| [ |
| { |
| "name": "inspect_evals/assistant_bench_closed_book_one_shot - Sample fb9ba3ab6a13d0adc677f993e90d54914a5cdf211305a1bba6bf16ec4ccb9b7c", |
| "description": "Task from inspect_evals/assistant_bench_closed_book_one_shot dataset, sample fb9ba3ab6a13d0adc677f993e90d54914a5cdf211305a1bba6bf16ec4ccb9b7c", |
| "prompt": "Which member of the famous Beckham couple has the most Instagram followers?\n\nOn which social media platform does Andrew Ng have the most followers?", |
| "mcp_config": { |
| "inspect": { |
| "command": "docker", |
| "args": [ |
| "run", |
| "--rm", |
| "-i", |
| "--init", |
| "-v", |
| "/var/run/docker.sock:/var/run/docker.sock", |
| "-v", |
| "/home/genteki/.cache/inspect_evals:/root/.cache/inspect_evals", |
| "hud-inspect:latest" |
| ] |
| } |
| }, |
| "setup_tool": { |
| "name": "load_task", |
| "arguments": { |
| "task_name": "inspect_evals/assistant_bench_closed_book_one_shot", |
| "sample_id": "fb9ba3ab6a13d0adc677f993e90d54914a5cdf211305a1bba6bf16ec4ccb9b7c", |
| "model": "claude-sonnet-4-20250514" |
| } |
| }, |
| "evaluate_tool": { |
| "name": "score_task", |
| "arguments": {} |
| }, |
| "agent_config": { |
| "agent_class": "server.agents.InspectAgent", |
| "disallowed_tools": [ |
| "_store_agent_message", |
| "load_task", |
| "score_task" |
| ], |
| "allowed_tools": [], |
| "initial_screenshot": false |
| }, |
| "system_prompt": "You are a knowledgeable AI assistant tasked with providing concise answers to a wide range of questions. Always provide an answer, even if you're not entirely certain. If you're unsure, make an educated guess based on your knowledge.\n\nFollow these guidelines:\n1. Don't ask the user any questions.\n2. Structure your response as a series of intermediate questions and answers, followed by a final answer.\n3. Do not generate the same intermediate question twice.\n4. Keep the final answer concise. It should be either: a number, a string, a list of strings, or a list of JSONs.\n5. Ensure the answer can be parsed with the Python method: json.loads(input_str).\n6. If you truly can't provide any answer, use an empty string as the final answer.\n\nThis is a closed-book task, so rely on your existing knowledge without asking for additional information.\n\nUse the format:\nIntermediate question: [Your question]\nIntermediate answer: [Your answer]\n(Repeat as needed)\nThe final answer is: [Your concise final answer]\n\nRemember, it's better to provide a potentially outdated or slightly inaccurate answer than no answer at all. Your goal is to maximize the answer rate and accuracy to the best of your ability. Always provide at least one intermediate question and answer before giving the final answer." |
| }, |
| { |
| "name": "inspect_evals/assistant_bench_closed_book_one_shot - Sample 929b45f34805280d77c61d1e093e3d4e551d77ddb6ecd73552b12b1af286388d", |
| "description": "Task from inspect_evals/assistant_bench_closed_book_one_shot dataset, sample 929b45f34805280d77c61d1e093e3d4e551d77ddb6ecd73552b12b1af286388d", |
| "prompt": "Which member of the famous Beckham couple has the most Instagram followers?\n\nThe dog genome was first mapped in 2004 and has been updated several times since. What is the link to the files that were most relevant in May 2020?", |
| "mcp_config": { |
| "inspect": { |
| "command": "docker", |
| "args": [ |
| "run", |
| "--rm", |
| "-i", |
| "--init", |
| "-v", |
| "/var/run/docker.sock:/var/run/docker.sock", |
| "-v", |
| "/home/genteki/.cache/inspect_evals:/root/.cache/inspect_evals", |
| "hud-inspect:latest" |
| ] |
| } |
| }, |
| "setup_tool": { |
| "name": "load_task", |
| "arguments": { |
| "task_name": "inspect_evals/assistant_bench_closed_book_one_shot", |
| "sample_id": "929b45f34805280d77c61d1e093e3d4e551d77ddb6ecd73552b12b1af286388d", |
| "model": "claude-sonnet-4-20250514" |
| } |
| }, |
| "evaluate_tool": { |
| "name": "score_task", |
| "arguments": {} |
| }, |
| "agent_config": { |
| "agent_class": "server.agents.InspectAgent", |
| "disallowed_tools": [ |
| "_store_agent_message", |
| "load_task", |
| "score_task" |
| ], |
| "allowed_tools": [], |
| "initial_screenshot": false |
| }, |
| "system_prompt": "You are a knowledgeable AI assistant tasked with providing concise answers to a wide range of questions. Always provide an answer, even if you're not entirely certain. If you're unsure, make an educated guess based on your knowledge.\n\nFollow these guidelines:\n1. Don't ask the user any questions.\n2. Structure your response as a series of intermediate questions and answers, followed by a final answer.\n3. Do not generate the same intermediate question twice.\n4. Keep the final answer concise. It should be either: a number, a string, a list of strings, or a list of JSONs.\n5. Ensure the answer can be parsed with the Python method: json.loads(input_str).\n6. If you truly can't provide any answer, use an empty string as the final answer.\n\nThis is a closed-book task, so rely on your existing knowledge without asking for additional information.\n\nUse the format:\nIntermediate question: [Your question]\nIntermediate answer: [Your answer]\n(Repeat as needed)\nThe final answer is: [Your concise final answer]\n\nRemember, it's better to provide a potentially outdated or slightly inaccurate answer than no answer at all. Your goal is to maximize the answer rate and accuracy to the best of your ability. Always provide at least one intermediate question and answer before giving the final answer." |
| }, |
| { |
| "name": "inspect_evals/assistant_bench_closed_book_one_shot - Sample 9baaa267c95f9d8b75741ee9169c50563d297cfa592c20deaffd30dbc5984c74", |
| "description": "Task from inspect_evals/assistant_bench_closed_book_one_shot dataset, sample 9baaa267c95f9d8b75741ee9169c50563d297cfa592c20deaffd30dbc5984c74", |
| "prompt": "Which member of the famous Beckham couple has the most Instagram followers?\n\nBased on recent years (2020-2023), how likely am I to hit a day with a max temperature (in Fahrenheit) over 95 if I travel to Houston, Texas during June? (provide the answer in percentage)", |
| "mcp_config": { |
| "inspect": { |
| "command": "docker", |
| "args": [ |
| "run", |
| "--rm", |
| "-i", |
| "--init", |
| "-v", |
| "/var/run/docker.sock:/var/run/docker.sock", |
| "-v", |
| "/home/genteki/.cache/inspect_evals:/root/.cache/inspect_evals", |
| "hud-inspect:latest" |
| ] |
| } |
| }, |
| "setup_tool": { |
| "name": "load_task", |
| "arguments": { |
| "task_name": "inspect_evals/assistant_bench_closed_book_one_shot", |
| "sample_id": "9baaa267c95f9d8b75741ee9169c50563d297cfa592c20deaffd30dbc5984c74", |
| "model": "claude-sonnet-4-20250514" |
| } |
| }, |
| "evaluate_tool": { |
| "name": "score_task", |
| "arguments": {} |
| }, |
| "agent_config": { |
| "agent_class": "server.agents.InspectAgent", |
| "disallowed_tools": [ |
| "_store_agent_message", |
| "load_task", |
| "score_task" |
| ], |
| "allowed_tools": [], |
| "initial_screenshot": false |
| }, |
| "system_prompt": "You are a knowledgeable AI assistant tasked with providing concise answers to a wide range of questions. Always provide an answer, even if you're not entirely certain. If you're unsure, make an educated guess based on your knowledge.\n\nFollow these guidelines:\n1. Don't ask the user any questions.\n2. Structure your response as a series of intermediate questions and answers, followed by a final answer.\n3. Do not generate the same intermediate question twice.\n4. Keep the final answer concise. It should be either: a number, a string, a list of strings, or a list of JSONs.\n5. Ensure the answer can be parsed with the Python method: json.loads(input_str).\n6. If you truly can't provide any answer, use an empty string as the final answer.\n\nThis is a closed-book task, so rely on your existing knowledge without asking for additional information.\n\nUse the format:\nIntermediate question: [Your question]\nIntermediate answer: [Your answer]\n(Repeat as needed)\nThe final answer is: [Your concise final answer]\n\nRemember, it's better to provide a potentially outdated or slightly inaccurate answer than no answer at all. Your goal is to maximize the answer rate and accuracy to the best of your ability. Always provide at least one intermediate question and answer before giving the final answer." |
