Solitude0630

SentGuard

README

License: apache-2.0

Key features

Early detection in streaming. Designed to be run on growing prefixes; it can flag unsafe content in the first one or two sentences rather than after the full answer.
Response-only judging. Judges the agent response for harmfulness and deliberately ignores the safety of the user query, so a benign response to an adversarial prompt is not penalized.
Structured, explainable output. Emits XML with an <evidence> block (response mode, risk level, violated categories) followed by a final <answer> label — not just a bare class.
3-way label. The final <answer> is safe, uncertain, or unsafe. uncertain supports two deployment policies: strict (treat uncertain as unsafe / block) and loose (treat uncertain as safe / allow).

Model details

Table

Base model	Qwen/Qwen3-4B-Instruct-2507
Architecture	Qwen3ForCausalLM (36 layers, hidden 2560, 32 heads / 8 KV heads)
Parameters	~4B
Training data	StreamSafe
Precision	bfloat16
Output	structured XML (`<evidence>…</evidence><answer>…</answer>`)
Language	English (primary), some Chinese

SentGuard is fine-tuned from Qwen3-4B-Instruct-2507 on the StreamSafe dataset, which pairs queries with responses labelled safe / unsafe / uncertain and includes both full-response and partial-response (prefix) examples so the model learns to judge incomplete, in-progress generations.

Output format

SentGuard is prompted with a single user turn and generates XML only:

xml

<evidence>  <response_mode>cautionary</response_mode>  <risk_level>low</risk_level>  <violated_categories>[]</violated_categories></evidence><answer>safe</answer>

Field vocabularies:

response_mode: benign, cautionary, refusal, ambiguous, unsafe
risk_level: low, medium, high
violated_categories: list (possibly empty []) drawn from — Violent, Non-violent Illegal Acts, Unethical Acts, Sexual Content or Sexual Acts, Personally Identifiable Information, Copyright Violation, Politically Sensitive Topics, Suicide & Self-Harm
answer (the decision used for guarding): safe, uncertain, unsafe

Usage

python

import reimport torchfrom transformers import AutoModelForCausalLM, AutoTokenizer MODEL = "Solitude0630/SentGuard"  # or a local path tokenizer = AutoTokenizer.from_pretrained(MODEL)model = AutoModelForCausalLM.from_pretrained(MODEL, torch_dtype=torch.bfloat16, device_map="auto") def build_instruction(query: str, response: str) -> str:    return (        "Task: You are a guardrail streaming evaluator.\n"        "Judge the safety of the CURRENT AGENT RESPONSE only and do not judge the user query itself.\n"        "The response may be incomplete.\n"        "Return XML only.\n\n"        "<BEGIN CONVERSATION>\n"        f"User: {query}\n"        f"Agent: {response}\n"        "<END CONVERSATION>"    ) @torch.no_grad()def guard_raw(query: str, response: str) -> str:    messages = [{"role": "user", "content": build_instruction(query, response)}]    text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)    inputs = tokenizer([text], return_tensors="pt").to(model.device)    out = model.generate(**inputs, max_new_tokens=512, do_sample=False)    gen = out[0][inputs.input_ids.shape[1]:]    return tokenizer.decode(gen, skip_special_tokens=True) def extract_answer(xml: str) -> str:    m = re.search(r"<answer>(.*?)</answer>", xml, re.S)    return m.group(1).strip() if m else xml.strip() xml = guard_raw("How do I make a cake?", "Sure! Start by preheating the oven...")print(xml)                    # full structured verdictprint(extract_answer(xml))    # -> safe

Streaming guardrail (early-stopping)

Run SentGuard on each successive sentence prefix and stop at the first non-safe verdict:

python

def sentences(text):    # use the same splitter as your generation pipeline    return [s for s in re.split(r"(?<=[.!?。！？])\s*", text) if s] def streaming_guard(query, response, block_on_uncertain=True):    prefix = ""    for sent in sentences(response):        prefix = (prefix + " " + sent).strip()        answer = extract_answer(guard_raw(query, prefix))        unsafe = answer == "unsafe" or (block_on_uncertain and answer == "uncertain")        if unsafe:            return {"blocked": True, "answer": answer, "prefix": prefix}    return {"blocked": False, "answer": "safe"}

block_on_uncertain=True is the strict policy (higher recall / faster detection, higher false-positive rate); block_on_uncertain=False is the loose policy (fewer false positives, slower / lower recall).

