Agentic Knowledgeable Tree Search Algorithm

• The Agentic Knowledgeable Tree Search Algorithm is a framework that recasts sequence tagging as a sequential decision-making problem using MCTS and MDP.
• It leverages dual LSTM encoders for word and tag representation to enable precise policy and value predictions at each decision step.
• Reinforcement learning and exploratory tree search drive enhanced performance, achieving superior accuracy on benchmarks like the CoNLL 2000 chunking task.

The Agentic Knowledgeable Tree Search Algorithm is exemplified by the MM-Tag model, which integrates Monte Carlo Tree Search (MCTS) with a Markov Decision Process (MDP) framework to address sequence tagging tasks in natural language processing. This approach formulates tag assignment as a sequential decision-making problem, enhancing the agent’s capacity to make globally informed and exploratory decisions through lookahead search, policy-value learning, and reinforcement optimization.

1. Integration of MCTS with Markov Decision Processes in Sequence Tagging

MM-Tag recasts the sequence labeling task as an MDP, where each state encapsulates the sequence of processed words and assigned tags up to the current position. The action at each state corresponds to choosing a tag for the current word in the sequence. Unlike traditional sequence models that employ greedy or locally conditioned strategies, MM-Tag incorporates MCTS to perform exploratory lookahead at every decision step.

• States: Embedded representations of the current prefix (words and tags assigned so far).
• Actions: Assignment of a tag to the next word in the sentence.
• Transition: Moving to the next state by applying the chosen tag.
• Reward: Computed as the accuracy of the complete sequence of tags after finishing the assignment.

By running MCTS at each word position, the algorithm simulates alternative sequences of future tag assignments, which allows the agent to plan ahead and anticipate the impact of current decisions on the overall tagging performance.

2. Representation and Learning with LSTM Encoders

To facilitate informed search, MM-Tag uses two LSTM networks:

• Word LSTM ($\mathrm{LSTM}_X$): Encodes the word sequence up to the current position, providing a summary of semantic content.
• Tag LSTM ($\mathrm{LSTM}_Y$): Encodes the sequence of tags assigned up to but not including the current step, capturing contextual dependencies in labeling.

The state vector is given by:

$$\mathbf{g}(s) = \left[\mathrm{LSTM}_X(\mathbf{X}_t)^T, \mathrm{LSTM}_Y(\mathbf{Y}_{t-1})^T\right]^T$$

Based on this representation:

• The value function $$V(s) = \sigma(\langle \mathbf{w}, \mathbf{g}(s) \rangle + b_v)$$ predicts the expected final tagging accuracy starting from the current state.
• The policy function $p(a|s)$ outputs a probability distribution over possible next tags.

Both the value and policy functions are learned and refined throughout training.

3. Reinforcement Learning and Loss Function

MM-Tag is trained via reinforcement learning, optimizing both the policy and value functions based on global, sequence-level rewards:

• At each time step, after MCTS search, the algorithm obtains a "search policy" $\bm{\pi}_t$ that reflects improved, exploration-augmented tag probabilities derived from simulated lookahead.
• After the complete sequence is labeled, a reward ($r$) is assigned as the final sequence tagging accuracy.

The overall objective combines value prediction error (mean-squared error between predicted and true accuracy) and policy learning (cross-entropy between predicted and search policies):

$$\ell(E, r) = \sum_{t=1}^{|E|} \left[ (V(s_t) - r)^2 + \sum_{a\in\mathcal{A}(s_t)}\pi_t(a|s_t) \log \frac{1}{p(a|s_t)} \right]$$

Parameter optimization is performed using AdaGrad-based stochastic gradient descent. This learning scheme aligns single-step predictions with global, sequence-level accuracy, counteracting local optima that can hinder myopic models.

4. Exploratory Tree Search and Decision Policy

At each labeling step, MCTS operates as follows:

• Selection: Traverses the search tree using an upper confidence bound to balance exploitation (high Q-value/anticipated reward) and exploration (low visit count).
• Expansion: Adds new nodes (possible tag assignments) to the tree, guided by the policy network.
• Evaluation: Uses the value network to assess leaf or newly expanded nodes, avoiding the need for full trajectory rollout.
• Backpropagation: Updates the statistics (value estimates, visit counts) along the traversed path.
• Policy Extraction: The root node's improved policy is computed from normalized visit counts:

$$\pi(a|s_R) = \frac{N(s_R, a)}{\sum_{a'} N(s_R, a')}$$

This mechanism ensures that the final tag assignment at each position is not just locally optimal but also takes into account its anticipated effect on the full sequence, as simulated by the agentic tree search.

5. Empirical Evaluation and Performance Benefits

MM-Tag was evaluated on the CoNLL 2000 chunking benchmark, showing the following results:

Model	Precision	Recall	F1	Accuracy
CRF	93.98%	94.47%	94.13%	94.54%
LSTM+CRF	88.04%	88.43%	88.17%	89.99%
BI-LSTM+CRF	89.57%	89.81%	89.65%	90.61%
MM-Tag	95.75%	95.47%	94.82%	95.77%

These results established that MM-Tag surpasses both traditional (CRF) and neural (LSTM-CRF, BI-LSTM-CRF) baselines in all metrics. Ablation tests attribute these improvements specifically to the exploratory mechanism of MCTS; removing this component dropped accuracy to 27%-30%, demonstrating that the search-driven exploration is critical to MM-Tag's effectiveness.

6. Applications Beyond Sequence Labeling and Broader Implications

While developed for sequence tagging tasks such as chunking, part-of-speech tagging, and named-entity recognition, the method extends to any problem where:

• Each decision impacts future outcomes (sequential dependencies).
• Greedy or locally conditioned predictions are suboptimal.
• Deep neural representations and search-based planning can uncover superior global strategies.

Potential application domains include text generation, machine translation, multi-label classification in vision, and sequential planning/control in reinforcement learning. The general approach demonstrates how deep learning and planning (via tree search) can be combined to address long-term consequence reasoning in structured prediction tasks, illustrating broader potential for agentic, knowledgeable decision-making systems.

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MM-Tag thus serves as a prototype for agentic knowledgeable tree search, merging sequential representation learning, reinforcement reward optimization, and strategic lookahead, and establishing a framework that has influenced subsequent research in structured prediction and planning.