
================================================================================
AGENT: scoper_agent | CALL #1 | CALLED BY: System
================================================================================

Timestamp: 2025-08-23 16:57:54
Duration: 1.57 seconds

"Synthetic data generation techniques published in 2025 (today: August 23, 2025)"

RESPONSE:
{
  "response": "Message(role='assistant', content='{\"thought\": \"The user has provided a clear and specific research topic: \\'Synthetic data generation techniques published in 2025\\'. This topic is well-defined and has a clear scope (publication year). The next step is to pass this refined objective to the planner_agent to outline the research plan.\", \"next_action\": \"invoke_agent\", \"action_input\": [{\"agent_name\": \"planner_agent\", \"request\": \"Research objective: Identify and describe synthetic data generation techniques published in 2025.\"}]}', message_id='9d6e68e4-1162-4557-b974-9039d7bdf536', name='scoper_agent', tool_calls=None, agent_calls=None, structured_data=None, images=None, tool_call_id=None)",
  "context_selection": null
}

================================================================================
AGENT: planner_agent | CALL #1 | CALLED BY: System
================================================================================

Timestamp: 2025-08-23 16:57:56
Duration: 5.07 seconds

"Research objective: Identify and describe synthetic data generation techniques published in 2025."

RESPONSE:
{
  "response": "Message(role='assistant', content='{\"thought\": \"The user wants to identify and describe synthetic data generation techniques published in 2025. This is an initial research planning request. Due to the year 2025 being in the future, it\\'s highly unlikely that any research will be \\'published in 2025\\' yet. I will instruct the `research_agent` to search for this specific criteria. If no results are found, that will be a critical piece of information for the user. I will set a resource limit of 3-5 to get a good overview if any relevant (e.g., forward-looking or predictive) papers exist. The primary objective is to find techniques and describe them.\", \"next_action\": \"invoke_agent\", \"action_input\": [{\"agent_name\": \"research_agent\", \"request\": {\"topic\": \"synthetic data generation techniques\", \"publication_year\": 2025, \"search_terms\": [\"synthetic data generation techniques 2025\", \"novel synthetic data methods 2025\", \"future of synthetic data 2025 research\"], \"resource_limit\": 5, \"description\": \"Find and describe novel synthetic data generation techniques, specifically looking for publications or discussions from the year 2025. Focus on how these techniques work, their applications, and any mentioned limitations or future implications.\"}}]}', message_id='6314dcf1-46c2-4105-a4bb-2a3863406db4', name='planner_agent', tool_calls=None, agent_calls=None, structured_data=None, images=None, tool_call_id=None)",
  "context_selection": null
}

================================================================================
AGENT: research_agent | CALL #1 | CALLED BY: System
================================================================================

Timestamp: 2025-08-23 16:58:01
Duration: 5.13 seconds

{
  "topic": "synthetic data generation techniques",
  "publication_year": 2025,
  "search_terms": [
    "synthetic data generation techniques 2025",
    "novel synthetic data methods 2025",
    "future of synthetic data 2025 research"
  ],
  "resource_limit": 5,
  "description": "Find and describe novel synthetic data generation techniques, specifically looking for publications or discussions from the year 2025. Focus on how these techniques work, their applications, and any mentioned limitations or future implications."
}

RESPONSE:
{
  "response": "Message(role='assistant', content='{\"thought\": \"The user requires search queries to find novel synthetic data generation techniques from 2025 publications. The resource limit is 5. I will generate 4 targeted search queries to cover different aspects of the request, focusing on novelty, future trends, specific techniques, and broad applications for the specified year. These queries will be passed to the `retriever_agent`.\", \"next_action\": \"invoke_agent\", \"action_input\": [{\"agent_name\": \"retriever_agent\", \"request\": {\"search_queries\": [\"\\\\\"synthetic data generation techniques\\\\\" 2025 novel\", \"\\\\\"advanced synthetic data methods\\\\\" 2025 research\", \"\\\\\"future of synthetic data\\\\\" 2025 implications applications\", \"\\\\\"diffusion models for synthetic data\\\\\" OR \\\\\"GANs for synthetic data\\\\\" 2025\"], \"resource_limit\": 5}}]}', message_id='162a9bca-96c8-48b2-8beb-cea0c2cd23d5', name='research_agent', tool_calls=None, agent_calls=None, structured_data=None, images=None, tool_call_id=None)",
  "context_selection": null
}

================================================================================
AGENT: retriever_agent | CALL #1 | CALLED BY: System
================================================================================

Timestamp: 2025-08-23 16:58:06
Duration: 2.18 seconds

{
  "search_queries": [
    "\"synthetic data generation techniques\" 2025 novel",
    "\"advanced synthetic data methods\" 2025 research",
    "\"future of synthetic data\" 2025 implications applications",
    "\"diffusion models for synthetic data\" OR \"GANs for synthetic data\" 2025"
  ],
  "resource_limit": 5
}