| }, |
| { |
| "name": "inspect_evals/assistant_bench_closed_book_one_shot - Sample e6bc98089608217e45b6956a46518fe3cce64a799b3ac43c6974c449ae14c408", |
| "description": "Task from inspect_evals/assistant_bench_closed_book_one_shot dataset, sample e6bc98089608217e45b6956a46518fe3cce64a799b3ac43c6974c449ae14c408", |
| "prompt": "Which member of the famous Beckham couple has the most Instagram followers?\n\nWhat's the highest price a high-rise apartment was sold for in Mission Bay, San Francisco, in 2021?", |
| "mcp_config": { |
| "inspect": { |
| "command": "docker", |
| "args": [ |
| "run", |
| "--rm", |
| "-i", |
| "--init", |
| "-v", |
| "/var/run/docker.sock:/var/run/docker.sock", |
| "-v", |
| "/home/genteki/.cache/inspect_evals:/root/.cache/inspect_evals", |
| "hud-inspect:latest" |
| ] |
| } |
| }, |
| "setup_tool": { |
| "name": "load_task", |
| "arguments": { |
| "task_name": "inspect_evals/assistant_bench_closed_book_one_shot", |
| "sample_id": "e6bc98089608217e45b6956a46518fe3cce64a799b3ac43c6974c449ae14c408", |
| "model": "claude-sonnet-4-20250514" |
| } |
| }, |
| "evaluate_tool": { |
| "name": "score_task", |
| "arguments": {} |
| }, |
| "agent_config": { |
| "agent_class": "server.agents.InspectAgent", |
| "disallowed_tools": [ |
| "_store_agent_message", |
| "load_task", |
| "score_task" |
| ], |
| "allowed_tools": [], |
| "initial_screenshot": false |
| }, |
| "system_prompt": "You are a knowledgeable AI assistant tasked with providing concise answers to a wide range of questions. Always provide an answer, even if you're not entirely certain. If you're unsure, make an educated guess based on your knowledge.\n\nFollow these guidelines:\n1. Don't ask the user any questions.\n2. Structure your response as a series of intermediate questions and answers, followed by a final answer.\n3. Do not generate the same intermediate question twice.\n4. Keep the final answer concise. It should be either: a number, a string, a list of strings, or a list of JSONs.\n5. Ensure the answer can be parsed with the Python method: json.loads(input_str).\n6. If you truly can't provide any answer, use an empty string as the final answer.\n\nThis is a closed-book task, so rely on your existing knowledge without asking for additional information.\n\nUse the format:\nIntermediate question: [Your question]\nIntermediate answer: [Your answer]\n(Repeat as needed)\nThe final answer is: [Your concise final answer]\n\nRemember, it's better to provide a potentially outdated or slightly inaccurate answer than no answer at all. Your goal is to maximize the answer rate and accuracy to the best of your ability. Always provide at least one intermediate question and answer before giving the final answer." |
| }, |
| { |
| "name": "inspect_evals/assistant_bench_closed_book_one_shot - Sample 291b53e665b4dd4365cde995042db4a6f6fecef3fe3a6f4482f23d61bd673918", |
| "description": "Task from inspect_evals/assistant_bench_closed_book_one_shot dataset, sample 291b53e665b4dd4365cde995042db4a6f6fecef3fe3a6f4482f23d61bd673918", |
| "prompt": "Which member of the famous Beckham couple has the most Instagram followers?\n\nWhat is the link to the GFF3 file for beluga whales that was the most recent one on 20/10/2020?", |
| "mcp_config": { |
| "inspect": { |
| "command": "docker", |
| "args": [ |
| "run", |
| "--rm", |
| "-i", |
| "--init", |
| "-v", |
| "/var/run/docker.sock:/var/run/docker.sock", |
| "-v", |
| "/home/genteki/.cache/inspect_evals:/root/.cache/inspect_evals", |
| "hud-inspect:latest" |
| ] |
| } |
| }, |
| "setup_tool": { |
| "name": "load_task", |
| "arguments": { |
| "task_name": "inspect_evals/assistant_bench_closed_book_one_shot", |
| "sample_id": "291b53e665b4dd4365cde995042db4a6f6fecef3fe3a6f4482f23d61bd673918", |
| "model": "claude-sonnet-4-20250514" |
| } |
| }, |
| "evaluate_tool": { |
| "name": "score_task", |
| "arguments": {} |
| }, |
| "agent_config": { |
| "agent_class": "server.agents.InspectAgent", |
| "disallowed_tools": [ |
| "_store_agent_message", |
| "load_task", |
| "score_task" |
| ], |
| "allowed_tools": [], |
| "initial_screenshot": false |
| }, |
| "system_prompt": "You are a knowledgeable AI assistant tasked with providing concise answers to a wide range of questions. Always provide an answer, even if you're not entirely certain. If you're unsure, make an educated guess based on your knowledge.\n\nFollow these guidelines:\n1. Don't ask the user any questions.\n2. Structure your response as a series of intermediate questions and answers, followed by a final answer.\n3. Do not generate the same intermediate question twice.\n4. Keep the final answer concise. It should be either: a number, a string, a list of strings, or a list of JSONs.\n5. Ensure the answer can be parsed with the Python method: json.loads(input_str).\n6. If you truly can't provide any answer, use an empty string as the final answer.\n\nThis is a closed-book task, so rely on your existing knowledge without asking for additional information.\n\nUse the format:\nIntermediate question: [Your question]\nIntermediate answer: [Your answer]\n(Repeat as needed)\nThe final answer is: [Your concise final answer]\n\nRemember, it's better to provide a potentially outdated or slightly inaccurate answer than no answer at all. Your goal is to maximize the answer rate and accuracy to the best of your ability. Always provide at least one intermediate question and answer before giving the final answer." |
| }, |
| { |
| "name": "inspect_evals/assistant_bench_closed_book_one_shot - Sample 4dbedc5e1a0205e14b7ff3ba89bce3060dab15d0ada3b7e1351a6f2aa8287aec", |
| "description": "Task from inspect_evals/assistant_bench_closed_book_one_shot dataset, sample 4dbedc5e1a0205e14b7ff3ba89bce3060dab15d0ada3b7e1351a6f2aa8287aec", |
| "prompt": "Which member of the famous Beckham couple has the most Instagram followers?\n\nHow much did I save by purchasing a season pass instead of daily tickets for California's Great America in San Jose, if I planned to visit once a month in June, July, August, and September during the summer of 2024?", |
| "mcp_config": { |
| "inspect": { |
| "command": "docker", |
| "args": [ |
| "run", |
| "--rm", |
| "-i", |
| "--init", |
| "-v", |
| "/var/run/docker.sock:/var/run/docker.sock", |
| "-v", |
| "/home/genteki/.cache/inspect_evals:/root/.cache/inspect_evals", |
| "hud-inspect:latest" |
| ] |
| } |
| }, |
| "setup_tool": { |
| "name": "load_task", |
| "arguments": { |
| "task_name": "inspect_evals/assistant_bench_closed_book_one_shot", |
| "sample_id": "4dbedc5e1a0205e14b7ff3ba89bce3060dab15d0ada3b7e1351a6f2aa8287aec", |
| "model": "claude-sonnet-4-20250514" |
| } |
| }, |
| "evaluate_tool": { |
| "name": "score_task", |
| "arguments": {} |
| }, |
| "agent_config": { |
| "agent_class": "server.agents.InspectAgent", |
| "disallowed_tools": [ |
| "_store_agent_message", |
| "load_task", |
| "score_task" |
| ], |
| "allowed_tools": [], |
| "initial_screenshot": false |
| }, |
| "system_prompt": "You are a knowledgeable AI assistant tasked with providing concise answers to a wide range of questions. Always provide an answer, even if you're not entirely certain. If you're unsure, make an educated guess based on your knowledge.\n\nFollow these guidelines:\n1. Don't ask the user any questions.\n2. Structure your response as a series of intermediate questions and answers, followed by a final answer.\n3. Do not generate the same intermediate question twice.\n4. Keep the final answer concise. It should be either: a number, a string, a list of strings, or a list of JSONs.\n5. Ensure the answer can be parsed with the Python method: json.loads(input_str).\n6. If you truly can't provide any answer, use an empty string as the final answer.\n\nThis is a closed-book task, so rely on your existing knowledge without asking for additional information.\n\nUse the format:\nIntermediate question: [Your question]\nIntermediate answer: [Your answer]\n(Repeat as needed)\nThe final answer is: [Your concise final answer]\n\nRemember, it's better to provide a potentially outdated or slightly inaccurate answer than no answer at all. Your goal is to maximize the answer rate and accuracy to the best of your ability. Always provide at least one intermediate question and answer before giving the final answer." |
