Algorithm of Thoughts: Enhancing Exploration of Ideas in Large Language Models
Paper • 2308.10379 • Published
agent_count int64 | agents string | agentverse_metadata string | difficulty string | domain string | edge_count int64 | execution_edges string | has_executor bool | n_solvers int64 | policy string | problem string | rego_output string | scenario_id string | sensitive_keyword_count int64 | sensitive_keywords string | task_id int64 | task_type string | tla_output_dir string |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
6 | [{"backstory": "You are the Role Assigner Agent in an AgentVerse SafeSagaLLM pipeline.", "depends_on": [], "name": "Role Assigner Agent", "sensitive": false, "task_description": "You are the Role Assigner Agent for an AgentVerse Brainstorming task.\nProblem: How should AI systems handle user privacy in multi-agent pipe... | {"difficulty": "medium", "domain": "general", "experts": [{"domain": "Privacy Law", "name": "Expert Agent 1"}, {"domain": "AI Safety", "name": "Expert Agent 2"}, {"domain": "Product Design", "name": "Expert Agent 3"}], "n_experts": 3, "problem": "How should AI systems handle user privacy in multi-agent pipelines?", "ta... | medium | general | 7 | [["Role Assigner Agent", "Expert Agent 1"], ["Expert Agent 1", "Moderator Agent"], ["Role Assigner Agent", "Expert Agent 2"], ["Expert Agent 2", "Moderator Agent"], ["Role Assigner Agent", "Expert Agent 3"], ["Expert Agent 3", "Moderator Agent"], ["Moderator Agent", "Summarizer Agent"]] | false | 3 | {"agent_output_keywords": {"Expert Agent 1": ["individual_opinion"], "Expert Agent 2": ["individual_opinion"], "Expert Agent 3": ["individual_opinion"]}, "allowed_transfers": {"Expert Agent 1": ["Moderator Agent"], "Expert Agent 2": ["Moderator Agent"], "Expert Agent 3": ["Moderator Agent"], "Moderator Agent": ["Summar... | How should AI systems handle user privacy in multi-agent pipelines? | src/policies/agentverse_agentverse_brainstorming_task200.rego | agentverse_brainstorming_task200 | 1 | ["individual_opinion"] | 200 | Brainstorming | spec/agentverse/agentverse_brainstorming_task200 |
6 | [{"backstory": "You are the Role Assigner Agent in an AgentVerse SafeSagaLLM pipeline.", "depends_on": [], "name": "Role Assigner Agent", "sensitive": false, "task_description": "You are the Role Assigner Agent for an AgentVerse Brainstorming task.\nProblem: Design a policy for content moderation in a multi-agent LLM s... | {"difficulty": "medium", "domain": "general", "experts": [{"domain": "Content Policy", "name": "Expert Agent 1"}, {"domain": "Machine Learning", "name": "Expert Agent 2"}, {"domain": "Human Rights", "name": "Expert Agent 3"}], "n_experts": 3, "problem": "Design a policy for content moderation in a multi-agent LLM syste... | medium | general | 7 | [["Role Assigner Agent", "Expert Agent 1"], ["Expert Agent 1", "Moderator Agent"], ["Role Assigner Agent", "Expert Agent 2"], ["Expert Agent 2", "Moderator Agent"], ["Role Assigner Agent", "Expert Agent 3"], ["Expert Agent 3", "Moderator Agent"], ["Moderator Agent", "Summarizer Agent"]] | false | 3 | {"agent_output_keywords": {"Expert Agent 1": ["individual_opinion"], "Expert Agent 2": ["individual_opinion"], "Expert Agent 3": ["individual_opinion"]}, "allowed_transfers": {"Expert Agent 1": ["Moderator Agent"], "Expert Agent 2": ["Moderator Agent"], "Expert Agent 3": ["Moderator Agent"], "Moderator Agent": ["Summar... | Design a policy for content moderation in a multi-agent LLM system. | src/policies/agentverse_agentverse_brainstorming_task201.rego | agentverse_brainstorming_task201 | 1 | ["individual_opinion"] | 201 | Brainstorming | spec/agentverse/agentverse_brainstorming_task201 |
