ReDAct: Uncertainty-Aware Deferral for LLM Agents | Dzianis Piatrashyn et al. | ResearchPod

Transcript: ReDAct: Uncertainty-Aware Deferral for LLM Agents

Alex: Welcome to another episode of ResearchPod.

Sam: Today we're looking at a paper called ReDAct: Uncertainty-Aware Deferral for LLM Agents. It addresses a problem with AI agents powered by large language models: they sometimes hallucinate, making up wrong facts, and in step-by-step tasks, one error can derail the whole sequence.

Alex: So these are AIs that handle chains of decisions, like navigating a virtual house? The challenge is knowing when to switch from a cheap small AI to a pricier large one?

Sam: Yes. Picture a warehouse robot picking items: a wrong grab early on ruins the delivery. Large AIs make fewer mistakes but cost more per step, so the paper uses a small AI for most decisions and switches to the large one only when unsure.

Alex: Like a driver using a basic map but pulling up a premium one for tricky spots. How does the small AI spot its own uncertainty?

Sam: It checks simple scores from its predictions, like how spread out its guesses are for the next word or step. If doubt passes a limit, it defers. In ALFWorld tests—virtual household chores where errors compound—this matches full large-AI performance by deferring just 15% of steps, at much lower cost.

Alex: Okay, so it suggests an action, scores its confidence, then decides. But where in the process do they check—during planning or at the action itself?

Sam: They tested both. Internal planning is vague and wordy, so doubt scores there mix good and bad ideas. Action-picking is concrete, like "pick up cup," so scores like word-spread or prediction surprise clearly flag shaky choices. Labeled ALFWorld steps confirmed this.

Alex: So action-stage checks work better, like judging a basketball shot by the release, not team huddle talk.

Sam: Right. They tuned cutoffs on validation data to average five large-AI calls per task. This beats random deferrals.

Alex: How does that perform on chores?

Sam: Perplexity-guided deferrals matched large-AI success rates, calling it for 15% of steps. All uncertainty methods beat the small AI alone; random helped less. Slight call-count shifts occur in real runs as fixes alter later doubts, but it works in practice.

Alex: Like a filter: cheap routine, expert fixes where needed.

Sam: Trade-off plots show these methods balance wins and calls best, even over tuned random.

Alex: What about real costs?

Sam: For ALFWorld, full large-AI runs cost over $500; small AI alone, $9 to $45. Guided deferrals hit $15 to $30 while matching success—a clear saving.

ReDAct: Uncertainty-Aware Deferral for LLM Agents

Dzianis Piatrashyn, Nikita Kotelevskii, Kirill Grishchenkov, Nikita Glazkov, Ivan Nasonov, Ilya Makarov, Timothy Baldwin, Preslav Nakov, Roman Vashurin, Maxim Panov

Apr 8, 2026·4:37·arxiv·Original Paper

LLM AgentsHallucinations in LLMsReDAct FrameworkUncertainty-Aware DeferralText-based Embodied EnvironmentsUncertainty Quantification (UQ)

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5 min

Briefing

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.

ReDAct: Uncertainty-Aware Deferral for LLM Agents: Research Paper Podcast

ReDAct: Uncertainty-Aware Deferral for LLM Agents

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