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---
license: mit
language:
- en
- zh
- multilingual
pipeline_tag: text-ranking
library_name: transformers
tags:
- reranker
- retrieval
- rag
- agentic-search
- qwen3.5
- sentence-transformers
---
# Prism-Reranker
**Beyond Relevance Scoring — Jointly Producing Contributions and Evidence for Agentic Retrieval.**
A reranker family that, unlike standard rerankers that emit only a relevance score, returns three things in a single forward pass: a calibrated score, a one-sentence *contribution*, and a self-contained *evidence* passage extracted from the document.
![Model Architecture](./model_architecture.png)
## Released models
Five checkpoints are released on the Hugging Face Hub. Four are fine-tuned from the **Qwen3.5** backbone; one (`-4B-exp`) is an experimental extension built on top of **Qwen3-Reranker-4B**, demonstrating that the same recipe transfers to an existing LLM-based reranker without losing ranking quality.
| Model | Backbone | Parameters | Hugging Face |
|---|---|---|---|
| Prism-Qwen3.5-Reranker-0.8B | Qwen3.5 | 0.8B | [infgrad/Prism-Qwen3.5-Reranker-0.8B](https://huggingface.co/infgrad/Prism-Qwen3.5-Reranker-0.8B) |
| Prism-Qwen3.5-Reranker-2B | Qwen3.5 | 2B | [infgrad/Prism-Qwen3.5-Reranker-2B](https://huggingface.co/infgrad/Prism-Qwen3.5-Reranker-2B) |
| Prism-Qwen3.5-Reranker-4B | Qwen3.5 | 4B | [infgrad/Prism-Qwen3.5-Reranker-4B](https://huggingface.co/infgrad/Prism-Qwen3.5-Reranker-4B) |
| Prism-Qwen3.5-Reranker-9B | Qwen3.5 | 9B | [infgrad/Prism-Qwen3.5-Reranker-9B](https://huggingface.co/infgrad/Prism-Qwen3.5-Reranker-9B) |
| Prism-Qwen3-Reranker-4B-exp | Qwen3-Reranker-4B | 4B | [infgrad/Prism-Qwen3-Reranker-4B-exp](https://huggingface.co/infgrad/Prism-Qwen3-Reranker-4B-exp) |
## Why this model?
In agentic / RAG pipelines, a relevance score is rarely the end goal. After deciding a document is relevant, the agent still has to read it, denoise it, and decide what to do next. Prism-Reranker folds that work into the reranker itself:
- **Relevance score** — `s(q, d) = σ(ℓ_yes − ℓ_no) ∈ (0, 1)`. Calibrated, ranking-ready.
- **`<contribution>`** — one sentence stating *every* core point the document contributes to the query. Useful for the agent to plan its next step without re-reading the doc.
- **`<evidence>`** — a self-contained, faithfully-rephrased rewrite of the query-relevant content. Drops irrelevant background, preserves verbatim proper nouns / numbers / dates / code / URLs. You can feed `<evidence>` directly to a downstream LLM and skip the raw document — saving context tokens and removing web-noise.
If the document is not relevant, the model outputs `no` and stops. No contribution/evidence is generated.
## Highlights
- **Backbones**: Qwen3.5 series for the four main sizes, no architectural changes; one extension variant on top of Qwen3-Reranker-4B.
- **Context length**: training data capped at **10K tokens** per example, covering most real-world documents.
- **Multilingual**: Chinese / English primary; other languages supported but with less coverage.
- **Keyword-query robust**: agents often emit keyword-style queries instead of well-formed questions. ~30% of training queries were rewritten by an LLM into keyword form, so the model handles both natural and keyword queries.
- **Real-world data distribution**: in addition to open reranker datasets (MS MARCO, T2Ranking, MIRACL, …), training includes synthetic queries paired with real Tavily / Exa web-search results, matching what an actual agent sees at inference time.
- **Length × score balanced**: training data was rebalanced so that document length is not a relevance shortcut.
- **Training recipe**: distillation (point-wise MSE on a strong commercial reranker's scores) + SFT on `yes/no` + `<contribution>` + `<evidence>`, supervised by a 5-LLM-as-judge ensemble.
## Quickstart
Two ways to call the model. Both produce the **same** relevance score `s(q, d) = σ(ℓ_yes − ℓ_no)`. Use **A** when you also want `<contribution>` / `<evidence>`. Use **B** when you only need a score and want a drop-in replacement for any other CrossEncoder reranker.
We use one shared example throughout so you can compare the outputs side by side:
```python
QUERY = "What is the boiling point of water at sea level?"
