Yunqi Zhu, Wen Tang, Jinghao Niu, Wensheng Zhang, Xuebing Yang
Evaluation of medical artificial intelligence (AI) agents remains predominantly answer-centric, assessing only the correctness of final outputs while overlooking the quality of intermediate reasoning. We propose MedTraj, a framework that treats reasoning trajectories as primary objects for modeling the progressive refinement of clinical evidence toward diagnostic decisions. A reasoning trajectory captures the state transitions among medical concepts---from clinical observations through evidential reasoning to a final conclusion---making the implicit reasoning chain of a medical agent explicit and inspectable. Each trajectory is scored across five quality dimensions: coherence, evidence support, hallucination, completeness, and traceability. Controlled error injection introduces targeted faults into otherwise correct trajectories to establish causal links between specific reasoning failures and measurable quality degradation. Step-level filtering based on marginal contribution identifies which individual reasoning steps drive or undermine reasoning trajectory quality. Quality-weighted reasoning trajectory context feeds these evaluations back into the model at inference time. Experiments across CareQA, PubMedQA, and CECMed demonstrate that reasoning trajectory context consistently improves reasoning coherence. For the severity assessment on the CECMed, quality-weighted reasoning trajectory context nearly doubles the correctness over the zero-shot baseline while cutting the hallucination ratio by 87%. Experiments show that 15.3% of reasoning steps carry most of the quality signal, and that extending chains beyond four steps yields diminishing returns.
Luoyang Sun, Guangyan Li, Cheng Deng University of Edinburgh, University of Edinburgh, Haifeng Zhang Institute of automation, Chinese academy of science, Chinese Academy of Sciences, Jian Zhao Zhongguancun Academy and Zhongguancun Institute of Artificial Intelligence, Yongqiang Tang, Wensheng Zhang, Jun Wang University College London
Large language models (LLMs) excel at natural language tasks but face deployment challenges due to computational demands. We introduce Dual Activation-Weight Sparsity (DAWS), a training-free framework that jointly exploits activation and weight sparsity through magnitude-based routing. Systematic analysis of pretrained transformers reveals two key observations: (1) the activation energy is concentrated in a few neurons, and (2) activation and weight sparsity patterns are complementary between attention and FFN layers. DAWS employs a three-tier routing strategy: high-magnitude activations pass through full-precision weights to preserve critical pathways, medium-magnitude activations use magnitude-pruned sparse weights for efficiency, and low-magnitude activations are directly discarded. Unlike prior work that uses activation-aware pruning methods like WANDA, our approach uses direct magnitude-based pruning, which we show is more robust to sample-level variations. Experiments on Llama and Mistral models demonstrate that DAWS maintains >98% of dense model performance at 50% sparsity, outperforming WANDA, TEAL, and R-Sparse.
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cs.CL
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表