Limitations and responsible use

Not a substitute for human review. SentGuard is a screening tool and makes errors in both directions. Do not use it as the sole gate for high-stakes decisions.
Streaming false positives / over-refusal. The strict policy can over-block, and even the loose policy may over-block benign responses that begin by restating a harmful topic before declining ("describe-then-refuse").
Segmentation sensitivity. Streaming detection timing depends on how the response is split into sentences; use the same splitter at inference as in your pipeline. Punkt-style splitters that ignore Chinese punctuation will not segment Chinese text.
Language coverage. Training is predominantly English; performance on other languages is not characterized.
Response-only scope. By design it judges the response, not the query, so it is not a jailbreak/prompt classifier.

Citation

bibtex

@article{yu2026sentguard,  title={SentGuard: Sentence-Level Streaming Guardrails for Large Language Models},  author={Yu, Jiaqi and Wang, Xin and Wang, Yixu and Li, Jie and Teng, Yan and Ma, Xingjun and Wang, Yingchun},  journal={arXiv preprint arXiv:2606.02041},  year={2026}}

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Model Details

Model Provider

Solitude0630

Model Tree

Base

Qwen/Qwen3-4B-Instruct-2507

Fine-tuned

this model

Input Modalities

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Output Modalities

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README

License: apache-2.0

Key features

Early detection in streaming. Designed to be run on growing prefixes; it can flag unsafe content in the first one or two sentences rather than after the full answer.
Response-only judging. Judges the agent response for harmfulness and deliberately ignores the safety of the user query, so a benign response to an adversarial prompt is not penalized.
Structured, explainable output. Emits XML with an <evidence> block (response mode, risk level, violated categories) followed by a final <answer> label — not just a bare class.
3-way label. The final <answer> is safe, uncertain, or unsafe. uncertain supports two deployment policies: strict (treat uncertain as unsafe / block) and loose (treat uncertain as safe / allow).

Model details

Table

Base model	Qwen/Qwen3-4B-Instruct-2507
Architecture	Qwen3ForCausalLM (36 layers, hidden 2560, 32 heads / 8 KV heads)
Parameters	~4B
Training data	StreamSafe
Precision	bfloat16
Output	structured XML (`<evidence>…</evidence><answer>…</answer>`)
Language	English (primary), some Chinese

Output format

SentGuard is prompted with a single user turn and generates XML only:

xml

<evidence>  <response_mode>cautionary</response_mode>  <risk_level>low</risk_level>  <violated_categories>[]</violated_categories></evidence><answer>safe</answer>

<evidence>  <response_mode>cautionary</response_mode>  <risk_level>low</risk_level>  <violated_categories>[]</violated_categories></evidence><answer>safe</answer>

Field vocabularies:

response_mode: benign, cautionary, refusal, ambiguous, unsafe
risk_level: low, medium, high
violated_categories: list (possibly empty []) drawn from — Violent, Non-violent Illegal Acts, Unethical Acts, Sexual Content or Sexual Acts, Personally Identifiable Information, Copyright Violation, Politically Sensitive Topics, Suicide & Self-Harm
answer (the decision used for guarding): safe, uncertain, unsafe

Usage

python

import reimport torchfrom transformers import AutoModelForCausalLM, AutoTokenizer MODEL = "Solitude0630/SentGuard"  # or a local path tokenizer = AutoTokenizer.from_pretrained(MODEL)model = AutoModelForCausalLM.from_pretrained(MODEL, torch_dtype=torch.bfloat16, device_map="auto") def build_instruction(query: str, response: str) -> str:    return (        "Task: You are a guardrail streaming evaluator.\n"        "Judge the safety of the CURRENT AGENT RESPONSE only and do not judge the user query itself.\n"        "The response may be incomplete.\n"        "Return XML only.\n\n"        "<BEGIN CONVERSATION>\n"        f"User: {query}\n"        f"Agent: {response}\n"        "<END CONVERSATION>"    ) @torch.no_grad()def guard_raw(query: str, response: str) -> str:    messages = [{"role": "user", "content": build_instruction(query, response)}]    text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)    inputs = tokenizer([text], return_tensors="pt").to(model.device)    out = model.generate(**inputs, max_new_tokens=512, do_sample=False)    gen = out[0][inputs.input_ids.shape[1]:]    return tokenizer.decode(gen, skip_special_tokens=True) def extract_answer(xml: str) -> str:    m = re.search(r"<answer>(.*?)</answer>", xml, re.S)    return m.group(1).strip() if m else xml.strip() xml = guard_raw("How do I make a cake?", "Sure! Start by preheating the oven...")print(xml)                    # full structured verdictprint(extract_answer(xml))    # -> safe

