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Why Reasoning Fails to Plan: A Planning-Centric Analysis of Long-Horizon Decision Making in LLM Agents

Zehong Wang, Fang Wu, Hongru Wang, Xiangru Tang, Bolian Li, Zhenfei Yin, Yijun Ma, Yiyang Li, Weixiang Sun, Xiusi Chen, Yanfang Ye

arXiv:2601.22311v1cs.AIcs.CLcs.LG

TL;DR

The paper asks why strong step-by-step reasoning fails to sustain coherent long-horizon decisions, focusing on the gap between local scoring and delayed consequences. It analyzes agents in deterministic, structured environments and introduces FLARE, whose explicit future-aware mechanisms consistently improve long-horizon decision making. The study concludes that reasoning and planning are structurally distinct.

  • Problem

    Step-wise reasoning can select locally appealing actions that create early commitments and fail on long-horizon tasks despite explicit state and transition information.

  • Method

    The paper formalizes reasoning as step-wise greedy decision making and introduces FLARE with explicit lookahead, backward value propagation, and limited commitment.

  • Results

    Across tasks, agent frameworks, and model backbones, FLARE consistently improves long-horizon decision making and mitigates failures identified in reasoning-based policies.

  • Takeaways & Limitations

    The findings establish a planning-centric distinction between reasoning and planning: coherent long-horizon behavior requires future-aware evaluation and revisable commitments.

  • Takeaways & Limitations

    The empirical study is limited to deterministic transitions and structured action spaces, assumes evaluative signals at planning time, and does not address stochasticity or partial observability.

Abstract

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Large language model (LLM)-based agents exhibit strong step-by-step reasoning capabilities over short horizons, yet often fail to sustain coherent behavior over long planning horizons. We argue that this failure reflects a fundamental mismatch: step-wise reasoning induces a form of step-wise greedy policy that is adequate for short horizons but fails in long-horizon planning, where early actions must account for delayed consequences. From this planning-centric perspective, we study LLM-based agents in deterministic, fully structured environments with explicit state transitions and evaluation signals. Our analysis reveals a core failure mode of reasoning-based policies: locally optimal choices induced by step-wise scoring lead to early myopic commitments that are systematically amplified over time and difficult to recover from. We introduce FLARE (Future-aware Lookahead with Reward Estimation) as a minimal instantiation of future-aware planning to enforce explicit lookahead, value propagation, and limited commitment in a single model, allowing downstream outcomes to influence early decisions. Across multiple benchmarks, agent frameworks, and LLM backbones, FLARE consistently improves task performance and planning-level behavior, frequently allowing LLaMA-8B with FLARE to outperform GPT-4o with standard step-by-step reasoning. These results establish a clear distinction between reasoning and planning.

1. Introduction

The paper frames long-horizon failures as a mismatch between step-wise reasoning and planning: locally scored actions create early commitments whose consequences compound over time. It formalizes this diagnosis and proposes FLARE, which adds explicit future-aware planning mechanisms.

  • Diagnosis: Long-horizon failures persist even in deterministic environments with explicit states, transitions, and evaluation signals.The controlled setting isolates agent-side decision processes from environmental uncertainty.
  • Planning principles: The paper identifies explicit lookahead, backward value propagation, and limited commitment as minimal mechanisms for coherent long-horizon planning.Existing reasoning paradigms omit at least one of these mechanisms.
  • Proposed approach: FLARE combines explicit lookahead, value propagation, and limited commitment, consistently improving performance across benchmarks and agent frameworks.The framework evaluates future trajectories, propagates outcomes backward, and commits only to the next action under receding-horizon replanning.
  • Motivation: LLM reasoning is formalized as a step-wise greedy policy based on local evaluation rather than long-term consequences.This formulation enables direct analysis of why locally strong reasoning may fail to produce coherent long-horizon behavior.
  • Diagnosis: Step-wise policies make early myopic commitments that are amplified over time and difficult to revise, producing brittle and inconsistent trajectories.Local improvements from reflection, bootstrapping, or self-improvement remain within step-wise decision making without explicit outcome evaluation.
  • Theoretical distinction: Theoretical analysis shows that step-wise greedy reasoning can be arbitrarily suboptimal, while even one-step explicit lookahead can strictly improve decision capability.Increasing beam-search width does not resolve the limitation.

