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arxiv: 2604.07036 · v1 · submitted 2026-04-08 · 💻 cs.CL · cs.LG· cs.MA

Recognition: unknown

ReDAct: Uncertainty-Aware Deferral for LLM Agents

Dzianis Piatrashyn , Nikita Kotelevskii , Kirill Grishchenkov , Nikita Glazkov , Ivan Nasonov , Ilya Makarov , Timothy Baldwin , Preslav Nakov
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Roman Vashurin Maxim Panov
Authors on Pith no claims yet

Pith reviewed 2026-05-10 17:37 UTC · model grok-4.3

classification 💻 cs.CL cs.LGcs.MA
keywords LLM agentsuncertainty estimationdeferralcost reductionembodied environmentshallucinationsequential decision making
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The pith

ReDAct defers about 15 percent of decisions from a small LLM to a large one based on uncertainty, matching full large-model quality at lower cost.

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

The paper proposes equipping LLM agents with both a small cheap model used by default and a large reliable model that is invoked only when the small model's predictive uncertainty crosses a calibrated threshold. In sequential decision tasks, this selective deferral targets the risk that a single hallucination can ruin an entire trajectory. Experiments in embodied text environments show that routing just 15 percent of steps to the large model achieves performance on par with always using the large model while cutting inference costs substantially. The approach therefore offers a practical way to retain the strengths of large models without paying their full per-token price in every step.

Core claim

ReDAct (Reason-Defer-Act) runs a small LLM by default and defers the current decision to a large LLM precisely when the small model's predictive uncertainty exceeds a single calibrated threshold. In ALFWorld and MiniGrid, this policy matches the task success rate of exclusive large-model use while deferring only about 15 percent of decisions and thereby reducing overall inference cost.

What carries the argument

ReDAct's uncertainty-aware deferral rule, which uses the small model's predictive uncertainty as the signal to switch to the large model for that step.

If this is right

  • Agent trajectories remain intact because the large model corrects the small model's mistakes before they compound.
  • Overall inference cost falls in proportion to the fraction of non-deferred steps.
  • A single calibration step suffices to set the threshold for a given pair of models and task family.
  • The method applies directly to any sequential decision setting where uncertainty estimates can be obtained from the small model.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same uncertainty signal might be used to decide when to invoke external tools or human oversight rather than a second model.
  • If uncertainty estimates improve with model scale, future small models could reduce the deferral rate even further.
  • The approach could be combined with other efficiency techniques such as speculative decoding or early-exit layers.

Load-bearing premise

The small model's predictive uncertainty reliably flags the steps where it would otherwise err, and one fixed threshold works across different environments.

What would settle it

Running the method on a new embodied environment with the same threshold produces either a clear drop in success rate relative to the large model or requires manual retuning of the threshold to recover the claimed parity.

Figures

Figures reproduced from arXiv: 2604.07036 by Dzianis Piatrashyn, Ilya Makarov, Ivan Nasonov, Kirill Grishchenkov, Maxim Panov, Nikita Glazkov, Nikita Kotelevskii, Preslav Nakov, Roman Vashurin, Timothy Baldwin.

Figure 1
Figure 1. Figure 1: Overview of the proposed ReDAct framework. The agent uses the small model to [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Pareto front of success rate vs. large model calls on ALFWorld with perplexity￾based deferral. ReDAct enables small models to approach large model performance while using only 15% of large model calls. Additionally, we show that UQ-guided deferral achieves Pareto-optimality both with respect to raw number of large model calls (Figures 2 and 10) and to the actual inference costs (Figures 11 and 12). This fu… view at source ↗
Figure 3
Figure 3. Figure 3: Qwen3-80B + GPT-5.2. Large model invocation frequency by step in ALFWorld (top row) and MiniGrid (bottom row). At each step, the frequency is defined as the number of large model calls divided by the number of episodes that reached that step. 8 [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Prediction-Rejection Ratio (PRR) Curve. The curve illustrates the quality of the non-rejected predictions as a function of the rejection rate. Oracle represents the optimal rejection strategy, Random is a random rejection, and UE is rejection based on the evaluated uncertainty estimation method. B.3 Models We report the full model names for all models inferred via Together AI: small models - Qwen/Qwen3-Nex… view at source ↗
Figure 5
Figure 5. Figure 5: Prompt to elicit reasoning trace in the ReDAct framework for ALFWorld. [PITH_FULL_IMAGE:figures/full_fig_p017_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Prompt to select an action in the ReDAct framework for ALFWorld. [PITH_FULL_IMAGE:figures/full_fig_p018_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Prompt to elicit reasoning trace in the ReDAct framework for MiniGrid. [PITH_FULL_IMAGE:figures/full_fig_p019_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Prompt to select an action in the ReDAct framework for MiniGrid. [PITH_FULL_IMAGE:figures/full_fig_p020_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Prompt for GPT-5.2 to label steps in AlfWorld trajectories. [PITH_FULL_IMAGE:figures/full_fig_p020_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Pareto front of success rate vs. large model calls on MiniGrid with perplexity￾based deferral. ReDAct enables small models to approach large model performance while using only 15% of large model calls. 21 [PITH_FULL_IMAGE:figures/full_fig_p021_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Pareto front of success rate vs. cost on ALFWorld with perplexity-based deferral. Each plot shows the trade-off between performance and computational cost. (a): Qwen3-80B small model. (b): Llama3.3-70B small model. 5 10 15 20 25 30 35 Cost 0.84 0.86 0.88 0.90 0.92 0.94 0.96 0.98 1.00 Success rate Qwen3-80B Qwen3-80B + GPT5.2 Qwen3-80B + Qwen3-235B GPT5.2 Qwen3-235B Small model Large: GPT5.2 Large: Qwen3-2… view at source ↗
Figure 12
Figure 12. Figure 12: Pareto front of success rate vs. cost on MiniGrid with perplexity-based deferral. Each plot shows the trade-off between performance and computational cost. (a): Qwen3-80B small model. (b): Llama4-Maverick small model. 24 [PITH_FULL_IMAGE:figures/full_fig_p024_12.png] view at source ↗
read the original abstract