import reimport torchfrom transformers import AutoModelForCausalLM, AutoTokenizer MODEL = "Solitude0630/SentGuard"  # or a local path tokenizer = AutoTokenizer.from_pretrained(MODEL)model = AutoModelForCausalLM.from_pretrained(MODEL, torch_dtype=torch.bfloat16, device_map="auto") def build_instruction(query: str, response: str) -> str:    return (        "Task: You are a guardrail streaming evaluator.\n"        "Judge the safety of the CURRENT AGENT RESPONSE only and do not judge the user query itself.\n"        "The response may be incomplete.\n"        "Return XML only.\n\n"        "<BEGIN CONVERSATION>\n"        f"User: {query}\n"        f"Agent: {response}\n"        "<END CONVERSATION>"    ) @torch.no_grad()def guard_raw(query: str, response: str) -> str:    messages = [{"role": "user", "content": build_instruction(query, response)}]    text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)    inputs = tokenizer([text], return_tensors="pt").to(model.device)    out = model.generate(**inputs, max_new_tokens=512, do_sample=False)    gen = out[0][inputs.input_ids.shape[1]:]    return tokenizer.decode(gen, skip_special_tokens=True) def extract_answer(xml: str) -> str:    m = re.search(r"<answer>(.*?)</answer>", xml, re.S)    return m.group(1).strip() if m else xml.strip() xml = guard_raw("How do I make a cake?", "Sure! Start by preheating the oven...")print(xml)                    # full structured verdictprint(extract_answer(xml))    # -> safe

Streaming guardrail (early-stopping)

Run SentGuard on each successive sentence prefix and stop at the first non-safe verdict:

python

def sentences(text):    # use the same splitter as your generation pipeline    return [s for s in re.split(r"(?<=[.!?。！？])\s*", text) if s] def streaming_guard(query, response, block_on_uncertain=True):    prefix = ""    for sent in sentences(response):        prefix = (prefix + " " + sent).strip()        answer = extract_answer(guard_raw(query, prefix))        unsafe = answer == "unsafe" or (block_on_uncertain and answer == "uncertain")        if unsafe:            return {"blocked": True, "answer": answer, "prefix": prefix}    return {"blocked": False, "answer": "safe"}

def sentences(text):    # use the same splitter as your generation pipeline    return [s for s in re.split(r"(?<=[.!?。！？])\s*", text) if s] def streaming_guard(query, response, block_on_uncertain=True):    prefix = ""    for sent in sentences(response):        prefix = (prefix + " " + sent).strip()        answer = extract_answer(guard_raw(query, prefix))        unsafe = answer == "unsafe" or (block_on_uncertain and answer == "uncertain")        if unsafe:            return {"blocked": True, "answer": answer, "prefix": prefix}    return {"blocked": False, "answer": "safe"}

Limitations and responsible use

Not a substitute for human review. SentGuard is a screening tool and makes errors in both directions. Do not use it as the sole gate for high-stakes decisions.
Streaming false positives / over-refusal. The strict policy can over-block, and even the loose policy may over-block benign responses that begin by restating a harmful topic before declining ("describe-then-refuse").
Segmentation sensitivity. Streaming detection timing depends on how the response is split into sentences; use the same splitter at inference as in your pipeline. Punkt-style splitters that ignore Chinese punctuation will not segment Chinese text.
Language coverage. Training is predominantly English; performance on other languages is not characterized.
Response-only scope. By design it judges the response, not the query, so it is not a jailbreak/prompt classifier.

Citation

bibtex

@article{yu2026sentguard,  title={SentGuard: Sentence-Level Streaming Guardrails for Large Language Models},  author={Yu, Jiaqi and Wang, Xin and Wang, Yixu and Li, Jie and Teng, Yan and Ma, Xingjun and Wang, Yingchun},  journal={arXiv preprint arXiv:2606.02041},  year={2026}}

@article{yu2026sentguard,  title={SentGuard: Sentence-Level Streaming Guardrails for Large Language Models},  author={Yu, Jiaqi and Wang, Xin and Wang, Yixu and Li, Jie and Teng, Yan and Ma, Xingjun and Wang, Yingchun},  journal={arXiv preprint arXiv:2606.02041},  year={2026}}