[None][doc] Fix broken links across example and test READMEs

examples/auto_deploy/README.md

@@ -176,7 +176,7 @@ For expert users, `build_and_run_ad.py` provides advanced configuration capabili
 
 #### CLI Arguments with Dot Notation
 
-The script supports flexible CLI argument parsing using dot notation to modify nested configurations dynamically. You can target any field in both the [`ExperimentConfig`](./build_and_run_ad.py) and nested [`AutoDeployConfig`](../../tensorrt_llm/_torch/auto_deploy/llm_args.py)/[`LlmArgs`](../../tensorrt_llm/_torch/auto_deploy/llm_args.) objects:
+The script supports flexible CLI argument parsing using dot notation to modify nested configurations dynamically. You can target any field in both the [`ExperimentConfig`](./build_and_run_ad.py) and nested [`AutoDeployConfig`](../../tensorrt_llm/_torch/auto_deploy/llm_args.py)/[`LlmArgs`](../../tensorrt_llm/_torch/auto_deploy/llm_args.py) objects:
 
 ```bash
 # Configure model parameters

examples/models/core/qwen/README.md

@@ -289,7 +289,7 @@ For further details, please refer to [speculative-decoding.md](../../../../docs/
 ### Dynamo
 
 NVIDIA Dynamo is a high-throughput low-latency inference framework designed for serving generative AI and reasoning models in multi-node distributed environments.
-Dynamo supports TensorRT LLM as one of its inference engine. For details on how to use TensorRT LLM with Dynamo please refer to [LLM Deployment Examples using TensorRT-LLM](https://github.com/ai-dynamo/dynamo/blob/main/examples/tensorrt_llm/README.md)
+Dynamo supports TensorRT LLM as one of its inference engine. For details on how to use TensorRT LLM with Dynamo please refer to [LLM Deployment Examples using TensorRT-LLM](https://github.com/ai-dynamo/dynamo/tree/main/examples/backends/trtllm/deploy)
 
 ## Qwen3-Next
 

examples/wide_ep/README.md

@@ -166,8 +166,8 @@ Refer to the [Troubleshooting and FAQ](https://github.com/NVIDIA/TensorRT-LLM/bl
 ## References
 
 To understand more details on wide EP and the optimizations we've added, refer to the technical blog series: Scaling Expert Parallelism in TensorRT-LLM
-  - [Part 1: Design and Implementation of Large-scale EP](https://github.com/NVIDIA/TensorRT-LLM/blob/main/docs/source/blogs/tech_blog/blog4_Scaling_Expert_Parallelism_in_TensorRT-LLM.md)
-  - [Part 2: Performance Status and Optimization](https://github.com/NVIDIA/TensorRT-LLM/blob/main/docs/source/blogs/tech_blog/blog8_Scaling_Expert_Parallelism_in_TensorRT-LLM_part2.md)
+  - [Part 1: Design and Implementation of Large-scale EP](https://github.com/NVIDIA/TensorRT-LLM/blob/main/docs/source/blogs/tech_blog/blog04_Scaling_Expert_Parallelism_in_TensorRT-LLM.md)
+  - [Part 2: Performance Status and Optimization](https://github.com/NVIDIA/TensorRT-LLM/blob/main/docs/source/blogs/tech_blog/blog08_Scaling_Expert_Parallelism_in_TensorRT-LLM_part2.md)
   - [Part 3: Pushing the Performance Boundary](https://github.com/NVIDIA/TensorRT-LLM/blob/main/docs/source/blogs/tech_blog/blog14_Scaling_Expert_Parallelism_in_TensorRT-LLM_part3.md)
 
 To review how wide EP helps with Blackwell's leading inference benchmarks, also read these recent blog posts:

tensorrt_llm/scaffolding/README.md

@@ -38,6 +38,6 @@ Future work includes the following aspects:
 
 - Provide information for combined performance optimization with backends. Scaffolding can provide some information which is helpful for LLM inference engine. For a example, Controller may aware the prefix relation between generation requests, that would be helpful for kvcache reuse.
 
-- Develop auxiliary components to support generic requirements. Now we have developed some interesting auxiliary components. For an [example](examples/scaffolding/token_budget_majority_vote.py), we developed `GenerationTokenCounter` as a task collection decorator so that Controller could get the output tokens count for itself and its Sub-Controller. There are still many such works waiting for us to do.
+- Develop auxiliary components to support generic requirements. Now we have developed some interesting auxiliary components. For an [example](../../examples/scaffolding/token_budget_majority_vote.py), we developed `GenerationTokenCounter` as a task collection decorator so that Controller could get the output tokens count for itself and its Sub-Controller. There are still many such works waiting for us to do.
 
 You can see more specific work in this [link](https://github.com/NVIDIA/TensorRT-LLM/issues/3706#issuecomment-2820015957).

tests/integration/defs/perf/README_release_test.md

@@ -109,8 +109,8 @@ if self._config.backend == "pytorch":
 
 ### 3.1 Full Test Cycles
 
-1. **llm_perf_full.yml** - Release performance test
-   - [test_lists/qa/llm_perf_full.yml](../../test_lists/qa/llm_perf_full.yml)
+1. **llm_perf_core.yml** - Release performance test
+   - [test_lists/qa/llm_perf_core.yml](../../test_lists/qa/llm_perf_core.yml)
 2. **llm_perf_cluster.yml** - Cluster performance test(for Blackwell)
    - [test_lists/qa/llm_perf_cluster.yml](../../test_lists/qa/llm_perf_cluster.yml)
 3. **llm_perf_nim.yml** - NIM performance test