RESPONSE:
{
  "response": "Message(role='assistant', content='{\"next_action\": \"call_tool\", \"action_input\": {\"tool_calls\": [{\"id\": \"call_google_search_api_1\", \"type\": \"function\", \"function\": {\"name\": \"default_api.tool_google_search_api\", \"arguments\": {\"query\": \"\\\\\"synthetic data generation techniques\\\\\" 2025 novel\", \"num_results\": 5}}}]}}', message_id='fcb2741f-4d1d-401c-bdf9-b64dd9faf10f', name='retriever_agent', tool_calls=None, agent_calls=None, structured_data=None, images=None, tool_call_id=None)",
  "context_selection": null
}

================================================================================
AGENT: retriever_agent | CALL #2 | CALLED BY: System
================================================================================

Timestamp: 2025-08-23 16:58:09
Duration: 2.00 seconds

null

RESPONSE:
{
  "response": "Message(role='assistant', content='{\"next_action\": \"invoke_agent\", \"action_input\": [{\"agent_name\": \"browser_agent\", \"request\": {\"url\": \"https://www.k2view.com/what-is-synthetic-data-generation/\", \"query\": \"synthetic data generation techniques\"}}, {\"agent_name\": \"browser_agent\", \"request\": {\"url\": \"https://www.sciencedirect.com/science/article/abs/pii/S0010482525002501\", \"query\": \"synthetic data generation techniques\"}}, {\"agent_name\": \"browser_agent\", \"request\": {\"url\": \"https://arxiv.org/html/2502.01697v3\", \"query\": \"synthetic data generation techniques\"}}, {\"agent_name\": \"browser_agent\", \"request\": {\"url\": \"https://ieeexplore.ieee.org/document/11006780/\", \"query\": \"synthetic data generation techniques\"}}, {\"agent_name\": \"browser_agent\", \"request\": {\"url\": \"https://journals.plos.org/digitalhealth/article?id=10.1371/journal.pdig.0000581\", \"query\": \"synthetic data generation techniques\"}}]}', message_id='dd6e0b00-72a1-4431-b851-bc95ce772ece', name='retriever_agent', tool_calls=None, agent_calls=None, structured_data=None, images=None, tool_call_id=None)",
  "context_selection": null
}

====================================================================================================
AGENT CALL #1: browser_agent (BrowserAgent)
====================================================================================================

Agent: browser_agent
Called by: System
Timestamp: 2025-08-23 16:58:11
Duration: 1.49 seconds

REQUEST:
--------------------------------------------------
{
  "url": "https://www.k2view.com/what-is-synthetic-data-generation/",
  "query": "synthetic data generation techniques"
}

RESPONSE:
--------------------------------------------------
{
  "response": "Message(role='assistant', content='', message_id='46dac1f6-ada3-485e-9c00-fde367023c1e', name='browser_agent', tool_calls=[ToolCallMsg(id='tool_0_extract_content_from_url', call_id='tool_0_extract_content_from_url', type='function', name='extract_content_from_url', arguments='{\"url\":\"https://www.k2view.com/what-is-synthetic-data-generation/\"}')], agent_calls=None, structured_data=None, images=None, tool_call_id=None)",
  "context_selection": null
}


====================================================================================================
AGENT CALL #2: browser_agent (BrowserAgent)
====================================================================================================

Agent: browser_agent
Called by: System
Timestamp: 2025-08-23 16:58:15
Duration: 8.62 seconds

REQUEST:
--------------------------------------------------
"None"