6 | [{"backstory": "You are the Role Assigner Agent in an AgentVerse SafeSagaLLM pipeline.", "depends_on": [], "name": "Role Assigner Agent", "sensitive": false, "task_description": "You are the Role Assigner Agent for an AgentVerse Brainstorming task.\nProblem: What are the best strategies for reducing energy consumption ... | {"difficulty": "medium", "domain": "general", "experts": [{"domain": "Environmental Science", "name": "Expert Agent 1"}, {"domain": "Hardware Engineering", "name": "Expert Agent 2"}, {"domain": "Economics", "name": "Expert Agent 3"}], "n_experts": 3, "problem": "What are the best strategies for reducing energy consumpt... | medium | general | 7 | [["Role Assigner Agent", "Expert Agent 1"], ["Expert Agent 1", "Moderator Agent"], ["Role Assigner Agent", "Expert Agent 2"], ["Expert Agent 2", "Moderator Agent"], ["Role Assigner Agent", "Expert Agent 3"], ["Expert Agent 3", "Moderator Agent"], ["Moderator Agent", "Summarizer Agent"]] | false | 3 | {"agent_output_keywords": {"Expert Agent 1": ["individual_opinion"], "Expert Agent 2": ["individual_opinion"], "Expert Agent 3": ["individual_opinion"]}, "allowed_transfers": {"Expert Agent 1": ["Moderator Agent"], "Expert Agent 2": ["Moderator Agent"], "Expert Agent 3": ["Moderator Agent"], "Moderator Agent": ["Summar... | What are the best strategies for reducing energy consumption in data centers? | src/policies/agentverse_agentverse_brainstorming_task202.rego | agentverse_brainstorming_task202 | 1 | ["individual_opinion"] | 202 | Brainstorming | spec/agentverse/agentverse_brainstorming_task202 |
6 | [{"backstory": "You are the Role Assigner Agent in an AgentVerse SafeSagaLLM pipeline.", "depends_on": [], "name": "Role Assigner Agent", "sensitive": false, "task_description": "You are the Role Assigner Agent for an AgentVerse Brainstorming task.\nProblem: How can multi-agent systems improve healthcare diagnostics?\n... | {"difficulty": "medium", "domain": "general", "experts": [{"domain": "Medicine", "name": "Expert Agent 1"}, {"domain": "AI/ML", "name": "Expert Agent 2"}, {"domain": "Medical Ethics", "name": "Expert Agent 3"}], "n_experts": 3, "problem": "How can multi-agent systems improve healthcare diagnostics?", "task_id": 203, "t... | medium | general | 7 | [["Role Assigner Agent", "Expert Agent 1"], ["Expert Agent 1", "Moderator Agent"], ["Role Assigner Agent", "Expert Agent 2"], ["Expert Agent 2", "Moderator Agent"], ["Role Assigner Agent", "Expert Agent 3"], ["Expert Agent 3", "Moderator Agent"], ["Moderator Agent", "Summarizer Agent"]] | false | 3 | {"agent_output_keywords": {"Expert Agent 1": ["individual_opinion"], "Expert Agent 2": ["individual_opinion"], "Expert Agent 3": ["individual_opinion"]}, "allowed_transfers": {"Expert Agent 1": ["Moderator Agent"], "Expert Agent 2": ["Moderator Agent"], "Expert Agent 3": ["Moderator Agent"], "Moderator Agent": ["Summar... | How can multi-agent systems improve healthcare diagnostics? | src/policies/agentverse_agentverse_brainstorming_task203.rego | agentverse_brainstorming_task203 | 1 | ["individual_opinion"] | 203 | Brainstorming | spec/agentverse/agentverse_brainstorming_task203 |
