MemReranker-4B

Introduction

MemReranker is a reasoning-aware reranking model family (0.6B / 4B) purpose-built for agent memory retrieval. It is fine-tuned from Qwen3-Reranker-4B through multi-stage LLM knowledge distillation.

In agent memory systems, the reranking model serves as the critical bridge connecting user queries with long-term memory. Most systems adopt the "retrieve-then-rerank" two-stage paradigm, but generic reranking models rely on semantic similarity matching and lack genuine reasoning capabilities. This leads to recalled results that are semantically relevant yet do not contain the key information needed to answer the question.

MemReranker addresses three specific problems in memory scenarios:

Score Miscalibration — Relevance scores from generic models are poorly calibrated, making threshold-based filtering difficult.
Complex Query Degradation — Ranking degrades when facing temporal constraints, causal reasoning, and other complex queries.
Context Disambiguation — The model cannot leverage dialogue context for semantic disambiguation.

📄 Paper: MemReranker: Reasoning-Aware Reranking for Agent Memory Retrieval

Model Family

Model	Base Model	Parameters	HuggingFace
MemReranker-4B	Qwen3-Reranker-4B	4B	IAAR-Shanghai/MemReranker-4B

💡 No GPU? Use our hosted API directly! Both models are available via the Memos Rerank API — no deployment needed. See the Memos API section below.

Training Methodology

MemReranker employs a two-stage training paradigm:

Stage 1: BCE Pointwise Distillation — Multi-teacher pairwise comparisons generate calibrated soft labels via a five-level scoring system based on Elo/Bradley-Terry, establishing well-distributed scores.
Stage 2: InfoNCE Contrastive Fine-tuning — Enhances hard-sample discrimination through contrastive learning.

Training data combines general corpora with memory-specific multi-turn dialogue data covering temporal constraints, causal reasoning, and coreference resolution.

Benchmark Results

LOCOMO Memory Retrieval Benchmark

Model	MAP	MRR	NDCG@1	NDCG@3	NDCG@10	NDCG	R@3	R@5	R@20	F1
BGE-v2-m3	0.671	0.699	0.607	0.672	0.714	0.736	0.716	0.768	0.863	0.504
Qwen3-Reranker-0.6B	0.643	0.673	0.576	0.638	0.689	0.714	0.681	0.748	0.857	0.472
Qwen3-Reranker-4B	0.689	0.716	0.623	0.691	0.732	0.750	0.735	0.796	0.873	0.522
Qwen3-Reranker-8B	0.721	0.748	0.666	0.724	0.759	0.775	0.763	0.813	0.880	0.552
GPT-4o-mini	0.715	0.742	0.657	0.719	0.753	0.770	0.760	0.812	0.868	0.544
Gemini-3-Flash	0.777	0.797	0.737	0.778	0.807	0.816	0.805	0.847	0.892	0.622
MemReranker-0.6B	0.715	0.738	0.650	0.717	0.754	0.770	0.758	0.809	0.885	0.555
MemReranker-4B	0.737	0.760	0.679	0.739	0.773	0.786	0.777	0.824	0.889	0.577

MemReranker-0.6B matches GPT-4o-mini and open-source 4B/8B models on key metrics. MemReranker-4B achieves 0.737 MAP, with several metrics on par with Gemini-3-Flash, while maintaining inference latency at only 10–20% of large models (~200ms).

Quickstart

Memos API

The easiest way to use MemReranker — no GPU or local deployment required. Both memos-reranker-0.6b and memos-reranker-4b are available through the Memos Rerank API.

import os
import requests
import json

os.environ["MEMOS_API_KEY"] = "YOUR_API_KEY"         # Get from https://memos-dashboard.openmem.net/apikeys/
os.environ["MEMOS_BASE_URL"] = "https://memos.memtensor.cn/api/openmem/v1"

url = f"{os.environ['MEMOS_BASE_URL']}/rerank"

payload = {
    "model": "memos-reranker-4b",   # or "memos-reranker-0.6b"
    "query": "用户有什么兴趣爱好",
    "documents": [
        "用户喜欢打羽毛球",
        "用户在杭州做后端开发",
        "用户偏好简洁的回复风格",
        "用户比较喜欢酱香型白酒",
        "用户下周三要去北京出差"
    ],
    "top_n": 3
}

headers = {
    "Content-Type": "application/json",
    "Authorization": f"Token {os.environ['MEMOS_API_KEY']}"
}

response = requests.post(url, headers=headers, data=json.dumps(payload))
print(response.json())