2. Problem Definition

The paper defines long-horizon decision making as action selection in a deterministic state-transition system with explicit evaluative feedback. It contrasts step-wise greedy policies and beam search with lookahead, asking whether local reasoning can support coherent planning.

  • Environment: The controlled setting exposes transition dynamics and evaluative feedback at planning time, isolating the agent’s decision mechanism from environmental uncertainty.This supports comparison of reasoning-based and planning-based policies under explicit conditions.
  • Environment: The environment is modeled as a deterministic state-transition system with discrete states, actions, transition dynamics, and evaluative signals.A trajectory is generated by applying the transition function after each selected action.
  • Agent policies: A reasoning-based policy selects actions from local evaluation at the current state, while beam search expands this approach to the top-k actions.Lookahead instead uses limited trajectory rollouts to estimate future return.
  • Problem statement: The central question is whether step-wise greedy reasoning can support coherent long-horizon decisions when states, transitions, and evaluative feedback are fully available.A negative result would establish a structural distinction between reasoning-based and planning-based decision making.

3. Failure Modes of Long-Horizon Planning

Long-horizon failures arise from step-wise policies that make early myopic commitments, amplify deviations, and rarely recover; explicit future-aware evaluation avoids these structural limits.

  • Empirical failure pattern: Reasoning-based policies introduce early myopic deviations that become amplified over time, producing irreversible trajectory collapse even in deterministic, fully structured environments.The analysis isolates agent-side decision mechanisms rather than environmental uncertainty or model capacity.
  • Empirical failure pattern: Accuracy decreases rapidly with planning horizon for single-step policies, while beam search offers only limited and transient improvement and lookahead degrades more slowly.This pattern indicates that longer trajectories alone do not explain failure; step-wise greedy decision-making is implicated.
  • Formal limits: Step-wise greedy reasoning can be arbitrarily suboptimal because locally preferred actions may force zero-return outcomes despite available trajectories with unbounded return.The formal result explains why locally rational choices can produce catastrophic long-horizon behavior.
  • Formal limits: Beam search preserves step-wise scoring and can irrevocably prune the optimal trajectory after the first decision for any fixed beam width.Increasing width delays commitment but cannot guarantee preservation of globally optimal paths.
  • Planning implication: One-step lookahead can achieve optimal return in environments where every policy based solely on step-wise surrogate scores achieves zero return.The result formally distinguishes planning from stronger reasoning or wider search by making downstream consequences explicit.
  • Planning implication: Future-aware planning requires explicit future evaluation, backward value propagation, and limited commitment, which together prevent irreversible local-score commitments.These mechanisms motivate FLARE as a minimal planning-based policy.

4. FLARE for Long-Horizon Planning

FLARE replaces local step-wise scoring with trajectory simulation, backward value propagation, and receding-horizon commitment, using downstream outcomes to guide early actions.

  • Receding-horizon commitment: FLARE commits only to the next action and replans after each transition, reducing brittleness from noisy evaluation and long-term commitment.The receding-horizon design limits the impact of early estimation errors.
  • Explicit lookahead: FLARE evaluates candidate actions by simulating the trajectories they induce and using trajectory-level outcomes rather than step-wise signals.This explicit lookahead is the framework’s core future-aware evaluation mechanism.
  • Explicit lookahead: FLARE allocates simulation effort using tree statistics in which Q(s_t, a) aggregates returns and N(s_t, a) records visit counts.The selection rule prioritizes branches with favorable long-term outcomes.
  • Explicit lookahead: Action pruning restricts expansion to a bounded feasible candidate set, limiting which futures are evaluated without directly changing action values or preferences.The LLM proposes feasible actions for expansion in the experiments.
  • Value propagation: FLARE propagates trajectory-level outcomes backward to update earlier action values, preventing irreversible commitment based only on local evidence.Backward propagation is necessary for simulated futures to revise early decisions.
  • Empirical outcome: FLARE achieves the strongest overall performance across CWQ, WebQSP, and GrailQA, with larger gains on more challenging datasets.The comparison uses identical base frameworks and LLM backbones across planning strategies.
  • Value propagation: Trajectory returns are evaluated by comparing sampled candidates with LLM-based relative preferences, rather than directly summing step-wise rewards.This makes action assessment depend on estimated downstream consequences.
  • Value propagation: FLARE uses bounded trajectory memory to reuse evaluations for sufficiently similar trajectories and amortize evaluation cost.Memory size and similarity-based reuse are explicitly bounded.

