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

This paper introduces MINT, a neuro-symbolic tree framework that combines symbolic interaction reasoning with neural planning uncertainty estimation and LLM-driven query generation to enable AI agents to actively elicit human inputs, thereby effectively bridging knowledge gaps and achieving near-expert performance in open-world joint planning tasks.

Original authors: Zeyu Fang, Mahdi Imani, Tian Lan

Published 2026-05-06

📖 4 min read☕ Coffee break read

CC BY 4.0

Original authors: Zeyu Fang, Mahdi Imani, Tian Lan

Original paper licensed under CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/). ✨ This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer

Imagine you and a robot are a team trying to solve a puzzle together. But here's the catch: the robot can't see the whole picture. There are hidden pieces, invisible walls, or secret rules it doesn't know about. In the paper's language, these are called "knowledge gaps."

If the robot guesses wrong, it might crash or fail. If it plays it too safe, it might take forever to finish. The paper introduces a new system called MINT (Minimal Information Neuro-Symbolic Tree) to help the robot figure out exactly what to ask you to solve the puzzle efficiently.

Here is how MINT works, broken down into simple steps:

1. The Problem: The Robot's "Blind Spot"

Imagine you are playing a video game where some walls are invisible. The robot knows the rules of the game, but it doesn't know where the invisible walls are or if a certain floor tile is a trap or a bonus.

Old ways: The robot either guesses blindly (often failing) or asks for help constantly (annoying and slow).
The Goal: The robot needs to ask the right questions at the right time to fill in its blind spots without wasting time.

2. The Solution: MINT (The "Detective's Sketchbook")

MINT acts like a detective's sketchbook that combines two superpowers:

The Brain (Neural Policy): A fast, intuitive AI that knows how to play the game well if it knew all the rules. It can estimate how "scary" or uncertain a situation is.
The Logic (Symbolic Tree): A structured map that breaks down the unknowns into a tree of possibilities.

How it builds the tree:

The Root: The robot starts with a big question mark (e.g., "Is that blue box a wall or a bonus?").
Branching Out: The robot simulates a game in its head (called "self-play"). It asks, "What if it's a wall?" and "What if it's a bonus?"
Checking the Impact: It uses its "Brain" to see: Does it matter if I guess wrong here?
- If the answer is "No, I'll be fine either way," it stops asking.
- If the answer is "Yes, this could ruin my plan," it keeps branching out.

3. The Magic Step: Asking the Human (The "Sherlock Holmes" Moment)

Once the robot has built this tree of "What ifs," it doesn't just guess. It calls in a Large Language Model (LLM)—think of this as a smart assistant who can read the robot's sketchbook and summarize it.

The LLM looks at the tree and says: "Hey, if we just ask one simple Yes/No question, we can eliminate half of these scary possibilities."

Example: Instead of asking, "Tell me everything about this room," the robot asks, "Is the floor in the hallway slippery?"
The Result: You say "Yes." The robot instantly crosses out all the "dry floor" branches of its tree. It now knows exactly what to do.

4. Why It's Better (The "Efficiency" Analogy)

Think of it like navigating a maze in the dark.

Pure Guessing (Old RL): You run into walls, hit your head, and hope you find the exit.
Constant Asking (Old Human-in-the-Loop): You stop every two steps to ask, "Is this the way?" It's exhausting and slow.
MINT: You pause, look at the map, and ask, "Is the exit to the left or right?" Once you answer, you sprint confidently to the exit.

What the Paper Found

The researchers tested MINT in three different "worlds":

MiniGrid: A simple grid maze with hidden blocks.
Atari Pacman: A classic game with hidden rewards or traps.
Isaac Gym: A realistic 3D simulation of a drone trying to rescue a person in a smoky warehouse.

The Results:

MINT achieved nearly the same success rate as an expert who knew everything about the world.
It did this by asking only 1 to 3 questions per unknown object.
It outperformed robots that just guessed and robots that asked for help too often.

In a Nutshell

MINT is a system that teaches AI to be a smart collaborator. Instead of guessing blindly or bothering humans with endless questions, it uses a mix of fast intuition and logical reasoning to figure out the single most important question to ask. This allows the human and the AI to work together to solve complex problems quickly and accurately, even when the AI doesn't know all the rules of the game.

Technical Summary: MINT for Objective-Driven Knowledge-Gap Reasoning

Problem Statement
In open-world human-AI teaming, joint planning is frequently hindered by incomplete information and unknowns, such as unseen objects, ambiguous human intents, or uncertain environmental constraints. These "knowledge gaps" create uncertainties in state transitions and reward functions, leading to conservative executions and suboptimal plans. While existing methods like Reinforcement Learning with Human Feedback (RLHF) or few-shot adaptation can process human input, they often lack the capacity to expressively reason about these gaps or actively elicit the specific information needed to bridge them. Conversely, traditional Human-in-the-Loop (HITL) approaches often focus on fine-tuning via demonstrations rather than addressing the root cause of knowledge gaps through objective-driven active elicitation.

