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arxiv: 2602.05048 · v2 · submitted 2026-02-04 · 💻 cs.AI · cs.HC

MINT: Minimal Information Neuro-Symbolic Tree for Objective-Driven Knowledge-Gap Reasoning and Active Elicitation

Zeyu Fang , Mahdi Imani , Tian Lan This is my paper

Pith reviewed 2026-05-16 07:09 UTC · model grok-4.3

classification 💻 cs.AI cs.HC
keywords neuro-symbolic planningactive elicitationknowledge gapshuman-AI teamingMarkov decision processesuncertainty estimationLLM reasoning
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The pith

MINT builds a symbolic interaction tree with neural uncertainty estimates to let AI agents elicit minimal human input and reach near-expert planning performance.

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

The paper introduces the Minimal Information Neuro-Symbolic Tree (MINT) to handle incomplete information in human-AI joint planning. MINT constructs a tree of possible human-AI interactions, consults a neural policy to measure how remaining knowledge gaps affect planning outcomes, and then uses an LLM to turn that reasoning into a small set of targeted elicitation queries. Self-play optimizes the overall strategy, and the approach is shown to deliver return guarantees in extended Markov decision processes that include knowledge gaps. On three benchmarks with increasing realism and unseen objects, MINT-based agents achieve near-expert returns, higher rewards, and better success rates while asking only a limited number of questions per task.

Core claim

MINT constructs a symbolic tree by proposing propositions about possible human-AI interactions, consults a neural planning policy to estimate uncertainty in outcomes due to knowledge gaps, and leverages an LLM to search and summarize the tree's reasoning into optimal elicitation queries, thereby enabling objective-driven active elicitation in open-world planning.

What carries the argument

The Minimal Information Neuro-Symbolic Tree (MINT), which builds propositions of human-AI interactions into a symbolic tree and pairs it with a neural policy that quantifies planning uncertainty caused by unresolved knowledge gaps.

If this is right

  • Agents using MINT issue a small number of questions per task yet reach near-expert returns on planning problems with unknown objects.
  • Return guarantees hold for any MINT-augmented policy in extended MDPs that model knowledge gaps.
  • Self-play on the MINT tree produces elicitation strategies that improve both reward and success rate over baselines without active elicitation.
  • The same tree-plus-LLM pipeline scales across benchmarks of increasing realism while keeping question counts low.

Where Pith is reading between the lines

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

  • The same structure could be applied to sensor-limited robotics tasks where the agent must decide which human clarification to request before acting.
  • Replacing the LLM summarizer with a smaller distilled model would test whether the performance gain depends on large-language-model quality.
  • Extending MINT to multi-turn conversations would allow the tree to be updated incrementally rather than rebuilt from scratch after each answer.

Load-bearing premise

A neural planning policy can reliably estimate how much uncertainty remains from knowledge gaps, and an LLM can accurately search and summarize the MINT tree to produce the best elicitation queries.

What would settle it

Run the three benchmark tasks with MINT disabled versus enabled; if the version without MINT matches or exceeds the reported rewards, success rates, and question counts, the central performance claim is false.

Figures

Figures reproduced from arXiv: 2602.05048 by Mahdi Imani, Tian Lan, Zeyu Fang.

Figure 1
Figure 1. Figure 1: Evaluating, expanding, curating, and acting with MINT. (a) How we build and expand MINT by first consulting a trained neural planning policy as an oracle, and then utilizing the LLM to curate the queries based on MINT and elicit human responses via natural-language interactions. (b) How MINT acts in the environment. AI agent implements the identified queries in its interaction with human in joint planning.… view at source ↗
Figure 2
Figure 2. Figure 2: Illustrations of how MINT acts in all 3 environments. (a) The agent faces unknown objects in MiniGrid and curates queries about its impact on transition; (b) The agent in Atari Pacman faces unseen targets (white) and curates queries about its impact on rewards; and (c) The agent in Isaac Search and Rescue reasons about the smoke, interacts with human, and plans its path accordingly. tages of both sides – i… view at source ↗
Figure 3
Figure 3. Figure 3: Screenshots of the environments used in this paper. (a)MiniGrid (b)Atari Pacman (c-1) an overview of NVIDIA Isaac environment (c-2) an example of drone view in Isaac environment. The Atari Pacman environment is mainly based on its original game setting. However, we inject an uncertain object marked as the white rectangle in the raw frames. This uncertain object either has an effect on the transition or rew… view at source ↗
read the original abstract

Joint planning through language-based interactions is a key area of human-AI teaming. Planning problems in the open world often involve various aspects of incomplete information and unknowns, e.g., objects involved, human goals/intents -- thus leading to knowledge gaps in joint planning. We consider the problem of discovering optimal interaction strategies for AI agents to actively elicit human inputs in object-driven planning. To this end, we propose Minimal Information Neuro-Symbolic Tree (MINT) to reason about the impact of knowledge gaps and leverage self-play with MINT to optimize the AI agent's elicitation strategies and queries. More precisely, MINT builds a symbolic tree by making propositions of possible human-AI interactions and by consulting a neural planning policy to estimate the uncertainty in planning outcomes caused by remaining knowledge gaps. Finally, we leverage LLM to search and summarize MINT's reasoning process and curate a set of queries to optimally elicit human inputs for best planning performance. By considering a family of extended Markov decision processes with knowledge gaps, we analyze the return guarantee for a given MINT with active human elicitation. Our evaluation on three benchmarks involving unseen/unknown objects of increasing realism shows that MINT-based planning attains near-expert returns by issuing a limited number of questions per task while achieving significantly improved rewards and success rates.

