Spaces:
Running
Running
DVampire
commited on
Commit
·
d3ff7fa
1
Parent(s):
26884bd
update database
Browse files- app.py +12 -12
- frontend/paper.html +1 -1
- frontend/paper.js +1 -1
- src/agents/evaluator.py +35 -35
- src/agents/prompt.py +270 -133
- workdir/paper_agent/papers_cache.db +3 -0
app.py
CHANGED
@@ -428,19 +428,19 @@ async def get_paper_score(paper_id: str) -> Dict[str, Any]:
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evaluation_content = paper.get('evaluation_content')
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if evaluation_content:
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evaluation_json = json.loads(evaluation_content)
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if '
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values = [
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]
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valid_scores = [v for v in values if v > 0]
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overall_score = sum(valid_scores) / len(valid_scores) if valid_scores else 0
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evaluation_content = paper.get('evaluation_content')
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if evaluation_content:
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evaluation_json = json.loads(evaluation_content)
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+
if 'scores' in evaluation_json:
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scores = evaluation_json['scores']
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values = [
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scores.get('task_formalization', 0),
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scores.get('data_resource_availability', 0),
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scores.get('input_output_complexity', 0),
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scores.get('real_world_interaction', 0),
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scores.get('existing_ai_coverage', 0),
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scores.get('human_originality', 0),
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scores.get('safety_ethics', 0),
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scores.get('technical_maturity_needed', 0),
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scores.get('three_year_feasibility_pct', 0) / 25, # Convert percentage to 0-4 scale
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scores.get('overall_automatability', 0)
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]
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valid_scores = [v for v in values if v > 0]
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overall_score = sum(valid_scores) / len(valid_scores) if valid_scores else 0
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frontend/paper.html
CHANGED
@@ -64,7 +64,7 @@
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<aside class="sidebar">
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<div class="scorecard-panel">
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<div class="panel-header">
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-
<h2><i class="fas fa-radar"></i>
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<div class="overall-score" id="overallScore">
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<span class="score-number">-</span>
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<span class="score-label">Overall</span>
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<aside class="sidebar">
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<div class="scorecard-panel">
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<div class="panel-header">
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+
<h2><i class="fas fa-radar"></i> Scores</h2>
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<div class="overall-score" id="overallScore">
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<span class="score-number">-</span>
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<span class="score-label">Overall</span>
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frontend/paper.js
CHANGED
@@ -292,7 +292,7 @@ class PaperEvaluationRenderer {
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if (!radarEl) return;
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try {
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-
const score = json.
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const d = parseMaybeJSON(json.dimensions) || {};
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const labels = [
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if (!radarEl) return;
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try {
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const score = json.scores || {};
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const d = parseMaybeJSON(json.dimensions) || {};
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const labels = [
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src/agents/evaluator.py
CHANGED
@@ -94,7 +94,7 @@ class Evaluator:
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# Call Anthropic API with tools (async)
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response = await self.client.messages.create(
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model=config.model_id,
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-
max_tokens=
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system=self.system_prompt,
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messages=messages,
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tools=TOOLS,
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@@ -158,26 +158,26 @@ async def save_node(state: ConversationState) -> ConversationState:
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# Try to extract score and tags from tool_result if available
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if state.tool_result:
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try:
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# Extract overall automatability score from
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if '
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evaluation_score = state.tool_result['
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# Extract overall score from
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if '
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overall_score = state.tool_result['
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# Create tags from key dimensions in
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tags = []
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if '
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if 'three_year_feasibility_pct' in
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tags.append(f"3yr_feasibility:{
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if 'task_formalization' in
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tags.append(f"task_formalization:{
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-
if 'data_resource_availability' in
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tags.append(f"data_availability:{
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-
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-
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except Exception as e:
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logger.warning(f"Warning: Could not extract structured data from tool_result: {e}")
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@@ -185,26 +185,26 @@ async def save_node(state: ConversationState) -> ConversationState:
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# Try to parse evaluation_content as JSON to extract structured data
