Uncertainty-Aware Clarification in LLM Agents with Information Gain
Mengyi Deng, Zhiwei Li, Xin Li, Tingyu Zhu, Ying Zhao, Zhijiang Guo, Wei Wang
Abstract
Large Language Model (LLM) agents often operate under underspecified user instructions, where latent uncertainty over user intent leads to erroneous tool actions. To address this challenge, we propose a goal-oriented clarification framework that aligns clarification behavior with ambiguity resolution. Central to our approach is the Information Gain Reward, a metric that quantifies the utility of clarification questions by measuring the Bayesian belief update towards the ground-truth goal induced by the clarification exchange. We train the clarifier (LLM) using this reward to optimize for high information gain, ensuring that clarifications effectively reduce uncertainty and improve task completion within the agent-tool-user environment. We validate our framework within a clarification-enhanced -Bench environment, conducting cross-agent evaluations across five heterogeneous backbones. Empirical results demonstrate that our method consistently improves the success rate by 3.7\% over the no-clarification baseline, while adding only 0.3 total interaction steps on average.
AI Impact Assessments
(1 models)Scientific Impact Assessment: Uncertainty-Aware Clarification in LLM Agents with Information Gain
1. Core Contribution
This paper addresses a genuine problem in LLM-based tool-using agents: underspecified user instructions leading to erroneous tool invocations. The core contribution is a Bayesian Information Gain Reward that quantifies clarification utility by measuring the shift in teacher-forced log-likelihood of the ground-truth goal before and after a clarification exchange. This reward is used within a DAPO (Decoupled Advantage Policy Optimization) framework to train a small (1.7B parameter) "clarifier" module that learns *when* and *what* to ask.
The key insight is reframing clarification as an amortized Bayesian experimental design problem, where the clarifier acts as a proposal distribution over questions, and the reward approximates expected information gain without requiring intractable posterior entropy computations. The mathematical connection to pointwise mutual information (PMI) / importance weighting (Eq. 9-10 in the appendix) is cleanly derived and provides theoretical grounding.
2. Methodological Rigor
Strengths in methodology:
Concerns:
3. Potential Impact
The paper addresses a relevant gap: most LLM agent frameworks assume well-specified instructions and lack mechanisms for interactive clarification. The decoupled clarifier design is practically appealing — it's pluggable, backbone-agnostic, and adds minimal overhead (0.3 steps on average).
Cross-agent generalization (Table 3) is the paper's strongest empirical result. The 1.7B trained clarifier consistently improves five heterogeneous agent backbones (from 8B to 671B parameters), demonstrating that clarification quality transfers across architectures. The finding that a tiny trained model matches or exceeds much larger general-purpose models (Table 2) in clarification utility is compelling.
The observation that interactive clarification can outperform full user intent in complex domains (airline: 17.3% vs. 16%) is an interesting finding suggesting that incremental information delivery may be preferable to information overload for LLM agents.
However, the practical impact is constrained by several factors: (1) the framework requires ground-truth goals for training, (2) the improvements, while consistent, are small in absolute terms, and (3) the evaluation scope is narrow (two domains, one benchmark).
4. Timeliness & Relevance
The paper is well-timed. As LLM agents move from research prototypes to deployment, handling ambiguous instructions becomes critical. The proliferation of tool-using agent frameworks (ReAct, Toolformer, etc.) creates demand for complementary modules that address input ambiguity. The use of DAPO for RL training aligns with current trends in LLM alignment research.
The connection to Bayesian experimental design is intellectually fresh in this context, though IGPO (Wang et al., 2025a) has explored similar information-gain optimization for multi-turn agents. The paper's distinction — operating with latent goals, free-form responses, and tool-grounded feedback — represents a meaningful extension.
5. Strengths & Limitations
Key Strengths:
Notable Weaknesses:
Additional Observations
The training dynamics analysis (Figures 3, 5-7) is thorough and demonstrates optimization stability, though this is expected given the relatively simple reward structure. The failure analysis in the appendix provides useful qualitative insight but is limited to two cases. The finding that joint agent-clarifier optimization could be beneficial is noted as future work but represents a significant limitation of the current decoupled design.
The paper is generally well-written and structured, though the experimental section could benefit from statistical rigor (confidence intervals, significance tests) given the small test sets and modest improvements.
Generated Jun 3, 2026
Comparison History (23)
Paper 1 presents a comprehensive, interpretable AI framework combining deep learning with statistical modeling for a significant clinical application (osteoarthritis). It demonstrates strong methodological rigor through conformal prediction for uncertainty quantification, substantially improves prediction accuracy, and enables large-scale longitudinal analysis yielding clinically meaningful findings about structure-pain relationships. Paper 2 addresses a useful but narrower problem (clarification in LLM agents) with modest improvements (3.7% success rate gain). Paper 1's clinical relevance, methodological depth, and potential to impact osteoarthritis research and treatment give it higher scientific impact.
