Papers by Mohsen Hariri
Ranking Reasoning LLMs under Test-Time Scaling (2026.acl-long)
Copied to clipboard
| Challenge: | Large language models (LLMs) are increasingly used as general-purpose reasoning systems for tasks such as programming and mathematical problem solving. |
| Approach: | They formalize dense benchmark ranking under test-time scaling and introduce a library that implements statistical ranking methods such as paired-comparison models, item response theory, voting rules, graph- and spectral-based methods. |
| Outcome: | The proposed method is based on paired-comparison models, item response theory (IRT) models, voting rules, graph- and spectral-based methods. |
Quantize What Counts: More for Keys, Less for Values (2026.findings-acl)
Copied to clipboard
| Challenge: | Empirical evaluations across various prominent LLMs and benchmarks show that key-favored allocations retain up to 98.3% accuracy compared to uniform allocations (e.g., 4-bit keys, 2-bit values). |
| Approach: | They propose two theorems that anchor mixed-precision KV quantization in the intrinsic geometry of Transformer models. |
| Outcome: | Empirical evaluations show that key-favored allocations retain up to 98.3% accuracy while conserving memory. |
LoRATK: LoRA Once, Backdoor Everywhere in the Share-and-Play Ecosystem (2025.findings-emnlp)
Copied to clipboard
Hongyi Liu, Shaochen Zhong, Xintong Sun, Minghao Tian, Mohsen Hariri, Zirui Liu, Ruixiang Tang, Zhimeng Jiang, Jiayi Yuan, Yu-Neng Chuang, Li Li, Soo-Hyun Choi, Rui Chen, Vipin Chaudhary, Xia Hu
| Challenge: | distributing LLMs without a proven track record like ‘meta-llama‘ or ‘qwen‘ rarely gains community traction. |
| Approach: | They propose a simple, efficient, yet specific recipe for a backdoor LoRA to be injected into task-enhancing LoRAs and examine the mechanisms of such infections. |
| Outcome: | The proposed model allows attackers to scale the distribution of compromised LoRAs with minimal effort by leveraging the rich pool of shared LoRA assets. |