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try:
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evaluation_json = json.loads(evaluation_content)
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# Extract overall automatability score from
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if '
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evaluation_score = evaluation_json['
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# Extract overall score from
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if '
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overall_score = evaluation_json['
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# Create tags from key dimensions in
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tags = []
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if '
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if 'three_year_feasibility_pct' in
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tags.append(f"3yr_feasibility:{
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if 'task_formalization' in
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tags.append(f"task_formalization:{
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if 'data_resource_availability' in
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tags.append(f"data_availability:{
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-
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-
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except Exception as e:
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logger.warning(f"Warning: Could not parse evaluation_content as JSON: {e}")
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# Call Anthropic API with tools (async)
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response = await self.client.messages.create(
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model=config.model_id,
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max_tokens=10000,
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system=self.system_prompt,
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messages=messages,
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tools=TOOLS,
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# Try to extract score and tags from tool_result if available
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if state.tool_result:
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try:
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# Extract overall automatability score from scores
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if 'scores' in state.tool_result and 'overall_automatability' in state.tool_result['scores']:
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evaluation_score = state.tool_result['scores']['overall_automatability']
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# Extract overall score from scores
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if 'scores' in state.tool_result and 'overall_automatability' in state.tool_result['scores']:
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overall_score = state.tool_result['scores']['overall_automatability']
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# Create tags from key dimensions in scores
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tags = []
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if 'scores' in state.tool_result:
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scores = state.tool_result['scores']
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if 'three_year_feasibility_pct' in scores:
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tags.append(f"3yr_feasibility:{scores['three_year_feasibility_pct']}%")
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if 'task_formalization' in scores:
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tags.append(f"task_formalization:{scores['task_formalization']}/4")
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if 'data_resource_availability' in scores:
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tags.append(f"data_availability:{scores['data_resource_availability']}/4")
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evaluation_tags = ",".join(tags) if tags else None
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except Exception as e:
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logger.warning(f"Warning: Could not extract structured data from tool_result: {e}")
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# Try to parse evaluation_content as JSON to extract structured data
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try:
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evaluation_json = json.loads(evaluation_content)
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# Extract overall automatability score from scores
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if 'scores' in evaluation_json and 'overall_automatability' in evaluation_json['scores']:
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evaluation_score = evaluation_json['scores']['overall_automatability']
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# Extract overall score from scores
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if 'scores' in evaluation_json and 'overall_automatability' in evaluation_json['scores']:
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overall_score = evaluation_json['scores']['overall_automatability']
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# Create tags from key dimensions in scores
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tags = []
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if 'scores' in evaluation_json:
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scores = evaluation_json['scores']
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if 'three_year_feasibility_pct' in scores:
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tags.append(f"3yr_feasibility:{scores['three_year_feasibility_pct']}%")
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if 'task_formalization' in scores:
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tags.append(f"task_formalization:{scores['task_formalization']}/4")
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if 'data_resource_availability' in scores:
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tags.append(f"data_availability:{scores['data_resource_availability']}/4")
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evaluation_tags = ",".join(tags) if tags else None
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except Exception as e:
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logger.warning(f"Warning: Could not parse evaluation_content as JSON: {e}")
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src/agents/prompt.py
CHANGED
@@ -11,9 +11,15 @@ Maintain critical thinking and provide detailed justifications for each score. Y
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EVALUATION_PROMPT_TEMPLATE = """
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# Systematic AI Automation Assessment Framework
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Please conduct a comprehensive evaluation of the provided academic work using the following 12-dimensional framework.
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-
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### 1. **Task Formalization** (Score: 0-4)
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**What to Evaluate**: Whether the task has clear rules/mathematical objectives
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@@ -141,91 +147,19 @@ Please conduct a comprehensive evaluation of the provided academic work using th
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**Analysis Required**: Synthesize all dimensions into overall assessment.