Paper 2 addresses a more fundamental and broadly applicable problem—explainability for agentic AI systems—which is a growing concern across the AI community. It bridges traditional XAI with emerging agentic paradigms, providing novel quantitative insights (e.g., state tracking inconsistency being 2.7x more prevalent in failures). Its conceptual contribution of shifting from feature-level to trajectory-level explainability has broader implications across multiple fields. Paper 1, while solid, offers incremental improvements (3.7% success rate gain) on a narrower clarification task with limited generalizability beyond the specific benchmark.
Paper 2 addresses a fundamental and widely-encountered problem in AI-driven molecular generation—invalid SMILES outputs from LLMs—with a novel framework (AMREC) that shifts the paradigm from validity repair to identity-preserving recovery. This has broad implications for drug discovery, materials science, and computational chemistry. Paper 1 makes a solid but more incremental contribution (3.7% improvement) to LLM agent clarification, a narrower problem space. Paper 2's cross-cutting relevance to both AI and chemistry, combined with its novel conceptual framing, gives it higher potential impact.
Paper 1 presents a novel, theoretically grounded methodological framework (Information Gain Reward based on Bayesian belief updates) to solve a pervasive issue in agentic AI: underspecified instructions. Its approach to uncertainty-aware clarification has broad applicability across virtually any LLM agent-tool environment, directly improving real-world usability and task completion. While Paper 2 introduces a valuable and diagnostically important benchmark for multimodal mathematical reasoning, Paper 1 offers a generalized algorithmic solution with a wider potential impact on the rapidly expanding field of autonomous agent design.
Paper 1 addresses a critical and widespread issue in LLM agents—handling ambiguous user instructions. By proposing a mathematically grounded framework using Bayesian information gain, it demonstrably improves agent success rates across multiple backbones. Its direct applicability to real-world, tool-augmented LLMs gives it a broader and more immediate scientific impact compared to Paper 2, which explores a more niche aspect of AI alignment within a specific game environment.
Paper 2 has higher potential impact due to a more novel architecture (multi-perspective, goal-conditioned memory with argumentation-based retrieval and explicit conflict surfacing) that generalizes across long-horizon agent settings, knowledge representation, and explainable AI. Its breadth spans agent memory, retrieval, ontology/knowledge graphs, and formal argumentation semantics, with clear real-world relevance in domains requiring auditability and handling conflicting evidence. Paper 1 is timely and methodologically clearer with quantified gains, but the contribution is narrower (clarification policy via information-gain reward) and shows modest improvements, suggesting more incremental impact.
Paper 1 identifies and rigorously characterizes a fundamental reliability issue in Large Reasoning Models—harmful overthinking—introducing a novel evaluation protocol and demonstrating significant accuracy improvements (up to 21%). The finding that existing efficiency strategies fail to address harmful overthinking opens important new research directions. Its breadth across multimodal and language-only benchmarks strengthens generalizability. Paper 2 addresses a relevant but narrower problem (clarification under ambiguity) with a modest 3.7% improvement. Paper 1's novelty, methodological depth, and broader implications for the rapidly growing LRM field give it substantially higher impact potential.
Paper 2 likely has higher impact: it introduces a principled, generalizable clarification framework for LLM agents using an information-gain reward grounded in Bayesian belief updates, with cross-backbone evaluation and measurable task-success improvements. This is timely for agent reliability and applies broadly across tool-using agents, HCI, and decision-making under uncertainty. Paper 1 provides a valuable dataset and capture pipeline, but its impact may be narrower (forensics/embodied-agent interaction) and more dependent on downstream adoption, whereas Paper 2 offers a reusable method that can be integrated across many agent systems.
Paper 2 (TriEval) has higher likely impact due to broader, immediate applicability and timeliness: lightweight, multi-axis evaluation (bias/toxicity/truthfulness) aligns with widespread deployment needs and can be adopted across academia/industry with minimal resources, amplifying real-world and cross-field impact. Open-sourcing further increases uptake. Paper 1 is novel and methodologically grounded (information-gain-driven clarification) but targets a narrower agent-clarification setting with modest reported gains, making its near-term impact more specialized.
Paper 2 introduces a novel architectural design principle ('score-level fusion') for hybrid language models that addresses a fundamental tension between attention and SSMs. This defines a new design axis with broad implications for the entire language modeling field. The approach is elegant (single SDPA call, no custom kernels), shows strong empirical results on standard benchmarks, and could influence how future foundation models are built. Paper 1, while useful, addresses a narrower problem (clarification in LLM agents) with more incremental improvements (3.7% success rate gain) and limited architectural novelty.
