A Next-Generation Training Engine Built for Ultra-Large MoE Models
[ICCV 2025] Are VLMs Ready for Autonomous Driving? An Empirical Study from the Reliability, Data, and Metric Perspectives
Higher performance OpenAI LLM service than vLLM serve: A pure C++ high-performance OpenAI LLM service implemented with GPRS+TensorRT-LLM+Tokenizers.cpp, supporting chat and function call, AI agents, distributed multi-GPU inference, multimodal capabilities, and a Gradio chat interface.
[ICLR 2026] OptMerge: data-free merging of multimodal LLM experts (VQA, Geometry, Chart, OCR, Grounding) and modalities, plus SWUDI / ASWUDI closed-form spectral merging for CLIP-ViT, Flan-T5, Llama-3.2 and MLLMs.
PriorTR (ECCV 2026): training-free, prior-corrected visual token reduction for accelerating multimodal LLMs — image & video.
[ICML 2026 Oral] DOUBT: Decoupled Object-level Understanding and Bridging via vMF-based Trustworthiness for Hallucination Detection in MLLMs
[ICML 2026] RLSF-V: Mitigating Hallucinations in MLLMs via Fuzzy Semantic Self-Feedback
Streamlit App Combining Vision, Language, and Audio AI Models
VLM (InternVL3) inference on Jetson Orin Nano (8GB) using TensorRT-LLM.
Automate Microsoft Account OAuth registration and token imports with this Chrome extension for the Microsoft Account Manager API.
Unified evaluation harness for Vision-Language Models — VQAv2, TextVQA, COCO, MMBench, MMBench-CN
天择多模态决策大模型框架 · TianZe-MJev: A Multimodal Jev-style Decision Model。七个官方基座、免额外训练、统一 API 与网页决策台。
[ACM AI 2026] Safety-audit framework for vision-language models on brain MRI, measuring calibration, confident errors, hallucination, abstention, and slice-level accuracy.
Fine-tuned sub-1B vision-language models that read CV/resume page images like a human and output ATS-ready JSON. No PDF text layer, runs on a laptop CPU.
帧防 FrameGuard · 视觉安全智能分析 — 用多模态大模型逐帧理解监控画面，对照自定义规则判定违规并输出分时段报告。更多炸裂应用尽在知识星球“AI创收私研社”
ACL 2025, SDP workshop Shared Task Submission
Unofficial PyTorch reproduction for InternVL3: Exploring Advanced Training and Test-Time Recipes for Open-Source Multimodal Models.
Description: Domain-shift failure analysis of LLaVA-1.5-7B and InternVL2-8B across photos, charts, medical, and screenshots. 945 probes, 6 failure categories. Chart OCR: LLaVA 0% vs InternVL2 71%. Domain-dependent failure confirmed via chi-square (p<0.0001).
上海人工智能实验室（SHLab）开源项目精选导航 · 含许可证风险标注与失效链接勘误
Code, generated data, raw predictions, and preprint for diagnosing observer-frame composition in vision-language models.
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