Methodology: Minimal Information Neuro-Symbolic Tree (MINT)
The authors propose MINT, a framework that combines symbolic reasoning, neural planning policies, and Large Language Models (LLMs) to optimize the elicitation of human inputs. The methodology operates in two primary phases: training and deployment.

Neural Policy Training (Uncertainty Estimation):
The system employs a bootstrapped Deep Q-Network (specifically UA-DQN) to estimate not only the optimal Q-values but also the aleatoric uncertainty (variance) of planning outcomes caused by knowledge gaps. During training, the agent is exposed to a family of extended Markov Decision Processes (MDPs) where the knowledge gap $u$ masks parts of the ground-truth descriptor $\phi$ . The network learns to estimate the mean and variance of Q-values over the distribution of possible descriptors $\Phi_u$ .
Symbolic Tree Construction and Expansion:
When a knowledge gap is detected (e.g., high variance in Q-values), MINT constructs a symbolic tree.
- Nodes: Represent states of the planning problem with specific knowledge gaps, characterized by descriptors (type, subtype, value ranges).
- Edges: Represent propositions (queries) proposed to narrow the gap.
- Expansion: The tree expands in a breadth-first manner. For each node, the neural policy estimates the mean return and variance. If the variance is high relative to the return gap between actions, the node is expanded by splitting the knowledge gap space (e.g., dividing value ranges or hypothesizing different types of uncertainty). This process continues until the variance is sufficiently low or a depth limit is reached.
LLM-Based Curation and Active Elicitation:
Once the tree is built, an LLM processes the structure to optimize the interaction strategy:
- Pruning: The LLM merges equivalent sub-trees or leaves that lead to identical optimal actions to eliminate redundancy.
- Query Generation: The LLM synthesizes binary (yes/no) questions designed to maximize information gain regarding the optimal action candidates.
- Interaction: The agent poses these questions to the human. The human response updates the knowledge gap, pruning inconsistent branches of the tree. This iterative process continues until the optimal action is identified with high confidence.

Theoretical Analysis
The paper provides a theoretical guarantee for MINT's performance. By defining a pseudo-metric between MDPs with different transition kernels and reward functions, the authors prove a local pseudo-Lipschitz continuity of the optimal Q-functions. They derive an upper bound on the return gap between MINT-based planning (with active elicitation) and an ideal case with no knowledge gaps. This analysis demonstrates that iteratively narrowing the knowledge gap through MINT's query strategy reduces the return gap, providing a performance guarantee.

Key Contributions

MINT Framework: The proposal of a neuro-symbolic tree that consults a neural planning policy to reason about knowledge gap impacts and leverages self-play to optimize human elicitation strategies.
Theoretical Guarantees: The proof of local pseudo-Lipschitz continuity for planning returns and the derivation of an upper bound on the return gap, offering a solid theoretical foundation for active human elicitation.
Empirical Validation: Demonstration that MINT achieves near-expert returns with significantly fewer queries (1–3 per object) compared to baselines across diverse environments.

Experimental Results
The authors evaluated MINT on three benchmarks with increasing realism:

MiniGrid: MINT achieved near-perfect success rates and high average rewards, significantly outperforming pure LLMs (which were often overly conservative) and pure RL methods (which struggled with uncertainty). MINT maintained high performance even with a limited query budget.
Atari Pacman: In an environment with raw pixel observations and unknown reward/transition zones, MINT achieved competitive returns compared to Human-in-the-Loop RL baselines (Query-A) but required significantly fewer queries.
Isaac Gym (Search and Rescue): In a high-fidelity drone rescue scenario involving smoke-obscured objects and hidden targets, MINT outperformed LLM-based planning strategies, successfully navigating uncertainty by reasoning about specific knowledge gaps.

Significance and Claims
The paper claims to be the first work to combine symbolic reasoning of knowledge gaps, neural planning policies, and LLMs to enable active and optimized human elicitation in language-based planning. The authors assert that MINT effectively bridges the gap between the interpretability of symbolic methods and the adaptability of neural networks. By systematically narrowing knowledge gaps through targeted queries, MINT allows AI agents to function as proactive teammates rather than passive tools, achieving near-expert performance while minimizing the communication burden on human partners. The work suggests that this approach is particularly vital for open-world scenarios where perfect information is unavailable, and conservative planning leads to non-optimal outcomes.