前几天你已经反复见到线索：无论 LastValue.update 还是 BinaryOperatorAggregate.update，参数都叫 values（复数、列表）。这不是巧合——它就是"本超步内该字段收到的所有写入"。

D07 见过 LastValue.update，现在从"并行写"视角重看它（channels/last_value.py:56）：

# channels/last_value.py:56
    def update(self, values):
        if len(values) == 0:
            return False
        if len(values) != 1:                              # ★本步收到超过 1 个写入
            msg = create_error_message(
                message=f"At key '{self.key}': Can receive only one value per step. "
                        "Use an Annotated key to handle multiple values.",
                error_code=ErrorCode.INVALID_CONCURRENT_GRAPH_UPDATE,
            )
            raise InvalidUpdateError(msg)                 # 直接抛错，并教你怎么修
        self.value = values[-1]
        return True

换成有 reducer 的字段（累加通道），同样的并行写就能合并。重看 D09 的 update（channels/binop.py:123）：

# channels/binop.py:123（聚焦多值循环）
    def update(self, values):
        if not values:
            return False
        if self.value is MISSING:
            self.value = values[0]
            values = values[1:]
        seen_overwrite = False
        for value in values:                 # ★本步的每个并行写入，依次叠进来
            ...
            if not seen_overwrite:
                self.value = self.operator(self.value, value)
        return True

多字段状态里还有个隐蔽问题：一个字段名可能出现在多个 schema 里（状态 schema、输入 schema、某节点的私有输入 schema……D11 细讲）。这些"同名字段"必须映射到同一个通道对象，否则并行写就乱套了。负责登记、对齐的是 _add_schema（graph/state.py:342）：

# graph/state.py:342
    def _add_schema(self, schema, /, allow_managed=True):
        if schema not in self.schemas:
            _warn_invalid_state_schema(schema)
            channels, managed, type_hints = _get_channels(schema)   # D07 的读取逻辑
            ...
            self.schemas[schema] = {**channels, **managed}
            for key, channel in channels.items():
                if key in self.channels:                    # 这个字段名之前登记过？
                    if self.channels[key] != channel:       # ★类型不一致？
                        if isinstance(channel, LastValue):
                            pass                            # 新的是 LastValue → 宽容放过
                        else:
                            raise ValueError(
                                f"Channel '{key}' already exists with a different type"
                            )
                else:
                    self.channels[key] = channel            # 首次见到 → 登记

官方性能基准里有个真实的多字段、含并行分支的图（bench/pydantic_state.py:249）。它同时演示了多个 reducer 字段 + 扇出扇入：

# bench/pydantic_state.py:249（结构节选）
builder = StateGraph(State)
builder.add_edge(START, "one")
builder.add_node("one",  partial(read_write, "messages", ["trigger_events", "primary_issue_medium"]))
builder.add_edge("one", "two")
builder.add_node("two",  partial(read_write, "trigger_events", ["autoresponse", "issue"]))
builder.add_edge("two", "three")     # ★扇出：two 后面同时接 three 和 four
builder.add_edge("two", "four")
builder.add_node("three", ...)
builder.add_node("four",  ...)
builder.add_node("five",  ...)
builder.add_edge(["three", "four"], "five")   # ★扇入：three 和 four 都完成后才进 five
builder.add_edge("five", "six")

而它的 State 里，不同字段配了不同 reducer（bench/pydantic_state.py:14）：

# bench/pydantic_state.py:14（字段节选）
class State(BaseModel):
    messages: Annotated[list, operator.add] = Field(default_factory=list)        # 列表累加
    trigger_events: Annotated[list, operator.add] = Field(default_factory=list)  # 列表累加
    primary_issue_medium: Annotated[str, lambda x, y: y or x] = Field(default="email")  # "有新值用新的"
    autoresponse: Annotated[dict | None, lambda _, y: y] = Field(default=None)   # 永远覆盖
    slack_participants: Annotated[dict, operator.or_] = Field(default_factory=dict)  # 字典合并
    bot_id: str | None = Field(default=None)                                     # 没 reducer → LastValue

多字段状态还牵涉"哪些字段算最终输出"。编译时 output_channels 会过滤掉托管值（graph/state.py:1256）：

# graph/state.py:1256
        output_channels = (
            "__root__"
            if len(self.schemas[self.output_schema]) == 1
            and "__root__" in self.schemas[self.output_schema]
            else [
                key
                for key, val in self.schemas[self.output_schema].items()
                if not is_managed_value(val)      # ★托管值不算对外输出
            ]
        )

# 1. 读并行冲突的两处 update 对照
sed -n '56,67p' libs/langgraph/langgraph/channels/last_value.py
sed -n '123,144p' libs/langgraph/langgraph/channels/binop.py

# 2. 读通道对齐逻辑
sed -n '342,372p' libs/langgraph/langgraph/graph/state.py

# 3. 读真实多字段并行图
sed -n '249,297p' libs/langgraph/bench/pydantic_state.py