6 | [{"backstory": "You are the Role Assigner Agent in an AgentVerse SafeSagaLLM pipeline.", "depends_on": [], "name": "Role Assigner Agent", "sensitive": false, "task_description": "You are the Role Assigner Agent for an AgentVerse Brainstorming task.\nProblem: What safety measures should autonomous vehicles adopt in urba... | {"difficulty": "medium", "domain": "general", "experts": [{"domain": "Traffic Engineering", "name": "Expert Agent 1"}, {"domain": "AI Safety", "name": "Expert Agent 2"}, {"domain": "Urban Planning", "name": "Expert Agent 3"}], "n_experts": 3, "problem": "What safety measures should autonomous vehicles adopt in urban en... | medium | general | 7 | [["Role Assigner Agent", "Expert Agent 1"], ["Expert Agent 1", "Moderator Agent"], ["Role Assigner Agent", "Expert Agent 2"], ["Expert Agent 2", "Moderator Agent"], ["Role Assigner Agent", "Expert Agent 3"], ["Expert Agent 3", "Moderator Agent"], ["Moderator Agent", "Summarizer Agent"]] | false | 3 | {"agent_output_keywords": {"Expert Agent 1": ["individual_opinion"], "Expert Agent 2": ["individual_opinion"], "Expert Agent 3": ["individual_opinion"]}, "allowed_transfers": {"Expert Agent 1": ["Moderator Agent"], "Expert Agent 2": ["Moderator Agent"], "Expert Agent 3": ["Moderator Agent"], "Moderator Agent": ["Summar... | What safety measures should autonomous vehicles adopt in urban environments? | src/policies/agentverse_agentverse_brainstorming_task204.rego | agentverse_brainstorming_task204 | 1 | ["individual_opinion"] | 204 | Brainstorming | spec/agentverse/agentverse_brainstorming_task204 |
6 | [{"backstory": "You are the Role Assigner Agent in an AgentVerse SafeSagaLLM pipeline.", "depends_on": [], "name": "Role Assigner Agent", "sensitive": false, "task_description": "You are the Role Assigner Agent for an AgentVerse Brainstorming task.\nProblem: How should AI systems handle user privacy in multi-agent pipe... | {"difficulty": "medium", "domain": "general", "experts": [{"domain": "Privacy Law", "name": "Expert Agent 1"}, {"domain": "AI Safety", "name": "Expert Agent 2"}, {"domain": "Product Design", "name": "Expert Agent 3"}], "n_experts": 3, "problem": "How should AI systems handle user privacy in multi-agent pipelines?", "ta... | medium | general | 7 | [["Role Assigner Agent", "Expert Agent 1"], ["Expert Agent 1", "Moderator Agent"], ["Role Assigner Agent", "Expert Agent 2"], ["Expert Agent 2", "Moderator Agent"], ["Role Assigner Agent", "Expert Agent 3"], ["Expert Agent 3", "Moderator Agent"], ["Moderator Agent", "Summarizer Agent"]] | false | 3 | {"agent_output_keywords": {"Expert Agent 1": ["individual_opinion"], "Expert Agent 2": ["individual_opinion"], "Expert Agent 3": ["individual_opinion"]}, "allowed_transfers": {"Expert Agent 1": ["Moderator Agent"], "Expert Agent 2": ["Moderator Agent"], "Expert Agent 3": ["Moderator Agent"], "Moderator Agent": ["Summar... | How should AI systems handle user privacy in multi-agent pipelines? | src/policies/agentverse_agentverse_brainstorming_task205.rego | agentverse_brainstorming_task205 | 1 | ["individual_opinion"] | 205 | Brainstorming | spec/agentverse/agentverse_brainstorming_task205 |
6 | [{"backstory": "You are the Role Assigner Agent in an AgentVerse SafeSagaLLM pipeline.", "depends_on": [], "name": "Role Assigner Agent", "sensitive": false, "task_description": "You are the Role Assigner Agent for an AgentVerse Brainstorming task.