5. Experiments

Experiments compare FLARE with reasoning, beam search, lookahead, and prior planning methods across structured KGQA and tool-use environments. FLARE consistently improves task performance, planning stability, and computational scalability.

  • Effectiveness: FLARE consistently outperforms single-step reasoning, beam search, and lookahead across KGQA datasets and agent frameworks.The comparisons cover ToG and PoG, with FLARE also matching or surpassing specialized MCTS and KGQA methods.
  • Planning behavior: FLARE improves planning behavior by reducing early trap selection, delaying first errors, and increasing recovery after mistakes.Table 3 evaluates first-step trap selection, first-error position, recovery probability, and correct decision prefixes.
  • Failure analysis: FLARE shifts residual failures away from premature commitment by evaluating future trajectories and propagating long-term outcomes backward.The remaining limitations are characterized as exploration and termination limits rather than primarily myopic commitment.
  • Planning dynamics: FLARE converts additional token budget into sustained performance gains, whereas beam search and lookahead saturate and single-step reasoning gains little.Figure 3 compares performance against total token budget on CWQ and WebQSP.
  • Efficiency analysis: Action pruning and trajectory memory provide complementary efficiency benefits under fixed computational budgets.Removing pruning requires nearly three times more tokens to recover accuracy, while removing memory raises token use and harms matched-budget performance.
  • Tool-use generalization: In ALFWorld, FLARE achieves higher success rates and later first errors than reasoning-based strategies across ReAct and Reflexion.This tests robustness beyond knowledge-graph traversal in long-horizon tool-use tasks.

6. Related Work

Related work improves reasoning, search, and offline training for LLM agents, but these approaches commonly select actions using local heuristics. This paper instead analyzes action-sequence coherence through a planning-centric distinction between reasoning and planning.

  • Reasoning in LLM-based agents: Chain-of-thought, self-consistency, tree and graph reasoning, and agent frameworks improve or structure multi-step reasoning but commonly retain step-wise local action selection.The related methods include CoT, self-consistency, Tree-of-Thoughts, Graph-of-Thoughts, ReAct, Reflexion, and AutoGen.
  • Planning in LLM-based agents: Explicit planning research adds beam search, plan revision, structured-state search, or MCTS-style planning to LLM agents.Examples include Think-on-Graph, Plan-on-Graph, and methods integrating LLMs with MCTS.
  • Offline vs. online planning: Offline reinforcement-learning approaches amortize long-horizon planning into model parameters, whereas online planners evaluate and revise decisions during inference.The distinction concerns when planning occurs and whether earlier actions are explicitly revised.
  • Position: This paper contributes a planning-centric and theoretical account that models LLM reasoning as a step-wise greedy policy and analyzes trajectories, commitments, and recoverability.The analysis targets the decision process over action sequences rather than the logical validity or faithfulness of intermediate reasoning.

7. Conclusion

The paper argues that step-by-step reasoning is not equivalent to planning because local evaluation produces irreversible early commitments in long-horizon tasks. FLARE addresses this gap with future-aware mechanisms and improves decision making across tasks, frameworks, and model backbones.

  • Conclusion: Step-by-step reasoning can make irreversible early commitments even with explicit states and deterministic transitions.The conclusion identifies local evaluation as the source of these long-horizon failures.
  • Conclusion: FLARE combines explicit lookahead, backward value propagation, and limited commitment so downstream outcomes can influence early decisions.These mechanisms are presented as the planning response to the identified conceptual gap.
  • Conclusion: Experiments across tasks, agent frameworks, and model backbones demonstrate consistent improvements in long-horizon decision making.The conclusion reports the cross-setting result without restricting it to a single benchmark or model.

