Messenger -1 to 1). Since Figure 2 and the associated text use this aggregation to claim that 'all models consistently improve on Instruction Following,' the aggregation should be validated with sensitivity analysis or the raw scores and weights should be made available.
minor comments (4)
  1. [References [2] and [11]] Reference [11] is cited for Reflexion in Section II and Section III-A-1, but [11] is the Agent-Pro paper; the correct reference for Reflexion is [2] (Shinn et al.).
  2. [Table II] In the 2-disk rows, the method column lists 'Planner' but the other rows use 'Reflection + Planner'; the label should be harmonized for consistency with the text.
  3. [§IV-C-1] The sentence 'except the Planner' should read 'except Reflection + Planner' to match the table and the rest of the section.
  4. [Abstract and §V] The claim that 'excessive reasoning harms performance' and the 'signal-to-noise ratio' interpretation would be more convincing if the paper reported prompt lengths or token counts per condition, since no quantitative measure of prompt length is given.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the empirical claims rest on external SmartPlay measurements and environment rewards, with no fitted parameter or self-citation chain doing the work.

full rationale

The paper's central claims ('little evidence for emergent reasoning', 'knowing-doing gap', 'language-embodiment gap') are derived from measured agent scores in the SmartPlay benchmark, an external test suite with environment-defined rewards and goals. The prompting strategies (Reflection, Oracle, Planner) are implemented and scored according to these environment rewards; the Oracle's heuristics are generated by an evolutionary loop and accepted or rejected based on actual episode reward, not fitted to the conclusion. No equation in the paper defines a target quantity in terms of an input quantity; Eq. (1) merely re-weights observed score differences by SmartPlay's stated challenge-dimension weights, which is a reporting aggregation, not a derivation of the findings. The citations to prior work by overlapping authors (SmartPlay [8], LLaMEA [16]) are contextual and do not carry the load of the main argument: SmartPlay is used as the benchmark environment (an external artifact), and LLaMEA is mentioned only as related work. The explicitly admitted non-comparability of the human baseline (Table I footnote) weakens the force of the human-relative framing, and the random-action baseline in Table II raises a validity concern about whether the text-only interface measures reasoning or interface comprehension; however, both are threats to the correctness or generality of the conclusions, not circularities. The derivation chain is self-contained against the environment: no fitted parameter is renamed as a prediction, no uniqueness theorem from the authors is invoked, and no result is assumed through its own definition.

Assumptions & free parameters 6 free parameters · 4 assumptions · 0 invented entities

The paper's central claims rest on environment and scoring design choices rather than on fitted model parameters. The reward-shaping constants and episode/horizon caps are hand-set and materially affect the additional experiments. The main conclusions also assume that SmartPlay's text-based games measure the intended reasoning dimensions and that the four models represent the broader LLM class. No entirely new physical or conceptual entities are introduced; Reflection, Oracle, and Planner are modular prompt components, not invented entities.

free parameters (6)
  • Messenger horizon = 10 steps
    Authors lengthened the SmartPlay default of 4 steps because the goal was often unreachable; this changes scores and weakens direct comparability with the human baseline.
  • Training episode cap = 20 episodes
    Capped after observing no further improvements; the cap is a design choice that constrains measured performance.
  • Planner rollout depth = 3 steps
    Lookahead horizon is chosen in the Planner module; deeper or shallower lookahead would likely change recommendations.
  • Hanoi reward shaping values = -2 invalid, +1 valid, +100 goal
    Hand-set dense reward constants in the additional Hanoi experiments; they affect goal rates and invalid-move counts.
  • Messenger reward shaping values = pickup +10, delivery +50, distance +0.50 per step closer
    Hand-set dense reward constants in the additional Messenger experiments; they improve pickup rates but not goal completion.
  • SmartPlay challenge dimension weights = 0.33, 0.67, 1.0
    Weights w_g,d from SmartPlay are used in Eq. (1) to aggregate per-challenge score differences; the aggregation depends on these weights.
assumptions (4)
  • domain assumption SmartPlay environments and their nine challenge dimensions are valid proxies for dynamic reasoning.
    The broad claims about LLM reasoning deficits are inferred from scores in four games, assuming these tasks capture the intended abilities.
  • domain assumption The four open-source models are representative of LLMs in general.
    Conclusions about 'LLMs' are drawn from Llama 3, Mistral-Nemo, DeepSeek-R1, and Llama 3.3-70B; other architectures or sizes may behave differently.
  • domain assumption Human baseline scores from SmartPlay remain a meaningful reference despite different episode lengths.
    The footnote says the human baseline is not directly comparable because it used 4-step, 100-episode settings, yet the paper still uses it as a reference point.
  • domain assumption Min-max scaling and weighted aggregation in Eq. (1) preserve meaningful comparisons across games.
    The normalized performance differences in Fig. 2 assume that min-max scaling across games makes diverse score scales comparable.

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Cite this review

Pith. "Pith review of Reasoning Capabilities of Large Language Models on Dynamic Tasks." pith.science (2026). https://pith.science/paper/6JH2K3KU

@misc{pith2026250510543,
  author       = {Pith},
  title        = {Pith review of: Reasoning Capabilities of Large Language Models on Dynamic Tasks},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/6JH2K3KU}},
  note         = {Machine review of arXiv:2505.10543}
}
read the original abstract

Large language models excel on static benchmarks, but their ability as self-learning agents in dynamic environments remains unclear. We evaluate three prompting strategies: self-reflection, heuristic mutation, and planning across dynamic tasks with open-source models. We find that larger models generally outperform smaller ones, but that strategic prompting can close this performance gap. Second, an overly long prompt can negatively impact smaller models on basic reactive tasks, while larger models show more robust behaviour. Third, advanced prompting techniques primarily benefit smaller models on complex games, but offer less improvement for already high-performing large language models. Yet, we find that advanced reasoning methods yield highly variable outcomes: while capable of significantly improving performance when reasoning and decision-making align, they also introduce instability and can lead to big performance drops. Compared to human performance, our findings reveal little evidence of true emergent reasoning. Instead, large language model performance exhibits persistent limitations in areas like planning and spatial coordination, suggesting that large language models still suffer fundamental shortcomings that may not be fully overcome through self-reflective prompting alone. Reasoning is a multi-faceted task, and while methods like Chain-of-thought improve multi-step reasoning on math word problems, our findings using dynamic benchmarks highlight important shortcomings in general reasoning capabilities, indicating a need to move beyond static benchmarks to capture the complexity of reasoning.

Figures

Figures reproduced from arXiv: 2505.10543 by the authors.

Figure 1
Figure 1. Top: Overview of the four prompting strategies, with each strategy’s added text shown in bold within the prompt. At every timestep in the environment, the agent chooses its next move solely from the text in its prompt: game manual, history as (state, action, reward, next state) tuples from episode start to the current step, current observation, and the list of legal actions. The example prompts in the figure omit th… view at source ↗
Figure 2
Figure 2. Normalized performance difference compared to baseline for various models and prompting strategies across nine challenges. L [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗

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Reviewed August 15, 2026 · model on record in the stance chip above.