| }, |
| { |
| "name": "inspect_evals/assistant_bench_closed_book_one_shot - Sample 55f4258484c5b398956133128a50462a767da211f8f72aa5ac5bbffb9bcbba1a", |
| "description": "Task from inspect_evals/assistant_bench_closed_book_one_shot dataset, sample 55f4258484c5b398956133128a50462a767da211f8f72aa5ac5bbffb9bcbba1a", |
| "prompt": "Which member of the famous Beckham couple has the most Instagram followers?\n\nWhat is the worst rated series (according to Rotten Tomatoes) with more than 1 season that Ted Danson has starred in and is available on Amazon Prime Video (US)?", |
| "mcp_config": { |
| "inspect": { |
| "command": "docker", |
| "args": [ |
| "run", |
| "--rm", |
| "-i", |
| "--init", |
| "-v", |
| "/var/run/docker.sock:/var/run/docker.sock", |
| "-v", |
| "/home/genteki/.cache/inspect_evals:/root/.cache/inspect_evals", |
| "hud-inspect:latest" |
| ] |
| } |
| }, |
| "setup_tool": { |
| "name": "load_task", |
| "arguments": { |
| "task_name": "inspect_evals/assistant_bench_closed_book_one_shot", |
| "sample_id": "55f4258484c5b398956133128a50462a767da211f8f72aa5ac5bbffb9bcbba1a", |
| "model": "claude-sonnet-4-20250514" |
| } |
| }, |
| "evaluate_tool": { |
| "name": "score_task", |
| "arguments": {} |
| }, |
| "agent_config": { |
| "agent_class": "server.agents.InspectAgent", |
| "disallowed_tools": [ |
| "_store_agent_message", |
| "load_task", |
| "score_task" |
| ], |
| "allowed_tools": [], |
| "initial_screenshot": false |
| }, |
| "system_prompt": "You are a knowledgeable AI assistant tasked with providing concise answers to a wide range of questions. Always provide an answer, even if you're not entirely certain. If you're unsure, make an educated guess based on your knowledge.\n\nFollow these guidelines:\n1. Don't ask the user any questions.\n2. Structure your response as a series of intermediate questions and answers, followed by a final answer.\n3. Do not generate the same intermediate question twice.\n4. Keep the final answer concise. It should be either: a number, a string, a list of strings, or a list of JSONs.\n5. Ensure the answer can be parsed with the Python method: json.loads(input_str).\n6. If you truly can't provide any answer, use an empty string as the final answer.\n\nThis is a closed-book task, so rely on your existing knowledge without asking for additional information.\n\nUse the format:\nIntermediate question: [Your question]\nIntermediate answer: [Your answer]\n(Repeat as needed)\nThe final answer is: [Your concise final answer]\n\nRemember, it's better to provide a potentially outdated or slightly inaccurate answer than no answer at all. Your goal is to maximize the answer rate and accuracy to the best of your ability. Always provide at least one intermediate question and answer before giving the final answer." |
| }, |
| { |
| "name": "inspect_evals/assistant_bench_closed_book_one_shot - Sample 52f7224e9c79431e7926afe317782711a0028750693e7456cde22ef6f4bd8bd5", |
| "description": "Task from inspect_evals/assistant_bench_closed_book_one_shot dataset, sample 52f7224e9c79431e7926afe317782711a0028750693e7456cde22ef6f4bd8bd5", |
| "prompt": "Which member of the famous Beckham couple has the most Instagram followers?\n\nWhat is the highest rated (according to IMDB) Isabelle Adjani feature film that is less than 2 hours and is available on Vudu (now called Fandango at Home) to buy or rent?", |
| "mcp_config": { |
| "inspect": { |
| "command": "docker", |
| "args": [ |
| "run", |
| "--rm", |
| "-i", |
| "--init", |
| "-v", |
| "/var/run/docker.sock:/var/run/docker.sock", |
| "-v", |
| "/home/genteki/.cache/inspect_evals:/root/.cache/inspect_evals", |
| "hud-inspect:latest" |
| ] |
| } |
| }, |
| "setup_tool": { |
| "name": "load_task", |
| "arguments": { |
| "task_name": "inspect_evals/assistant_bench_closed_book_one_shot", |
| "sample_id": "52f7224e9c79431e7926afe317782711a0028750693e7456cde22ef6f4bd8bd5", |
| "model": "claude-sonnet-4-20250514" |
| } |
| }, |
| "evaluate_tool": { |
| "name": "score_task", |
| "arguments": {} |
| }, |
| "agent_config": { |
| "agent_class": "server.agents.InspectAgent", |
| "disallowed_tools": [ |
| "_store_agent_message", |
| "load_task", |
| "score_task" |
| ], |
| "allowed_tools": [], |
| "initial_screenshot": false |
| }, |
| "system_prompt": "You are a knowledgeable AI assistant tasked with providing concise answers to a wide range of questions. Always provide an answer, even if you're not entirely certain. If you're unsure, make an educated guess based on your knowledge.\n\nFollow these guidelines:\n1. Don't ask the user any questions.\n2. Structure your response as a series of intermediate questions and answers, followed by a final answer.\n3. Do not generate the same intermediate question twice.\n4. Keep the final answer concise. It should be either: a number, a string, a list of strings, or a list of JSONs.\n5. Ensure the answer can be parsed with the Python method: json.loads(input_str).\n6. If you truly can't provide any answer, use an empty string as the final answer.\n\nThis is a closed-book task, so rely on your existing knowledge without asking for additional information.\n\nUse the format:\nIntermediate question: [Your question]\nIntermediate answer: [Your answer]\n(Repeat as needed)\nThe final answer is: [Your concise final answer]\n\nRemember, it's better to provide a potentially outdated or slightly inaccurate answer than no answer at all. Your goal is to maximize the answer rate and accuracy to the best of your ability. Always provide at least one intermediate question and answer before giving the final answer." |
| }, |
| { |
| "name": "inspect_evals/assistant_bench_closed_book_one_shot - Sample 557e78eceec08ca8b0da5f9fdaca6e1c7ec6140a8ce600983ee716327dab005e", |
| "description": "Task from inspect_evals/assistant_bench_closed_book_one_shot dataset, sample 557e78eceec08ca8b0da5f9fdaca6e1c7ec6140a8ce600983ee716327dab005e", |
| "prompt": "Which member of the famous Beckham couple has the most Instagram followers?\n\nWhich bar is closest to Mummers Museum in Philadelphia and is wheel chair accessible?", |
| "mcp_config": { |
| "inspect": { |
| "command": "docker", |
| "args": [ |
| "run", |
| "--rm", |
| "-i", |
| "--init", |
| "-v", |
| "/var/run/docker.sock:/var/run/docker.sock", |
| "-v", |
| "/home/genteki/.cache/inspect_evals:/root/.cache/inspect_evals", |
| "hud-inspect:latest" |
| ] |
| } |
| }, |
| "setup_tool": { |
| "name": "load_task", |
| "arguments": { |
| "task_name": "inspect_evals/assistant_bench_closed_book_one_shot", |
| "sample_id": "557e78eceec08ca8b0da5f9fdaca6e1c7ec6140a8ce600983ee716327dab005e", |
| "model": "claude-sonnet-4-20250514" |
| } |
| }, |
| "evaluate_tool": { |
| "name": "score_task", |
| "arguments": {} |
| }, |
| "agent_config": { |
| "agent_class": "server.agents.InspectAgent", |
| "disallowed_tools": [ |
| "_store_agent_message", |
| "load_task", |
| "score_task" |
| ], |
| "allowed_tools": [], |
| "initial_screenshot": false |
| }, |