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##
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Please structure your response as follows:
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# AI Automation Assessment Report
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## Executive Summary
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[Provide a concise 150-word summary of key findings and overall assessment]
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## Detailed Dimensional Analysis
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### 1. Task Formalization
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**Score: X/4**
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[Detailed analysis and justification]
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### 2. Data & Resource Availability
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**Score: X/4**
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[Detailed analysis and justification]
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### 3. Input-Output Complexity
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**Score: X/4**
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[Detailed analysis and justification]
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### 4. Real-World Interaction
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**Score: X/4**
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[Detailed analysis and justification]
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### 5. Existing AI Coverage
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**Score: X/4**
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[Detailed analysis with specific tools/models and coverage percentage]
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### 6. Automation Barriers
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[Comprehensive list and explanation of key barriers]
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### 7. Human Originality/Irreplaceability
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**Score: X/4**
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[Detailed analysis and justification]
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### 8. Safety & Ethical Criticality
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**Score: X/4**
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[Detailed risk analysis and justification]
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### 9. Societal/Economic Impact
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[Comprehensive impact analysis]
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### 10. Technical Maturity Needed
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**Score: X/4**
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[Detailed analysis of required advances]
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### 11. 3-Year Feasibility
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**Probability: X%**
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[Detailed probability assessment with reasoning]
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### 12. Overall Automatability
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**Score: X/4**
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[Synthesis of all dimensions with final assessment]
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## Summary Scorecard
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| Dimension | Score | Key Insight |
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|-----------|-------|-------------|
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| Task Formalization | X/4 | [Brief insight] |
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| Data & Resource Availability | X/4 | [Brief insight] |
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| Input-Output Complexity | X/4 | [Brief insight] |
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| Real-World Interaction | X/4 | [Brief insight] |
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| Existing AI Coverage | X/4 | [Brief insight] |
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| Human Originality | X/4 | [Brief insight] |
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| Safety & Ethics | X/4 | [Brief insight] |
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| Technical Maturity | X/4 | [Brief insight] |
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| 3-Year Feasibility | X% | [Brief insight] |
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| **Overall Automatability** | **X/4** | **[Key conclusion]** |
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## Strategic Recommendations
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### For Researchers
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-
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### For Institutions
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-
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### For AI Development
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-
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## Assessment Limitations and Uncertainties
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-
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---
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@@ -235,6 +169,8 @@ Please structure your response as follows:
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- Consider both current capabilities and realistic near-term developments
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- Justify all numerical scores with detailed reasoning
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- For qualitative dimensions, provide comprehensive analysis
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Now please begin the systematic evaluation of the provided academic work.
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"""
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@@ -244,79 +180,218 @@ Now please begin the systematic evaluation of the provided academic work.
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TOOLS = [
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{
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"name": "return_assessment",
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"description": "Return the complete
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"input_schema": {
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"type": "object",
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"properties": {
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"executive_summary": {
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"dimensions": {
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"type": "object",
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"properties": {
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"task_formalization": {
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"type": "object",
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"properties": {
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},
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"data_resource_availability": {
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"type": "object",
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"properties": {
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},
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"input_output_complexity": {
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"type": "object",
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"properties": {
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},
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"real_world_interaction": {
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"type": "object",
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"properties": {
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-
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},
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"existing_ai_coverage": {
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"type": "object",
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"properties": {
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"score": {
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},
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"required": [
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},
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"automation_barriers": {
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"type": "object",
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"properties": {
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},
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"human_originality": {
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"type": "object",
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"properties": {
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},
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"safety_ethics": {
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"type": "object",
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"properties": {
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},
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"societal_economic_impact": {
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"type": "object",
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"properties": {
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-
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},
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"technical_maturity_needed": {
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"type": "object",
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"properties": {
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},
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"three_year_feasibility": {
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"type": "object",
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"properties": {
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},
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"overall_automatability": {
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"type": "object",
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"properties": {
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},
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"required": [
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"task_formalization",
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@@ -330,22 +405,52 @@ TOOLS = [
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"societal_economic_impact",
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"technical_maturity_needed",
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"three_year_feasibility",
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"overall_automatability"
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]
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},
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"
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"type": "object",
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"properties": {
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"task_formalization": {
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},
|
350 |
"required": [
|
351 |
"task_formalization",
|
@@ -357,30 +462,62 @@ TOOLS = [
|
|
357 |
"safety_ethics",
|
358 |
"technical_maturity_needed",
|
359 |
"three_year_feasibility_pct",
|
360 |
-
"overall_automatability"
|
361 |
-
]
|
362 |
},
|
363 |
"recommendations": {
|
364 |
"type": "object",
|
365 |
"properties": {
|
366 |
-
"for_researchers": {
|
367 |
-
|
368 |
-
|
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|
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},
|
370 |
-
"required": [
|
|
|
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|
|
|
|
|
371 |
},
|
372 |
-
"limitations_uncertainties": {
|
|
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|
|
|
|
|
|
|
|
|
|
373 |
},
|
374 |
"required": [
|
375 |
"executive_summary",
|
376 |
"dimensions",
|
377 |
-
"
|
378 |
"recommendations",
|
379 |
-
"limitations_uncertainties"
|
380 |
],
|
381 |
"additionalProperties": False,
|
382 |
-
|
|
|
383 |
}
|
384 |
]
|
385 |
|
386 |
-
TOOL_CHOICE = {
|
|
|
|
|
|
|
|
11 |
EVALUATION_PROMPT_TEMPLATE = """
|
12 |
# Systematic AI Automation Assessment Framework
|
13 |
|
14 |
+
Please conduct a comprehensive evaluation of the provided academic work using the following 12-dimensional framework. Your output should be organized into four sections: executive_summary, dimensions, scores, recommendations, and limitations_uncertainties.
