The optimization achieves a 13% speedup through three key changes:

**1. Eliminated Function Call Overhead:**
Replaced custom `is_dict()` and `is_list()` wrapper functions with direct `isinstance()` calls. The line profiler shows `is_dict()` was called 1,809 times, consuming 358µs. Direct `isinstance()` calls eliminate this overhead since Python's C-implemented `isinstance()` is highly optimized.

**2. Optimized Set Membership Check:**
Changed `if key == "index" or key == "type":` to `if key in {"index", "type"}:`. Set membership testing is faster than chained equality comparisons, especially when called frequently (4,109 times according to the profiler).

**3. Improved List Type Checking:**
Replaced `all(isinstance(x, (str, int, float)) for x in acc_value)` with an explicit loop that breaks early. This avoids potential generator overhead and provides better control flow for the type checking logic.

**Performance Impact by Test Case:**
- **List operations see the biggest gains** (24-66% faster): The elimination of function call overhead in list processing provides substantial benefits for cases like `test_accumulate_simple_list_of_scalars` and `test_accumulate_lists_of_primitives`.
- **Nested dictionary operations** show 16-39% improvements due to reduced overhead in recursive calls.
- **Simple scalar operations** see modest 3-7% gains from the set membership optimization.
- **Large-scale tests** demonstrate consistent 2-27% improvements, showing the optimizations scale well.

These optimizations are particularly effective for workloads with frequent list processing and nested dictionary operations, which are common in streaming data accumulation scenarios.