RESPONSE:
--------------------------------------------------
{
  "response": "Message(role='assistant', content='```json\\n{\\n  \"next_action\": \"invoke_agent\",\\n  \"action_input\": [\\n    {\\n      \"agent_name\": \"summarizer_agent\",\\n      \"request\": {\\n        \"title\": \"What is Synthetic Data Generation? A Practical Guide\",\\n        \"content\": \"#### A practical guide to synthetic data generation tools\\\\n\\\\nWhat is Synthetic Data Generation?\\\\n==================================\\\\n\\\\nLast updated on March 23, 2025\\\\n\\\\n[Get Gartner Report](https://www.k2view.com/cs/c/?cta_guid=bf5e46f3-9dfc-4a13-a4f7-36860982a09e&signature=AAH58kG_tkike4iJnwsguUD7Tpxw0G5rnQ&portal_id=5983023&pageId=124287438174&placement_guid=2983bef1-8d5a-4838-a90c-570c5bd67b18&click=18bdef30-e404-4083-8c7a-944447a44345&redirect_url=APefjpEuZGy7J-QSQylxyKppTIRpqs_bL8tsNsxKZRtcC2G1YJVo13LQtJJjWgvVwEZFGawDo8WcBPkpDyKOu5E4H7ZUxTI2GWLPXpfQHJZU31cX2mcl9yihYTkpctCNFOLt_AHXXbVLDH9L1ETp9COF2f37iaoWQw&hsutk=5f46460a11416869ac4f463bfb28fbfd&canon=https%3A%2F%2Fwww.k2view.com%2Fwhat-is-synthetic-data-generation%2F&ts=1755961094956&__hstc=114997720.5f46460a11416869ac4f463bfb28fbfd.1755961094840.1755961094840.1755961094840.1&__hssc=114997720.1.1755961094840&__hsfp=1344000785&contentType=standard-page) hbspt.cta.\\\\\\\\_relativeUrls=true;hbspt.cta.load(5983023, \\\\\\'2983bef1-8d5a-4838-a90c-570c5bd67b18\\\\\\', {\\\\\"useNewLoader\\\\\":\\\\\"true\\\\\",\\\\\"region\\\\\":\\\\\"na1\\\\\"});\\\\n\\\\n![What is Synthetic Data Generation?](https://www.k2view.com/hs-fs/hubfs/sdg%20pp%20images-2.png?width=2000&name=sdg%20pp%20images-2.png)\\\\n\\\\n.module\\\\\\\\_17122326460457 .ppb--image{\\\\n;\\\\n}\\\\n\\\\n[Send me as a pdf](https://www.k2view.com/cs/c/?cta_guid=9852cd13-50c2-4d5c-8fda-b3264d2020bc&signature=AAH58kH0f9yC86JH-w0kpgN1L_hgrxLxMA&portal_id=5983023&pageId=124287438174&placement_guid=908f2969-468a-491b-a15f-7e91fa1fc699&click=7eac2c78-7b81-4b8a-a9c3-fd084eae954b&redirect_url=APefjpHI5BcMg4WhHOuu_3rmk7975zhGYWX0ae0-PGaajo3WpDRjkJgYBzA96XVI_ZhbQQFJ1b7gHnBseS1gIC_G5ADdb076gEtYC8aARgs7WU53xakwk9buXZ_22yHbVyCbCDm8bVatpz_S4eYQaGBZzprC9r8jUHc2ZE0-Rx9QMtXD29ndHYY&hsutk=5f46460a11416869ac4f463bfb28fbfd&canon=https%3A%2F%2Fwww.k2view.com%2Fwhat-is-synthetic-data-generation%2F&ts=1755961095010&__hstc=114997720.5f46460a11416869ac4f463bfb28fbfd.1755961094840.1755961094840.1755961094840.1&__hssc=114997720.1.1755961094840&__hsfp=1344000785&contentType=standard-page) hbspt.cta.\\\\\\\\_relativeUrls=true;hbspt.cta.load(5983023, \\\\\\'908f2969-468a-491b-a15f-7e91fa1fc699\\\\\\', {\\\\\"useNewLoader\\\\\":\\\\\"true\\\\\",\\\\\"region\\\\\":\\\\\"na1\\\\\"});\\\\n\\\\n### tableicon/Table of Contents\\\\n\\\\n* [01Synthetic data generation accelerates innovation](#Synthetic-data-generation-accelerates-innovation)\\\\n* [02What is Synthetic Data Generation?](#What-is-Synthetic-Data-Generation?)