|
15 |
|
16 |
+
IMPORTANT: Follow the exact JSON schema structure provided. The 'dimensions' section should contain detailed analysis objects with 'score' and 'analysis' fields. The 'scores' section should contain only the numerical scores as a flat object. Do not include dimension scores as top-level fields.
|
17 |
+
|
18 |
+
## Executive Summary
|
19 |
+
|
20 |
+
Please provide a concise 150-word summary of key findings and overall assessment.
|
21 |
+
|
22 |
+
## 12-Dimensional Evaluation
|
23 |
|
24 |
### 1. **Task Formalization** (Score: 0-4)
|
25 |
**What to Evaluate**: Whether the task has clear rules/mathematical objectives
|
|
|
147 |
|
148 |
**Analysis Required**: Synthesize all dimensions into overall assessment.
|
149 |
|
150 |
+
## Recommendations
|
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|
151 |
|
152 |
### For Researchers
|
153 |
+
Please provide specific recommendations for researchers in this field.
|
154 |
|
155 |
### For Institutions
|
156 |
+
Please provide recommendations for research institutions and funding bodies.
|
157 |
|
158 |
### For AI Development
|
159 |
+
Please provide recommendations for AI researchers and developers.
|
160 |
|
161 |
## Assessment Limitations and Uncertainties
|
162 |
+
Please list any limitations or uncertainties in your assessment.
|
163 |
|
164 |
---
|
165 |
|
|
|
169 |
- Consider both current capabilities and realistic near-term developments
|
170 |
- Justify all numerical scores with detailed reasoning
|
171 |
- For qualitative dimensions, provide comprehensive analysis
|
172 |
+
- Please use `return_assessment` tool to return the complete AI automation assessment as a single JSON object.
|
173 |
+
- Do not mention the tool in your response in order to avoid model hallucination.
|
174 |
|
175 |
Now please begin the systematic evaluation of the provided academic work.
|
176 |
"""
|
|
|
180 |
TOOLS = [
|
181 |
{
|
182 |
"name": "return_assessment",
|
183 |
+
"description": "Return the complete AI automation assessment as a single JSON object.",
|
184 |
"input_schema": {
|
185 |
"type": "object",
|
186 |
"properties": {
|
187 |
+
"executive_summary": {
|
188 |
+
"type": "string",
|
189 |
+
"description": "A concise 150-word summary of key findings and overall assessment."
|
190 |
+
},
|
191 |
"dimensions": {
|
192 |
"type": "object",
|
193 |
+
"description": "Detailed analysis of each dimension with scores and justifications.",
|
194 |
"properties": {
|
195 |
"task_formalization": {
|
196 |
"type": "object",
|
197 |
+
"properties": {
|
198 |
+
"score": {
|
199 |
+
"type": "number",
|
200 |
+
"description": "The score for the task formalization dimension, on a scale of 0-4."