📖 Full API documentation: https://memos-docs.openmem.net/cn/api_docs/core/rerank

Sentence Transformers (Recommended)

import torch
from sentence_transformers import CrossEncoder

model = CrossEncoder(
    "IAAR-Shanghai/MemReranker-4B",
    model_kwargs={"torch_dtype": torch.bfloat16, "device_map": "cuda"},
)

query = "What is the capital of China?"
documents = [
    "The capital of China is Beijing.",
    "Beijing has been the capital of various Chinese dynasties.",
    "Python is a popular programming language.",
]

# Get relevance scores
scores = model.predict([(query, doc) for doc in documents])
print(scores)
# tensor([2.969, 1.328, -3.141])

# Get 0-1 probability via sigmoid
probs = model.predict(
    [(query, doc) for doc in documents],
    activation_fn=torch.nn.Sigmoid(),
)

# Or get a sorted ranking directly
results = model.rank(query, documents)
print(results)

The default prompt is "query", which injects the instruction "Given a web search query, retrieve relevant passages that answer the query". A custom instruction can be passed via prompts={"my_task": "..."} and default_prompt_name="my_task" on CrossEncoder(...).

Transformers

Requires transformers>=4.51.0

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

def format_instruction(instruction, query, doc):
    if instruction is None:
        instruction = 'Given a web search query, retrieve relevant passages that answer the query'
    output = "<Instruct>: {instruction}\n<Query>: {query}\n<Document>: {doc}".format(
        instruction=instruction, query=query, doc=doc
    )
    return output

def process_inputs(pairs):
    inputs = tokenizer(
        pairs, padding=False, truncation='longest_first',
        return_attention_mask=False,
        max_length=max_length - len(prefix_tokens) - len(suffix_tokens)
    )
    for i, ele in enumerate(inputs['input_ids']):
        inputs['input_ids'][i] = prefix_tokens + ele + suffix_tokens
    inputs = tokenizer.pad(inputs, padding=True, return_tensors="pt", max_length=max_length)
    for key in inputs:
        inputs[key] = inputs[key].to(model.device)
    return inputs

def compute_logits(inputs):
    batch_scores = model(**inputs).logits[:, -1, :]
    true_vector = batch_scores[:, token_true_id]
    false_vector = batch_scores[:, token_false_id]
    batch_scores = torch.stack([false_vector, true_vector], dim=1)
    batch_scores = torch.nn.functional.log_softmax(batch_scores, dim=1)
    scores = batch_scores[:, 1].exp().tolist()
    return scores

tokenizer = AutoTokenizer.from_pretrained(
    "IAAR-Shanghai/MemReranker-4B", padding_side='left'
)
model = AutoModelForCausalLM.from_pretrained(
    "IAAR-Shanghai/MemReranker-4B"
).eval()

# We recommend enabling flash_attention_2 for better acceleration and memory saving:
# model = AutoModelForCausalLM.from_pretrained(
#     "IAAR-Shanghai/MemReranker-4B",
#     torch_dtype=torch.float16,
#     attn_implementation="flash_attention_2"
# ).cuda().eval()

token_false_id = tokenizer.convert_tokens_to_ids("no")
token_true_id = tokenizer.convert_tokens_to_ids("yes")
max_length = 8192

prefix = '<|im_start|>system\nJudge whether the Document meets the requirements based on the Query and the Instruct provided. Note that the answer can only be "yes" or "no".<|im_end|>\n<|im_start|>user\n'
suffix = '<|im_end|>\n<|im_start|>assistant\n<think>\n\n</think>\n\n'
prefix_tokens = tokenizer.encode(prefix, add_special_tokens=False)
suffix_tokens = tokenizer.encode(suffix, add_special_tokens=False)

task = 'Given a web search query, retrieve relevant passages that answer the query'
queries = ["What is the capital of China?", "Explain gravity"]
documents = [
    "The capital of China is Beijing.",
    "Gravity is a force that attracts two bodies towards each other. It gives weight to physical objects and is responsible for the movement of planets around the sun.",
]

pairs = [format_instruction(task, query, doc) for query, doc in zip(queries, documents)]
inputs = process_inputs(pairs)
scores = compute_logits(inputs)
print("scores:", scores)

vLLM

Requires vllm>=0.8.5

import math
import torch
from transformers import AutoTokenizer
from vllm import LLM, SamplingParams
from vllm.inputs.data import TokensPrompt

def format_instruction(instruction, query, doc):
    text = [
        {"role": "system", "content": 'Judge whether the Document meets the requirements based on the Query and the Instruct provided. Note that the answer can only be "yes" or "no".'},
        {"role": "user", "content": f"<Instruct>: {instruction}\n\n<Query>: {query}\n\n<Document>: {doc}"}
    ]
    return text