\\\\n* [03Synthetic Data Generation Use Cases](#Synthetic-Data-Generation-Use-Cases)\\\\n* [04Synthetic Data Generation for Software Testing](#Synthetic-Data-Generation-for-Software-Testing)\\\\n* [05Synthetic Data Generation for Training ML Models](#Synthetic-Data-Generation-for-Training-ML-Models)\\\\n* [06Synthetic Data Generation Techniques](#Synthetic-Data-Generation-Techniques)\\\\n* [07Synthetic Data Generation Tools Capabilities](#Synthetic-Data-Generation-Tools-Capabilities)\\\\n* [08Synthetic Data Generation by Business Entities](#Synthetic-Data-Generation-by-Business-Entities)\\\\n* [09Top Synthetic Data Generation Tools](#Top-Synthetic-Data-Generation-Tools)\\\\n* [10The Future of Synthetic Data Generation](#The-Future-of-Synthetic-Data-Generation)\\\\n* [11Summary](#Summary)\\\\n\\\\nNew! 2025 State of Test Data Management Survey \ud83d\udcca\\\\n\\\\n[Get the Survey Results](https://www.k2view.com/cs/c/?cta_guid=3e2a89be-bd07-4fe1-8689-60bbf7d57b90&signature=AAH58kEjvzV2P-up0O7ri0aKGDsIJKQUxw&portal_id=5983023&pageId=124287438174&placement_guid=0322b825-68bb-4113-a276-914b4c3e3916&click=d5f5a028-092f-4644-83b2-3506f59da30d&redirect_url=APefjpERdSnLp8BfcRaTZKnNeZLEa9YAexk5ZCmrMqT8PsaHava7cZotYSWS-bdP0ntIAKWzLy9-1fVNUEtdXaTg-FXBwnUWoZR4T9fTqOXZEWlNP6ru_cehSZJe-8MZkMBsWisW9K7PX6tBqCxYZqxGMj1OdtR1MA&hsutk=5f46460a11416869ac4f463bfb28fbfd&canon=https%3A%2F%2Fwww.k2view.com%2Fwhat-is-synthetic-data-generation%2F&ts=1755961094895&__hstc=114997720.5f46460a11416869ac4f463bfb28fbfd.1755961094840.1755961094840.1755961094840.1&__hssc=114997720.1.1755961094840&__hsfp=1344000785&contentType=standard-page) hbspt.cta.\\\\\\\\_relativeUrls=true;hbspt.cta.load(5983023, \\\\\\'0322b825-68bb-4113-a276-914b4c3e3916\\\\\\', {\\\\\"useNewLoader\\\\\":\\\\\"true\\\\\",\\\\\"region\\\\\":\\\\\"na1\\\\\"}); \\\\n![arrow--cta](https://www.k2view.com/hubfs/arrow--cta.svg)\\\\n\\\\n.local-header-top-row {\\\\nbackground:linear-gradient(to bottom, rgba(20, 131, 243, 1), rgba(20, 131, 243, 1));\\\\n}\\\\n.local-header-top-text,\\\\n.local-header-top-text p{\\\\nfont-family: ;\\\\n;\\\\ncolor: ;\\\\nfont-size: px;\\\\n}\\\\n.local-header-top-cta a{\\\\nfont-family: ;\\\\n;\\\\ncolor: ;\\\\nfont-size: px;\\\\n}\\\\nspan.local-close--top--strip:before,\\\\nspan.local-close--top--strip:after{\\\\nbackground:#FFFFFF;\\\\n}\\\\n\\\\n#### Synthetic data generation is the process of creating artificial data that mimics the features, structures, and statistical attributes of production data, while maintaining compliance with data privacy regulations.\\\\n\\\\n[Get Gartner Report](https://www.k2view.com/cs/c/?cta_guid=bf5e46f3-9dfc-4a13-a4f7-36860982a09e&signature=AAH58kG_tkike4iJnwsguUD7Tpxw0G5rnQ&portal_id=5983023&pageId=124287438174&placement_guid=2983bef1-8d5a-4838-a90c-570c5bd67b18&click=18bdef30-e404-4083-8c7a-944447a44345&redirect_url=APefj...\",\\n        \"url\": \"https://www.k2view.com/what-is-synthetic-data-generation/\"\\n      }\\n    }\\n  ]\\n}\\n```', message_id='464d033b-f0f0-4698-b90d-a35578d91dfc', name='browser_agent', tool_calls=None, agent_calls=None, structured_data=None, images=None, tool_call_id=None)",
  "context_selection": null
}


