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arxiv: 2605.14057 · v1 · submitted 2026-05-13 · 💻 cs.CL

Recognition: no theorem link

Dual Hierarchical Dialogue Policy Learning for Legal Inquisitive Conversational Agents

Xubo Lin , Zezhii Deng , Shihao Wang , Grace Hui Yang , Yang Deng
Authors on Pith no claims yet

Pith reviewed 2026-05-15 05:17 UTC · model grok-4.3

classification 💻 cs.CL
keywords inquisitive conversational agentsdual hierarchical reinforcement learninglegal dialogueproactive information extractionsupreme court oral argumentsdialogue policy learningjudicial questioning
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The pith

A dual hierarchical reinforcement learning method lets conversational agents proactively extract information by coordinating high-level strategy and low-level question generation in legal dialogues.

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

Standard dialogue systems respond to users but many real tasks require the agent to drive the exchange and gather needed details. This paper defines Inquisitive Conversational Agents that actively probe for information and tests the idea on U.S. Supreme Court oral arguments. The core proposal is a Dual Hierarchical Reinforcement Learning setup in which two cooperating agents share responsibility for overall dialogue policy and for producing specific utterances. Together the agents learn when to ask probing questions and how to phrase them so the exchange meets the agent's legal objectives. On a Supreme Court dataset the approach records higher scores than several baseline systems across the reported measures.

Core claim

The paper establishes that two cooperating reinforcement-learning agents, one managing high-level dialogue strategy and the other handling fine-grained utterance generation, can learn to emulate judicial questioning patterns, systematically uncover crucial information, and thereby achieve the agent's legal objectives in Supreme Court-style oral arguments.

What carries the argument

Dual Hierarchical Reinforcement Learning framework consisting of two cooperating RL agents that jointly learn dialogue-level policy and utterance-level generation.

If this is right

  • The agents learn both when to initiate probing questions and how to word them to serve legal goals.
  • Information extraction becomes more systematic than in standard user-driven dialogue systems.
  • The same dual-agent structure can be applied to other high-stakes domains that require proactive questioning.
  • Performance gains appear across multiple automatic metrics without additional human reward signals.

Where Pith is reading between the lines

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

  • The framework could be tested in medical intake or investigative interviews where the agent must also drive information gathering.
  • Removing the need for extra human feedback during training suggests the method may scale to additional specialized domains with only modest new data.
  • Real-time interaction logs could be fed back to refine the learned policies after deployment.

Load-bearing premise

The U.S. Supreme Court dataset captures representative judicial questioning patterns and the dual RL agents can learn effective strategies without further human feedback or validation.

What would settle it

Run the trained agents on a fresh set of Supreme Court transcripts and check whether the generated questions fail to elicit the same categories of information that actual justices obtain or whether performance falls when either the high-level or low-level agent is removed.

Figures

Figures reproduced from arXiv: 2605.14057 by Grace Hui Yang, Shihao Wang, Xubo Lin, Yang Deng, Zezhii Deng.

Figure 1
Figure 1. Figure 1: While this paper focuses on inquisitive dia [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: System Architecture of the Proposed Dual Hierarchical Inquisitive Conversational Agent. [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Coverage Score results [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: MR Score results 5.2 Main Results In this section, we test our method and all base￾lines on the US Supreme Court dataset and com￾pare their effectiveness in terms of the evaluation metrics. Detailed results are shown in [PITH_FULL_IMAGE:figures/full_fig_p008_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: A template of google form for manual label [PITH_FULL_IMAGE:figures/full_fig_p016_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Justice uses a counterexample to challenge [PITH_FULL_IMAGE:figures/full_fig_p017_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Justice continuously pressing attorney by [PITH_FULL_IMAGE:figures/full_fig_p017_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Learning Curves from Ablation Study. (a) Cumulative reward during early training stage; (b) Cumulative [PITH_FULL_IMAGE:figures/full_fig_p018_8.png] view at source ↗
read the original abstract

Most existing dialogue systems are user-driven, primarily designed to fulfill user requests. However, in many critical real-world scenarios, a conversational agent must proactively extract information to achieve its own objectives rather than merely respond. To address this gap, we introduce \emph{Inquisitive Conversational Agents (ICAs)} and develop an ICA specifically tailored to U.S. Supreme Court oral arguments. We propose a Dual Hierarchical Reinforcement Learning framework featuring two cooperating RL agents, each with its own policy, to coordinate strategic dialogue management and fine-grained utterance generation. By learning when and how to ask probing questions, the agent emulates judicial questioning patterns and systematically uncovers crucial information to fulfill its legal objectives. Evaluations on a U.S. Supreme Court dataset show that our method outperforms various baselines across multiple metrics. It represents an important first step toward broader high-stakes, domain-specific applications.

