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 # Evolve
 ***_Self-improving agents through iterations._***
 
-Evolve is a system designed to help agents improve over time by learning from their trajectories. It uses a combination of an MCP server for tool integration, vector storage for memory, and LLM-based conflict resolution to refine its knowledge base.
+Coding agents repeat the same mistakes because they start fresh every session. Evolve gives agents memory — they learn from what worked and what didn't, so each session is better than the last.
 
-## Features
+On the AppWorld benchmark, Evolve improved agent reliability by **+8.9 points** overall, with a **74% relative increase** on hard multi-step tasks. See the [full results](results/index.md) and the [paper (arXiv:2603.10600)](https://arxiv.org/abs/2603.10600).
 
-- **MCP Server**: Exposes tools to get guidelines and save trajectories.
-- **Conflict Resolution**: Intelligently merges new insights with existing guidelines using LLMs.
-- **Trajectory Analysis**: Automatically analyzes agent trajectories to generate guidelines and best practices.
-- **Milvus Integration**: Uses Milvus (or Milvus Lite) for efficient vector storage and retrieval.
-
-## Start Here
+## Get Started
 
 - [Installation](installation/index.md): Set up Evolve on Bob or Claude Code.
-- [Configuration](guides/configuration.md): Configure models, backends, and environment variables.
-- [CLI Reference](reference/cli.md): Manage namespaces, entities, and sync jobs from the command line.
+- [Hello World with IBM Bob](examples/hello_world/bob.md): A simple walkthrough that shows how memory gets learned.
+- [Hello World with Claude Code](examples/hello_world/claude.md): Get started with Evolve Lite in Claude Code.
 
 ## Guides
 
+- [Configuration](guides/configuration.md): Configure models, backends, and environment variables.
 - [Low-Code Tracing](guides/low-code-tracing.md): Instrument agents with Phoenix and verify end-to-end tracing.
 - [Phoenix Sync](guides/phoenix-sync.md): Pull trajectories from Phoenix and generate stored guidelines.
 - [Extract Trajectories](guides/extract-trajectories.md): Export Phoenix traces into an OpenAI-style message format.
 
-## Integrations and Tutorials
-
-- [Evolve Lite (Claude Code)](integrations/claude/evolve-lite.md): Lightweight Claude Code integration with local entity storage.
-- [Claude Code Demo](tutorials/claude-code-demo.md): Run the filesystem demo with Claude Code and the Evolve MCP server.
-- [Hello World with IBM Bob](examples/hello_world/bob.md): A simple Bob walkthrough that shows how memory gets learned.
-
 ## Reference
 
+- [CLI Reference](reference/cli.md): Manage namespaces, entities, and sync jobs from the command line.
 - [Policies](reference/policies.md): Structured policy entities and how to retrieve them with MCP tools.
 
-## Architecture
+## How It Works
+
+Evolve analyzes agent trajectories to extract guidelines and best practices, then recalls them in future sessions. It supports both a lightweight file-based mode (Evolve Lite) and a full mode backed by an MCP server with vector storage and LLM-based conflict resolution.
+
 ![Architecture](assets/architecture.png)
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+# Results
+
+## AppWorld Benchmark
+
+We evaluated Evolve on [AppWorld](https://appworld.dev), where agents complete realistic multi-step tasks via APIs, averaging 9.5 APIs across 1.8 apps. Hard tasks require more complex control flow across multiple services.
+
+A ReAct agent received the task instruction plus the top 5 retrieved guidelines generated from one prior run on train/dev and was tested on an unseen partition (test-normal). We report Scenario Goal Completion (SGC), a strict consistency metric requiring success across scenario variants.
+
+| Difficulty | Baseline SGC | + Evolve | Gain |
+|---|---:|---:|---:|
+| Easy | 79.0% | 84.2% | +5.2 |
+| Medium | 56.2% | 62.5% | +6.3 |
+| **Hard** | **19.1%** | **33.3%** | **+14.2** |
+| **Aggregate** | **50.0%** | **58.9%** | **+8.9** |
+
+### Key findings
+
+- **Generalization:** The agent improves on unseen test tasks, showing it learns transferable principles rather than memorizing solutions.
+- **Complexity scaling:** The harder the task, the more the agent benefits from learned guidelines. Hard tasks saw a 74% relative increase in success rate.
+- **Consistency:** SGC gains exceeded raw pass-rate improvements, reducing "flaky" behavior across scenario variants. Guidelines help the agent solve tasks reliably, not just occasionally.
+
+## Paper
+
+For full details on the architecture, experiments, and analysis, see:
+
+> [Trajectory-Informed Memory Generation for Self-Improving Agent Systems](https://arxiv.org/abs/2603.10600) (arXiv:2603.10600)
diff --git a/mkdocs.yaml b/mkdocs.yaml
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     - Hello World (IBM Bob IDE): examples/hello_world/bob.md
     - Claude Code Demo: tutorials/claude-code-demo.md
     - Starter Example 2 (Claude Code): examples/hello_world/claude.md
+  - Results:
+    - Benchmarks: results/index.md
   - Operations:
     - Docker Testing: operations/docker-testing.md