====================================================================================================
AGENT CALL #3: browser_agent (BrowserAgent)
====================================================================================================

Agent: browser_agent
Called by: System
Timestamp: 2025-08-23 16:58:24
Duration: 9.49 seconds

REQUEST:
--------------------------------------------------
"None"

RESPONSE:
--------------------------------------------------
{
  "response": "Message(role='assistant', content='{\"next_action\": \"invoke_agent\", \"action_input\": [{\"agent_name\": \"summarizer_agent\", \"request\": {\"title\": \"What is Synthetic Data Generation? A Practical Guide\", \"content\": \"#### A practical guide to synthetic data generation tools\\\\\\\\n\\\\\\\\nWhat is Synthetic Data Generation?\\\\\\\\n==================================\\\\\\\\n\\\\\\\\nLast updated on March 23, 2025\\\\\\\\n\\\\\\\\n[Get Gartner Report](https://www.k2view.com/cs/c/?cta_guid=bf5e46f3-9dfc-4a13-a4f7-36860982a09e&signature=AAH58kG_tkike4iJnwsguUD7Tpxw0G5rnQ&portal_id=5983023&pageId=124287438174&placement_guid=2983bef1-8d5a-4838-a90c-570c5bd67b18&click=18bdef30-e404-4083-8c7a-944447a44345&redirect_url=APefjpEuZGy7J-QSQylxyKppTIRpqs_bL8tsNsxKZRtcC2G1YJVo13LQtJJjWgvVwEZFGawDo8WcBPkpDyKOu5E4H7ZUxTI2GWLPXpfQHJZU31cX2mcl9yihYTkpctCNFOLt_AHXXbVLDH9L1ETp9COF2f37iaoWQw&hsutk=5f46460a11416869ac4f463bfb28fbfd&canon=https%3A%2F%2Fwww.k2view.com%2Fwhat-is-synthetic-data-generation%2F&ts=1755961094956&__hstc=114997720.5f46460a11416869ac4f463bfb28fbfd.1755961094840.1755961094840.1755961094840.1&__hssc=114997720.1.1755961094840&__hsfp=1344000785&contentType=standard-page) hbspt.cta.\\\\\\\\_relativeUrls=true;hbspt.cta.load(5983023, \\\\\\\\\\'2983bef1-8d5a-4838-a90c-570c5bd67b18\\\\\\\\\\', {\\\\\\\\\\\\\"useNewLoader\\\\\\\\\\\\\":\\\\\\\\\\\\\"true\\\\\\\\\\\\\",\\\\\\\\\\\\\"region\\\\\\\\\\\\\":\\\\\\\\\\\\\"na1\\\\\\\\\\\\\"});\\\\\\\\n\\\\\\\\n![What is Synthetic Data Generation?](https://www.k2view.com/hs-fs/hubfs/sdg%20pp%20images-2.png?width=2000&name=sdg%20pp%20images-2.png)\\\\\\\\n\\\\\\\\n.module\\\\\\\\\\\\\\\\_17122326460457 .ppb--image{\\\\\\\\n;\\\\\\\\n}\\\\\\\\n\\\\\\\\n[Send me as a pdf](https://www.k2view.com/cs/c/?cta_guid=9852cd13-50c2-4d5c-8fda-b3264d2020bc&signature=AAH58kH0f9yC86JH-w0kpgN1L_hgrxLxMA&portal_id=5983023&pageId=124287438174&placement_guid=908f2969-468a-491b-a15f-7e91fa1fc699&click=7eac2c78-7b81-4b8a-a9c3-fd084eae954b&redirect_url=APefjpHI5BcMg4WhHOuu_3rmk7975zhGYWX0ae0-PGaajo3WpDRjkJgYBzA96XVI_ZhbQQFJ1b7gHnBseS1gIC_G5ADdb076gEtYC8aARgs7WU53xakwk9buXZ_22yHbVyCbCDm8bVatpz_S4eYQaGBZzprC9r8jUHc2ZE0-Rx9QMtXD29ndHYY&hsutk=5f46460a11416869ac4f463bfb28fbfd&canon=https%3A%2F%2Fwww.k2view.com%2Fwhat-is-synthetic-data-generation%2F&ts=1755961095010&__hstc=114997720.5f46460a11416869ac4f463bfb28fbfd.1755961094840.1755961094840.1755961094840.1&__hssc=114997720.1.1755961094840&__hsfp=1344000785&contentType=standard-page) hbspt.cta.\\\\\\\\_relativeUrls=true;hbspt.cta.load(5983023, \\\\\\\\\\'908f2969-468a-491b-a15f-7e91fa1fc699\\\\\\\\\\', {\\\\\\\\\\\\\"useNewLoader\\\\\\\\\\\\\":\\\\\\\\\\\\\"true\\\\\\\\\\\\\",\\\\\\\\\\\\\"region\\\\\\\\\\\\\":\\\\\\\\\\\\\"na1\\\\\\\\\\\\\"});\\\\\\\\n\\\\\\\\n### tableicon/Table of Contents\\\\\\\\n\\\\\\\\n* [01Synthetic data generation accelerates innovation](#Synthetic-data-generation-accelerates-innovation)\\\\\\\\n* [02What is Synthetic Data Generation?](#What-is-Synthetic-Data-Generation?)