Ianvs new release note v0.3.0 added to docs

docs/roadmap.md

@@ -1,4 +1,5 @@
 # Ianvs v0.1.0 release
+
 ## 1. Release the Ianvs distributed synergy AI benchmarking framework.
    a) Release test environment management and configuration.
    b) Release test case management and configuration.
@@ -31,4 +32,37 @@ This version of Ianvs supports the following functions of unstructured lifelong
 
 ## 3. Provide real-world datasets and rich examples for lifelong learning testing, to better evaluate the effectiveness of lifelong learning algorithms in real environments.
    - Provide cloud-robotics datasets in [this website](https://kubeedge-ianvs.github.io/).
-   - Provide cloud-robotics semantic segmentation examples in [this example](https://github.com/kubeedge/ianvs/tree/main/examples/robot/lifelong_learning_bench).
+   - Provide cloud-robotics semantic segmentation examples in [this example](https://github.com/kubeedge/ianvs/tree/main/examples/robot/lifelong_learning_bench).
+
+# Ianvs v0.3.0 release
+
+## What's New in v0.3.0
+
+Ianvs v0.3.0 brings powerful new LLM-related features, including comprehensive (1) LLM testing and benchmarking tools, (2) advanced cloud-edge collaborative inference paradigms, and (3) innovative algorithms tailored for large model optimization.
+
+### 1. Support for LLM Testing and Benchmarks
+Ianvs now supports robust testing for both locally deployed LLMs and public LLM APIs (e.g., OpenAI). This release introduces three specialized benchmarks for evaluating LLM capabilities in diverse scenarios:
+
+- Government-Specific Large Model Benchmark: Designed to assess LLM accuracy and reasoning in [government-specific scenarios](https://github.com/kubeedge/ianvs/blob/main/docs/proposals/scenarios/llm-benchmarks/llm-benchmarks.md). using objective (multiple-choice) and subjective (Q&A) tests. [Explore the benchmark dataset](https://www.kaggle.com/datasets/kubeedgeianvs/the-government-affairs-dataset-govaff/data?select=government_benchmark), [try the example](https://github.com/kubeedge/ianvs/tree/main/examples/government/singletask_learning_bench).
+
+- Smart Coding Benchmark: This benchmark evaluates the debugging capabilities of LLMs using real-world coding issues from GitHub repositories. [Learn more through the example](https://github.com/kubeedge/ianvs/tree/main/examples/smart_coding/smart_coding_learning_bench) and [read the background documentation](https://github.com/kubeedge/ianvs/blob/main/docs/proposals/scenarios/Smart_Coding/Smart%20Coding%20benchmark%20suite%20Proposal.md).
+
+- Large Language Model Edge Benchmark: Focused on testing LLM performance in edge environments, this benchmark evaluates resource efficiency and deployment performance. [Access datasets and examples here](https://github.com/kubeedge/ianvs/tree/main/examples/llm_simple_qa) and check out the [detailed documentation](https://github.com/kubeedge/ianvs/blob/main/docs/proposals/scenarios/llm-benchmark-suite/llm-edge-benchmark-suite.md).
+
+### 2. Enhanced Cloud-Edge Collaborative Inference
+This release introduces new paradigms and algorithms for collaborative inference to optimize cloud-edge cooperation and improve performance:
+
+- Cloud-Edge Collaborative Inference Paradigm: A new architecture enables [efficient cloud-edge collaboration for LLM inference](https://github.com/kubeedge/ianvs/blob/main/docs/proposals/algorithms/joint-inference/cloud-edge-collaboration-inference-for-llm.md), featuring a baseline algorithm that delivers up to 50% token cost savings without compromising accuracy. [Try the example](https://github.com/kubeedge/ianvs/tree/main/examples/cloud-edge-collaborative-inference-for-llm).
+
+- Speculative Decoding Algorithm (EAGLE, ICML'24): Integrated within the collaborative inference framework, this algorithm accelerates inference speeds by 20% or more. [Try the example](https://github.com/kubeedge/ianvs/tree/main/examples/cloud-edge-collaborative-inference-for-llm) and explore [detailed documentation](https://github.com/kubeedge/ianvs/blob/main/docs/proposals/algorithms/joint-inference/cloud-edge-speculative-decoding-for-llm.md).
+
+- Joint Inference Paradigm for Pedestrian Tracking: A multi-edge inference paradigm for pedestrian tracking utilizing the pretrained ByteTrack model (ECCV'22). [See the pedestrian tracking example](https://github.com/kubeedge/ianvs/tree/main/examples/MOT17/multiedge_inference_bench/pedestrian_tracking) or refer to the [background documentation](https://github.com/kubeedge/ianvs/blob/main/docs/proposals/algorithms/multi-edge-inference/Heterogeneous%20Multi-Edge%20Collaborative%20Neural%20Network%20Inference%20for%20High%20Mobility%20Scenarios.md).
+
+### 3. Support for New Large Model Algorithms
+Ianvs includes new algorithms to improve LLM performance and usability in various scenarios:
+
+- Personalized LLM Agent Algorithm: This algorithm supports single-task learning using the pretrained Bloom model, enabling personalized LLM operations. [Explore the example](https://github.com/kubeedge/ianvs/tree/main/examples/llm-agent/singletask_learning_bench) and review the [documentation](https://github.com/Frank-lilinjie/ianvs/blob/main/docs/proposals/algorithms/single-task-learning/Personalized%20LLM%20Agent%20based%20on%20KubeEdge-Ianvs%20Cloud-Edge%20Collaboration.md).
+
+- Multimodal Large Model Joint Learning Algorithm: A joint learning algorithm for multimodal understanding with the pretrained RFNet model. [Try the example here](https://github.com/aryan0931/ianvs/tree/main/examples/Cloud_Robotics/singletask_learning_bench/Semantic_Segmentation) and learn more in the [documentation](https://github.com/kubeedge/ianvs/blob/main/docs/proposals/scenarios/Cloud_robotics/single_task_learning.md).
+
+- Unseen Task Processing Algorithm: Supports lifelong learning with pretrained models to handle unseen tasks effectively. [Access the example](https://github.com/kubeedge/ianvs/tree/main/examples/cityscapes/lifelong_learning_bench/unseen_task_processing-GANwithSelfTaughtLearning) and gain insights from the [background documentation](https://github.com/kubeedge/ianvs/blob/main/docs/proposals/algorithms/lifelong-learning/Unknown_Task_Processing_Algorithm_based_on_Lifelong_Learning_of_Ianvs.md).