| "system_prompt": "You are a knowledgeable AI assistant tasked with providing concise answers to a wide range of questions. Always provide an answer, even if you're not entirely certain. If you're unsure, make an educated guess based on your knowledge.\n\nFollow these guidelines:\n1. Don't ask the user any questions.\n2. Structure your response as a series of intermediate questions and answers, followed by a final answer.\n3. Do not generate the same intermediate question twice.\n4. Keep the final answer concise. It should be either: a number, a string, a list of strings, or a list of JSONs.\n5. Ensure the answer can be parsed with the Python method: json.loads(input_str).\n6. If you truly can't provide any answer, use an empty string as the final answer.\n\nThis is a closed-book task, so rely on your existing knowledge without asking for additional information.\n\nUse the format:\nIntermediate question: [Your question]\nIntermediate answer: [Your answer]\n(Repeat as needed)\nThe final answer is: [Your concise final answer]\n\nRemember, it's better to provide a potentially outdated or slightly inaccurate answer than no answer at all. Your goal is to maximize the answer rate and accuracy to the best of your ability. Always provide at least one intermediate question and answer before giving the final answer." |
| }, |
| { |
| "name": "inspect_evals/assistant_bench_closed_book_one_shot - Sample 797f7a5b65ca28b7e7156e7db1e9f117bd4a021de0cd512bfdbb0be897d89eab", |
| "description": "Task from inspect_evals/assistant_bench_closed_book_one_shot dataset, sample 797f7a5b65ca28b7e7156e7db1e9f117bd4a021de0cd512bfdbb0be897d89eab", |
| "prompt": "Which member of the famous Beckham couple has the most Instagram followers?\n\nWhich restaurants (not takeaway only) within 1 blocks of Washington Square Park have vegan mains for under $15?", |
| "mcp_config": { |
| "inspect": { |
| "command": "docker", |
| "args": [ |
| "run", |
| "--rm", |
| "-i", |
| "--init", |
| "-v", |
| "/var/run/docker.sock:/var/run/docker.sock", |
| "-v", |
| "/home/genteki/.cache/inspect_evals:/root/.cache/inspect_evals", |
| "hud-inspect:latest" |
| ] |
| } |
| }, |
| "setup_tool": { |
| "name": "load_task", |
| "arguments": { |
| "task_name": "inspect_evals/assistant_bench_closed_book_one_shot", |
| "sample_id": "797f7a5b65ca28b7e7156e7db1e9f117bd4a021de0cd512bfdbb0be897d89eab", |
| "model": "claude-sonnet-4-20250514" |
| } |
| }, |
| "evaluate_tool": { |
| "name": "score_task", |
| "arguments": {} |
| }, |
| "agent_config": { |
| "agent_class": "server.agents.InspectAgent", |
| "disallowed_tools": [ |
| "_store_agent_message", |
| "load_task", |
| "score_task" |
| ], |
| "allowed_tools": [], |
| "initial_screenshot": false |
| }, |
| "system_prompt": "You are a knowledgeable AI assistant tasked with providing concise answers to a wide range of questions. Always provide an answer, even if you're not entirely certain. If you're unsure, make an educated guess based on your knowledge.\n\nFollow these guidelines:\n1. Don't ask the user any questions.\n2. Structure your response as a series of intermediate questions and answers, followed by a final answer.\n3. Do not generate the same intermediate question twice.\n4. Keep the final answer concise. It should be either: a number, a string, a list of strings, or a list of JSONs.\n5. Ensure the answer can be parsed with the Python method: json.loads(input_str).\n6. If you truly can't provide any answer, use an empty string as the final answer.\n\nThis is a closed-book task, so rely on your existing knowledge without asking for additional information.\n\nUse the format:\nIntermediate question: [Your question]\nIntermediate answer: [Your answer]\n(Repeat as needed)\nThe final answer is: [Your concise final answer]\n\nRemember, it's better to provide a potentially outdated or slightly inaccurate answer than no answer at all. Your goal is to maximize the answer rate and accuracy to the best of your ability. Always provide at least one intermediate question and answer before giving the final answer." |
| }, |
| { |
| "name": "inspect_evals/assistant_bench_web_search_zero_shot - Sample 3af8028c2a59e28ca88baff0e6d91f2a9f170c5ef91003f1c8406755a2760ad4", |
| "description": "Task from inspect_evals/assistant_bench_web_search_zero_shot dataset, sample 3af8028c2a59e28ca88baff0e6d91f2a9f170c5ef91003f1c8406755a2760ad4", |
| "prompt": "Which Magic: The Gathering Standard card (non-foil paper version released in its original set) that was banned at the same time as Oko, Thief of Crowns (including Oko, Crown of Thorns) had the highest price decrease from its all-time high to its all-time low?", |
| "mcp_config": { |
| "inspect": { |
| "command": "docker", |
| "args": [ |
| "run", |
| "--rm", |
| "-i", |
| "--init", |
| "-v", |
| "/var/run/docker.sock:/var/run/docker.sock", |
| "-v", |
| "/home/genteki/.cache/inspect_evals:/root/.cache/inspect_evals", |
| "hud-inspect:latest" |
| ] |
| } |
| }, |
| "setup_tool": { |
| "name": "load_task", |
| "arguments": { |
| "task_name": "inspect_evals/assistant_bench_web_search_zero_shot", |
| "sample_id": "3af8028c2a59e28ca88baff0e6d91f2a9f170c5ef91003f1c8406755a2760ad4", |
| "model": "claude-sonnet-4-20250514" |
| } |
| }, |
| "evaluate_tool": { |
| "name": "score_task", |
| "arguments": {} |
| }, |
| "agent_config": { |
| "agent_class": "server.agents.InspectAgent", |
| "disallowed_tools": [ |
| "_store_agent_message", |
| "load_task", |
| "score_task" |
| ], |
| "allowed_tools": [], |
| "initial_screenshot": false |
| }, |
| "system_prompt": "You are a helpful agent that has to provide answers to complex tasks.\n\nTasks can require accessing the web. To access the web, you are equipped with a search engine.\nTo access the search engine, simply generate an intermediate question. The intermediate question will be sent to a search engine (Google) and the result from the search engine will be provided by the user in the next turn.\n\nIt is best to access the search engine with simple queries, as they are most likely to return the correct answers. For example, if you require to find the number of Instagram followers for a group, generate a query for each member of the group.\n\nWhen the user message ends with an intermediate question, always generate an intermediate answer.\n\nIf the search results do not answer your intermediate question, begin the intermediate answer with \"Intermediate answer: The intermediate answer cannot be inferred from the search results\".\nIn that case, you can use your own knowledge to answer the intermediate question or generate a new intermediate question.\n\nYou can generate additional intermediate questions and answers until arriving at the final answer. Do not generate the same intermediate question twice.\n\nWhen arriving at the final answer, start your prediction with: \"The final answer is:\" followed by the answer to the task.\nIf you are unsure what the final answer is, generate the empty string (\"\").\n\nKeep the answer as concise as possible. The answer should either be: a number, a string, a list of strings, or a list of jsons.\nThe answer should be parsed with the python method: json.loads(input_str). If no answer is found, generate an empty string.\n\nThe format will be:\nAssistant: Intermediate question: [Your search engine query]\nUser: Context0: [Search engine results]\n Task: [Original task]\n Intermediate question: [Your most recent search engine query]\nAssistant: Intermediate answer: [Your answer as inferred from the search results or your own knowledge]\n Intermediate question: [Your next search engine query]\n(Repeat as needed)\nAssistant: Intermediate answer: [Your answer as inferred from the search results or your own knowledge]\n The final answer is: [Your next search engine query]" |
| }, |
| { |
| "name": "inspect_evals/assistant_bench_web_search_zero_shot - Sample 4dbedc5e1a0205e14b7ff3ba89bce3060dab15d0ada3b7e1351a6f2aa8287aec", |
| "description": "Task from inspect_evals/assistant_bench_web_search_zero_shot dataset, sample 4dbedc5e1a0205e14b7ff3ba89bce3060dab15d0ada3b7e1351a6f2aa8287aec", |
| "prompt": "How much did I save by purchasing a season pass instead of daily tickets for California's Great America in San Jose, if I planned to visit once a month in June, July, August, and September during the summer of 2024?", |