DOCUMENTS = [
"Water boils at 100 C (212 F) at standard atmospheric pressure (1 atm), "
"which corresponds to sea-level conditions.",
"Mount Everest is the highest mountain on Earth, with a peak elevation "
"of 8,848 meters above sea level.",
]
```
### A. Transformers (full output: score + contribution + evidence)
```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
MODEL_PATH = "infgrad/Prism-Qwen3.5-Reranker-4B" # or any sibling repo above
SYSTEM_PROMPT = (
"Judge whether the Document meets the requirements based on "
"the Query and the Instruct provided. "
)
INSTRUCTION = (
'Judge if the document is relevant to the query. Reply "yes" or "no".\n'
'On "yes", also emit:\n'
"<contribution>One sentence covering every core point the document "
"contributes to the query, without elaboration.</contribution>\n"
"<evidence>Self-contained rewrite of the query-relevant content. Rules:\n"
"- Faithful: rephrase only; add or infer nothing.\n"
"- Self-contained: evidence alone must fully answer the query.\n"
"- Concise: drop query-irrelevant background.\n"
"- Verbatim (no translation): proper nouns, terms, abbreviations, "
"numbers, dates, code, URLs.\n"
"- Output language: multilingual doc → query's language; else doc's language."
"</evidence>"
)
PROMPT_TEMPLATE = (
"<|im_start|>system\n{system}<|im_end|>\n"
"<|im_start|>user\n"
"<Instruct>: {instruction}\n"
"<Query>: {query}\n"
"<Document>: {doc}<|im_end|>\n"
"<|im_start|>assistant\n<think>\n\n</think>\n\n"
)
def build_prompt(query: str, doc: str) -> str:
return PROMPT_TEMPLATE.format(
system=SYSTEM_PROMPT, instruction=INSTRUCTION, query=query, doc=doc
)
tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH)
model = AutoModelForCausalLM.from_pretrained(
MODEL_PATH,
torch_dtype=torch.bfloat16,
device_map="cuda",
attn_implementation="sdpa",
).eval()
yes_id = tokenizer.encode("yes", add_special_tokens=False)[0]
no_id = tokenizer.encode("no", add_special_tokens=False)[0]
@torch.no_grad()
def rerank(query: str, doc: str, max_new_tokens: int = 512):
prompt = build_prompt(query, doc)
input_ids = tokenizer(prompt, return_tensors="pt").input_ids.to(model.device)
out = model.generate(
input_ids=input_ids,
max_new_tokens=max_new_tokens,
do_sample=False,
return_dict_in_generate=True,
output_scores=True,
pad_token_id=tokenizer.pad_token_id or tokenizer.eos_token_id,
)
# Relevance score = softmax over {yes, no} at the first generated token.
first_logprobs = torch.log_softmax(out.scores[0][0].float(), dim=-1)
yes_p = first_logprobs[yes_id].exp()
no_p = first_logprobs[no_id].exp()
score = (yes_p / (yes_p + no_p)).item()
# Decoded text holds yes/no plus <contribution>...</contribution><evidence>...</evidence>
gen_ids = out.sequences[0, input_ids.shape[1]:]
text = tokenizer.decode(gen_ids, skip_special_tokens=True)
return {"score": score, "text": text}
for doc in DOCUMENTS:
print(rerank(QUERY, doc))
```
Expected output (one dict per document):
```text
{"score": 0.98, "text": "yes\n<contribution>...</contribution>\n<evidence>...</evidence>"}
{"score": 0.01, "text": "no"}
```
For irrelevant pairs the score is close to 0 and `text` is just `"no"`.
### B. Sentence Transformers CrossEncoder (score only)
If you only need the score and want a drop-in CrossEncoder, the same model works directly with `sentence-transformers >= 5.4.0`. **Note:** in this mode `<contribution>` and `<evidence>` are not produced — only the calibrated relevance score.
The system prompt and instruction are baked into the model's `chat_template.jinja` and are **not configurable** — the model was trained with one fixed prompt and only that prompt produces calibrated scores. You only pass `(query, document)`; the rest is hardcoded.
