GraphDx: A Cost-Aware Knowledge-Enhanced Multi-Agent Framework for Sequential Diagnosis (2026)
In sequential diagnosis, a clinician asks about symptoms and orders tests over several turns, trying to get the diagnosis right without spending too much.
Look Within or Beyond? A Theoretical Comparison Between Parameter-Efficient and Full Fine-Tuning (2026)
Parameter-efficient fine-tuning (PEFT) methods such as LoRA often match full fine-tuning (FFT) on standard benchmarks. It is less clear whether they hold up on harder tasks like reasoning and instruction-following, or under adversarial inputs. The authors address this with a theoretical analysis backed by experiments.

All works

3 works of theirs in our library, the most cited first; 2 with a page here, the others link their original until we summarise them.

GraphDx: A Cost-Aware Knowledge-Enhanced Multi-Agent Framework for Sequential DiagnosisFindings of the Association for Computational LinguisticsShaoting Tan, Ning Liu, Yuntao Du, et al.2026
Look Within or Beyond? A Theoretical Comparison Between Parameter-Efficient and Full Fine-TuningAnnual Meeting of the Association for Computational Linguistics (ACL)YongKang Liu, Xingle Xu, Ercong Nie, et al.2026
Internalizing Multi-Agent Reasoning for Accurate and Efficient LLM-based Recommendation ↗Findings of the Association for Computational Linguistics: ACL 2026 · no summary yetYang Wu et al.2026

Co-authors here

8 co-authors with a page, the most papers together first.

Where their works here were published, the most works first, with the editions of those that have a page.

Updated 1 November 2026