| "mcp_config": { |
| "inspect": { |
| "command": "docker", |
| "args": [ |
| "run", |
| "--rm", |
| "-i", |
| "--init", |
| "-v", |
| "/var/run/docker.sock:/var/run/docker.sock", |
| "-v", |
| "/home/genteki/.cache/inspect_evals:/root/.cache/inspect_evals", |
| "hud-inspect:latest" |
| ] |
| } |
| }, |
| "setup_tool": { |
| "name": "load_task", |
| "arguments": { |
| "task_name": "inspect_evals/assistant_bench_web_search_zero_shot", |
| "sample_id": "4dbedc5e1a0205e14b7ff3ba89bce3060dab15d0ada3b7e1351a6f2aa8287aec", |
| "model": "claude-sonnet-4-20250514" |
| } |
| }, |
| "evaluate_tool": { |
| "name": "score_task", |
| "arguments": {} |
| }, |
| "agent_config": { |
| "agent_class": "server.agents.InspectAgent", |
| "disallowed_tools": [ |
| "_store_agent_message", |
| "load_task", |
| "score_task" |
| ], |
| "allowed_tools": [], |
| "initial_screenshot": false |
| }, |
| "system_prompt": "You are a helpful agent that has to provide answers to complex tasks.\n\nTasks can require accessing the web. To access the web, you are equipped with a search engine.\nTo access the search engine, simply generate an intermediate question. The intermediate question will be sent to a search engine (Google) and the result from the search engine will be provided by the user in the next turn.\n\nIt is best to access the search engine with simple queries, as they are most likely to return the correct answers. For example, if you require to find the number of Instagram followers for a group, generate a query for each member of the group.\n\nWhen the user message ends with an intermediate question, always generate an intermediate answer.\n\nIf the search results do not answer your intermediate question, begin the intermediate answer with \"Intermediate answer: The intermediate answer cannot be inferred from the search results\".\nIn that case, you can use your own knowledge to answer the intermediate question or generate a new intermediate question.\n\nYou can generate additional intermediate questions and answers until arriving at the final answer. Do not generate the same intermediate question twice.\n\nWhen arriving at the final answer, start your prediction with: \"The final answer is:\" followed by the answer to the task.\nIf you are unsure what the final answer is, generate the empty string (\"\").\n\nKeep the answer as concise as possible. The answer should either be: a number, a string, a list of strings, or a list of jsons.\nThe answer should be parsed with the python method: json.loads(input_str). If no answer is found, generate an empty string.\n\nThe format will be:\nAssistant: Intermediate question: [Your search engine query]\nUser: Context0: [Search engine results]\n Task: [Original task]\n Intermediate question: [Your most recent search engine query]\nAssistant: Intermediate answer: [Your answer as inferred from the search results or your own knowledge]\n Intermediate question: [Your next search engine query]\n(Repeat as needed)\nAssistant: Intermediate answer: [Your answer as inferred from the search results or your own knowledge]\n The final answer is: [Your next search engine query]" |
| }, |
| { |
| "name": "inspect_evals/assistant_bench_web_search_zero_shot - Sample 55f4258484c5b398956133128a50462a767da211f8f72aa5ac5bbffb9bcbba1a", |
| "description": "Task from inspect_evals/assistant_bench_web_search_zero_shot dataset, sample 55f4258484c5b398956133128a50462a767da211f8f72aa5ac5bbffb9bcbba1a", |
| "prompt": "What is the worst rated series (according to Rotten Tomatoes) with more than 1 season that Ted Danson has starred in and is available on Amazon Prime Video (US)?", |
| "mcp_config": { |
| "inspect": { |
| "command": "docker", |
| "args": [ |
| "run", |
| "--rm", |
| "-i", |
| "--init", |
| "-v", |
| "/var/run/docker.sock:/var/run/docker.sock", |
| "-v", |
| "/home/genteki/.cache/inspect_evals:/root/.cache/inspect_evals", |
| "hud-inspect:latest" |
| ] |
| } |
| }, |
| "setup_tool": { |
| "name": "load_task", |
| "arguments": { |
| "task_name": "inspect_evals/assistant_bench_web_search_zero_shot", |
| "sample_id": "55f4258484c5b398956133128a50462a767da211f8f72aa5ac5bbffb9bcbba1a", |
| "model": "claude-sonnet-4-20250514" |
| } |
| }, |
| "evaluate_tool": { |
| "name": "score_task", |
| "arguments": {} |
| }, |
| "agent_config": { |
| "agent_class": "server.agents.InspectAgent", |
| "disallowed_tools": [ |
| "_store_agent_message", |
| "load_task", |
| "score_task" |
| ], |
| "allowed_tools": [], |
| "initial_screenshot": false |
| }, |
| "system_prompt": "You are a helpful agent that has to provide answers to complex tasks.\n\nTasks can require accessing the web. To access the web, you are equipped with a search engine.\nTo access the search engine, simply generate an intermediate question. The intermediate question will be sent to a search engine (Google) and the result from the search engine will be provided by the user in the next turn.\n\nIt is best to access the search engine with simple queries, as they are most likely to return the correct answers. For example, if you require to find the number of Instagram followers for a group, generate a query for each member of the group.\n\nWhen the user message ends with an intermediate question, always generate an intermediate answer.\n\nIf the search results do not answer your intermediate question, begin the intermediate answer with \"Intermediate answer: The intermediate answer cannot be inferred from the search results\".\nIn that case, you can use your own knowledge to answer the intermediate question or generate a new intermediate question.\n\nYou can generate additional intermediate questions and answers until arriving at the final answer. Do not generate the same intermediate question twice.\n\nWhen arriving at the final answer, start your prediction with: \"The final answer is:\" followed by the answer to the task.\nIf you are unsure what the final answer is, generate the empty string (\"\").\n\nKeep the answer as concise as possible. The answer should either be: a number, a string, a list of strings, or a list of jsons.\nThe answer should be parsed with the python method: json.loads(input_str). If no answer is found, generate an empty string.\n\nThe format will be:\nAssistant: Intermediate question: [Your search engine query]\nUser: Context0: [Search engine results]\n Task: [Original task]\n Intermediate question: [Your most recent search engine query]\nAssistant: Intermediate answer: [Your answer as inferred from the search results or your own knowledge]\n Intermediate question: [Your next search engine query]\n(Repeat as needed)\nAssistant: Intermediate answer: [Your answer as inferred from the search results or your own knowledge]\n The final answer is: [Your next search engine query]" |
| }, |
| { |
| "name": "inspect_evals/assistant_bench_web_search_zero_shot - Sample 2ddae3b7a208e3c25f14d82d7a1faaaa1832fbf950b4dac345e755c4c361f294", |
| "description": "Task from inspect_evals/assistant_bench_web_search_zero_shot dataset, sample 2ddae3b7a208e3c25f14d82d7a1faaaa1832fbf950b4dac345e755c4c361f294", |
| "prompt": "What's the lowest price a Single Family house was sold in Queen Anne in January 2023?", |
| "mcp_config": { |
| "inspect": { |
| "command": "docker", |
| "args": [ |
| "run", |
| "--rm", |
| "-i", |
| "--init", |
| "-v", |
| "/var/run/docker.sock:/var/run/docker.sock", |
| "-v", |
| "/home/genteki/.cache/inspect_evals:/root/.cache/inspect_evals", |
| "hud-inspect:latest" |
| ] |
| } |
| }, |
| "setup_tool": { |
| "name": "load_task", |
| "arguments": { |
| "task_name": "inspect_evals/assistant_bench_web_search_zero_shot", |
| "sample_id": "2ddae3b7a208e3c25f14d82d7a1faaaa1832fbf950b4dac345e755c4c361f294", |
| "model": "claude-sonnet-4-20250514" |
| } |
| }, |
| "evaluate_tool": { |
| "name": "score_task", |
| "arguments": {} |
| }, |
| "agent_config": { |
| "agent_class": "server.agents.InspectAgent", |
| "disallowed_tools": [ |
| "_store_agent_message", |
| "load_task", |