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 introduces the Minimal Information Neuro-Symbolic Tree (MINT) framework to address knowledge gaps in joint human-AI planning tasks involving incomplete information about objects, goals, and intents. MINT constructs symbolic trees of possible interactions, consults a neural planning policy to estimate outcome uncertainty from remaining gaps, optimizes elicitation strategies through self-play, and uses an LLM to search and summarize the tree for generating optimal queries. It provides a return-guarantee analysis for a family of extended MDPs and reports empirical results on three benchmarks with unseen objects, claiming near-expert returns with a limited number of questions per task along with significantly improved rewards and success rates.

Significance. If the empirical performance claims and the return guarantee hold under independent verification, the work would represent a meaningful advance in objective-driven active elicitation for open-world planning, demonstrating how neuro-symbolic trees combined with self-play and LLM summarization can reduce interaction overhead while preserving high task performance. The integration of uncertainty estimation over knowledge gaps with formal MDP analysis is a constructive direction for human-AI teaming.

major comments (3)
  1. [Abstract] Abstract: The central empirical claims (near-expert returns, significantly improved rewards and success rates on three benchmarks) are stated without any quantitative numbers, baseline comparisons, error bars, or specific metrics, preventing verification of the magnitude and statistical reliability of the reported gains.
  2. [Return Guarantee Analysis] Return guarantee analysis (extended-MDP setting): The guarantee is derived from the same family of MDPs used for self-play optimization of the MINT policy; without explicit independence between the policy parameters and the bound (e.g., via a separate derivation or worst-case analysis), the guarantee risks reducing to a tautological or fitted quantity rather than an independent performance certificate.
  3. [Methods and Evaluation] Methods and evaluation sections: No ablation experiments isolate the neural planning policy's uncertainty estimation or the LLM's tree-search/summarization steps, both of which are load-bearing for attributing benchmark improvements unambiguously to MINT rather than to the quality of the underlying LLM or neural policy.
minor comments (2)
  1. [Abstract] The acronym MINT is expanded in the title but the abstract introduces it without immediate expansion; define on first use for clarity.
  2. [Preliminaries] Notation for the extended MDP family and the propositions in the symbolic tree should be introduced with explicit definitions and an example tree diagram to aid reproducibility.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive feedback. We address each major comment below and have revised the manuscript to improve clarity, rigor, and verifiability.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The central empirical claims (near-expert returns, significantly improved rewards and success rates on three benchmarks) are stated without any quantitative numbers, baseline comparisons, error bars, or specific metrics, preventing verification of the magnitude and statistical reliability of the reported gains.

    Authors: We agree that the abstract should include quantitative support. In the revised manuscript, we have updated the abstract to incorporate the specific metrics, baseline comparisons, and error bars already reported in the evaluation section, allowing readers to directly assess the magnitude and reliability of the gains. revision: yes

  2. Referee: [Return Guarantee Analysis] Return guarantee analysis (extended-MDP setting): The guarantee is derived from the same family of MDPs used for self-play optimization of the MINT policy; without explicit independence between the policy parameters and the bound (e.g., via a separate derivation or worst-case analysis), the guarantee risks reducing to a tautological or fitted quantity rather than an independent performance certificate.

    Authors: We thank the referee for this observation. The return guarantee is derived analytically for the entire family of extended MDPs with knowledge gaps and holds for any MINT-based policy in that family; self-play is used only to select a high-performing policy within the family and does not enter the bound derivation. We have revised the relevant section to explicitly separate the general worst-case analysis from the optimization procedure and to restate the independence of the certificate. revision: yes

  3. Referee: [Methods and Evaluation] Methods and evaluation sections: No ablation experiments isolate the neural planning policy's uncertainty estimation or the LLM's tree-search/summarization steps, both of which are load-bearing for attributing benchmark improvements unambiguously to MINT rather than to the quality of the underlying LLM or neural policy.

    Authors: We acknowledge that targeted ablations would strengthen attribution. In the revised manuscript we have added ablation experiments that (i) replace the neural uncertainty estimator with a uniform heuristic and (ii) replace LLM summarization with exhaustive tree traversal, reporting the resulting drops in return and success rate on all three benchmarks. These results confirm the contribution of each component. revision: yes

Circularity Check

0 steps flagged

No significant circularity detected

full rationale

The provided abstract and context describe MINT construction via neural policy uncertainty estimates, self-play optimization of elicitation strategies, LLM summarization of the tree, and a separate analysis of return guarantees over a family of extended MDPs with knowledge gaps. No equations, self-citations, or derivations are quoted that reduce the central performance claims or guarantees to fitted inputs by construction, self-definitional loops, or load-bearing self-citations. The self-play step and MDP-family analysis follow standard RL practice and remain independent of the reported benchmark results. This is the expected non-finding for a paper whose core claims rest on empirical evaluation rather than a closed mathematical reduction.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 1 invented entities

The central claim rests on the assumption that neural policies can estimate planning uncertainty from knowledge gaps and that LLM summarization produces optimal queries; these components are introduced by the paper without independent external validation in the provided abstract.

axioms (1)
  • domain assumption Planning problems can be modeled as extended Markov decision processes that include explicit knowledge gaps
    Invoked when analyzing the return guarantee for MINT with active elicitation.
invented entities (1)
  • Minimal Information Neuro-Symbolic Tree (MINT) no independent evidence
    purpose: To represent possible human-AI interactions and quantify uncertainty from remaining knowledge gaps
    New structure proposed by the paper; no independent evidence supplied in the abstract.

pith-pipeline@v0.9.0 · 5539 in / 1343 out tokens · 43237 ms · 2026-05-16T07:09:28.265681+00:00 · methodology

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Forward citations

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