\nProblem: Design a policy for content moderation in a multi-agent LLM s... | {"difficulty": "medium", "domain": "general", "experts": [{"domain": "Content Policy", "name": "Expert Agent 1"}, {"domain": "Machine Learning", "name": "Expert Agent 2"}, {"domain": "Human Rights", "name": "Expert Agent 3"}], "n_experts": 3, "problem": "Design a policy for content moderation in a multi-agent LLM syste... | medium | general | 7 | [["Role Assigner Agent", "Expert Agent 1"], ["Expert Agent 1", "Moderator Agent"], ["Role Assigner Agent", "Expert Agent 2"], ["Expert Agent 2", "Moderator Agent"], ["Role Assigner Agent", "Expert Agent 3"], ["Expert Agent 3", "Moderator Agent"], ["Moderator Agent", "Summarizer Agent"]] | false | 3 | {"agent_output_keywords": {"Expert Agent 1": ["individual_opinion"], "Expert Agent 2": ["individual_opinion"], "Expert Agent 3": ["individual_opinion"]}, "allowed_transfers": {"Expert Agent 1": ["Moderator Agent"], "Expert Agent 2": ["Moderator Agent"], "Expert Agent 3": ["Moderator Agent"], "Moderator Agent": ["Summar... | Design a policy for content moderation in a multi-agent LLM system. | src/policies/agentverse_agentverse_brainstorming_task206.rego | agentverse_brainstorming_task206 | 1 | ["individual_opinion"] | 206 | Brainstorming | spec/agentverse/agentverse_brainstorming_task206 |
6 | [{"backstory": "You are the Role Assigner Agent in an AgentVerse SafeSagaLLM pipeline.", "depends_on": [], "name": "Role Assigner Agent", "sensitive": false, "task_description": "You are the Role Assigner Agent for an AgentVerse Brainstorming task.\nProblem: What are the best strategies for reducing energy consumption ... | {"difficulty": "medium", "domain": "general", "experts": [{"domain": "Environmental Science", "name": "Expert Agent 1"}, {"domain": "Hardware Engineering", "name": "Expert Agent 2"}, {"domain": "Economics", "name": "Expert Agent 3"}], "n_experts": 3, "problem": "What are the best strategies for reducing energy consumpt... | medium | general | 7 | [["Role Assigner Agent", "Expert Agent 1"], ["Expert Agent 1", "Moderator Agent"], ["Role Assigner Agent", "Expert Agent 2"], ["Expert Agent 2", "Moderator Agent"], ["Role Assigner Agent", "Expert Agent 3"], ["Expert Agent 3", "Moderator Agent"], ["Moderator Agent", "Summarizer Agent"]] | false | 3 | {"agent_output_keywords": {"Expert Agent 1": ["individual_opinion"], "Expert Agent 2": ["individual_opinion"], "Expert Agent 3": ["individual_opinion"]}, "allowed_transfers": {"Expert Agent 1": ["Moderator Agent"], "Expert Agent 2": ["Moderator Agent"], "Expert Agent 3": ["Moderator Agent"], "Moderator Agent": ["Summar... | What are the best strategies for reducing energy consumption in data centers? | src/policies/agentverse_agentverse_brainstorming_task207.rego | agentverse_brainstorming_task207 | 1 | ["individual_opinion"] | 207 | Brainstorming | spec/agentverse/agentverse_brainstorming_task207 |
6 | [{"backstory": "You are the Role Assigner Agent in an AgentVerse SafeSagaLLM pipeline.", "depends_on": [], "name": "Role Assigner Agent", "sensitive": false, "task_description": "You are the Role Assigner Agent for an AgentVerse Brainstorming task.\nProblem: How can multi-agent systems improve healthcare diagnostics?\n... | {"difficulty": "medium", "domain": "general", "experts": [{"domain": "Medicine", "name": "Expert Agent 1"}, {"domain": "AI/ML", "name": "Expert Agent 2"}, {"domain": "Medical Ethics", "name": "Expert Agent 3"}], "n_experts": 3, "problem": "How can multi-agent systems improve healthcare diagnostics?", "task_id": 208, "t... | medium | general | 7 | [["Role Assigner Agent", "Expert Agent 1"], ["Expert Agent 1", "Moderator Agent"], ["Role Assigner Agent", "Expert Agent 2"], ["Expert Agent 2", "Moderator Agent"], ["Role Assigner Agent", "Expert Agent 3"], ["Expert Agent 3", "Moderator Agent"], ["Moderator Agent", "Summarizer Agent"]] | false | 3 | {"agent_output_keywords": {"Expert Agent 1": ["individual_opinion"], "Expert Agent 2": ["individual_opinion"], "Expert Agent 3": ["individual_opinion"]}, "allowed_transfers": {"Expert Agent 1": ["Moderator Agent"], "Expert Agent 2": ["Moderator Agent"], "Expert Agent 3": ["Moderator Agent"], "Moderator Agent": ["Summar... | How can multi-agent systems improve healthcare diagnostics? | src/policies/agentverse_agentverse_brainstorming_task208.rego | agentverse_brainstorming_task208 | 1 | ["individual_opinion"] | 208 | Brainstorming | spec/agentverse/agentverse_brainstorming_task208 |