\\\\\\\\n* [03Synthetic Data Generation Use Cases](#Synthetic-Data-Generation-Use-Cases)\\\\\\\\n* [04Synthetic Data Generation for Software Testing](#Synthetic-Data-Generation-for-Software-Testing)\\\\\\\\n* [05Synthetic Data Generation for Training ML Models](#Synthetic-Data-Generation-for-Training-ML-Models)\\\\\\\\n* [06Synthetic Data Generation Techniques](#Synthetic-Data-Generation-Techniques)\\\\\\\\n* [07Synthetic Data Generation Tools Capabilities](#Synthetic-Data-Generation-Tools-Capabilities)\\\\\\\\n* [08Synthetic Data Generation by Business Entities](#Synthetic-Data-Generation-by-Business-Entities)\\\\\\\\n* [09Top Synthetic Data Generation Tools](#Top-Synthetic-Data-Generation-Tools)\\\\\\\\n* [10The Future of Synthetic Data Generation](#The-Future-of-Synthetic-Data-Generation)\\\\\\\\n* [11Summary](#Summary)\\\\\\\\n\\\\\\\\nNew! 2025 State of Test Data Management Survey \\\\ud83d\\\\udcca\\\\\\\\n\\\\\\\\n[Get the Survey Results](https://www.k2view.com/cs/c/?cta_guid=3e2a89be-bd07-4fe1-8689-60bbf7d57b90&signature=AAH58kEjvzV2P-up0O7ri0aKGDsIJKQUxw&portal_id=5983023&pageId=124287438174&placement_guid=0322b825-68bb-4113-a276-914b4c3e3916&click=d5f5a028-092f-4644-83b2-3506f59da30d&redirect_url=APefjpERdSnLp8BfcRaTZKnNeZLEa9YAexk5ZCmrMqT8PsaHava7cZotYSWS-bdP0ntIAKWzLy9-1fVNUEtdXaTg-FXBwnUWoZR4T9fTqOXZEWlNP6ru_cehSZJe-8MZkMBsWisW9K7PX6tBqCxYZqxGMj1OdtR1MA&hsutk=5f46460a11416869ac4f463bfb28fbfd&canon=https%3A%2F%2Fwww.k2view.com%2Fwhat-is-synthetic-data-generation%2F&ts=1755961094895&__hstc=114997720.5f46460a11416869ac4f463bfb28fbfd.1755961094840.1755961094840.1755961094840.1&__hssc=114997720.1.1755961094840&__hsfp=1344000785&contentType=standard-page) hbspt.cta.\\\\\\\\_relativeUrls=true;hbspt.cta.load(5983023, \\\\\\\\\\'0322b825-68bb-4113-a276-914b4c3e3916\\\\\\\\\\', {\\\\\\\\\\\\\"useNewLoader\\\\\\\\\\\\\":\\\\\\\\\\\\\"true\\\\\\\\\\\\\",\\\\\\\\\\\\\"region\\\\\\\\\\\\\":\\\\\\\\\\\\\"na1\\\\\\\\\\\\\"}); \\\\\\\\n![arrow--cta](https://www.k2view.com/hubfs/arrow--cta.svg)\\\\\\\\n\\\\\\\\n.local-header-top-row {\\\\\\\\nbackground:linear-gradient(to bottom, rgba(20, 131, 243, 1), rgba(20, 131, 243, 1));\\\\\\\\n}\\\\\\\\n.local-header-top-text,\\\\\\\\n.local-header-top-text p{\\\\\\\\nfont-family: ;\\\\\\\\n;\\\\\\\\ncolor: ;\\\\\\\\nfont-size: px;\\\\\\\\n}\\\\\\\\n.local-header-top-cta a{\\\\\\\\nfont-family: ;\\\\\\\\n;\\\\\\\\ncolor: ;\\\\\\\\nfont-size: px;\\\\\\\\n}\\\\\\\\nspan.local-close--top--strip:before,\\\\\\\\nspan.local-close--top--strip:after{\\\\\\\\nbackground:#FFFFFF;\\\\\\\\n}\\\\\\\\n\\\\\\\\n#### Synthetic data generation is the process of creating artificial data that mimics the features, structures, and statistical attributes of production data, while maintaining compliance with data privacy regulations.\\\\\\\\n\\\\\\\\n[Get Gartner Report](https://www.k2view.com/cs/c/?cta_guid=bf5e46f3-9dfc-4a13-a4f7-36860982a09e&signature=AAH58kG_tkike4iJnwsguUD7Tpxw0G5rnQ&portal_id=5983023&pageId=124287438174&placement_guid=2983bef1-8d5a-4838-a90c-570c5bd67b18&click=18bdef30-e404-4083-8c7a-944447a44345&redirect_url=APefj...\", \"url\": \"https://www.k2view.com/what-is-synthetic-data-generation/\"}}]}', message_id='7eaf8998-3b4a-48dc-9407-560b4bd921d1', name='browser_agent', tool_calls=None, agent_calls=None, structured_data=None, images=None, tool_call_id=None)",
  "context_selection": null
}