| "score_task" |
| ], |
| "allowed_tools": [], |
| "initial_screenshot": false |
| }, |
| "system_prompt": "You are a helpful agent that has to provide answers to complex tasks.\n\nTasks can require accessing the web. To access the web, you are equipped with a search engine.\nTo access the search engine, simply generate an intermediate question. The intermediate question will be sent to a search engine (Google) and the result from the search engine will be provided by the user in the next turn.\n\nIt is best to access the search engine with simple queries, as they are most likely to return the correct answers. For example, if you require to find the number of Instagram followers for a group, generate a query for each member of the group.\n\nWhen the user message ends with an intermediate question, always generate an intermediate answer.\n\nIf the search results do not answer your intermediate question, begin the intermediate answer with \"Intermediate answer: The intermediate answer cannot be inferred from the search results\".\nIn that case, you can use your own knowledge to answer the intermediate question or generate a new intermediate question.\n\nYou can generate additional intermediate questions and answers until arriving at the final answer. Do not generate the same intermediate question twice.\n\nWhen arriving at the final answer, start your prediction with: \"The final answer is:\" followed by the answer to the task.\nIf you are unsure what the final answer is, generate the empty string (\"\").\n\nKeep the answer as concise as possible. The answer should either be: a number, a string, a list of strings, or a list of jsons.\nThe answer should be parsed with the python method: json.loads(input_str). If no answer is found, generate an empty string.\n\nThe format will be:\nAssistant: Intermediate question: [Your search engine query]\nUser: Context0: [Search engine results]\n Task: [Original task]\n Intermediate question: [Your most recent search engine query]\nAssistant: Intermediate answer: [Your answer as inferred from the search results or your own knowledge]\n Intermediate question: [Your next search engine query]\n(Repeat as needed)\nAssistant: Intermediate answer: [Your answer as inferred from the search results or your own knowledge]\n The final answer is: [Your next search engine query]" |
| }, |
| { |
| "name": "inspect_evals/assistant_bench_web_search_zero_shot - Sample 748899d9d70c09beb3bd48ac8a3658bdcfd2f9114fe6dc4c4b8d2f9541ef4607", |
| "description": "Task from inspect_evals/assistant_bench_web_search_zero_shot dataset, sample 748899d9d70c09beb3bd48ac8a3658bdcfd2f9114fe6dc4c4b8d2f9541ef4607", |
| "prompt": "How much does it cost to send an envelope with 1-week delivery from Rio de Janeiro to NYC with DHL, USPS, or Fedex? (Format your answers as a list of jsons with the keys \"sender\" and \"price (usd)\" for each option you find)", |
| "mcp_config": { |
| "inspect": { |
| "command": "docker", |
| "args": [ |
| "run", |
| "--rm", |
| "-i", |
| "--init", |
| "-v", |
| "/var/run/docker.sock:/var/run/docker.sock", |
| "-v", |
| "/home/genteki/.cache/inspect_evals:/root/.cache/inspect_evals", |
| "hud-inspect:latest" |
| ] |
| } |
| }, |
| "setup_tool": { |
| "name": "load_task", |
| "arguments": { |
| "task_name": "inspect_evals/assistant_bench_web_search_zero_shot", |
| "sample_id": "748899d9d70c09beb3bd48ac8a3658bdcfd2f9114fe6dc4c4b8d2f9541ef4607", |
| "model": "claude-sonnet-4-20250514" |
| } |
| }, |
| "evaluate_tool": { |
| "name": "score_task", |
| "arguments": {} |
| }, |
| "agent_config": { |
| "agent_class": "server.agents.InspectAgent", |
| "disallowed_tools": [ |
| "_store_agent_message", |
| "load_task", |
| "score_task" |
| ], |
| "allowed_tools": [], |
| "initial_screenshot": false |
| }, |
| "system_prompt": "You are a helpful agent that has to provide answers to complex tasks.\n\nTasks can require accessing the web. To access the web, you are equipped with a search engine.\nTo access the search engine, simply generate an intermediate question. The intermediate question will be sent to a search engine (Google) and the result from the search engine will be provided by the user in the next turn.\n\nIt is best to access the search engine with simple queries, as they are most likely to return the correct answers. For example, if you require to find the number of Instagram followers for a group, generate a query for each member of the group.\n\nWhen the user message ends with an intermediate question, always generate an intermediate answer.\n\nIf the search results do not answer your intermediate question, begin the intermediate answer with \"Intermediate answer: The intermediate answer cannot be inferred from the search results\".\nIn that case, you can use your own knowledge to answer the intermediate question or generate a new intermediate question.\n\nYou can generate additional intermediate questions and answers until arriving at the final answer. Do not generate the same intermediate question twice.\n\nWhen arriving at the final answer, start your prediction with: \"The final answer is:\" followed by the answer to the task.\nIf you are unsure what the final answer is, generate the empty string (\"\").\n\nKeep the answer as concise as possible. The answer should either be: a number, a string, a list of strings, or a list of jsons.\nThe answer should be parsed with the python method: json.loads(input_str). If no answer is found, generate an empty string.\n\nThe format will be:\nAssistant: Intermediate question: [Your search engine query]\nUser: Context0: [Search engine results]\n Task: [Original task]\n Intermediate question: [Your most recent search engine query]\nAssistant: Intermediate answer: [Your answer as inferred from the search results or your own knowledge]\n Intermediate question: [Your next search engine query]\n(Repeat as needed)\nAssistant: Intermediate answer: [Your answer as inferred from the search results or your own knowledge]\n The final answer is: [Your next search engine query]" |
| }, |
| { |
| "name": "inspect_evals/assistant_bench_web_search_zero_shot - Sample 291b53e665b4dd4365cde995042db4a6f6fecef3fe3a6f4482f23d61bd673918", |
| "description": "Task from inspect_evals/assistant_bench_web_search_zero_shot dataset, sample 291b53e665b4dd4365cde995042db4a6f6fecef3fe3a6f4482f23d61bd673918", |
| "prompt": "What is the link to the GFF3 file for beluga whales that was the most recent one on 20/10/2020?", |
| "mcp_config": { |
| "inspect": { |
| "command": "docker", |
| "args": [ |
| "run", |
| "--rm", |
| "-i", |
| "--init", |
| "-v", |
| "/var/run/docker.sock:/var/run/docker.sock", |
| "-v", |
| "/home/genteki/.cache/inspect_evals:/root/.cache/inspect_evals", |
| "hud-inspect:latest" |
| ] |
| } |
| }, |
| "setup_tool": { |
| "name": "load_task", |
| "arguments": { |
| "task_name": "inspect_evals/assistant_bench_web_search_zero_shot", |
| "sample_id": "291b53e665b4dd4365cde995042db4a6f6fecef3fe3a6f4482f23d61bd673918", |
| "model": "claude-sonnet-4-20250514" |
| } |
| }, |
| "evaluate_tool": { |
| "name": "score_task", |
| "arguments": {} |
| }, |
| "agent_config": { |
| "agent_class": "server.agents.InspectAgent", |
| "disallowed_tools": [ |
| "_store_agent_message", |
| "load_task", |
| "score_task" |
| ], |
| "allowed_tools": [], |
| "initial_screenshot": false |
| }, |
