Methods for improving knowledge use in large language models typically fall into two regimes. Non-parametric retrieval offers flexible access to external knowledge, but adds retrieval latency, context overhead, and only shallow integration with the backbone. Parametric adaptation is efficient at inference time, but ent...
Ming-Yuan Li, Guang-Sheng Yu, Xu Wang et al.· 3 citations
Generalized least squares (GLS) analysis shows that, under exchangeability, the optimal symmetric linear combiner of latent embeddings is uniform, supporting majority vote as the natural default in standard SC while leaving room for weighting or pruning under heterogeneous prompt-template branches.
Guang-Sheng Yu, Litianyi Zhang, Qin Wang et al.· 0 citations
A label-free bias-only TTRL, which uses majority-vote pseudolabels as rewards and optimizes only ~100K bias parameters while keeping the pretrained backbone frozen, demonstrating that substantial test-time adaptation can emerge from optimizing a tiny bias-only subspace using entirely label-free rewards.
This work shows that specialist optimization implicitly selects from this latent trajectory space, and establishes a new view of specialist training: when gold reasoning is absent, tuning choices directly control the latent supervision passed to downstream models.
Yi-Le Tu, Zi-Hao Li, Shao-Xiong Ji et al.· 0 citations
PhysAssistBench is introduced, a benchmark for interactive doctor-patient-EHR assistance that uses a scalable pipeline to construct agentic patients: interactive, record-grounded agents that turn static EHR records into multi-turn clinical scenarios while preserving clinical factuality.
T. Du, Peijie Yu, Sihan Shang et al.· arXiv.org· 0 citations
This work proposes a pipeline for automatically generating step-by-step linguistic reasoning traces from Universal Dependencies treebanks, dictionaries, and grammar-rule banks and shows that linguistic reasoning traces are most effective as inference-time guidance in ICL, which substantially improve translation perform...
It is found that standard benchmarks do not adequately capture the strengths of the dataset, but expert judgment shows that SQPsych makes LLMs significantly better at therapist roleplaying.
Doan Nam Long Vu, Rui Tan, L. Mary Moench et al.· arXiv.org· 5 citations· ⚡2
The results suggest that Engram can serve as a reusable external knowledge artifact, provided that the target has access to a compatible reader interface and target-side adaptation can further improve alignment when direct reader reuse is insufficient.
Mingyuan Li, Guangsheng Yu, Xu Wang et al.· 0 citations
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