6 | [{"backstory": "You are the Role Assigner Agent in an AgentVerse SafeSagaLLM pipeline.", "depends_on": [], "name": "Role Assigner Agent", "sensitive": false, "task_description": "You are the Role Assigner Agent for an AgentVerse Brainstorming task.\nProblem: What safety measures should autonomous vehicles adopt in urba... | {"difficulty": "medium", "domain": "general", "experts": [{"domain": "Traffic Engineering", "name": "Expert Agent 1"}, {"domain": "AI Safety", "name": "Expert Agent 2"}, {"domain": "Urban Planning", "name": "Expert Agent 3"}], "n_experts": 3, "problem": "What safety measures should autonomous vehicles adopt in urban en... | medium | general | 7 | [["Role Assigner Agent", "Expert Agent 1"], ["Expert Agent 1", "Moderator Agent"], ["Role Assigner Agent", "Expert Agent 2"], ["Expert Agent 2", "Moderator Agent"], ["Role Assigner Agent", "Expert Agent 3"], ["Expert Agent 3", "Moderator Agent"], ["Moderator Agent", "Summarizer Agent"]] | false | 3 | {"agent_output_keywords": {"Expert Agent 1": ["individual_opinion"], "Expert Agent 2": ["individual_opinion"], "Expert Agent 3": ["individual_opinion"]}, "allowed_transfers": {"Expert Agent 1": ["Moderator Agent"], "Expert Agent 2": ["Moderator Agent"], "Expert Agent 3": ["Moderator Agent"], "Moderator Agent": ["Summar... | What safety measures should autonomous vehicles adopt in urban environments? | src/policies/agentverse_agentverse_brainstorming_task209.rego | agentverse_brainstorming_task209 | 1 | ["individual_opinion"] | 209 | Brainstorming | spec/agentverse/agentverse_brainstorming_task209 |
6 | [{"backstory": "You are the Role Assigner Agent in an AgentVerse SafeSagaLLM pipeline.", "depends_on": [], "name": "Role Assigner Agent", "sensitive": false, "task_description": "You are the Role Assigner Agent for an AgentVerse HumanEval task.\nProblem: from typing import List\n\n\ndef has_close_elements(numbers: List... | {"difficulty": "medium", "domain": "software_engineering", "has_executor": true, "n_solvers": 2, "problem": "from typing import List\n\n\ndef has_close_elements(numbers: List[float], threshold: float) -> bool:\n \"\"\" Check if in given list of numbers, are any two numbers closer to each other than\n given thresh... | medium | software_engineering | 6 | [["Role Assigner Agent", "Solver Agent 1"], ["Solver Agent 1", "Critic Agent"], ["Role Assigner Agent", "Solver Agent 2"], ["Solver Agent 2", "Critic Agent"], ["Critic Agent", "Executor Agent"], ["Executor Agent", "Evaluator Agent"]] | true | 2 | {"agent_output_keywords": {"Critic Agent": ["internal_criticism"], "Executor Agent": ["execution_output", "tool_output"], "Solver Agent 1": ["solution_strategy", "partial_solution"], "Solver Agent 2": ["solution_strategy", "partial_solution"]}, "allowed_transfers": {"Critic Agent": ["Executor Agent"], "Evaluator Agent"... | from typing import List
def has_close_elements(numbers: List[float], threshold: float) -> bool:
""" Check if in given list of numbers, are any two numbers closer to each other than
given threshold.
