# Qworld Criteria Generation

> Generate question-specific evaluation criteria using the Qworld Recursive Expansion Tree (RET) algorithm. Decomposes any open-ended question into scenarios, perspectives, and fine-grained binary criteria through structured horizontal and hierarchical expansion. Use when asked to create evaluation rubrics, generate grading criteria, build scoring rubrics for LLM responses, assess answer quality for open-ended questions, or when the user mentions "criteria generation", "evaluation rubrics", "evaluation criteria", "Qworld", or "RET algorithm".

- Skill: `mims-harvard/qworld-criteria-generation` (Agent Skill)
- Install (CLI): `npx skillmds@latest add mims-harvard/qworld-criteria-generation`
- Raw SKILL.md: https://api.skillmd.com/api/skills/mims-harvard/qworld-criteria-generation/raw
- Safety review: PASS (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: mims-harvard (https://skillmd.com/u/mims-harvard)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/mims-harvard/qworld-criteria-generation

---


# Qworld: Question-Specific Criteria Generation

Generate evaluation criteria tailored to a specific question using the Recursive Expansion Tree (RET)
algorithm. Each question defines its own "evaluation world" -- a set of scenarios, perspectives, and
criteria that capture what matters for judging answers to that specific question.

## When to Use

- User asks to create evaluation criteria or rubrics for one or more questions
- User wants to assess or score LLM responses to open-ended questions
- User needs fine-grained, binary (YES/NO) evaluation dimensions for a question
- User mentions Qworld, RET, or criteria generation

## Core Principles

1. **Question-specific**: Every scenario, perspective, and criterion must be derived from the question's
   content, intent, and context. Never reuse a fixed set of dimensions across questions.
2. **Binary and verifiable**: Each final criterion must be answerable YES or NO by an evaluator.
3. **Non-redundant**: At every level, check for overlap before adding new items.
4. **Balanced**: Include both positive criteria (what a good answer must do) and negative criteria
   (what constitutes harmful or misleading content). Positive total points must outweigh negative.
5. **Coverage-driven**: At each level, repeatedly ask "what's missing?" to ensure the evaluation
   space implied by the question is fully explored.

## Algorithm Overview

The RET builds a 3-level tree from the question:

```
Question
  -> Level 1: Scenarios (contextual framings that change what "good" means)
       -> Level 2: Perspectives (evaluation dimensions per scenario)
            -> Level 3: Criteria (concrete, binary rubric items with scores)
```

At each level, two operators apply:
- **Hierarchical decomposition**: break a node into finer-grained children at the next level
- **Horizontal expansion**: ask "what else is missing?" and add sibling nodes for coverage

---

## Step-by-Step Workflow

Copy this checklist and track progress as you work:

```
Task Progress:
- [ ] Step 1: Scenario grounding
- [ ] Step 2: Scenario expansion (3 rounds)
- [ ] Step 3: Perspective generation
- [ ] Step 4: Perspective expansion (4 rounds)
- [ ] Step 5: Perspective review and consolidation
- [ ] Step 6: Criteria generation
- [ ] Step 7: Criteria expansion (3 rounds)
- [ ] Step 8: Criteria review and consolidation
- [ ] Step 9: Polarity check
- [ ] Step 10: Score calibration
```

---

### Step 1: Scenario Grounding

**Goal**: Identify the distinct real-world contexts in which this question could arise, where each
context would materially change what constitutes a good answer.

**Method**:
- Read the question carefully. Infer its domain, intent, audience, stakes, and implicit constraints.
- Ask: "In what different situations could someone ask this question, and how would the situation
  change what a good answer looks like?"
- Produce a minimal, non-redundant set of scenarios. Merge any that would lead to the same
  evaluation criteria.

**Output format** for each scenario:
- `scenario_name`: short descriptive label
- `scenario_description`: 3-5 sentences explaining what is unique about this context and why it
  changes what "good" means

---

### Step 2: Scenario Expansion (3 rounds)

**Goal**: Ensure comprehensive coverage of the evaluation space at the scenario level.

**Method** (repeat 3 times):
1. Review all existing scenarios.
2. Ask: "What contexts or framings are missing that would require different evaluation criteria?"
3. Identify gaps along context axes (audience, setting, stakes, constraints, domain variation, etc.).
4. Generate ONLY new scenarios that differ materially from all existing ones. Do not repeat or
   rephrase existing scenarios.