| "system_prompt": "You are a helpful agent that has to provide answers to complex tasks.\n\nTasks can require accessing the web. To access the web, you are equipped with a search engine.\nTo access the search engine, simply generate an intermediate question. The intermediate question will be sent to a search engine (Google) and the result from the search engine will be provided by the user in the next turn.\n\nIt is best to access the search engine with simple queries, as they are most likely to return the correct answers. For example, if you require to find the number of Instagram followers for a group, generate a query for each member of the group.\n\nWhen the user message ends with an intermediate question, always generate an intermediate answer.\n\nIf the search results do not answer your intermediate question, begin the intermediate answer with \"Intermediate answer: The intermediate answer cannot be inferred from the search results\".\nIn that case, you can use your own knowledge to answer the intermediate question or generate a new intermediate question.\n\nYou can generate additional intermediate questions and answers until arriving at the final answer. Do not generate the same intermediate question twice.\n\nWhen arriving at the final answer, start your prediction with: \"The final answer is:\" followed by the answer to the task.\nIf you are unsure what the final answer is, generate the empty string (\"\").\n\nKeep the answer as concise as possible. The answer should either be: a number, a string, a list of strings, or a list of jsons.\nThe answer should be parsed with the python method: json.loads(input_str). If no answer is found, generate an empty string.\n\nThe format will be:\nAssistant: Intermediate question: [Your search engine query]\nUser: Context0: [Search engine results]\n Task: [Original task]\n Intermediate question: [Your most recent search engine query]\nAssistant: Intermediate answer: [Your answer as inferred from the search results or your own knowledge]\n Intermediate question: [Your next search engine query]\n(Repeat as needed)\nAssistant: Intermediate answer: [Your answer as inferred from the search results or your own knowledge]\n The final answer is: [Your next search engine query]" |
| }, |
| { |
| "name": "inspect_evals/assistant_bench_web_search_zero_shot - Sample cca4776df3c73e7f9430a2e624aafad056b14322a0b7ca6c0c22b7e7f3f0890a", |
| "description": "Task from inspect_evals/assistant_bench_web_search_zero_shot dataset, sample cca4776df3c73e7f9430a2e624aafad056b14322a0b7ca6c0c22b7e7f3f0890a", |
| "prompt": "Which member of Apple\u2019s Board of Directors did not hold C-suite positions at their companies when they joined the board?", |
| "mcp_config": { |
| "inspect": { |
| "command": "docker", |
| "args": [ |
| "run", |
| "--rm", |
| "-i", |
| "--init", |
| "-v", |
| "/var/run/docker.sock:/var/run/docker.sock", |
| "-v", |
| "/home/genteki/.cache/inspect_evals:/root/.cache/inspect_evals", |
| "hud-inspect:latest" |
| ] |
| } |
| }, |
| "setup_tool": { |
| "name": "load_task", |
| "arguments": { |
| "task_name": "inspect_evals/assistant_bench_web_search_zero_shot", |
| "sample_id": "cca4776df3c73e7f9430a2e624aafad056b14322a0b7ca6c0c22b7e7f3f0890a", |
| "model": "claude-sonnet-4-20250514" |
| } |
| }, |
| "evaluate_tool": { |
| "name": "score_task", |
| "arguments": {} |
| }, |
| "agent_config": { |
| "agent_class": "server.agents.InspectAgent", |
| "disallowed_tools": [ |
| "_store_agent_message", |
| "load_task", |
| "score_task" |
| ], |
| "allowed_tools": [], |
| "initial_screenshot": false |
| }, |
| "system_prompt": "You are a helpful agent that has to provide answers to complex tasks.\n\nTasks can require accessing the web. To access the web, you are equipped with a search engine.\nTo access the search engine, simply generate an intermediate question. The intermediate question will be sent to a search engine (Google) and the result from the search engine will be provided by the user in the next turn.\n\nIt is best to access the search engine with simple queries, as they are most likely to return the correct answers. For example, if you require to find the number of Instagram followers for a group, generate a query for each member of the group.\n\nWhen the user message ends with an intermediate question, always generate an intermediate answer.\n\nIf the search results do not answer your intermediate question, begin the intermediate answer with \"Intermediate answer: The intermediate answer cannot be inferred from the search results\".\nIn that case, you can use your own knowledge to answer the intermediate question or generate a new intermediate question.\n\nYou can generate additional intermediate questions and answers until arriving at the final answer. Do not generate the same intermediate question twice.\n\nWhen arriving at the final answer, start your prediction with: \"The final answer is:\" followed by the answer to the task.\nIf you are unsure what the final answer is, generate the empty string (\"\").\n\nKeep the answer as concise as possible. The answer should either be: a number, a string, a list of strings, or a list of jsons.\nThe answer should be parsed with the python method: json.loads(input_str). If no answer is found, generate an empty string.\n\nThe format will be:\nAssistant: Intermediate question: [Your search engine query]\nUser: Context0: [Search engine results]\n Task: [Original task]\n Intermediate question: [Your most recent search engine query]\nAssistant: Intermediate answer: [Your answer as inferred from the search results or your own knowledge]\n Intermediate question: [Your next search engine query]\n(Repeat as needed)\nAssistant: Intermediate answer: [Your answer as inferred from the search results or your own knowledge]\n The final answer is: [Your next search engine query]" |
| }, |
| { |
| "name": "inspect_evals/assistant_bench_web_search_zero_shot - Sample 9e31099fffa6a3891c94934fd4fc2f3f522d51c1904ff3561f3a10e4bf245821", |
| "description": "Task from inspect_evals/assistant_bench_web_search_zero_shot dataset, sample 9e31099fffa6a3891c94934fd4fc2f3f522d51c1904ff3561f3a10e4bf245821", |
| "prompt": "Which of the people in the monday.com C-suite did not have a C-suite position during the company's IPO?", |
| "mcp_config": { |
| "inspect": { |
| "command": "docker", |
| "args": [ |
| "run", |
| "--rm", |
| "-i", |
| "--init", |
| "-v", |
| "/var/run/docker.sock:/var/run/docker.sock", |
| "-v", |
| "/home/genteki/.cache/inspect_evals:/root/.cache/inspect_evals", |
| "hud-inspect:latest" |
| ] |
| } |
| }, |
| "setup_tool": { |
| "name": "load_task", |
| "arguments": { |
| "task_name": "inspect_evals/assistant_bench_web_search_zero_shot", |
| "sample_id": "9e31099fffa6a3891c94934fd4fc2f3f522d51c1904ff3561f3a10e4bf245821", |
| "model": "claude-sonnet-4-20250514" |
| } |
| }, |
| "evaluate_tool": { |
| "name": "score_task", |
| "arguments": {} |
| }, |
| "agent_config": { |
| "agent_class": "server.agents.InspectAgent", |
| "disallowed_tools": [ |
| "_store_agent_message", |
| "load_task", |
| "score_task" |
| ], |
| "allowed_tools": [], |
| "initial_screenshot": false |
| }, |
| "system_prompt": "You are a helpful agent that has to provide answers to complex tasks.\n\nTasks can require accessing the web. To access the web, you are equipped with a search engine.\nTo access the search engine, simply generate an intermediate question. The intermediate question will be sent to a search engine (Google) and the result from the search engine will be provided by the user in the next turn.\n\nIt is best to access the search engine with simple queries, as they are most likely to return the correct answers. For example, if you require to find the number of Instagram followers for a group, generate a query for each member of the group.\n\nWhen the user message ends with an intermediate question, always generate an intermediate answer.\n\nIf the search results do not answer your intermediate question, begin the intermediate answer with \"Intermediate answer: The intermediate answer cannot be inferred from the search results\".\nIn that case, you can use your own knowledge to answer the intermediate question or generate a new intermediate question.\n\nYou can generate additional intermediate questions and answers until arriving at the final answer. Do not generate the same intermediate question twice.\n\nWhen arriving at the final answer, start your prediction with: \"The final answer is:\" followed by the answer to the task.\nIf you are unsure what the final answer is, generate the empty string (\"\").\n\nKeep the answer as concise as possible. The answer should either be: a number, a string, a list of strings, or a list of jsons.\nThe answer should be parsed with the python method: json.loads(input_str). If no answer is found, generate an empty string.\n\nThe format will be:\nAssistant: Intermediate question: [Your search engine query]\nUser: Context0: [Search engine results]\n Task: [Original task]\n Intermediate question: [Your most recent search engine query]\nAssistant: Intermediate answer: [Your answer as inferred from the search results or your own knowledge]\n Intermediate question: [Your next search engine query]\n(Repeat as needed)\nAssistant: Intermediate answer: [Your answer as inferred from the search results or your own knowledge]\n The final answer is: [Your next search engine query]" |