|
201 |
+
},
|
202 |
+
"analysis": {
|
203 |
+
"type": "string",
|
204 |
+
"description": "A detailed analysis of the task formalization dimension, including the score and the justification for the score."
|
205 |
+
}
|
206 |
+
},
|
207 |
+
"required": [
|
208 |
+
"score",
|
209 |
+
"analysis"
|
210 |
+
]
|
211 |
},
|
212 |
"data_resource_availability": {
|
213 |
"type": "object",
|
214 |
+
"properties": {
|
215 |
+
"score": {
|
216 |
+
"type": "number",
|
217 |
+
"description": "The score for the data resource availability dimension, on a scale of 0-4."
|
218 |
+
},
|
219 |
+
"analysis": {
|
220 |
+
"type": "string",
|
221 |
+
"description": "A detailed analysis of the data resource availability dimension, including the score and the justification for the score."
|
222 |
+
}
|
223 |
+
},
|
224 |
+
"required": [
|
225 |
+
"score",
|
226 |
+
"analysis"
|
227 |
+
]
|
228 |
},
|
229 |
"input_output_complexity": {
|
230 |
"type": "object",
|
231 |
+
"properties": {
|
232 |
+
"score": {
|
233 |
+
"type": "number",
|
234 |
+
"description": "The score for the input output complexity dimension, on a scale of 0-4."
|
235 |
+
},
|
236 |
+
"analysis": {
|
237 |
+
"type": "string",
|
238 |
+
"description": "A detailed analysis of the input output complexity dimension, including the score and the justification for the score."
|
239 |
+
}
|
240 |
+
},
|
241 |
+
"required": [
|
242 |
+
"score",
|
243 |
+
"analysis"
|
244 |
+
]
|
245 |
},
|
246 |
"real_world_interaction": {
|
247 |
"type": "object",
|
248 |
+
"properties": {
|
249 |
+
"score": {
|
250 |
+
"type": "number",
|
251 |
+
"description": "The score for the real world interaction dimension, on a scale of 0-4."
|
252 |
+
},
|
253 |
+
"analysis": {
|
254 |
+
"type": "string",
|
255 |
+
"description": "A detailed analysis of the real world interaction dimension, including the score and the justification for the score."
|
256 |
+
}
|
257 |
+
},
|
258 |
+
"required": [
|
259 |
+
"score",
|
260 |
+
"analysis"
|
261 |
+
]
|
262 |
},
|
263 |
"existing_ai_coverage": {
|
264 |
"type": "object",
|
265 |
"properties": {
|
266 |
+
"score": {
|
267 |
+
"type": "number",
|
268 |
+
"description": "The score for the existing AI coverage dimension, on a scale of 0-4."
|
269 |
+
},
|
270 |
+
"analysis": {
|
271 |
+
"type": "string",
|
272 |
+
"description": "A detailed analysis of the existing AI coverage dimension, including the score and the justification for the score."
|
273 |
+
},
|
274 |
+
"tools_models": {
|
275 |
+
"type": "array",
|
276 |
+
"items": {
|
277 |
+
"type": "string"
|
278 |
+
}
|
279 |
+
},
|
280 |
+
"coverage_pct_estimate": {
|
281 |
+
"type": "number"
|
282 |
+
}
|
283 |
},
|
284 |
+
"required": [
|
285 |
+
"score",
|
286 |
+
"analysis"
|
287 |
+
]
|
288 |
},
|
289 |
"automation_barriers": {
|
290 |
"type": "object",
|
291 |
+
"properties": {
|
292 |
+
"analysis": {
|
293 |
+
"type": "string",
|
294 |
+
"description": "A detailed analysis of the automation barriers dimension, including the score and the justification for the score."
|
295 |
+
}
|
296 |
+
},
|
297 |
+
"required": [
|
298 |
+
"analysis"
|
299 |
+
]
|
300 |
},
|
301 |
"human_originality": {
|
302 |
"type": "object",
|
303 |
+
"properties": {
|
304 |
+
"score": {
|
305 |
+
"type": "number",
|
306 |
+
"description": "The score for the human originality dimension, on a scale of 0-4."