def process_inputs(pairs, instruction, max_length, suffix_tokens):
    messages = [format_instruction(instruction, query, doc) for query, doc in pairs]
    messages = tokenizer.apply_chat_template(
        messages, tokenize=True, add_generation_prompt=False, enable_thinking=False
    )
    messages = [ele[:max_length] + suffix_tokens for ele in messages]
    messages = [TokensPrompt(prompt_token_ids=ele) for ele in messages]
    return messages

def compute_logits(model, messages, sampling_params, true_token, false_token):
    outputs = model.generate(messages, sampling_params, use_tqdm=False)
    scores = []
    for output in outputs:
        final_logits = output.outputs[0].logprobs[-1]
        true_logit = final_logits[true_token].logprob if true_token in final_logits else -10
        false_logit = final_logits[false_token].logprob if false_token in final_logits else -10
        true_score = math.exp(true_logit)
        false_score = math.exp(false_logit)
        score = true_score / (true_score + false_score)
        scores.append(score)
    return scores

number_of_gpu = torch.cuda.device_count()
tokenizer = AutoTokenizer.from_pretrained("IAAR-Shanghai/MemReranker-4B")
model = LLM(
    model="IAAR-Shanghai/MemReranker-4B",
    tensor_parallel_size=number_of_gpu,
    max_model_len=10000,
    enable_prefix_caching=True,
    gpu_memory_utilization=0.8,
)
tokenizer.padding_side = "left"
tokenizer.pad_token = tokenizer.eos_token

suffix = '<|im_end|>\n<|im_start|>assistant\n<think>\n\n</think>\n\n'
max_length = 8192
suffix_tokens = tokenizer.encode(suffix, add_special_tokens=False)
true_token = tokenizer("yes", add_special_tokens=False).input_ids[0]
false_token = tokenizer("no", add_special_tokens=False).input_ids[0]
sampling_params = SamplingParams(
    temperature=0,
    max_tokens=1,
    logprobs=20,
    allowed_token_ids=[true_token, false_token],
)

task = 'Given a web search query, retrieve relevant passages that answer the query'
queries = ["What is the capital of China?", "Explain gravity"]
documents = [
    "The capital of China is Beijing.",
    "Gravity is a force that attracts two bodies towards each other.",
]

pairs = list(zip(queries, documents))
inputs = process_inputs(pairs, task, max_length - len(suffix_tokens), suffix_tokens)
scores = compute_logits(model, inputs, sampling_params, true_token, false_token)
print("scores:", scores)

vLLM OpenAI-Compatible Server

You can also deploy MemReranker-4B as an OpenAI-compatible API server using vLLM:

python -m vllm.entrypoints.openai.api_server \
  --model IAAR-Shanghai/MemReranker-4B \
  --tensor-parallel-size 1 \
  --gpu-memory-utilization 0.9 \
  --served-model-name MemReranker-4B \
  --host 0.0.0.0 \
  --port 8089 \
  --chat-template /path/to/qwen3_reranker.jinja \
  --hf_overrides '{"architectures": ["Qwen3ForSequenceClassification"], "classifier_from_token": ["no", "yes"], "is_original_qwen3_reranker": true}'

How It Works

MemReranker uses a yes/no logit-score reranking head inherited from Qwen3-Reranker. Given a query-document pair, the model outputs logits at the final token position. The score is computed as:

$\text{score} = \frac{e^{\log p(\text{yes})}}{e^{\log p(\text{yes})} + e^{\log p(\text{no})}}$

where yes (token id: 9693) and no (token id: 2152) are the two classification tokens.

Use Cases

Agent Memory Retrieval — Rerank retrieved memory fragments for LLM agents with long-term memory.
Multi-turn Dialogue Search — Handle temporal, causal, and coreference queries in conversational history.
RAG Pipelines — Serve as a second-stage reranker in Retrieve-then-Rerank architectures.
General Text Retrieval — Also effective as a general-purpose reranker for web search and document retrieval tasks.

Citation

@article{li2026memreranker,
  title={MemReranker: Reasoning-Aware Reranking for Agent Memory Retrieval},
  author={Li, Chunyu and Kang, Jingyi and Chen, Ding and Zhang, Mengyuan and Shen, Jiajun and Tang, Bo and Zhou, Xuanhe and Xiong, Feiyu and Li, Zhiyu},
  journal={arXiv preprint arXiv:2605.06132},
  year={2026}
}

Acknowledgements

MemReranker is built upon Qwen3-Reranker by the Qwen Team. We thank the Sentence Transformers community for the CrossEncoder integration support.

license: apache-2.0

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Paper for IAAR-Shanghai/MemReranker-4B

MemReranker: Reasoning-Aware Reranking for Agent Memory Retrieval

Paper • 2605.06132 • Published 6 days ago