[Bug]:AutoContextMemory Strategy 6 breaks ReAct tool_use/tool_result structure, causing LLM to repeat tool calls infinitely

## Bug Description

When `AutoContextMemory` triggers **Strategy 6** (`summaryCurrentRoundMessages`),
it compresses `[tool_use + tool_result]` message pairs into a single plain
`ASSISTANT` text message. This destroys the structural information that the LLM
relies on to recognize a completed tool execution, causing it to re-invoke the
same tool in a loop.

## Version

`agentscope-extensions-autocontext-memory: 1.0.9`

## Steps to Reproduce

1. Configure `AutoContextMemory` with a low `msgThreshold` (e.g. 6) so that
Strategy 6 is triggered after a few rounds.
2. Call a tool that returns a large result (e.g. a search or file-read tool).
3. Trigger compression — Strategy 6 fires and compresses the current round.
4. On the next user turn, the LLM re-invokes the same tool with the same
arguments, even though the result already exists in history.

## Root Cause

In `summaryCurrentRoundMessages`, the compressed output is written as a single
`ASSISTANT` role message:

**Before compression (valid ReAct structure):**
ASSISTANT → { type: tool_use, call_id: "abc", name: "search", input: {...} }
USER → { type: tool_result, call_id: "abc", content: "" }

**After Strategy 6 compression (structure destroyed):**
ASSISTANT → "我调用了 search 工具，返回："

The LLM no longer sees a `tool_use` / `tool_result` pair. From its perspective,
no tool call has been made in the current context, so it issues a new `tool_use`
request — triggering an infinite loop.

## Expected Behavior

The message role structure should be preserved after compression. Only the
**content** of the `tool_result` should be replaced with a reference/summary:

ASSISTANT → { type: tool_use, call_id: "abc", name: "search", input: {...} }
USER → { type: tool_result, call_id: "abc", content: "" }

This way the LLM still recognizes the completed tool invocation and does not
repeat it.

## Current Workaround

Adding explicit instructions to the system prompt asking the LLM to treat
compressed messages as completed tool executions. This is fragile and
model-dependent.