|
307 |
+
},
|
308 |
+
"analysis": {
|
309 |
+
"type": "string",
|
310 |
+
"description": "A detailed analysis of the human originality dimension, including the score and the justification for the score."
|
311 |
+
}
|
312 |
+
},
|
313 |
+
"required": [
|
314 |
+
"score",
|
315 |
+
"analysis"
|
316 |
+
]
|
317 |
},
|
318 |
"safety_ethics": {
|
319 |
"type": "object",
|
320 |
+
"properties": {
|
321 |
+
"score": {
|
322 |
+
"type": "number",
|
323 |
+
"description": "The score for the safety and ethics dimension, on a scale of 0-4."
|
324 |
+
},
|
325 |
+
"analysis": {
|
326 |
+
"type": "string",
|
327 |
+
"description": "A detailed analysis of the safety and ethics dimension, including the score and the justification for the score."
|
328 |
+
}
|
329 |
+
},
|
330 |
+
"required": [
|
331 |
+
"score",
|
332 |
+
"analysis"
|
333 |
+
]
|
334 |
},
|
335 |
"societal_economic_impact": {
|
336 |
"type": "object",
|
337 |
+
"properties": {
|
338 |
+
"analysis": {
|
339 |
+
"type": "string"
|
340 |
+
}
|
341 |
+
},
|
342 |
+
"required": [
|
343 |
+
"analysis"
|
344 |
+
]
|
345 |
},
|
346 |
"technical_maturity_needed": {
|
347 |
"type": "object",
|
348 |
+
"properties": {
|
349 |
+
"score": {
|
350 |
+
"type": "number"
|
351 |
+
},
|
352 |
+
"analysis": {
|
353 |
+
"type": "string"
|
354 |
+
}
|
355 |
+
},
|
356 |
+
"required": [
|
357 |
+
"score",
|
358 |
+
"analysis"
|
359 |
+
]
|
360 |
},
|
361 |
"three_year_feasibility": {
|
362 |
"type": "object",
|
363 |
+
"properties": {
|
364 |
+
"probability_pct": {
|
365 |
+
"type": "number",
|
366 |
+
"description": "The probability of AI reaching expert level within 3 years, on a scale of 0-100%."
|
367 |
+
},
|
368 |
+
"analysis": {
|
369 |
+
"type": "string",
|
370 |
+
"description": "A detailed analysis of the three year feasibility dimension, including the probability and the justification for the probability."
|
371 |
+
}
|
372 |
+
},
|
373 |
+
"required": [
|
374 |
+
"probability_pct",
|
375 |
+
"analysis"
|
376 |
+
]
|
377 |
},
|
378 |
"overall_automatability": {
|
379 |
"type": "object",
|
380 |
+
"properties": {
|
381 |
+
"score": {
|
382 |
+
"type": "number",
|
383 |
+
"description": "The score for the overall automatability dimension, on a scale of 0-4."
|
384 |
+
},
|
385 |
+
"analysis": {
|
386 |
+
"type": "string",
|
387 |
+
"description": "A detailed analysis of the overall automatability dimension, including the score and the justification for the score."
|
388 |
+
}
|
389 |
+
},
|
390 |
+
"required": [
|
391 |
+
"score",
|
392 |
+
"analysis"
|
393 |
+
]
|
394 |
+
}
|
395 |
},
|
396 |
"required": [
|
397 |
"task_formalization",
|
|
|
405 |
"societal_economic_impact",
|
406 |
"technical_maturity_needed",
|
407 |
"three_year_feasibility",
|
408 |
+
"overall_automatability"
|
409 |
+
]
|
410 |
},
|
411 |
+
"scores": {
|
412 |
"type": "object",
|
413 |
"properties": {
|
414 |
+
"task_formalization": {
|
415 |
+
"type": "number",
|
416 |
+
"description": "The score for the task formalization dimension, on a scale of 0-4."
|
417 |
+
},
|
418 |
+
"data_resource_availability": {
|
419 |
+
"type": "number",
|
420 |
+
"description": "The score for the data resource availability dimension, on a scale of 0-4."
|
421 |
+
},
|
422 |
+
"input_output_complexity": {
|
423 |
+
"type": "number",
|
424 |
+
"description": "The score for the input output complexity dimension, on a scale of 0-4."