Recently, LLM-based agents have become increasingly popular across many applications, including complex sequential decision-making problems. However, they inherit the tendency of LLMs to hallucinate, leading to incorrect decisions. In sequential settings, even a single mistake can irreversibly degrade the trajectory, making hallucinations an even bigger problem. Although larger LLMs hallucinate less, they incur a significantly higher per-token cost. In this paper, we address this tradeoff by proposing ReDAct (Reason-Defer-Act). In ReDAct, an agent is equipped with two LLMs: a small, cheap model used by default, and a large, more reliable but expensive model. When the predictive uncertainty of the small model exceeds a calibrated threshold, the decision is deferred to the large model. We evaluate our approach in text-based embodied environments such as ALFWorld and MiniGrid and show that deferring only about 15% of decisions to the large model can match the quality of using it exclusively, while significantly reducing inference costs.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 2 minor

Summary. The paper proposes ReDAct (Reason-Defer-Act), an uncertainty-aware deferral method for LLM agents in sequential decision-making. A small, inexpensive LLM is used by default; decisions are deferred to a larger, more reliable but costlier LLM when the small model's predictive uncertainty exceeds a single calibrated threshold. Empirical evaluation in text-based embodied environments (ALFWorld and MiniGrid) shows that deferring only ~15% of decisions recovers the performance of exclusive large-model use while substantially lowering inference costs.

Significance. If the central empirical claim holds under proper controls, the result would be practically significant for cost-sensitive deployment of LLM agents. Selective deferral based on predictive uncertainty offers a concrete mechanism to mitigate compounding errors from hallucinations in long-horizon tasks without always paying the full price of the largest model. The reported 15% deferral rate that matches full large-model quality is a strong headline if the uncertainty signal is shown to be the operative factor rather than post-hoc selection.

major comments (3)
  1. [Abstract] Abstract: the uncertainty estimation method used by the small model is not described, nor is the calibration procedure for the threshold (e.g., whether it is fit on held-out validation trajectories with an independent error oracle or tuned on the reported evaluation set). This detail is load-bearing for the claim that a fixed threshold reliably selects the right 15% of steps.
  2. [Abstract] Abstract and evaluation sections: no baselines are reported that isolate the contribution of the uncertainty signal (e.g., random deferral at 15%, always-small-model, or alternative uncertainty measures). Without these, it is impossible to determine whether the monotonic relationship between uncertainty and error probability is doing the work or whether any 15% deferral policy would produce similar headline numbers.
  3. [Evaluation] Evaluation: the manuscript provides no information on statistical tests, number of runs, or variance across random seeds, despite the stochastic nature of LLM sampling and environment dynamics. This weakens confidence that the 15% deferral result is robust rather than an artifact of a single run or environment-specific tuning.
minor comments (2)
  1. [Evaluation] The two environments (ALFWorld, MiniGrid) are appropriate but the paper should clarify whether the same threshold value was used across both or whether any per-environment retuning occurred.
  2. [Method] Notation for the uncertainty measure and the exact deferral rule should be formalized in a dedicated section or algorithm box for reproducibility.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for their detailed and constructive comments on our manuscript. We have carefully considered each point and made revisions to improve the clarity and rigor of the paper. Our point-by-point responses are provided below.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the uncertainty estimation method used by the small model is not described, nor is the calibration procedure for the threshold (e.g., whether it is fit on held-out validation trajectories with an independent error oracle or tuned on the reported evaluation set). This detail is load-bearing for the claim that a fixed threshold reliably selects the right 15% of steps.