Paper 1 addresses a critical and highly timely societal issue (multimodal fake news and generative AI manipulation) with a novel approach focusing on intrinsic semantic and physical conflicts. Its ability to generalize to unseen manipulation types in zero-shot settings offers broader, more immediate real-world impact compared to Paper 2, which presents a valuable but somewhat more incremental improvement (3.7% success rate increase) in LLM agent instruction clarification.
Paper 2 addresses a more fundamental and broadly applicable problem—understanding *why* AI agents fail through process-level error analysis, rather than just improving outcomes. TELBench provides a reusable benchmark (2,790 trajectories), and DRIFT offers a generalizable auditing framework applicable across agent types. The 30 percentage point improvement in error localization is substantial. Paper 1's 3.7% improvement on a specific task is more incremental. Paper 2's focus on interpretability and reliability of deep-research agents is timely given rapid agent deployment, and its methodology likely influences broader agent evaluation practices.
Paper 1 addresses a foundational, cross-disciplinary problem—formalizing Machine Theory of Mind—which has broad implications across AI, cognitive science, and neuroscience. Providing a rigorous formal definition and meta-model for a concept that has lacked one establishes a conceptual framework that can guide an entire research agenda. Paper 2 presents a useful but incremental contribution (3.7% improvement) to LLM agent clarification, with narrower scope and more limited generalizability. The foundational nature and breadth of impact of Paper 1 gives it higher long-term scientific impact potential.
Paper 1 (DeskCraft) likely has higher impact because it introduces a large, realistic, long-horizon benchmark for professional creative/engineering desktop workflows with a formalized human-in-the-loop interaction protocol and broad evaluation across many agents. Such benchmarks often become community standards, enabling reproducible comparison and accelerating progress across UI agents, HCI, and applied ML. Paper 2 proposes a focused training objective (information-gain reward) with moderate gains in a specific environment; it is methodologically interesting but narrower in scope and likely less field-shaping than a widely adopted benchmark.
Paper 2 introduces a novel framework (Information Gain Reward for clarification in LLM agents) that addresses a broadly relevant problem in AI—handling ambiguous user instructions. It combines Bayesian information theory with LLM training in a principled way, has wide applicability across LLM agent systems, and is highly timely given the rapid deployment of LLM agents. Paper 1, while methodologically sound, provides incremental findings (LSTM outperforms Transformer for a specific hydrology task) with narrower scope and limited novelty beyond an architectural comparison on a domain-specific problem.
Paper 2 addresses a fundamental and timely gap in legal theory for agentic AI systems, proposing a comprehensive liability framework that bridges AI capabilities with tort law. Its breadth of impact spans law, AI policy, and technology governance—fields with enormous societal relevance as agentic AI proliferates. Paper 1 offers a solid but incremental technical contribution (3.7% improvement) within a narrow LLM agent clarification setting. Paper 2's novel conceptual framework (interaction types, Reasonable Agent standard, forensic logging) has greater potential to shape policy, legal precedent, and interdisciplinary discourse.
Paper 2 demonstrates a substantially higher performance improvement (over 25% increase in success rate compared to Paper 1's 3.7%) and tackles a foundational problem in agentic workflows: iterative skill refinement. The proposed execution-grounded framework and the demonstration of strong cross-model transferability suggest broader applicability and potential to significantly advance how autonomous agents acquire and perfect procedural knowledge.
Paper 2 likely has higher impact due to a clearer, broadly applicable core contribution: an information-theoretic (Bayesian) reward for training clarification behavior that can transfer across many agent/tool settings and model backbones. It targets a central, timely failure mode (underspecified instructions) and provides a principled optimization objective with measurable gains and low interaction overhead. Paper 1 is useful for cloud-edge deployment, but is more system-specific and its improvements appear task-dependent; novelty is more in engineering design than a general learning objective.
Paper 2 proposes a paradigm shift in geospatial AI by unifying raster and vector data modalities for foundation models, addressing a fundamental gap in Earth Observation. As a perspective paper, it has broader potential impact across remote sensing, urban planning, environmental science, and GIS communities. Its vision for joint spatial representation learning could reshape how geospatial foundation models are built. Paper 1, while solid, offers incremental improvement (3.7% success rate gain) on a specific LLM agent clarification task with narrower scope and applicability.
TAPS addresses a fundamental efficiency bottleneck in LLM inference (speculative decoding), offering substantial speedups (up to 7.9x) with lossless quality. The methodological insight about the mismatch between marginal probabilities and prefix-conditioned verification is novel and well-validated across diverse settings. Its practical impact is broader since inference speed affects all LLM deployments. Paper 1's clarification framework, while useful, shows modest improvements (3.7%) on a narrower problem. Paper 2's combination of strong empirical gains, theoretical grounding, and wide applicability gives it higher impact potential.