+
+| Algorithm | Performance Comparison |
+
+
+
+| fib |
+
+
+ 11.72 s
+
+ 5.47 s
+ |
+
+
+
+| fib_memo |
+
+
+ 0.93 ms
+
+ 0.55 ms
+ |
+
+
+
+| fib_iter |
+
+
+ 0.375 ms
+
+ 0.100 ms
+ |
+
+
+
+| fib_tail |
+
+
+ 0.401 ms
+
+ 0.157 ms
+ |
+
+
+
+
+---
+
+## Detailed Analysis
+
+### 1. Naive Recursion (`fib`)
+
+* **Time:** 5.471 seconds (5471 ms)
+* **Algorithm Complexity:** O(2ⁿ) exponential
+* **Performance Notes:**
+
+ * Reduced by roughly half compared to 0.4.2-alpha
+ * Function call overhead optimization effective, but exponential growth remains the bottleneck
+
+### 2. Memoized Recursion (`fib_memo`)
+
+* **Time:** 0.550 ms
+* **Algorithm Complexity:** O(n) linear
+* **Performance Notes:**
+
+ * Hash table / cache access efficiency improved
+ * Sub-millisecond execution suitable for overlapping subproblems
+
+### 3. Iterative (`fib_iter`)
+
+* **Time:** 0.100 ms
+* **Algorithm Complexity:** O(n) linear
+* **Performance Notes:**
+
+ * Fastest implementation, ~3.7× improvement over 0.4.2-alpha
+ * Loop and arithmetic operation optimization significant
+
+### 4. Tail Recursion (`fib_tail`)
+
+* **Time:** 0.157 ms
+* **Algorithm Complexity:** O(n) linear
+* **Performance Notes:**
+
+ * Slightly slower than iterative, ~2.5× improvement over 0.4.2-alpha
+ * Tree traversal interpreter optimizations for recursion effective; TCO not implemented
+
+---
+
+## Technical Insights
+
+* Function call overhead significantly reduced
+* Loop and arithmetic operations show greatest efficiency gains
+* Hash table / cache access highly efficient
+* Algorithm choice remains the dominant factor for performance
+
+---
+
+## Recommendations for Developers
+
+1. Prioritize iterative solutions for performance-critical code
+2. Use memoization for recursion with overlapping subproblems
+3. Tail recursion is suitable for moderate depth, but TCO is not implemented
+4. Avoid exponential algorithms in interpreted code
+5. Benchmark different implementations, as algorithm choice dominates performance
+
+---
+
+## Conclusion
+
+Fig v0.4.3-alpha tree traversal interpreter shows significant improvements in function call and loop optimizations, particularly benefiting iterative and tail-recursive implementations.
+O(n) algorithms execute at sub-millisecond speeds, while exponential recursion remains limited. Overall interpreter performance is adequate for practical applications.
+
+**Report Generated:** Based on actual benchmark execution
+**Interpreter Type:** Tree Traversal Interpreter
+**Version:** 0.4.3-alpha
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+# Fig 语言性能基准测试报告
+
+## 版本: 0.4.3-alpha (树遍历解释器)
+
+### 前言
+
+本报告基于 Fig v0.4.3-alpha 树遍历解释器对 Fibonacci 算法进行了基准测试，并与 0.4.2-alpha 版本对比。结果显示 0.4.3-alpha 在函数调用、循环和递归优化方面都有显著提升，尤其是迭代和尾递归实现性能改善明显。
+
+### 测试环境
+
+* **CPU:** Intel Core i5-13490F
+* **操作系统:** Windows 11
+* **编译器/解释器:** Fig 树遍历解释器 v0.4.3-alpha
+* **测试日期:** 当前测试执行
+
+### 执行摘要
+
+本基准测试评估了 Fig 中四种不同斐波那契算法实现的性能，计算第30个斐波那契数（832,040）。结果显示算法选择仍是性能主导因素，同时反映了解释器在函数调用和循环方面的优化效果。
+
+## 性能结果
+
+### 最新浮动执行时间 (0.4.3-alpha)
+
+| 算法 | 时间(秒) | 时间(毫秒) | 相对速度 |
+| ------------------- | ----------- | ---------- | ------------ |
+| `fib` (朴素递归) | 5.471 s | 5471.37 ms | 1.00× (基准) |
+| `fib_memo` (记忆化) | 0.0005503 s | 0.5503 ms | 9,950× 更快 |
+| `fib_iter` (迭代) | 0.0001004 s | 0.1004 ms | 54,500× 更快 |
+| `fib_tail` (尾递归) | 0.0001573 s | 0.1573 ms | 34,800× 更快 |
+
+### 与 0.4.2-alpha 对比
+
+| 算法 | 0.4.2-alpha 时间 | 0.4.3-alpha 时间 | 性能提升倍数 |
+| ------------------- | ---------------- | ---------------- | ------------ |
+| `fib` (朴素递归) | 11.721 s | 5.471 s | ~2.14× |
+| `fib_memo` (记忆化) | 0.930 ms | 0.550 ms | ~1.69× |
+| `fib_iter` (迭代) | 0.375 ms | 0.100 ms | ~3.73× |
+| `fib_tail` (尾递归) | 0.401 ms | 0.157 ms | ~2.55× |
+
+---
+
+## 可视化性能对比（横向柱状图占位符）
+0.4.2-alpha vs 0.4.3-alpha
+
+**说明:** 每行两条条形：灰色表示 0.4.2-alpha，蓝色表示 0.4.3-alpha
+
+