Impact Statement

The paper presents planning as a route to more reliable and interpretable long-horizon agents, while noting risks from stronger sustained behavior and limits of its controlled evaluation setting. It identifies stochasticity, partial observability, imperfect signals, and open-ended environments as important boundaries for extension.

  • Potential benefits: Explicit lookahead and value propagation reduce irreversible commitments and make decision processes more transparent for debugging, auditing, and analysis.The paper also describes FLARE as modular and integrable into existing agent frameworks.
  • Risks and misuse considerations: Stronger long-horizon planning may amplify unintended or misaligned behavior when evaluation signals imperfectly represent human intent.The paper highlights reward hacking, harmful long-term objectives, unsafe tool chaining, and evaluator blind spots.
  • Limitations and scope: The empirical study is limited to deterministic transitions, structured action spaces, and evaluative signals available during planning.It does not address stochastic dynamics, partial observability, non-stationarity, or learning environment models from interaction.
  • Limitations and scope: KGQA isolates decision mechanisms but does not establish performance in open-ended environments with less explicit states and feedback.This is the nearest practical boundary stated for generalizing the empirical findings.
  • Future directions: Future extensions may combine lookahead with belief-state inference, uncertainty-aware evaluation, learned world models, or learning-based value estimation.These directions target stochastic and partially observed settings while preserving future-aware decision making.

B. Suboptimality of Truncated Lookahead

Truncated lookahead can remain systematically suboptimal when decisive rewards lie beyond its search horizon. In the worst case, its performance gap grows with the episode horizon relative to lookahead depth.

  • Motivation: Finite computational budgets make practical lookahead depth k smaller than episode horizon H, motivating analysis of truncated policies.The analysis considers rewards delayed beyond the lookahead horizon.
  • Proposition B.1: In the worst case, truncated k-lookahead has an additive suboptimality gap that grows linearly with H/(k + 1).This occurs when critical rewards are systematically delayed beyond depth k.
  • Construction: A one-step greedy policy can choose a locally higher-scoring action that yields return 0, while an alternative action yields arbitrary return M.The construction makes the suboptimality gap arbitrarily large because M is arbitrary.
  • Construction: Beam search can prune the optimal prefix when B + 1 decoy prefixes have higher step-wise scores, after which the optimal trajectory cannot be recovered.The resulting final return is zero while the optimal return M can be arbitrarily large.

C.3. Proof of Proposition 3.3

The proof constructs a deterministic delayed-reward setting where step-wise policies fail but one-step lookahead succeeds. FLARE extends this future-aware principle through trajectory simulation, value propagation, and receding-horizon execution.

  • Proof of Proposition 3.3: A step-wise policy and finite-width beam search select the locally preferred action and receive total return 0, despite an alternative yielding M.The separation applies because both methods rank actions using local surrogate scores.
  • Proof of Proposition 3.3: With k = 1 lookahead, simulating candidate actions reveals the delayed reward, so the policy selects b and achieves optimal return M.The separation between step-wise decision-making and lookahead planning is strict because M can be arbitrarily large.
  • FLARE: FLARE performs explicit lookahead, evaluates future trajectories, propagates trajectory-level outcomes backward, and commits only to the next action before replanning.Its search uses candidate expansion, simulation, trajectory evaluation, and value updates.
  • FLARE: FLARE’s selection allocates simulation effort across futures without directly deciding executable actions, using future trajectory outcomes rather than step-wise rewards.The UCB-style rule balances exploration and exploitation within the search tree.
  • FLARE: Action pruning restricts candidate futures for computational efficiency without assigning action values or introducing additional decision signals.The proposal function defines the scope of explicit lookahead rather than determining preferences.

D.4. Time Complexity Analysis

FLARE has a bounded per-step computational budget under fixed simulation, horizon, lookahead, and memory parameters. Compared with step-wise methods, it trades additional computation for explicit future evaluation and value propagation.