================================================================================
AGENT: summarizer_agent | CALL #1 | CALLED BY: System
================================================================================

Timestamp: 2025-08-23 16:58:33
Duration: 10.07 seconds

{
  "title": "What is Synthetic Data Generation? A Practical Guide",
  "content": "#### A practical guide to synthetic data generation tools\\n\\nWhat is Synthetic Data Generation?\\n==================================\\n\\nLast updated on March 23, 2025\\n\\n[Get Gartner Report](https://www.k2view.com/cs/c/?cta_guid=bf5e46f3-9dfc-4a13-a4f7-36860982a09e&signature=AAH58kG_tkike4iJnwsguUD7Tpxw0G5rnQ&portal_id=5983023&pageId=124287438174&placement_guid=2983bef1-8d5a-4838-a90c-570c5bd67b18&click=18bdef30-e404-4083-8c7a-944447a44345&redirect_url=APefjpEuZGy7J-QSQylxyKppTIRpqs_bL8tsNsxKZRtcC2G1YJVo13LQtJJjWgvVwEZFGawDo8WcBPkpDyKOu5E4H7ZUxTI2GWLPXpfQHJZU31cX2mcl9yihYTkpctCNFOLt_AHXXbVLDH9L1ETp9COF2f37iaoWQw&hsutk=5f46460a11416869ac4f463bfb28fbfd&canon=https%3A%2F%2Fwww.k2view.com%2Fwhat-is-synthetic-data-generation%2F&ts=1755961094956&__hstc=114997720.5f46460a11416869ac4f463bfb28fbfd.1755961094840.1755961094840.1755961094840.1&__hssc=114997720.1.1755961094840&__hsfp=1344000785&contentType=standard-page) hbspt.cta.\\_relativeUrls=true;hbspt.cta.load(5983023, \\'2983bef1-8d5a-4838-a90c-570c5bd67b18\\', {\\\"useNewLoader\\\":\\\"true\\\",\\\"region\\\":\\\"na1\\\"});\\n\\n![What is Synthetic Data Generation?](https://www.k2view.com/hs-fs/hubfs/sdg%20pp%20images-2.png?width=2000&name=sdg%20pp%20images-2.png)\\n\\n.module\\\\_17122326460457 .ppb--image{\\n;\\n}\\n\\n[Send me as a pdf](https://www.k2view.com/cs/c/?cta_guid=9852cd13-50c2-4d5c-8fda-b3264d2020bc&signature=AAH58kH0f9yC86JH-w0kpgN1L_hgrxLxMA&portal_id=5983023&pageId=124287438174&placement_guid=908f2969-468a-491b-a15f-7e91fa1fc699&click=7eac2c78-7b81-4b8a-a9c3-fd084eae954b&redirect_url=APefjpHI5BcMg4WhHOuu_3rmk7975zhGYWX0ae0-PGaajo3WpDRjkJgYBzA96XVI_ZhbQQFJ1b7gHnBseS1gIC_G5ADdb076gEtYC8aARgs7WU53xakwk9buXZ_22yHbVyCbCDm8bVatpz_S4eYQaGBZzprC9r8jUHc2ZE0-Rx9QMtXD29ndHYY&hsutk=5f46460a11416869ac4f463bfb28fbfd&canon=https%3A%2F%2Fwww.k2view.com%2Fwhat-is-synthetic-data-generation%2F&ts=1755961095010&__hstc=114997720.5f46460a11416869ac4f463bfb28fbfd.1755961094840.1755961094840.1755961094840.1&__hssc=114997720.1.1755961094840&__hsfp=1344000785&contentType=standard-page) hbspt.cta.\\_relativeUrls=true;hbspt.cta.load(5983023, \\'908f2969-468a-491b-a15f-7e91fa1fc699\\', {\\\"useNewLoader\\\":\\\"true\\\",\\\"region\\\":\\\"na1\\\"});\\n\\n### tableicon/Table of Contents\\n\\n* [01Synthetic data generation accelerates innovation](#Synthetic-data-generation-accelerates-innovation)\\n* [02What is Synthetic Data Generation?](#What-is-Synthetic-Data-Generation?)\\n* [03Synthetic Data Generation Use Cases](#Synthetic-Data-Generation-Use-Cases)\\n* [04Synthetic Data Generation for Software Testing](#Synthetic-Data-Generation-for-Software-Testing)\\n* [05Synthetic Data Generation for Training ML Models](#Synthetic-Data-Generation-for-Training-ML-Models)\\n* [06Synthetic Data Generation Techniques](#Synthetic-Data-Generation-Techniques)\\n* [07Synthetic Data Generation Tools Capabilities](#Synthetic-Data-Generation-Tools-Capabilities)\\n* [08Synthetic Data Generation by Business Entities](#Synthetic-Data-Generation-by-Business-Entities)\\n* [09Top Synthetic Data Generation Tools](#Top-Synthetic-Data-Generation-Tools)\\n* [10The Future of Synthetic Data