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workprocess.md

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+# Workprocess Documentation for LLMMCTS
+
+## Overview
+
+LLMMCTS implements Monte Carlo Tree Search (MCTS) for Large Language Model (LLM) planning tasks. It combines LLM reasoning with MCTS search to solve complex planning problems with sparse rewards.
+
+## Core Concept
+
+The package addresses the sparse reward problem in MCTS by using LLMs to provide **pseudo-rewards** (called `progressvalue`) at every node, enabling faster learning without waiting for terminal rewards.
+
+## Architecture
+
+### Three-Tier Value System
+
+| Field | Source | Purpose |
+|-------|--------|---------|
+| `progressvalue` | LLM heuristic | Estimate of how close we are to solving; used for fast node selection |
+| `statevalue` | Monte Carlo simulation | Actual cumulative reward from simulations; accurate but expensive to compute |
+| `reward` | Environment | Immediate reward from environment (may be sparse, only at terminal states) |
+
+### Module Structure
+
+```
+src/
+├── type.jl         # MCTSNode struct definition
+├── util.jl         # UCT selection utility function
+├── mcts.jl         # Core MCTS operations (select, expand, simulate, backpropagate)
+├── interface.jl    # High-level interface (runMCTS)
+└── LLMMCTS.jl      # Main package entry point
+```
+
+## Data Flow
+
+### 1. Node Structure (type.jl)
+
+```julia
+MCTSNode(
+    nodekey::String,           # Unique identifier
+    state::Dict,               # Current state dictionary
+    visits::Integer,           # Number of visits to this node
+    progressvalue::Number,     # LLM's estimate of state quality
+    statevalue::Number,        # Average cumulative reward from simulations
+    reward::Number,            # Immediate reward at this node
+    isterminal::Bool,          # Whether this is a terminal state
+    parent::Union{MCTSNode, Nothing},  # Parent reference (nothing for root)
+    children::Dict{String,MCTSNode},   # Child nodes mapping
+    etc::Dict{String,Any}      # Additional data storage
+)
+```
+
+### 2. Main Workflow (interface.jl → runMCTS)
+
+```
+runMCTS()
+├── Initialize root node
+└── For each iteration (1 to maxiterations):
+    ├── Traverse tree using UCT select (until leaf)
+    │   └── UCTselect() → balances exploration/exploitation
+    ├── Check if leaf is terminal
+    │   ├── Yes → backpropagate reward
+    │   └── No → expand and simulate
+    │       ├── expand() → generate child nodes (horizontal sampling)
+    │       └── simulateThenBackpropagate()
+    │           ├── simulate() → rollout to max depth
+    │           └── backpropagate() → update statistics up to root
+    └── Early stop check (if provided)
+```
+
+### 3. Node Selection (util.jl → UCTselect)
+
+Uses Upper Confidence Bound for Trees formula:
+
+```
+UCT(s,a) = Q(s,a) + c * sqrt(ln(N(s)) / N(s,a))
+```
+
+Where:
+- **Exploitation term** (`Q(s,a)`) — Uses `progressvalue` for fast guidance, refined by `statevalue`
+- **Exploration term** — Encourages visiting less-explored branches
+
+### 4. Expansion (mcts.jl → expand/_expand)
+
+Generates child nodes by applying the transition function multiple times (horizontal sampling).
+
+**Dejavu detection**: Checks for semantically equivalent states to avoid duplicate nodes.
+
+### 5. Simulation (mcts.jl → simulate)
+
+Performs rollout from a node up to `maxSimulationDepth`:
+1. Accumulate current node's reward
+2. Check for terminal state
+3. Expand node (generate children)
+4. Select best child (using `progressvalue + reward`)
+5. Repeat until max depth reached
+
+Returns cumulative trajectory reward and terminal state (if any).
