Papers by Aosong Feng
IPR: Intelligent Prompt Routing with User-Controlled Quality-Cost Trade-offs (2025.emnlp-industry)
Copied to clipboard
Aosong Feng, Balasubramaniam Srinivasan, Yun Zhou, Zhichao Xu, Kang Zhou, Sheng Guan, Yueyan Chen, Xian Wu, Ninad Kulkarni, Yi Zhang, Zhengyuan Shen, Dmitriy Bespalov, Soumya Smruti Mishra, Yifei Teng, Darren Yow-Bang Wang, Haibo Ding, Lin Lee Cheong
| Challenge: | Existing systems require users to manually select models or employ rigid routing rules that fail to capture the continuous spectrum of query complexity. |
| Approach: | They propose a quality-constrained intelligent prompt routing framework that automatically selects optimal models based on predicted response quality and user-specified tolerance levels. |
| Outcome: | The proposed framework achieves 43.9% cost reduction while maintaining quality parity with strongest model in the Claude family and processes requests with sub-150ms latency. |
Diffusion Language Model Inference with Monte Carlo Tree Search (2026.findings-eacl)
Copied to clipboard
Zheng Huang, Kiran Ramnath, Yueyan Chen, Aosong Feng, Sangmin Woo, Balasubramaniam Srinivasan, Zhichao Xu, Kang Zhou, Shuai Wang, Haibo Ding, Lin Lee Cheong
| Challenge: | Existing methods for inference use heuristics to determine which positions to unmask and which tokens to commit . MEDAL is an inference-time scaling framework that integrates Monte Carlo Tree SEarch initialization for Diffusion Language Model inference. |
| Approach: | They propose a framework that integrates Monte Carlo Tree SEarch initialization for Diffusion Language Model inference. |
| Outcome: | The proposed framework achieves 22.0% improvement over existing inference strategies across multiple benchmarks. |
BayesFlow: A Probability Inference Framework for Meta-Agent Assisted Workflow Generation (2026.findings-eacl)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) have demonstrated remarkable generality, often solving tasks with a single carefully engineered prompt. |
| Approach: | They propose to cast automatic workflow generation as Bayesian inference over a posterior distribution on workflows and instantiate BayesFlow as Bayer-based workflow generation framework. |
| Outcome: | The proposed framework improves accuracy by 9 percentage points over baselines and 65 percentage points on pool-wide benchmarks. |
MMLU-ProX: A Multilingual Benchmark for Advanced Large Language Model Evaluation (2025.emnlp-main)
Copied to clipboard
Weihao Xuan, Rui Yang, Heli Qi, Qingcheng Zeng, Yunze Xiao, Aosong Feng, Dairui Liu, Yun Xing, Junjue Wang, Fan Gao, Jinghui Lu, Yuang Jiang, Huitao Li, Xin Li, Kunyu Yu, Ruihai Dong, Shangding Gu, Yuekang Li, Xiaofei Xie, Felix Juefei-Xu, Foutse Khomh, Osamu Yoshie, Qingyu Chen, Douglas Teodoro, Nan Liu, Randy Goebel, Lei Ma, Edison Marrese-Taylor, Shijian Lu, Yusuke Iwasawa, Yutaka Matsuo, Irene Li
| Challenge: | Existing large language model evaluation benchmarks focus on English, while current multilingual tasks lack parallel questions that specifically assess cross-lingual reasoning abilities. |
| Approach: | They propose a comprehensive benchmark covering 29 languages, built on an English benchmark. |
| Outcome: | The MMLU-ProX is a comprehensive benchmark covering 29 languages, built on an English benchmark. |
HiPool: Modeling Long Documents Using Graph Neural Networks (2023.acl-short)
Copied to clipboard
| Challenge: | Recent work on pretraining languages have achieved satisfying results in many NLP tasks, but they are still restricted by a pre-defined maximum length. |
| Approach: | They propose a graph-based method to model sentence-level information using a fixed length and graphs to model intra- and cross-sentence correlations. |
| Outcome: | The proposed model outperforms baseline models by 2.6% in F1 score, and 4.8% on the longest sequence dataset. |
Long Sequence Modeling with Attention Tensorization: From Sequence to Tensor Learning (2024.findings-emnlp)
Copied to clipboard
| Challenge: | a lack of attention-based models for long sequences poses challenges for long-sequence modeling . attention tensorization can be used to extend context lengths with improved efficiency . tenorization enables training of LLMs with context length longer than those trained on . |
| Approach: | They propose to tensorize long input sequences into compact tenses followed by attention on each transformed dimension. |
| Outcome: | The proposed model can be used as efficient transformer backbones to extend input context length with improved memory and time efficiency. |
SafeKey: Amplifying Aha-Moment Insights for Safety Reasoning (2025.emnlp-main)
Copied to clipboard
| Challenge: | Large Reasoning Models (LRMs) introduce a new paradigm of explicitly reasoning before answering, but they pose great safety risks against harmful queries and adversarial attacks. |
| Approach: | They propose a safety aha moment that activates safety reasoning and leads to a safe response. |
| Outcome: | The proposed model can generalize to unseen jailbreak prompts while maintaining general abilities. |