docs(benchmark): add quick start guides for halumem and longmemeval experiments

- Created HaluMem experiment quick start guide with ReMe integration setup
- Added detailed steps for installing ReMe environment using conda
- Included repository cloning instructions for HaluMem benchmark
- Provided complete command examples for running HaluMem experiments
- Created LongMeMEval quick start guide with data download procedures
- Added wget commands for downloading cleaned dataset files
- Included evaluation script instructions for computing experiment statistics
- Documented parameter configurations for different model types and batch sizes

benchmark/halumem/quickstart.md

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+# Halumem
+Experiment Quick Start Guide
+This guide helps you quickly set up and run Halumem experiments with ReMe integration.
+
+### 1. Start ReMe Service
+Install ReMe (if not already installed)
+If you haven't installed the ReMe environment yet, follow these steps:
+```bash
+# Create ReMe environment
+conda create -p ./reme-env python==3.12
+conda activate ./reme-env
+
+# Install ReMe
+pip install .
+```
+
+### 2. Clone the Repository
+```bash
+cd ./benchmark/halumem
+git clone https://github.com/MemTensor/HaluMem.git
+```
+
+### 3. Run Experiments
+Launch the ReMe service to enable memory library functionality:
+```bash
+clear && python benchmark/halumem/eval_reme.py \
+    --data_path benchmark/halumem/HaluMem/data/HaluMem-Medium.jsonl \
+    --reme_model_name gpt-4o-mini-2024-07-18 \
+    --eval_model_name gpt-4o-mini-2024-07-18 \
+    --batch_size 40 \
+    --algo_version default
+```
+

benchmark/longmemeval/quickstart.md

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+# Halumem
+Experiment Quick Start Guide
+This guide helps you quickly set up and run Halumem experiments with ReMe integration.
+
+### 1. Start ReMe Service
+Install ReMe (if not already installed)
+If you haven't installed the ReMe environment yet, follow these steps:
+```bash
+# Create ReMe environment
+conda create -p ./reme-env python==3.12
+conda activate ./reme-env
+
+# Install ReMe
+pip install .
+```
+
+### 2. Clone the Repository
+```bash
+cd ./benchmark/longmemeval
+mkdir -p data/
+cd data/
+wget https://huggingface.co/datasets/xiaowu0162/longmemeval-cleaned/resolve/main/longmemeval_oracle.json
+wget https://huggingface.co/datasets/xiaowu0162/longmemeval-cleaned/resolve/main/longmemeval_s_cleaned.json
+wget https://huggingface.co/datasets/xiaowu0162/longmemeval-cleaned/resolve/main/longmemeval_m_cleaned.json
+cd ..
+```
+
+### 3. Run Experiments
+Launch the ReMe service to enable memory library functionality:
+```bash
+clear && python benchmark/longmemeval/eval_longmemeval_reme.py \
+    --data_path benchmark/longmemeval/data/longmemeval_s_cleaned.json \
+    --reme_model_name qwen-flash \
+    --reme_model_name retrieve_model_name \
+    --eval_model_name gpt-4o-mini-2024-07-18 \
+    --batch_size 20 \
+    --algo_version default
+```
+
+
+### 4. Evaluate Results
+Evaluate the results of the experiments:
+```bash
+python benchmark/longmememeval/compute_stats.py \
+    --results_dir bench_results/longmemeval_reme \
+    --output_file bench_results/longmemeval_reme/statistics.json
+```
+The `compute_stats.py` script computes various statistics from the evaluation results.