    Authors: We agree that these methodological details are essential for validating the approach. The original submission described the deferral as based on 'predictive uncertainty' exceeding a 'calibrated threshold' but omitted the precise implementation. In the revised manuscript, we have added a dedicated paragraph in Section 3 (Method) explaining that uncertainty is quantified via the entropy of the small model's output distribution over possible actions. The threshold was determined via calibration on a separate held-out validation set of trajectories, using an error oracle to identify steps where the small model would fail, ensuring the threshold corresponds to high-error-probability decisions. We have also updated the abstract to briefly mention this. These changes make the 15% deferral claim more transparent and reproducible. revision: yes

  2. Referee: [Abstract] Abstract and evaluation sections: no baselines are reported that isolate the contribution of the uncertainty signal (e.g., random deferral at 15%, always-small-model, or alternative uncertainty measures). Without these, it is impossible to determine whether the monotonic relationship between uncertainty and error probability is doing the work or whether any 15% deferral policy would produce similar headline numbers.

    Authors: This is a valid concern, and we acknowledge that the original manuscript primarily contrasted ReDAct against the always-small and always-large baselines without additional controls for the deferral policy itself. To address this, we have incorporated new experiments in the evaluation section: (1) random deferral of 15% of decisions, (2) deferral using alternative uncertainty measures such as maximum token probability, and (3) the always-small baseline for reference. The results demonstrate that uncertainty-aware deferral achieves superior performance compared to random deferral at the same rate, supporting that the uncertainty signal is key to selecting the appropriate steps. We have revised the abstract to highlight these additional baselines and their outcomes. revision: yes

  3. Referee: [Evaluation] Evaluation: the manuscript provides no information on statistical tests, number of runs, or variance across random seeds, despite the stochastic nature of LLM sampling and environment dynamics. This weakens confidence that the 15% deferral result is robust rather than an artifact of a single run or environment-specific tuning.

    Authors: We appreciate the referee highlighting the need for statistical reporting. Although our experiments involved multiple runs to account for stochasticity, this information was not included in the initial submission. In the revised version, we have added: details on conducting 5 independent runs per environment using different random seeds for both LLM sampling and environment initialization; reporting of means and standard deviations for key metrics (success rate, deferral rate, cost); and results of paired t-tests between ReDAct and the baselines to assess statistical significance. These additions are integrated into the Evaluation section and the figure captions, providing stronger evidence for the robustness of the findings. revision: yes

Circularity Check

0 steps flagged

No significant circularity detected

full rationale

The paper presents ReDAct as an empirical method: a small LLM is used by default and decisions are deferred to a large LLM when predictive uncertainty exceeds a calibrated threshold. The headline result (15% deferral matching full large-model quality) is reported as an experimental outcome from evaluations on ALFWorld and MiniGrid. No equations or claims reduce the central result to a self-definition, a fitted parameter renamed as a prediction, or a load-bearing self-citation chain. The threshold calibration is described as external to the reported test results, and the uncertainty-error correlation is treated as an assumption validated by experiment rather than imposed by construction. This is the normal case of a self-contained empirical paper.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 0 invented entities

The approach rests on standard assumptions about LLM behavior and uncertainty estimation, with the threshold as the main tunable element calibrated to data.

free parameters (1)
  • uncertainty threshold
    Calibrated value that determines when to defer; directly controls the 15% deferral rate and performance match.
axioms (1)
  • domain assumption LLM predictive uncertainty correlates with actual decision error probability
    Required for deferral to improve reliability rather than add noise.

pith-pipeline@v0.9.0 · 5518 in / 1207 out tokens · 50251 ms · 2026-05-10T17:37:22.306477+00:00 · methodology

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Reference graph

Works this paper leans on

7 extracted references · 1 canonical work pages · 1 internal anchor

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    Association for Computational Linguistics. Roman Vashurin, Ekaterina Fadeeva, Artem Vazhentsev, Lyudmila Rvanova, Daniil Vasilev, Akim Tsvigun, Sergey Petrakov, Rui Xing, Abdelrahman Sadallah, Kirill Grishchenkov, Alexander Panchenko, Timothy Baldwin, Preslav Nakov, Maxim Panov, and Artem Shelmanov. Benchmarking uncertainty quantification methods for larg...

    work page internal anchor Pith review Pith/arXiv arXiv 2022

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    Check whether the last few actions show useless repetition or left-right oscillation

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    Choose the one action that makes the most direct progress toward the current subgoal

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    Reasoning rules: - Turning changes orientation only; it does not move the agent

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