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

2 major / 1 minor

Summary. The paper introduces Inquisitive Conversational Agents (ICAs) tailored to U.S. Supreme Court oral arguments. It proposes a Dual Hierarchical Reinforcement Learning framework with two cooperating agents—one handling strategic dialogue management and the other managing fine-grained utterance generation—to proactively extract information by emulating judicial questioning patterns. The central claim is that this approach outperforms various baselines across multiple metrics on a U.S. Supreme Court dataset.

Significance. If the results hold with proper validation, the work could advance objective-driven dialogue systems for high-stakes legal domains by addressing coordination between high-level strategy and low-level generation. The dual-agent RL setup offers a structured way to pursue agent goals rather than purely respond to users, potentially informing broader applications in specialized conversational AI.

major comments (2)
  1. Abstract: The assertion that 'Evaluations on a U.S. Supreme Court dataset show that our method outperforms various baselines across multiple metrics' supplies no numerical results, baseline descriptions, error analysis, tables, or statistical tests. This absence is load-bearing for the central empirical claim and prevents verification of the dual hierarchical RL superiority.
  2. Evaluation section: The assessment relies solely on automatic metrics from the U.S. Supreme Court dataset without human expert validation or independent grounding of the reward function for legal objectives. This leaves unverified whether the learned policies align with actual judicial questioning patterns or merely optimize for fluency on an unrepresentative dataset.
minor comments (1)
  1. Abstract: The acronym ICA is defined on first use but the motivation for shifting from user-driven to objective-driven agents could be stated more explicitly in the opening sentence to improve accessibility.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive comments on our work. We address each major comment point by point below and have revised the manuscript to strengthen the presentation of our empirical results and evaluation methodology.

read point-by-point responses

  1. Referee: Abstract: The assertion that 'Evaluations on a U.S. Supreme Court dataset show that our method outperforms various baselines across multiple metrics' supplies no numerical results, baseline descriptions, error analysis, tables, or statistical tests. This absence is load-bearing for the central empirical claim and prevents verification of the dual hierarchical RL superiority.

    Authors: We agree that the abstract would benefit from greater specificity to support the central claim. In the revised manuscript, we have updated the abstract to reference key quantitative improvements (e.g., higher task success rates and probing question quality scores relative to the listed baselines) and direct readers to the corresponding tables and statistical comparisons in the evaluation section. This change preserves the abstract's brevity while enabling verification of the reported superiority. revision: yes

  2. Referee: Evaluation section: The assessment relies solely on automatic metrics from the U.S. Supreme Court dataset without human expert validation or independent grounding of the reward function for legal objectives. This leaves unverified whether the learned policies align with actual judicial questioning patterns or merely optimize for fluency on an unrepresentative dataset.

    Authors: We acknowledge the value of human validation for high-stakes domains. Our evaluation follows standard practice in reinforcement learning for dialogue by using automatic metrics (task success, information extraction efficiency, and utterance quality) computed directly on the Supreme Court dataset. The reward function is explicitly derived from observed judicial objectives in the data, such as information gain per turn. We have revised the evaluation section to include a detailed derivation of the reward components, an expanded error analysis comparing learned policies to dataset patterns, and a limitations paragraph noting the absence of human expert studies as future work. We maintain that the dataset is representative of U.S. Supreme Court questioning, as it consists of real oral argument transcripts. revision: partial

Circularity Check

0 steps flagged

No significant circularity detected

full rationale

The paper describes a Dual Hierarchical Reinforcement Learning framework consisting of two cooperating agents for dialogue strategy and utterance generation, applied to a U.S. Supreme Court oral arguments dataset. No mathematical derivations, equations, or parameter-fitting steps are presented that reduce predictions or results to the inputs by construction. The central claims rest on standard RL training followed by empirical evaluation against baselines, with no self-definitional loops, fitted inputs renamed as predictions, or load-bearing self-citations that collapse the argument. The approach is self-contained as an application of existing RL methods to a domain-specific task.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on standard RL assumptions and the representativeness of the court dataset; no free parameters, invented entities, or ad-hoc axioms are explicitly introduced in the abstract.

axioms (1)
  • domain assumption Reinforcement learning policies can be learned to emulate judicial questioning strategies from transcript data
    Invoked in the design of the dual hierarchical framework and evaluation on Supreme Court data.

pith-pipeline@v0.9.0 · 5450 in / 1040 out tokens · 41065 ms · 2026-05-15T05:17:18.907754+00:00 · methodology

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