UCSC-VLAA

ClinSeek-35B-A3B

README

License: apache-2.0

Release information

Table with columns: Item, Value
Item	Value
Base model	`Qwen/Qwen3.5-35B-A3B`
Teacher model	GPT-5.6 Sol (reasoning effort high)
Training method	Full-parameter supervised fine-tuning
Checkpoint	End of epoch 2 (lowest validation loss)
Architecture	`Qwen3_5MoeForConditionalGeneration`, bf16, 14 shards
Paper	https://arxiv.org/abs/2605.20176
Code	https://github.com/UCSC-VLAA/ClinSeekAgent
Benchmark metadata	https://huggingface.co/datasets/UCSC-VLAA/ClinSeek-Bench
Project page	https://ucsc-vlaa.github.io/ClinSeekAgent/

The weights on main are the checkpoint reported in the revised paper (arXiv v2). The checkpoint released with arXiv v1 (May 2026) is kept under the v1 tag.

Training data

GPT-5.6 Sol runs ClinSeekAgent with the 16 EHR tools and the strict temporal policy on the 3,000 questions of the AgentEHR-Bench training split, which cover six tasks (diagnoses, procedures, laboratory tests, microbiology tests, prescriptions, and transfers). We keep the 2,947 trajectories that end with an answer and drop those longer than 52K tokens, leaving 2,681, split at random into 2,628 for training and 53 for validation. None of the evaluation questions appears in the training data.

During data generation the teacher records its reasoning with a think tool before each action. For training, this reasoning is moved into the student's native thinking block of the following turn, so the student reasons in its thinking channel instead of calling the tool. Tool calls are rendered in the Qwen3.5 native tool-call format. The loss is computed on assistant tokens only (reasoning, tool calls, and the final answer).

The trajectories are not released: they contain records from credentialed clinical datasets.

Training configuration

Table with columns: Component, Configuration
Component	Configuration
Training / validation size	2,628 / 53 trajectories
Maximum sequence length	52,000 tokens
Epochs	3 (checkpoint after epoch 2 released)
Global / micro batch size	32 / 1 per GPU
Optimizer	Adam (β1 = 0.9, β2 = 0.999); optimizer states offloaded to CPU
Learning rate	2e-5, cosine decay to 2e-6, 10 warmup steps
Weight decay / gradient clipping	0.1 / 1.0
MoE auxiliary loss / z-loss	0.01 / 0.001

Evaluation

AgentEHR-Bench test split, 100 questions per task, F1 (%). All models run ClinSeekAgent with the same tools, temporal policy, and inference settings. The base and fine-tuned models are served locally with identical decoding settings.

Table with columns: Model, Diagnoses, Labs, Microbiology, Procedures, Transfers, Avg.
Model	Diagnoses	Labs	Microbiology	Procedures	Transfers	Avg.
Closed-source
GPT-5.6 Sol (teacher)	47.0	43.1	31.1	45.5	27.3	38.8

Every gain over the base model is significant under a paired bootstrap (95% CI).

Compared with its base model on the same 500 questions, ClinSeek-35B-A3B improves both precision (18.6 → 32.4) and recall (31.1 → 53.3). It writes SQL with joins, aggregation, or time filters in 53% of the questions (base: 0.8%), reads 72% of the available tables (base: 36%), and reads imaging or discharge notes in 74% of the questions (base: 12%).

Usage

ClinSeek-35B-A3B is an agent model: it expects the ClinSeekAgent EHR tools and a multi-turn tool loop. Use the ClinSeekAgent repository, which provides the EHR MCP server, tool schemas, prompts, the AgentEHR-Bench and ClinSeek-Bench runners, and scoring.

Serving with vLLM

The paper's evaluation served the model with vLLM 0.28.1 as follows (scripts/run_vllm_server.sh in the repository applies the same flags):

bash

vllm serve UCSC-VLAA/ClinSeek-35B-A3B \  --served-model-name ClinSeek-35B-A3B \  --max-model-len 196608 \  --reasoning-parser qwen3 \  --enable-auto-tool-choice \  --tool-call-parser qwen3_coder \  --language-model-only

Inference settings used in the paper: thinking enabled with a 4,096-token budget, temperature 0, at most 8,192 output tokens per call, tool results truncated to 100,000 characters, and at most 200 rounds per question.

The model was trained with an EHR-only tool space (no web browsing). Do not add an instruction to call ehr.think: the model reasons in its thinking block instead.

Loading with transformers

python

import torchfrom transformers import AutoModelForImageTextToText, AutoTokenizer model_id = "UCSC-VLAA/ClinSeek-35B-A3B"tokenizer = AutoTokenizer.from_pretrained(model_id)model = AutoModelForImageTextToText.from_pretrained(    model_id, dtype=torch.bfloat16, device_map="auto")

The checkpoint keeps the Qwen3.5 architecture (Qwen3_5MoeForConditionalGeneration); ClinSeek-35B-A3B was trained and evaluated on text only.

Limitations and responsible use

The model was trained on trajectories over MIMIC-IV-derived patient records (PhysioNet credentialed data). Users must comply with the PhysioNet data-use agreement and must not attempt to recover patient-level information.
It was trained on AgentEHR-Bench tasks only; performance on other clinical tasks is not established.
It is a research model, not a medical device. Its outputs must not be used for clinical diagnosis, treatment, triage, or patient management.

Citation

bibtex

@misc{wu2026clinseekagent,  title         = {ClinSeekAgent: Automating Multimodal Evidence Seeking for Agentic Clinical Reasoning},  author        = {Wu, Juncheng and Zhang, Letian and Wang, Yuhan and Tu, Haoqin and Chen, Hardy and Wang, Zijun and Xie, Cihang and He, Yufan and Zhao, Can and Xu, Daguang and Tang, Yucheng and Zhou, Yuyin},  year          = {2026},  eprint        = {2605.20176},  archivePrefix = {arXiv},  primaryClass  = {cs.CL},  url           = {https://arxiv.org/abs/2605.20176}}

Please also cite AgentEHR-Bench, MIMIC-IV, and Qwen3.5 where applicable.

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Model Details

Model Provider

UCSC-VLAA

Model Tree

Base

Qwen/Qwen3.5-35B-A3B

Fine-tuned

this model

Input Modalities

TextImageVideo

Output Modalities

Text

Supported Functionality