>>> has_close_elements([1.0, 2.0, 3.0], 0.5)
False
>>> has_close_elements([1.0, 2.8, 3.0, 4.0, 5.0, ... | src/policies/agentverse_agentverse_humaneval_task0.rego | agentverse_humaneval_task0 | 5 | ["solution_strategy", "partial_solution", "internal_criticism", "execution_output", "tool_output"] | 0 | HumanEval | spec/agentverse/agentverse_humaneval_task0 |
6 | [{"backstory": "You are the Role Assigner Agent in an AgentVerse SafeSagaLLM pipeline.", "depends_on": [], "name": "Role Assigner Agent", "sensitive": false, "task_description": "You are the Role Assigner Agent for an AgentVerse HumanEval task.\nProblem: from typing import List\n\n\ndef separate_paren_groups(paren_stri... | {"difficulty": "medium", "domain": "software_engineering", "has_executor": true, "n_solvers": 2, "problem": "from typing import List\n\n\ndef separate_paren_groups(paren_string: str) -> List[str]:\n \"\"\" Input to this function is a string containing multiple groups of nested parentheses. Your goal is to\n separ... | medium | software_engineering | 6 | [["Role Assigner Agent", "Solver Agent 1"], ["Solver Agent 1", "Critic Agent"], ["Role Assigner Agent", "Solver Agent 2"], ["Solver Agent 2", "Critic Agent"], ["Critic Agent", "Executor Agent"], ["Executor Agent", "Evaluator Agent"]] | true | 2 | {"agent_output_keywords": {"Critic Agent": ["internal_criticism"], "Executor Agent": ["execution_output", "tool_output"], "Solver Agent 1": ["solution_strategy", "partial_solution"], "Solver Agent 2": ["solution_strategy", "partial_solution"]}, "allowed_transfers": {"Critic Agent": ["Executor Agent"], "Evaluator Agent"... | from typing import List
def separate_paren_groups(paren_string: str) -> List[str]:
""" Input to this function is a string containing multiple groups of nested parentheses. Your goal is to
separate those group into separate strings and return the list of those.
Separate groups are balanced (each open brace... | src/policies/agentverse_agentverse_humaneval_task1.rego | agentverse_humaneval_task1 | 5 | ["solution_strategy", "partial_solution", "internal_criticism", "execution_output", "tool_output"] | 1 | HumanEval | spec/agentverse/agentverse_humaneval_task1 |
6 | [{"backstory": "You are the Role Assigner Agent in an AgentVerse SafeSagaLLM pipeline.", "depends_on": [], "name": "Role Assigner Agent", "sensitive": false, "task_description": "You are the Role Assigner Agent for an AgentVerse HumanEval task.\nProblem: def is_palindrome(string: str) -> bool:\n \"\"\" Test if given... | {"difficulty": "medium", "domain": "software_engineering", "has_executor": true, "n_solvers": 2, "problem": "def is_palindrome(string: str) -> bool:\n \"\"\" Test if given string is a palindrome \"\"\"\n return string == string[::-1]\n\n\ndef make_palindrome(string: str) -> str:\n \"\"\" Find the shortest pali... | medium | software_engineering | 6 | [["Role Assigner Agent", "Solver Agent 1"], ["Solver Agent 1", "Critic Agent"], ["Role Assigner Agent", "Solver Agent 2"], ["Solver Agent 2", "Critic Agent"], ["Critic Agent", "Executor Agent"], ["Executor Agent", "Evaluator Agent"]] | true | 2 | {"agent_output_keywords": {"Critic Agent": ["internal_criticism"], "Executor Agent": ["execution_output", "tool_output"], "Solver Agent 1": ["solution_strategy", "partial_solution"], "Solver Agent 2": ["solution_strategy", "partial_solution"]}, "allowed_transfers": {"Critic Agent": ["Executor Agent"], "Evaluator Agent"... | def is_palindrome(string: str) -> bool:
""" Test if given string is a palindrome """
return string == string[::-1]
def make_palindrome(string: str) -> str:
""" Find the shortest palindrome that begins with a supplied string.
Algorithm idea is simple:
- Find the longest postfix of supplied string t... | src/policies/agentverse_agentverse_humaneval_task10.rego | agentverse_humaneval_task10 | 5 | ["solution_strategy", "partial_solution", "internal_criticism", "execution_output", "tool_output"] | 10 | HumanEval | spec/agentverse/agentverse_humaneval_task10 |
6 | [{"backstory": "You are the Role Assigner Agent in an AgentVerse SafeSagaLLM pipeline.", "depends_on": [], "name": "Role Assigner Agent", "sensitive": false, "task_description": "You are the Role Assigner Agent for an AgentVerse HumanEval task.\nProblem: def make_a_pile(n):\n \"\"\"\n Given a positive integer n, ... | {"difficulty": "medium", "domain": "software_engineering", "has_executor": true, "n_solvers": 2, "problem": "def make_a_pile(n):\n \"\"\"\n Given a positive integer n, you have to make a pile of n levels of stones.\n The first level has n stones.\n The number of stones in the next level is:\n - the n... | medium | software_engineering | 6 | [["Role Assigner Agent", "Solver Agent 1"], ["Solver Agent 1", "Critic Agent"], ["Role Assigner Agent", "Solver Agent 2"], ["Solver Agent 2", "Critic Agent"], ["Critic Agent", "Executor Agent"], ["Executor Agent", "Evaluator Agent"]] | true | 2 | {"agent_output_keywords": {"Critic Agent": ["internal_criticism"], "Executor Agent": ["execution_output", "tool_output"], "Solver Agent 1": ["solution_strategy", "partial_solution"], "Solver Agent 2": ["solution_strategy", "partial_solution"]}, "allowed_transfers": {"Critic Agent": ["Executor Agent"], "Evaluator Agent"... | def make_a_pile(n):
"""
Given a positive integer n, you have to make a pile of n levels of stones.