```python
import torch
from sentence_transformers import CrossEncoder
MODEL_PATH = "infgrad/Prism-Qwen3.5-Reranker-4B" # or any sibling repo above
ce = CrossEncoder(MODEL_PATH, model_kwargs={"torch_dtype": torch.bfloat16})
# 1) Score (q, d) pairs. The default activation is Sigmoid, so scores are in (0, 1)
# and equal to s(q, d) = sigmoid(logit_yes - logit_no) — identical to path A above.
pairs = [(QUERY, doc) for doc in DOCUMENTS]
scores = ce.predict(pairs)
print(scores)
# array([0.98, 0.01], dtype=float32)
# 2) Rank documents directly.
ranked = ce.rank(QUERY, DOCUMENTS, return_documents=True)
for r in ranked:
print(f"{r['score']:.3f}\t{r['corpus_id']}\t{r['text'][:80]}")
```
To get raw logit differences instead of [0, 1] probabilities, pass `activation_fn=torch.nn.Identity()` to `ce.predict(...)`.
#### A note on numerical parity with path A
In **fp32**, paths A and B produce the same score to within ~1e-6 (verified across all five checkpoints).
In **bf16** with the default batched call (`batch_size > 1`), CE scores can drift from path A by **~1–3%** for individual pairs. The cause is bf16 SDPA: when CrossEncoder pads shorter sequences to the longest in the batch, the bf16 attention numerics differ by a few ULPs vs running each pair alone, and the difference accumulates across layers before the final sigmoid. **Ranking order is unaffected.** If you need bit-for-bit parity with path A:
```python
# Option 1: keep bf16, disable batching
ce.predict(pairs, batch_size=1)
# Option 2: use fp32 (slower, larger memory)
ce = CrossEncoder(MODEL_PATH, model_kwargs={"torch_dtype": torch.float32})
```
## Notes on usage
- The first generated token is always `yes` or `no` — the score is well-defined even if you stop generation immediately (cheap mode: `max_new_tokens=1`). Generate further only when you also want contribution/evidence.
- Inputs longer than 10K tokens may degrade — truncate the document side first.
- Greedy decoding is fine for ranking. For diverse evidence rephrasings, use `temperature=0.3-0.5`.
## Citation
If you use Prism-Reranker in your research, please cite:
```bibtex
@misc{zhang2025prismreranker,
title = {Prism-Reranker: Beyond Relevance Scoring -- Jointly Producing Contributions and Evidence for Agentic Retrieval},
author = {Dun Zhang},
year = {2025},
eprint = {2604.23734},
archivePrefix = {arXiv},
primaryClass = {cs.IR},
url = {https://arxiv.org/abs/2604.23734},
}
```
## Contact
Dun Zhang — `dunnzhang0@gmail.com` (independent researcher).

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{%- set query_text = messages | selectattr("role", "eq", "query") | map(attribute="content") | first -%}
{%- set document_text = messages | selectattr("role", "eq", "document") | map(attribute="content") | first -%}
<|im_start|>system
Judge whether the Document meets the requirements based on the Query and the Instruct provided. <|im_end|>
<|im_start|>user
<Instruct>: Judge if the document is relevant to the query. Reply "yes" or "no".
On "yes", also emit:
<contribution>One sentence covering every core point the document contributes to the query, without elaboration.</contribution>
<evidence>Self-contained rewrite of the query-relevant content. Rules:
- Faithful: rephrase only; add or infer nothing.
- Self-contained: evidence alone must fully answer the query.
- Concise: drop query-irrelevant background.
- Verbatim (no translation): proper nouns, terms, abbreviations, numbers, dates, code, URLs.
- Output language: multilingual doc → query's language; else doc's language.</evidence>
<Query>: {{ query_text }}
<Document>: {{ document_text }}<|im_end|>
<|im_start|>assistant
<think>
</think>

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"architectures": [
"Qwen3ForCausalLM"
],
"attention_bias": false,
"attention_dropout": 0.0,
"bos_token_id": 151643,
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"eos_token_id": 151645,
"head_dim": 128,
"hidden_act": "silu",
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"max_position_embeddings": 40960,
"max_window_layers": 36,
"model_type": "qwen3",
"num_attention_heads": 32,
"num_hidden_layers": 36,
"num_key_value_heads": 8,
"rms_norm_eps": 1e-06,
"rope_scaling": null,
"rope_theta": 1000000,
"sliding_window": null,
"tie_word_embeddings": true,
"transformers_version": "4.57.6",
"use_cache": true,
"use_sliding_window": false,
"vocab_size": 151669
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{
"__version__": {
"sentence_transformers": "5.4.1"
},
"activation_fn": "torch.nn.modules.activation.Sigmoid",
"model_type": "CrossEncoder"
}

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{
"bos_token_id": 151643,
"do_sample": true,
"eos_token_id": [
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151643
],
"pad_token_id": 151643,
"temperature": 0.6,
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"top_p": 0.95,
"transformers_version": "4.57.6"
}

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