|
425 |
+
},
|
426 |
+
"real_world_interaction": {
|
427 |
+
"type": "number",
|
428 |
+
"description": "The score for the real world interaction dimension, on a scale of 0-4."
|
429 |
+
},
|
430 |
+
"existing_ai_coverage": {
|
431 |
+
"type": "number",
|
432 |
+
"description": "The score for the existing AI coverage dimension, on a scale of 0-4."
|
433 |
+
},
|
434 |
+
"human_originality": {
|
435 |
+
"type": "number",
|
436 |
+
"description": "The score for the human originality dimension, on a scale of 0-4."
|
437 |
+
},
|
438 |
+
"safety_ethics": {
|
439 |
+
"type": "number",
|
440 |
+
"description": "The score for the safety and ethics dimension, on a scale of 0-4."
|
441 |
+
},
|
442 |
+
"technical_maturity_needed": {
|
443 |
+
"type": "number",
|
444 |
+
"description": "The score for the technical maturity needed dimension, on a scale of 0-4."
|
445 |
+
},
|
446 |
+
"three_year_feasibility_pct": {
|
447 |
+
"type": "number",
|
448 |
+
"description": "The probability of AI reaching expert level within 3 years, on a scale of 0-100%."
|
449 |
+
},
|
450 |
+
"overall_automatability": {
|
451 |
+
"type": "number",
|
452 |
+
"description": "The score for the overall automatability dimension, on a scale of 0-4."
|
453 |
+
}
|
454 |
},
|
455 |
"required": [
|
456 |
"task_formalization",
|
|
|
462 |
"safety_ethics",
|
463 |
"technical_maturity_needed",
|
464 |
"three_year_feasibility_pct",
|
465 |
+
"overall_automatability"
|
466 |
+
]
|
467 |
},
|
468 |
"recommendations": {
|
469 |
"type": "object",
|
470 |
"properties": {
|
471 |
+
"for_researchers": {
|
472 |
+
"type": "array",
|
473 |
+
"items": {
|
474 |
+
"type": "string",
|
475 |
+
"description": "A specific recommendation for researchers in this field."
|
476 |
+
}
|
477 |
+
},
|
478 |
+
"for_institutions": {
|
479 |
+
"type": "array",
|
480 |
+
"items": {
|
481 |
+
"type": "string",
|
482 |
+
"description": "A recommendation for research institutions and funding bodies."
|
483 |
+
}
|
484 |
+
},
|
485 |
+
"for_ai_development": {
|
486 |
+
"type": "array",
|
487 |
+
"items": {
|
488 |
+
"type": "string",
|
489 |
+
"description": "A recommendation for AI researchers and developers."
|
490 |
+
}
|
491 |
+
}
|
492 |
},
|
493 |
+
"required": [
|
494 |
+
"for_researchers",
|
495 |
+
"for_institutions",
|
496 |
+
"for_ai_development"
|
497 |
+
]
|
498 |
},
|
499 |
+
"limitations_uncertainties": {
|
500 |
+
"type": "array",
|
501 |
+
"items": {
|
502 |
+
"type": "string",
|
503 |
+
"description": "A limitation or uncertainty in the assessment."
|
504 |
+
}
|
505 |
+
}
|
506 |
},
|
507 |
"required": [
|
508 |
"executive_summary",
|
509 |
"dimensions",
|
510 |
+
"scores",
|
511 |
"recommendations",
|
512 |
+
"limitations_uncertainties"
|
513 |
],
|
514 |
"additionalProperties": False,
|
515 |
+
"description": "Complete evaluation output with executive summary, detailed dimensions analysis, numerical scores, recommendations, and limitations."
|
516 |
+
}
|
517 |
}
|
518 |
]
|
519 |
|
520 |
+
TOOL_CHOICE = {
|
521 |
+
"type": "tool",
|
522 |
+
"name": "return_assessment"
|
523 |
+
}
|
workdir/paper_agent/papers_cache.db
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:63089ac0211f69a8086daf1e54751e1a9cb67ca01a5a81b6765beb8dae6fe818
|
3 |
+
size 282624
|