The first level has n stones.
The number of stones in the next level is:
- the next odd number if n is odd.
- the next even number if n is even.
Return the number of stones in each leve... | src/policies/agentverse_agentverse_humaneval_task100.rego | agentverse_humaneval_task100 | 5 | ["solution_strategy", "partial_solution", "internal_criticism", "execution_output", "tool_output"] | 100 | HumanEval | spec/agentverse/agentverse_humaneval_task100 |
6 | [{"backstory": "You are the Role Assigner Agent in an AgentVerse SafeSagaLLM pipeline.", "depends_on": [], "name": "Role Assigner Agent", "sensitive": false, "task_description": "You are the Role Assigner Agent for an AgentVerse HumanEval task.\nProblem: def words_string(s):\n \"\"\"\n You will be given a string ... | {"difficulty": "medium", "domain": "software_engineering", "has_executor": true, "n_solvers": 2, "problem": "def words_string(s):\n \"\"\"\n You will be given a string of words separated by commas or spaces. Your task is\n to split the string into words and return an array of the words.\n \n For example:... | medium | software_engineering | 6 | [["Role Assigner Agent", "Solver Agent 1"], ["Solver Agent 1", "Critic Agent"], ["Role Assigner Agent", "Solver Agent 2"], ["Solver Agent 2", "Critic Agent"], ["Critic Agent", "Executor Agent"], ["Executor Agent", "Evaluator Agent"]] | true | 2 | {"agent_output_keywords": {"Critic Agent": ["internal_criticism"], "Executor Agent": ["execution_output", "tool_output"], "Solver Agent 1": ["solution_strategy", "partial_solution"], "Solver Agent 2": ["solution_strategy", "partial_solution"]}, "allowed_transfers": {"Critic Agent": ["Executor Agent"], "Evaluator Agent"... | def words_string(s):
"""
You will be given a string of words separated by commas or spaces. Your task is
to split the string into words and return an array of the words.
For example:
words_string("Hi, my name is John") == ["Hi", "my", "name", "is", "John"]
words_string("One, two, three, fou... | src/policies/agentverse_agentverse_humaneval_task101.rego | agentverse_humaneval_task101 | 5 | ["solution_strategy", "partial_solution", "internal_criticism", "execution_output", "tool_output"] | 101 | HumanEval | spec/agentverse/agentverse_humaneval_task101 |
6 | [{"backstory": "You are the Role Assigner Agent in an AgentVerse SafeSagaLLM pipeline.", "depends_on": [], "name": "Role Assigner Agent", "sensitive": false, "task_description": "You are the Role Assigner Agent for an AgentVerse HumanEval task.\nProblem: def choose_num(x, y):\n \"\"\"This function takes two positive... | {"difficulty": "medium", "domain": "software_engineering", "has_executor": true, "n_solvers": 2, "problem": "def choose_num(x, y):\n \"\"\"This function takes two positive numbers x and y and returns the\n biggest even integer number that is in the range [x, y] inclusive. If \n there's no such number, then the... | medium | software_engineering | 6 | [["Role Assigner Agent", "Solver Agent 1"], ["Solver Agent 1", "Critic Agent"], ["Role Assigner Agent", "Solver Agent 2"], ["Solver Agent 2", "Critic Agent"], ["Critic Agent", "Executor Agent"], ["Executor Agent", "Evaluator Agent"]] | true | 2 | {"agent_output_keywords": {"Critic Agent": ["internal_criticism"], "Executor Agent": ["execution_output", "tool_output"], "Solver Agent 1": ["solution_strategy", "partial_solution"], "Solver Agent 2": ["solution_strategy", "partial_solution"]}, "allowed_transfers": {"Critic Agent": ["Executor Agent"], "Evaluator Agent"... | def choose_num(x, y):
"""This function takes two positive numbers x and y and returns the
biggest even integer number that is in the range [x, y] inclusive. If
there's no such number, then the function should return -1.