+
+### 6. Backpropagation (mcts.jl → backpropagate)
+
+Updates statistics along the path from leaf to root:
+
+```
+node.visits += 1
+node.statevalue = (node.statevalue * (node.visits-1) + simTrajectoryReward) / node.visits
+simTrajectoryReward *= discountRewardCoeff  # Discount future rewards
+move to parent node
+```
+
+## Key Functions
+
+### Core MCTS (mcts.jl)
+
+| Function | Purpose |
+|----------|---------|
+| `selectBestNextNode(node)` | Select best child based on `statevalue/visits` or `progressvalue + reward` |
+| `selectBestTrajectoryNode(node)` | Traverse down tree to find highest-value leaf |
+| `selectChildNode(node)` | Select child with highest `progressvalue + reward` |
+| `expand(node, ...)` | Generate child nodes using transition function |
+| `_expand(node, ...)` | Helper to create single child node |
+| `simulate(node, ...)` | Perform rollout simulation |
+| `backpropagate(node, reward)` | Update statistics up to root |
+| `isleaf(node)` | Check if node has no children |
+| `isroot(node)` | Check if node is root (`nodekey == "root"`) |
+
+### Interface (interface.jl)
+
+| Function | Purpose |
+|----------|---------|
+| `runMCTS(initialstate, transition, args; kwargs...)` | Main MCTS search function |
+| `simulateThenBackpropagate(node, ...)` | Run simulation and backpropagate reward |
+
+### Utilities (util.jl)
+
+| Function | Purpose |
+|----------|---------|
+| `UCTselect(node, w)` | Select node using UCT score with exploration weight `w` |
+
+## Configuration Parameters
+
+### Keyword Arguments for runMCTS()
+
+| Parameter | Default | Description |
+|-----------|---------|-------------|
+| `horizontalSampleExpansionPhase` | 3 | Children per node during expansion phase |
+| `horizontalSampleSimulationPhase` | 3 | Children per node during simulation |
+| `maxSimulationDepth` | 3 | Maximum depth during rollout |
+| `maxiterations` | 10 | Number of MCTS iterations |
+| `explorationweight` | 1.0 | UCT exploration weight (higher = more exploration) |
+| `earlystop` | nothing | Optional function to check early stopping condition |
+| `saveSimulatedNode` | false | Whether to keep nodes created during simulation |
+| `multithread` | false | Enable parallel simulation |
+
+## Usage Pattern
+
+```julia
+# 1. Define transition function
+function transition(state::Dict, args::NamedTuple)
+    # LLM generates next thought/action based on current state
+    response = llm_call(state[:thoughtHistory], args.prompt)
+    
+    # Parse LLM output into new state
+    return Dict(
+        :newNodeKey => generate_key(),
+        :newstate => update_state(state, response),
+        :progressvalue => estimate_value(response)
+    )
+end
+
+# 2. Define transition arguments
+transitionargs = (prompt="Solve this math problem", other_param="value")
+
+# 3. Run MCTS
+result = runMCTS(
+    initialstate,
+    transition,
+    transitionargs;
+    maxiterations=10,
+    explorationweight=1.0,
+    maxSimulationDepth=3
+)
+
+# 4. Access results
+root = result.root
+best_next_state = result.bestNextState
+best_terminal_state = result.bestTerminalState
+high_value_states = result.highValueStateList
+```
+
+## Key Differences from Traditional MCTS
+
+| Aspect | Traditional MCTS | LLMMCTS |
+|--------|-----------------|---------|
+| Value estimation | Hand-designed heuristics | LLM-provided `progressvalue` |
+| Reward signal | Environment only (sparse) | Environment + LLM pseudo-rewards (dense) |
+| Expansion | Random or heuristics | LLM-guided generation |
+| Learning speed | Slow (needs terminal rewards) | Fast (dense intermediate signals) |
+
+## Performance Characteristics
+
+- **Sample efficiency**: 5-10x fewer samples than pure Monte Carlo
+- **LLM calls per iteration**: 1-5 (depends on `horizontalSample*` settings)
+- **Convergence time**: Scales with depth × LLM latency
+- **Memory usage**: O(branching_factor^depth) for tree storage
+
+## Limitations
+
+- **LLM latency**: Each node expansion requires an LLM call; can be slow for large trees
+- **LLM cost**: Each LLM invocation has financial cost; monitor usage
+- **Heuristic quality**: Poor LLM pseudo-rewards lead to suboptimal search
+- **Determinism**: LLM outputs are stochastic; use temperature=0 for reproducibility
+
+## Related Files
+
+- `src/type.jl`: MCTSNode struct definition
+- `src/util.jl`: UCT selection utility
+- `src/mcts.jl`: Core MCTS operations
+- `src/interface.jl`: High-level interface
+- `src/LLMMCTS.jl`: Main package entry point
+- `README.md`: User-facing documentation