  • Per-simulation cost: Each simulation constructs a trajectory of length at most H, while expansion may terminate the inner loop earlier at an unexpanded state.The per-simulation cost is upper-bounded using H.
  • Per-simulation cost: Tree traversal costs O(Hk + HCT), where k bounds actions considered per state and CT is the transition-call cost.Selection is performed over at most k actions at each step.
  • Per-simulation cost: Trajectory-memory retrieval can scan up to M stored trajectories, while direct evaluation adds cost O(Cr) when reuse fails.The memory size is bounded by M.
  • Total complexity: Under fixed S, H, k, and M, FLARE has a bounded per-step compute budget, with dominant terms scaling linearly in S and H.The total complexity includes traversal, expansion, trajectory evaluation, memory retrieval, and backpropagation.
  • Comparison: Compared with step-wise methods, FLARE trades additional computation O(SH) for explicit lookahead and value propagation.Step-wise methods invoke the proposal function once per decision and select greedily or via beam search.

E.1. Benchmarks

The evaluation uses structured KGQA and tool-use environments to compare decision paradigms that incorporate future outcomes differently. The benchmarks expose long-horizon consequences, irreversible actions, and differences between local scoring and planning.

  • Knowledge Graph Question Answering: KGQA evaluation covers CWQ, WebQSP, and GrailQA, which require multi-hop traversal over Freebase with differing path-length distributions.WebQSP is relatively shallow, while CWQ and GrailQA include more compositional or longer-horizon queries.
  • Tool Usage: ALFWorld evaluates goal-oriented tool use through discrete text actions, often requiring long action sequences and structured exploration.Tasks use deterministic environments and predefined terminal conditions.
  • Planning Strategies: Single-step greedy policies optimize immediate outcomes using local surrogate scores and do not account for downstream consequences.They instantiate the step-wise greedy decision mechanism.
  • Planning Strategies: Beam search retains the top B = 8 prefixes by accumulated step-wise scores but still lacks explicit future evaluation.Its wider search can delay premature commitment without removing the underlying local-scoring failure mode.
  • Planning Strategies: Shallow lookahead uses fixed-depth forward simulation, with k = 2 by default, but lacks backward value propagation from deeper trajectory outcomes.Early decisions therefore remain governed by truncated estimates.
  • Knowledge Graph Question Answering: The KGQA experiments use LLaMA-3.1-8B, LLaMA-3.1-70B, GPT-4o-mini, and GPT-4, reporting Hits@1 as the primary metric.Think-on-Graph and Plan-on-Graph provide standardized agent interfaces for traversal and reasoning.
  • Planning Behavior: Across datasets, FLARE attains the lowest trap-selection rate, latest first-error position, and highest recovery probability.CWQ and GrailQA show more severe long-horizon behavior across methods than WebQSP.

F.2. Comprehensive Ablation Study

The ablation study shows that action pruning and trajectory memory improve FLARE’s performance–cost trade-off through complementary efficiency effects, while failure analysis shows explicit planning reduces early myopic deviations but leaves later procedural failures.

  • Ablation Study: Action pruning improves exploration efficiency, raising Hits@1 under comparable budgets and requiring substantially more tokens to recover accuracy when removed.Removing pruning causes unconstrained expansion to spend budget on low-quality branches; recovery requires more than doubling tokens on CWQ and tripling them on WebQSP.
  • Ablation Study: Trajectory memory improves evaluation efficiency and value accumulation, with memory-free variants using more tokens and performing worse under matched budgets.Caching previously evaluated trajectories reduces redundant evaluation, stabilizes trajectory-level estimates, and reduces noise in value propagation.
  • Ablation Study: Action pruning and trajectory memory jointly produce a superior efficiency–performance frontier without changing FLARE’s planning objective.Pruning reallocates computation toward better branches, while memory converts a fixed budget into more reliable value estimates and higher final accuracy.
  • Failure Pattern Analysis: Failure analysis categorizes unsuccessful trajectories into myopic deviation, dead-end, loop, and premature termination using 50 randomly sampled failures per method and dataset.The analysis covers CWQ, WebQSP, and GrailQA.
  • Failure Pattern Analysis: FLARE reduces myopic deviations to the lowest level among methods, while remaining failures shift toward later looping and premature termination.Dead-end failures remain present but form a smaller fraction across planning-based methods; exploration efficiency and termination control are not directly targeted by the current mechanism.
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