Update README.md

README.md

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 ---
-license: apache-2.0
-dataset_info:
-  features:
-  - name: task_id
-    dtype: string
-  - name: suite_type
-    dtype: string
-  - name: category
-    dtype: string
-  - name: prompt_explicitness
-    dtype: string
-  - name: acceptable_ui_archetypes
-    dtype: string
-  - name: user_prompt
-    dtype: string
-  - name: mock_tool_context
-    dtype: string
-  - name: expected_tool_trigger
-    dtype: bool
-  - name: outcome_dom_assertions
-    dtype: string
-  - name: outcome_interaction_steps
-    dtype: string
-  - name: expected_callback_schema
-    dtype: string
-  - name: model_grader_rubrics
-    dtype: string
-  - name: reference_solution
-    dtype: string
-  splits:
-  - name: train
-    num_bytes: 26713
-    num_examples: 20
-  download_size: 24801
-  dataset_size: 26713
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
+license: mit
+task_categories:
+  - text-generation
+  - question-answering
+language:
+  - en
+tags:
+  - hermes-agent
+  - nous-research
+  - generative-ui
+  - agent-evals
+  - interactive-micro-apps
+  - human-in-the-loop
+  - active-learning
+pretty_name: Agent Generative UI Evaluation Suite
+size_categories:
+  - n<1K
 ---
+
+# Hermes Agent — Generative UI (Interactive Micro-Apps) Evaluation Suite
+
+## Dataset Summary
+
+**`hermes-genui-evals`** is an **Eval-Driven Development (EDD)** benchmark designed to evaluate **Bidirectional Generative UI (Interactive Micro-Apps)** plugins for [**Hermes Agent**](https://github.com/NousResearch/hermes-agent) by **Nous Research**.
+
+While Hermes Agent excels at backend execution, persistent memory (`MEMORY.md`), cron scheduling, and multi-channel messaging, complex cognitive workflows—such as **studying a technical topic in depth**, **what-if parameter simulations**, and **Human-in-the-Loop (HITL) batch triage**—suffer when constrained to static walls of Markdown text.
+
+This evaluation suite tests an agent's ability to:
+1. **Recognize when to trigger (or abstain from triggering) an interactive micro-app** across varying levels of prompt explicitness (`explicit_ui`, `semi_guided`, and `implicit_intent`).
+2. **Ground the generated UI** in real tool outputs (`read_file`, `web_search`, `execute_command`, `memory_read`) without hallucinating data.
+3. **Generate self-contained, error-free HTML/JS micro-apps** that execute cleanly in a headless browser (Playwright).
+4. **Maintain a bidirectional state contract** via `window.hermes.sendAction(payload)`, feeding user interactions (e.g., quiz diagnostic weak spots, confirmed file deletions, cron schedules) back into Hermes Agent's execution loop.
+
+---
+
+## Evaluation Methodology (Anthropic Agent Evals Framework)
+
+This dataset is structured following the nomenclature and best practices from Anthropic's paper [*"Demystifying evals for AI agents"*](https://www.anthropic.com/engineering/demystifying-evals-for-ai-agents):
+
+* **Agent Harness:** Hermes Agent equipped with the Generative UI plugin (`render_micro_app` tool + `SKILL.md`).
+* **Evaluation Harness:** An automated Python + Playwright runner that executes $k$ **Trials** per **Task**, captures the **Transcript** (tool call trace) and the **Outcome** (rendered DOM state + bidirectional callback payload), and runs three tiers of **Graders**:
+  1. **Code-Based Graders (Deterministic):**
+     * *Transcript Check:* Verifies `render_micro_app` invocation (`expected_tool_trigger`) to measure both **undertriggering** and **overtriggering**.
+     * *Outcome DOM Check:* Loads the generated micro-app in headless Chromium, asserts zero runtime console/page errors (`zero_js_errors`), verifies data grounding (`must_contain_strings`), and checks interactive controls (`required_selectors`).
+     * *Outcome Callback Check:* Executes `outcome_interaction_steps` in Playwright and validates the resulting `window.hermes.sendAction(...)` payload against `expected_callback_schema` (JSON Schema).
+  2. **Model-Based Graders (Binary `PASS`/`FAIL` Rubrics):**
+     * Instead of noisy 1–5 Likert scales, an LLM judge evaluates the rendered UI and code against strict **binary (`PASS`/`FAIL`) rubrics** (`model_grader_rubrics`) assessing pedagogical scaffolding, active recall enforcement, and autonomous UI selection.
+  3. **Reference Solutions:**
+     * Every task includes a verified `reference_solution` that passes 100% of the deterministic and model-based graders to guarantee task solvability and grader calibration.
+
+### Target Metrics: `pass@k` vs `pass^k`
+* **`pass@k`** ($1 - (1 - p)^k$): Measures the agent's capability ceiling (at least 1 successful trial out of $k$).
+* **`pass^k`** ($p^k$): Measures production reliability and consistency ($k$ out of $k$ consecutive trials succeeding without a single JS error or broken callback).
+
+---
+
+## Dataset Taxonomy & Distribution (v1 — 20 Tasks)
+
+### 1. By Suite Type (`suite_type`)
+* **`capability` (10 tasks):** Challenging tasks testing multi-step interactive simulations, literature contradiction matrices, and autonomous pedagogical UI initiative (`implicit_intent`).
+* **`regression` (10 tasks):** Core functional tasks (6 positive `explicit_ui` tasks + 4 `negative_control` tasks) that must maintain a ~100% pass rate across plugin iterations.