After each round, append new scenarios to the list.

---

### Step 3: Perspective Generation

**Goal**: For each scenario, derive the evaluation dimensions that matter for judging an answer
in that context.

**Method**:
- For EACH scenario, produce 4-7 non-overlapping evaluation perspectives.
- Each perspective must be grounded in the scenario and the question. Derive perspectives entirely
  from the question's content and context -- do not apply a pre-set list of dimensions.
- Aim for maximally diverse themes, including unconventional angles that are specific to this
  question.
- Each perspective should be high-level enough to spawn multiple criteria, yet narrow enough to
  avoid overlap with other perspectives.

**Output format** for each perspective:
- `perspective_name`: 2-5 word descriptive label
- `perspective_description`: 3-5 sentences explaining what this perspective evaluates and listing
  3-5 specific sub-aspects it covers

---

### Step 4: Perspective Expansion (4 rounds)

**Goal**: Fill coverage gaps in the perspective set.

**Method** (repeat 4 times):
1. Review all perspectives across all scenarios.
2. Map coverage across quality dimensions implied by the question.
3. Ask: "What evaluation angles are missing that would yield materially different criteria?"
4. Generate ONLY new perspectives that provide distinct evaluation value. Do not repeat existing ones.

After each round, append new perspectives to the collection.

---

### Step 5: Perspective Review and Consolidation

**Goal**: Produce a clean, non-redundant set of perspectives ready for criteria generation.

**Method**:
- If two perspectives target the same evaluation angle but under different scenarios, combine
  scenario-specific details into each description but keep them as separate perspectives.
- Remove perspectives that are off-topic, redundant with others, or too vague to guide concrete
  criteria generation.
- Each kept perspective must contribute unique evaluation value for this question.
- Assign each kept perspective a unique ID (p0, p1, p2, ...).

---

### Step 6: Criteria Generation

**Goal**: For each reviewed perspective, generate concrete, binary evaluation criteria.

**Method**:
- For EACH perspective, generate criteria that an evaluator can check YES or NO against a response.
- Cover all sub-aspects listed in the perspective description.

**Criteria rules**:
1. **Binary**: Each criterion must be answerable YES/NO.
2. **Scenario-specific**: Explicitly reference features of the question and scenario. Make criteria
   as detailed and specific as possible.
3. **Self-contained**: Each criterion is a standalone statement in the form [Verb + Specific Requirement].
   Start with one clear action verb, then state the exact required or forbidden content with qualifiers.
4. **Balanced**: Include positive criteria (required content/behavior) and negative criteria (harmful,
   misleading, or critically wrong content). Only add negative criteria when the issue represents
   harmful, dangerous, or significantly quality-reducing behavior -- not minor stylistic concerns.
5. **Diverse**: Cover all sub-aspects of the perspective.

**Negative criteria phrasing**: Describe the bad behavior directly. Instead of "Avoids doing X",
write "Does X" (where X is the harmful behavior). The criterion text states the behavior; its negative
score indicates that meeting it is bad.

**Scoring standard**:
- Positive: 1-10 (10 = critical safety/core requirement; 8-9 = important completeness; 5-7 = quality enhancer; 1-4 = minor nice-to-have)
- Negative: -1 to -10 (-10 = dangerous/harmful; -8 to -9 = major omission or error; -5 to -7 = quality issue; -1 to -4 = minor issue)

**Output format** for each criterion:
- `criterion`: the criterion text
- `points`: integer score (positive or negative)
- `reasoning`: 2-3 sentences explaining why this criterion matters for this question and why it
  received this weight

---

### Step 7: Criteria Expansion (3 rounds)

**Goal**: Fill coverage gaps in the criteria set.

**Method** (repeat 3 times):
1. Review all existing criteria.
2. Ask: "What concrete content or behavior, if present or absent in a response, would change
   whether the response passes or fails -- and is not yet covered?"
3. Generate ONLY new criteria that are non-overlapping with existing ones.
4. Follow the same rules and scoring standard as Step 6.