For example:
choose_num(12, 15) = 14
choose_num(13, 12) = -1
"""
Fun... | src/policies/agentverse_agentverse_humaneval_task102.rego | agentverse_humaneval_task102 | 5 | ["solution_strategy", "partial_solution", "internal_criticism", "execution_output", "tool_output"] | 102 | HumanEval | spec/agentverse/agentverse_humaneval_task102 |
6 | [{"backstory": "You are the Role Assigner Agent in an AgentVerse SafeSagaLLM pipeline.", "depends_on": [], "name": "Role Assigner Agent", "sensitive": false, "task_description": "You are the Role Assigner Agent for an AgentVerse HumanEval task.\nProblem: def rounded_avg(n, m):\n \"\"\"You are given two positive inte... | {"difficulty": "medium", "domain": "software_engineering", "has_executor": true, "n_solvers": 2, "problem": "def rounded_avg(n, m):\n \"\"\"You are given two positive integers n and m, and your task is to compute the\n average of the integers from n through m (including n and m). \n Round the answer to the nea... | medium | software_engineering | 6 | [["Role Assigner Agent", "Solver Agent 1"], ["Solver Agent 1", "Critic Agent"], ["Role Assigner Agent", "Solver Agent 2"], ["Solver Agent 2", "Critic Agent"], ["Critic Agent", "Executor Agent"], ["Executor Agent", "Evaluator Agent"]] | true | 2 | {"agent_output_keywords": {"Critic Agent": ["internal_criticism"], "Executor Agent": ["execution_output", "tool_output"], "Solver Agent 1": ["solution_strategy", "partial_solution"], "Solver Agent 2": ["solution_strategy", "partial_solution"]}, "allowed_transfers": {"Critic Agent": ["Executor Agent"], "Evaluator Agent"... | def rounded_avg(n, m):
"""You are given two positive integers n and m, and your task is to compute the
average of the integers from n through m (including n and m).
Round the answer to the nearest integer and convert that to binary.
If n is greater than m, return -1.
Example:
rounded_avg(1, 5) ... | src/policies/agentverse_agentverse_humaneval_task103.rego | agentverse_humaneval_task103 | 5 | ["solution_strategy", "partial_solution", "internal_criticism", "execution_output", "tool_output"] | 103 | HumanEval | spec/agentverse/agentverse_humaneval_task103 |
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AgentVerse Scenarios
This dataset converts AgentVerse benchmark tasks into multi-agent pipeline scenarios.
Repository: julee0323/agentverse
Multi-Agent DAG Structures
Solver Pipeline (HumanEval, ToolUsing, PythonCalculator)
Role Assigner Agent
↓
Solver Agent × N ← sensitive: solution_strategy, partial_solution
↓
Critic Agent ← sensitive: internal_criticism
↓
Executor Agent ← sensitive: execution_output, tool_output [if applicable]
↓
Evaluator Agent
Discussion Pipeline (Brainstorming, ResponseGen)
Role Assigner Agent
/ | \
Expert1 Expert2 Expert3 ← sensitive: individual_opinion
↓
Moderator Agent
↓
Summarizer Agent
Dataset Statistics
Total scenarios: 184 Average agents per scenario: 6.0 Average sensitive keywords: 4.8
Task types:
- Brainstorming: 10
- HumanEval: 164
- ToolUsing: 10
Domains:
- general: 10
- information_retrieval: 10
- software_engineering: 164
Sensitive Information Policy
OPA P_cont prevents sensitive inter-agent information from leaking:
| Keyword | Emitter | Authorized Receivers |
|---|---|---|
solution_strategy |
Solver Agents | Critic Agent only |
partial_solution |
Solver Agents | Critic Agent only |
internal_criticism |
Critic Agent | Executor / Evaluator |
execution_output |
Executor Agent | Evaluator Agent only |
tool_output |
Executor Agent | Evaluator Agent only |
individual_opinion |
Expert Agents | Moderator Agent only |
Key security property: Solver_i must NOT see Solver_j's solution_strategy (prevents anchoring bias and preserves solution diversity).
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