+
+### 2. By Functional Category (`category`)
+* **`topic_study` (11 tasks — Core Focus):** Interactive learning environments coupled with Hermes's long-term memory and cron scheduler:
+  * *Explorable Explanations & Mathematical Simulators* (e.g., `pass@k` vs `pass^k`, Softmax Temperature, Neural Network Backpropagation).
+  * *Active Recall & Gap Diagnostics* (e.g., Flashcards & quizzes with hidden answers that send `weak_concepts` back to `MEMORY.md`).
+  * *Concept Graph Explorers* (e.g., Distributed consensus Raft state & Pre-Vote exploration).
+  * *Literature Comparison Matrices* (e.g., Cross-examining RAG evaluation papers like RAGAS vs ARES).
+  * *Spaced Repetition Planners* (e.g., Turning memory weak spots into automated Telegram review crons).
+* **`human_in_the_loop` (2 tasks):** Safe destructive file cleanup (`~/Downloads`) and batch email triage with editable draft replies.
+* **`what_if_simulation` (1 task):** Cloud GPU (RunPod A100) vs Local Hardware (Mac Studio) break-even calculator.
+* **`ops_monitoring` (1 task):** Visual process manager (`ps aux`) isolating runaway processes while protecting production databases.
+* **`workflow_builder` (1 task):** Visual Cron & delivery channel configurator for automated ArXiv digests.
+* **`negative_control` (4 tasks):** Simple factual questions, CLI lookups, and unambiguous single-step commands where spawning a micro-app is over-engineering (`expected_tool_trigger: false`).
+
+### 3. By Prompt Explicitness (`prompt_explicitness`)
+A critical dimension of this benchmark is evaluating whether the agent only builds UIs when explicitly told which widgets to render, or whether it can diagnose a user's cognitive bottleneck on its own:
+* **`explicit_ui` (7 tasks):** The user explicitly requests an interactive component (tests pure code & callback implementation).
+* **`semi_guided` (6 tasks):** The user expresses an intent to practice, compare, or triage without naming specific UI widgets (tests archetype mapping).
+* **`implicit_intent` (3 positive tasks + 4 negative controls):** The user states a raw problem or learning bottleneck with **zero mention of UI** (e.g., *"I have my midterm tomorrow and I still get confused about Raft split votes..."*). Tests true agentic initiative vs. overtriggering.
+
+---
+
+## Schema & Column Definitions (13 Columns)
+
+| Column | Type | Description |
+| :--- | :--- | :--- |
+| **`task_id`** | `string` | Unique identifier for the task (e.g., `study_cap_06`, `hitl_reg_01`, `neg_reg_01`). |
+| **`suite_type`** | `string` | Evaluation suite classification: `"capability"` or `"regression"`. |
+| **`category`** | `string` | Functional domain (`"topic_study"`, `"human_in_the_loop"`, `"what_if_simulation"`, `"ops_monitoring"`, `"workflow_builder"`, `"negative_control"`). |
+| **`prompt_explicitness`** | `string` | Level of UI guidance in the prompt: `"explicit_ui"`, `"semi_guided"`, or `"implicit_intent"`. |
+| **`acceptable_ui_archetypes`** | `string` (JSON list) | List of valid UI patterns for the task (allows flexibility on `implicit_intent` prompts, or `["none"]` for negative controls). |
+| **`user_prompt`** | `string` | The natural language prompt sent to Hermes Agent. |
+| **`mock_tool_context`** | `string` (JSON list) | Simulated outputs from prior Hermes tools (`read_file`, `web_search`, `execute_command`, `memory_read`) that must ground the micro-app. |
+| **`expected_tool_trigger`** | `bool` | **Transcript Grader:** `true` if the agent should invoke `render_micro_app`, `false` for negative controls. |
+| **`outcome_dom_assertions`** | `string` (JSON dict) | **Outcome Grader (Playwright):** Deterministic checks (`zero_js_errors`, `must_contain_strings`, `required_selectors`, `min_interactive_elements`). |
+| **`outcome_interaction_steps`** | `string` (JSON list) | Sequential Playwright actions (`click`, `set_range`) executed to test reactivity and trigger the callback. |
+| **`expected_callback_schema`** | `string` (JSON Schema) | **Bidirectional Outcome Grader:** JSON Schema validating the payload sent via `window.hermes.sendAction(payload)`. |
+| **`model_grader_rubrics`** | `string` (JSON list) | **Model-Based Grader:** Strict binary (`PASS`/`FAIL`) rubrics evaluated by an LLM judge. |
+| **`reference_solution`** | `string` | Verified self-contained HTML/JS micro-app (or plain text response for negative controls) that passes 100% of the task's graders. |
+
+---
+
+## Quick Usage
+
+```python
+import json
+from datasets import load_dataset
+
+# Load the dataset from Hugging Face Hub
+dataset = load_dataset("your-username/hermes-genui-evals", split="train")
+
+for task in dataset:
+    print(f"[{task['task_id']}] ({task['suite_type']} | {task['prompt_explicitness']})")
+    print(f"Prompt: {task['user_prompt']}")
+    dom_checks = json.loads(task["outcome_dom_assertions"])
+    rubrics = json.loads(task["model_grader_rubrics"])
+    print(f"Expected Trigger: {task['expected_tool_trigger']} | Rubrics: {[r['id'] for r in rubrics]}\n")