After each round, append new criteria to the collection.

---

### Step 8: Criteria Review and Consolidation

**Goal**: Produce a concise, non-redundant final rubric.

**Method**:
- **Deduplicate and merge**: Combine criteria that assess the same aspect. Keep the most precise
  wording and include all distinct details from merged criteria.
- **Positive/negative overlap**: If a positive and negative criterion cover the same aspect, keep
  only the positive.
- **Neutralize fixed facts**: Replace hard-coded numbers, dates, versions, limits, or placeholders
  with a requirement that the response states the current/official/latest value or standard.
- **Balance check**: Ensure total positive points outweigh total negative points. Retain all
  distinct, non-overlapping items.
- Assign each final criterion a unique ID (c0, c1, c2, ...).

---

### Step 9: Polarity Check

**Goal**: Verify that every criterion's score sign correctly reflects whether meeting it is good or bad.

**Method**:
- For each criterion, determine: does meeting this criterion improve the response (positive) or
  indicate a problem (negative)?
- A criterion that describes desirable behavior should have a positive score.
- A criterion that describes harmful, wrong, or misleading behavior should have a negative score.
- Phrasing like "Avoids doing X" describes desirable behavior (positive). Phrasing like "Does X"
  (where X is bad) describes undesirable behavior (negative).
- Adjust the sign of points if misclassified. Do not change the criterion text.

---

### Step 10: Score Calibration

**Goal**: Ensure score magnitudes accurately reflect importance.

**Method**:
- Review each criterion's absolute score against the scoring standard from Step 6.
- A more important or severe issue must always have a higher absolute score than a less important one.
- Adjust magnitudes where needed. Do not change the sign (positive/negative direction) or the
  criterion text.
- Verify the scoring standard is applied consistently:
  - Positive: 10 = critical, 8-9 = important, 5-7 = quality, 1-4 = minor
  - Negative: -10 = dangerous, -8 to -9 = major, -5 to -7 = moderate, -1 to -4 = minor

---

## Final Output Format

Present the final criteria as a structured list. Each item contains:

- `criterion_id`: unique identifier (c0, c1, ...)
- `criterion`: the full criterion text
- `points`: calibrated integer score
- `reasoning`: why this criterion matters for this specific question and why this weight

Also include intermediate outputs for transparency:
- List of scenarios (with IDs, names, descriptions)
- List of reviewed perspectives (with IDs, names, descriptions)
- Summary statistics: number of scenarios, perspectives, raw criteria, and final criteria

---

## Handling Special Inputs

**Multi-turn conversations**: If the question is a conversation (user-assistant exchange), treat
the full conversation as the question context. The last user message is typically the primary intent;
earlier messages provide context that may affect evaluation dimensions.

**Questions with images**: If the question includes an image, factor image content into scenario
analysis and criteria generation. Reference visual elements where relevant in criteria.

**Questions with retrieved web context**: If web-retrieved context is provided alongside the
question, use it to inform factual grounding of scenarios and criteria. Do not limit analysis to
only the retrieved content -- also apply general reasoning.

---

## Key Constraints

- **No fixed dimension lists**: Never apply a pre-defined set of evaluation dimensions. Every
  scenario, perspective, and criterion must be freshly derived from the question.
- **Expansion counts matter**: Complete all specified expansion rounds (3 for scenarios, 4 for
  perspectives, 3 for criteria). Skipping rounds reduces coverage.
- **Deduplication is mandatory**: After each expansion phase and before finalization, check for
  and remove redundant items.
- **Criteria must be binary**: Every criterion in the final set must be checkable as YES or NO.
  Do not produce criteria that require subjective degree judgments.

---

## Citation

If you use this skill or the Qworld method in your work, please cite our paper:

```bibtex
@inproceedings{su2026qworld,
      title={Qworld: Question-Specific Evaluation Criteria for {LLM}s},
      author={Yuchang Su and Shanghua Gao and Pengwei Sui and Curtis R Ginder and Marinka Zitnik},
      booktitle={Third Conference on Language Modeling},
      year={2026},
}
```

<sub>\*Yuchang Su and Shanghua Gao are joint first authors and contributed equally to this work.</sub>