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XINLI1997
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Model Details
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XINLI1997
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Qwen/Qwen2.5-7B
Input Modalities
Output Modalities
Supported Functionality
| Evaluation (WirelessMathBench-XL test) | Accuracy |
|---|---|
| Full 800-item test split (paper Tab. 3, row D10) | 47.88% |
| 310-item public test subset (paper Appendix L) | 46.45% |
Locked protocol: 2k-token answer budget, T = 0.6, GPT-4.1-mini judge on every response.
Training-time check under greedy model selection (paper Tab. 7): base 21.88% → GRPO 39.50%. These values use a different protocol from the locked scores.
The paper queries these checkpoints through a raw completion endpoint (no chat template), using the dataset's prompt field.
python
from datasets import load_datasetfrom transformers import AutoModelForCausalLM, AutoTokenizer repo = "XINLI1997/WirelessMathLM-7B"tok = AutoTokenizer.from_pretrained(repo)model = AutoModelForCausalLM.from_pretrained(repo, torch_dtype="auto", device_map="auto") ex = load_dataset("XINLI1997/WirelessMATHBench-XL", "cc_by", split="test")[0]prompt = ex["prompt"]inputs = tok(prompt, return_tensors="pt").to(model.device)out = model.generate(**inputs, max_new_tokens=2048, do_sample=True, temperature=0.6)print(tok.decode(out[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))Apache 2.0, following the base model.
bibtex
@inproceedings{li2026wirelessmathbenchxl,title={WirelessMathBench-XL: An Auditable Benchmark for Wireless Mathematical Reasoning},author={Xin Li and Mengbing Liu and Yiyang Zhu and Wenhe Zhang and Li Wei and Jiancheng An and Chau Yuen},booktitle={The Fortieth Annual Conference on Neural Information Processing Systems Evaluations and Datasets Track},year={2026}}from datasets import load_datasetfrom transformers import AutoModelForCausalLM, AutoTokenizer repo = "XINLI1997/WirelessMathLM-7B"tok = AutoTokenizer.from_pretrained(repo)model = AutoModelForCausalLM.from_pretrained(repo, torch_dtype="auto", device_map="auto") ex = load_dataset("XINLI1997/WirelessMATHBench-XL", "cc_by", split="test")[0]prompt = ex["prompt"]inputs = tok(prompt, return_tensors="pt").to(model.device)out = model.generate(**inputs, max_new_tokens=2048, do_sample=True, temperature=0.6)print(tok.decode(out[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))@inproceedings{li2026wirelessmathbenchxl,title={WirelessMathBench-XL: An Auditable Benchmark for Wireless Mathematical Reasoning},author={Xin Li and Mengbing Liu and Yiyang Zhu and Wenhe Zhang and Li Wei and Jiancheng An and Chau Yuen},booktitle={The Fortieth Annual Conference on Neural Information Processing Systems Evaluations and Datasets Track},year={2026}}