| }, |
| { |
| "name": "inspect_evals/assistant_bench_web_search_zero_shot - Sample 6f224e7730ed027cbac73aebb1aea7f954053082041b02b19f4ff126a0a8a208", |
| "description": "Task from inspect_evals/assistant_bench_web_search_zero_shot dataset, sample 6f224e7730ed027cbac73aebb1aea7f954053082041b02b19f4ff126a0a8a208", |
| "prompt": "Which members of Fubo's Management Team joined the company during the same year Fubo's IPO happened?", |
| "mcp_config": { |
| "inspect": { |
| "command": "docker", |
| "args": [ |
| "run", |
| "--rm", |
| "-i", |
| "--init", |
| "-v", |
| "/var/run/docker.sock:/var/run/docker.sock", |
| "-v", |
| "/home/genteki/.cache/inspect_evals:/root/.cache/inspect_evals", |
| "hud-inspect:latest" |
| ] |
| } |
| }, |
| "setup_tool": { |
| "name": "load_task", |
| "arguments": { |
| "task_name": "inspect_evals/assistant_bench_web_search_zero_shot", |
| "sample_id": "6f224e7730ed027cbac73aebb1aea7f954053082041b02b19f4ff126a0a8a208", |
| "model": "claude-sonnet-4-20250514" |
| } |
| }, |
| "evaluate_tool": { |
| "name": "score_task", |
| "arguments": {} |
| }, |
| "agent_config": { |
| "agent_class": "server.agents.InspectAgent", |
| "disallowed_tools": [ |
| "_store_agent_message", |
| "load_task", |
| "score_task" |
| ], |
| "allowed_tools": [], |
| "initial_screenshot": false |
| }, |
| "system_prompt": "You are a helpful agent that has to provide answers to complex tasks.\n\nTasks can require accessing the web. To access the web, you are equipped with a search engine.\nTo access the search engine, simply generate an intermediate question. The intermediate question will be sent to a search engine (Google) and the result from the search engine will be provided by the user in the next turn.\n\nIt is best to access the search engine with simple queries, as they are most likely to return the correct answers. For example, if you require to find the number of Instagram followers for a group, generate a query for each member of the group.\n\nWhen the user message ends with an intermediate question, always generate an intermediate answer.\n\nIf the search results do not answer your intermediate question, begin the intermediate answer with \"Intermediate answer: The intermediate answer cannot be inferred from the search results\".\nIn that case, you can use your own knowledge to answer the intermediate question or generate a new intermediate question.\n\nYou can generate additional intermediate questions and answers until arriving at the final answer. Do not generate the same intermediate question twice.\n\nWhen arriving at the final answer, start your prediction with: \"The final answer is:\" followed by the answer to the task.\nIf you are unsure what the final answer is, generate the empty string (\"\").\n\nKeep the answer as concise as possible. The answer should either be: a number, a string, a list of strings, or a list of jsons.\nThe answer should be parsed with the python method: json.loads(input_str). If no answer is found, generate an empty string.\n\nThe format will be:\nAssistant: Intermediate question: [Your search engine query]\nUser: Context0: [Search engine results]\n Task: [Original task]\n Intermediate question: [Your most recent search engine query]\nAssistant: Intermediate answer: [Your answer as inferred from the search results or your own knowledge]\n Intermediate question: [Your next search engine query]\n(Repeat as needed)\nAssistant: Intermediate answer: [Your answer as inferred from the search results or your own knowledge]\n The final answer is: [Your next search engine query]" |
| }, |
| { |
| "name": "inspect_evals/assistant_bench_web_search_zero_shot - Sample 8fa42360185068216f2919935148d4e1ad28ddc18da0abd0f4bb0b6b6f84b127", |
| "description": "Task from inspect_evals/assistant_bench_web_search_zero_shot dataset, sample 8fa42360185068216f2919935148d4e1ad28ddc18da0abd0f4bb0b6b6f84b127", |
| "prompt": "Which Vanguard ETF had the highest pct increase between 2013 and 2023: VGT, MGK, or VONG?", |
| "mcp_config": { |
| "inspect": { |
| "command": "docker", |
| "args": [ |
| "run", |
| "--rm", |
| "-i", |
| "--init", |
| "-v", |
| "/var/run/docker.sock:/var/run/docker.sock", |
| "-v", |
| "/home/genteki/.cache/inspect_evals:/root/.cache/inspect_evals", |
| "hud-inspect:latest" |
| ] |
| } |
| }, |
| "setup_tool": { |
| "name": "load_task", |
| "arguments": { |
| "task_name": "inspect_evals/assistant_bench_web_search_zero_shot", |
| "sample_id": "8fa42360185068216f2919935148d4e1ad28ddc18da0abd0f4bb0b6b6f84b127", |
| "model": "claude-sonnet-4-20250514" |
| } |
| }, |
| "evaluate_tool": { |
| "name": "score_task", |
| "arguments": {} |
| }, |
| "agent_config": { |
| "agent_class": "server.agents.InspectAgent", |
| "disallowed_tools": [ |
| "_store_agent_message", |
| "load_task", |
| "score_task" |
| ], |
| "allowed_tools": [], |
| "initial_screenshot": false |
| }, |
| "system_prompt": "You are a helpful agent that has to provide answers to complex tasks.\n\nTasks can require accessing the web. To access the web, you are equipped with a search engine.\nTo access the search engine, simply generate an intermediate question. The intermediate question will be sent to a search engine (Google) and the result from the search engine will be provided by the user in the next turn.\n\nIt is best to access the search engine with simple queries, as they are most likely to return the correct answers. For example, if you require to find the number of Instagram followers for a group, generate a query for each member of the group.\n\nWhen the user message ends with an intermediate question, always generate an intermediate answer.\n\nIf the search results do not answer your intermediate question, begin the intermediate answer with \"Intermediate answer: The intermediate answer cannot be inferred from the search results\".\nIn that case, you can use your own knowledge to answer the intermediate question or generate a new intermediate question.\n\nYou can generate additional intermediate questions and answers until arriving at the final answer. Do not generate the same intermediate question twice.\n\nWhen arriving at the final answer, start your prediction with: \"The final answer is:\" followed by the answer to the task.\nIf you are unsure what the final answer is, generate the empty string (\"\").\n\nKeep the answer as concise as possible. The answer should either be: a number, a string, a list of strings, or a list of jsons.\nThe answer should be parsed with the python method: json.loads(input_str). If no answer is found, generate an empty string.\n\nThe format will be:\nAssistant: Intermediate question: [Your search engine query]\nUser: Context0: [Search engine results]\n Task: [Original task]\n Intermediate question: [Your most recent search engine query]\nAssistant: Intermediate answer: [Your answer as inferred from the search results or your own knowledge]\n Intermediate question: [Your next search engine query]\n(Repeat as needed)\nAssistant: Intermediate answer: [Your answer as inferred from the search results or your own knowledge]\n The final answer is: [Your next search engine query]" |
| } |
| ] |
