The most plausible near-term role of medical LLMs is to assist rather than replace physicians, yet current evaluations often test isolated capabilities: clinical knowledge, EHR system interaction, or patient communication. Physician assistance instead requires coordinating these capabilities within the same interaction, where physicians issue underspecified requests, patients describe symptoms ambiguously, and EHR systems demand precise tool use. We introduce PhysAssistBench, a benchmark for interactive doctor-patient-EHR assistance. Built from real MIMIC-IV cases, PhysAssistBench 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. PhysAssistBench provides a curated evaluation set of 1,296 manually reviewed and physician-validated turns. Experiments with leading LLMs show that current models remain unreliable in this setting, struggling to infer physician intent, clarify vague patient descriptions, formulate grounded EHR queries, and integrate evidence across sources. These results expose a key bottleneck for clinical LLMs: reliable assistance requires coordination across knowledge, communication, and systems, not isolated gains in any of them.

Autonomous agents powered by large language models (LLM-based agents) are capable of using off-the-shelf tools to interact with the environment, solve real-world problems, and boost work efficiency. However, current approaches to enhancing tool use for LLM-based agents primarily focus on post-training fine-tuning or test-time context extension. These methods overlook the fundamental tool knowledge acquisition during the early training phase, where models actually learn and internalize core knowledge representations, restricting model performance on out-of-distribution tool usage. To solve such a problem, we introduce enhancing tool knowledge for LLM-based agents during continuous pre-training (ToolCPT). We identify and bridge a key gap in current LLM training by shifting focus from tool-calling patterns to deep internalization of core tool-knowledge representations. We begin by curating 5.1 million code artifacts from large-scale, high-quality code repositories. These artifacts are selected based on a set of criteria that defines a usable “proxy agent tool”, thereby forming a comprehensive agent tool library. For each proxy tool, we then create a detailed playbook covering implementation specifications, core functionalities, interaction protocols with other tools, and illustrative positive and negative examples. This process yields a large-scale tool knowledge corpus comprising 18 billion tokens, which is used to continuously pre-train our model. Experiments show our playbook-enhanced corpus catalyzes deep knowledge internalization, driving the model to notable performance gains on multiple standard benchmarks.

We introduce the task of correcting named entity recognition (NER) errors without re-training model. After an NER model is trained and deployed in production,it makes prediction errors, which usually need to be fixed quickly. To address this problem, we firstly construct a gazetteer containing named entities and corresponding possible entity types. And then, we propose type enhanced BERT (TyBERT),a method that integrates the named entity’s type information into BERT by an adapter layer. When errors are identified, we can repair the model by updating the gazetteer. In other words, the gazetteer becomes a trigger to control NER model’s output. The experiment results in multiple corpus show the effectiveness of our method, which outperforms strong baselines.x