diff --git a/README.md b/README.md
index f497d6b67..12ebfcece 100644
--- a/README.md
+++ b/README.md
@@ -1,4 +1,5 @@
-# cuOpt - GPU accelerated Optimization Engine
+<!-- AI agents: Start by reading AGENTS.md -->
+# cuOpt - GPU-accelerated Optimization
 
 [![Build Status](https://github.com/NVIDIA/cuopt/actions/workflows/build.yaml/badge.svg)](https://github.com/NVIDIA/cuopt/actions/workflows/build.yaml)
 [![Version](https://img.shields.io/badge/version-26.02.00-blue)](https://github.com/NVIDIA/cuopt/releases)
@@ -11,12 +12,15 @@
 
 
 
-NVIDIA® cuOpt™ is a GPU-accelerated optimization engine that excels in mixed integer linear programming (MILP), linear programming (LP), quadratic programming (QP) and vehicle routing problems (VRP). It enables near real-time solutions for large-scale challenges with millions of variables and constraints, offering
-easy integration into existing solvers and seamless deployment across hybrid and multi-cloud environments.
+NVIDIA® cuOpt™ is a GPU-accelerated optimization engine that excels in mixed integer linear programming (MILP), linear programming (LP), quadratic programming (QP) and vehicle routing problems (VRP). It enables near real-time solutions for large-scale LPs with millions of variables and constraints, and MIPs with hundreds of thousands of variables. cuOpt offers easy integration into existing modeling languages and seamless deployment across hybrid and multi-cloud environments.
 
 The core engine is written in C++ and wrapped with a C API, Python API and Server API.
 
-For the latest stable version ensure you are on the `main` branch.
+For the latest version ensure you are on the `main` branch.
+
+## Latest Documentation
+
+[cuOpt Documentation](https://docs.nvidia.com/cuopt/user-guide/latest/introduction.html))
 
 ## Supported APIs
 
@@ -153,6 +157,10 @@ cuOpt follows the RAPIDS release schedule and is part of the **"others"** catego
 
 For current release timelines and dates, refer to the [RAPIDS Maintainers Docs](https://docs.rapids.ai/maintainers/).
 
+## For AI Coding Agents
+
+See [AGENTS.md](./AGENTS.md) for agent-specific guidelines.
+
 ## Contributing Guide
 
 Review the [CONTRIBUTING.md](CONTRIBUTING.md) file for information on how to contribute code and issues to the project.
diff --git a/RELEASE-NOTES.md b/RELEASE-NOTES.md
index 3112b56ec..ca9d080b7 100644
--- a/RELEASE-NOTES.md
+++ b/RELEASE-NOTES.md
@@ -1,6 +1,51 @@
 # Release Notes
 
-
+## Release Notes 26.02
+
+### New Features (26.02)
+
+- New parallel reliability branching inside MIP solver
+- Mixed Integer Gomory, Mixed Integer Rounding, Knapsack and Strong Chvatal Gomory cuts are now added at root node
+- Added an option to use batch PDLP when running strong branching at the root. Based on [Batched First-Order Methods for Parallel LP Solving in MIP](https://arxiv.org/abs/2601.21990) ([Nicolas Blin](https://github.com/Kh4ster), [Stefano Gualandi](https://github.com/stegua), [Christopher Maes](https://github.com/chris-maes), [Andrea Lodi](https://github.com/andrealodi), [Bartolomeo Stellato](https://github.com/bstellato))
+- New infeasibility detection option for PDLP's default solver mode Stable3
+- Solutions callbacks added to C API. Users can now retrieve the dual bound and pass in user data.
+- Multiple new diving techniques added for finding integer feasible solutions
+- The [PSLP presolver](https://github.com/dance858/PSLP) is enabled by default for LP problems. Use the presolve option to select Papilo or disable.   
+- Added a batch solve for routing to boost throughput for many similar instances
+
+### Breaking Changes (26.02)
+
+- The signature of the solution callbacks have changed for the Python API
+
+### Improvements (26.02)
+
+- Improved primal/dual warm start for PDLP's default solver mode Stable3
+- Quadratic objectives can now be constructed via a matrix in Python API
+- QP barrier now updates and solves augmented system on the GPU
+- Improved performance for LP folding
+- Probing implications and better variable ordering to strengthen presolve and branching
+- Replace deprecated cuDF Column/Buffer APIs with pylibcudf and public cuDF interfaces
+- Modernize dependency pinnings; make CUDA runtime linkage static for portability
+- Build/tooling: add `--split-compile`, `--jobserver`, Clang host build, ThreadSanitizer, improved container scripts, and branch/commit metadata in images
+- Use explicit `cudaStream_t` with `cub::DeviceTransform` and non-blocking streams for GPU control
+- Enable barrier LP tests, add regression testing, and add SonarQube static analysis
+
+### Bug Fixes (26.02)
+
+- Fix out-of-bounds in dense-column detection in barrier
+- Correct infeasible-list handling to avoid incorrect infeasibility reports in dual simplex
+- Fix race conditions found via Clang host build + ThreadSanitizer
+- Resolve CUDA–Numba version mismatches with cuDF
+- Fix device code to include required trailing return types
+- Fix issue in crossover after dualization in barrier
+- Repair container build and test failures
+- Miscellaneous additional fixes and stability improvements
+
+### Documentation (26.02)
+
+- Update README and top-level docs for current build and usage
+- Document new repository branching strategies and release-cycle details in README and CONTRIBUTING
+- Add best practices for batch solving
 
 ## Release Notes 25.12