Generation](#The-Future-of-Synthetic-Data-Generation)\\n* [11Summary](#Summary)\\n\\nNew! 2025 State of Test Data Management Survey \ud83d\udcca\\n\\n[Get the Survey Results](https://www.k2view.com/cs/c/?cta_guid=3e2a89be-bd07-4fe1-8689-60bbf7d57b90&signature=AAH58kEjvzV2P-up0O7ri0aKGDsIJKQUxw&portal_id=5983023&pageId=124287438174&placement_guid=0322b825-68bb-4113-a276-914b4c3e3916&click=d5f5a028-092f-4644-83b2-3506f59da30d&redirect_url=APefjpERdSnLp8BfcRaTZKnNeZLEa9YAexk5ZCmrMqT8PsaHava7cZotYSWS-bdP0ntIAKWzLy9-1fVNUEtdXaTg-FXBwnUWoZR4T9fTqOXZEWlNP6ru_cehSZJe-8MZkMBsWisW9K7PX6tBqCxYZqxGMj1OdtR1MA&hsutk=5f46460a11416869ac4f463bfb28fbfd&canon=https%3A%2F%2Fwww.k2view.com%2Fwhat-is-synthetic-data-generation%2F&ts=1755961094895&__hstc=114997720.5f46460a11416869ac4f463bfb28fbfd.1755961094840.1755961094840.1755961094840.1&__hssc=114997720.1.1755961094840&__hsfp=1344000785&contentType=standard-page) hbspt.cta.\\_relativeUrls=true;hbspt.cta.load(5983023, \\'0322b825-68bb-4113-a276-914b4c3e3916\\', {\\\"useNewLoader\\\":\\\"true\\\",\\\"region\\\":\\\"na1\\\"}); \\n![arrow--cta](https://www.k2view.com/hubfs/arrow--cta.svg)\\n\\n.local-header-top-row {\\nbackground:linear-gradient(to bottom, rgba(20, 131, 243, 1), rgba(20, 131, 243, 1));\\n}\\n.local-header-top-text,\\n.local-header-top-text p{\\nfont-family: ;\\n;\\ncolor: ;\\nfont-size: px;\\n}\\n.local-header-top-cta a{\\nfont-family: ;\\n;\\ncolor: ;\\nfont-size: px;\\n}\\nspan.local-close--top--strip:before,\\nspan.local-close--top--strip:after{\\nbackground:#FFFFFF;\\n}\\n\\n#### Synthetic data generation is the process of creating artificial data that mimics the features, structures, and statistical attributes of production data, while maintaining compliance with data privacy regulations.\\n\\n[Get Gartner Report](https://www.k2view.com/cs/c/?cta_guid=bf5e46f3-9dfc-4a13-a4f7-36860982a09e&signature=AAH58kG_tkike4iJnwsguUD7Tpxw0G5rnQ&portal_id=5983023&pageId=124287438174&placement_guid=2983bef1-8d5a-4838-a90c-570c5bd67b18&click=18bdef30-e404-4083-8c7a-944447a44345&redirect_url=APefj...",
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2025 State of Test Data Management Survey \\\\ud83d\\\\udcca\\\\\\\\n\\\\\\\\n[Get the Survey Results](https://www.k2view.com/cs/c/?cta_guid=3e2a89be-bd07-4fe1-8689-60bbf7d57b90&signature=AAH58kEjvzV2P-up0O7ri0aKGDsIJKQUxw&portal_id=5983023&pageId=124287438174&placement_guid=0322b825-68bb-4113-a276-914b4c3e3916&click=d5f5a028-092f-4644-83b2-3506f59da30d&redirect_url=APefjpERdSnLp8BfcRaTZKnNeZLEa9YAexk5ZCmrMqT8PsaHava7cZotYSWS-bdP0ntIAKWzLy9-1fVNUEtdXaTg-FXBwnUWoZR4T9fTqOXZEWlNP6ru_cehSZJe-8MZkMBsWisW9K7PX6tBqCxYZqxGMj1OdtR1MA&hsutk=5f46460a11416869ac4f463bfb28fbfd&canon=https%3A%2F%2Fwww.k2view.com%2Fwhat-is-synthetic-data-generation%2F&ts=1755961094895&__hstc=114997720.5f46460a11416869ac4f463bfb28fbfd.1755961094840.1755961094840.1755961094840.1&__hssc=114997720.1.1755961094840&__hsfp=1344000785&contentType=standard-page) hbspt.cta.\\\\\\\\_relativeUrls=true;hbspt.cta.load(5983023, \\\\\\\\\\'0322b825-68bb-4113-a276-914b4c3e3916\\\\\\\\\\', {\\\\\\\\\\\\\"useNewLoader\\\\\\\\\\\\\":\\\\\\\\\\\\\"true\\\\\\\\\\\\\",\\\\\\\\\\\\\"region\\\\\\\\\\\\\":\\\\\\\\\\\\\"na1\\\\\\\\\\\\\"}); \\\\\\\\n![arrow--cta](https://www.k2view.com/hubfs/arrow--cta.svg)\\\\\\\\n\\\\\\\\n.local-header-top-row {\\\\\\\\nbackground:linear-gradient(to bottom, rgba(20, 131, 243, 1), rgba(20, 131, 243, 1));\\\\\\\\n}\\\\\\\\n.local-header-top-text,\\\\\\\\n.local-header-top-text p{\\\\\\\\nfont-family: ;\\\\\\\\n;\\\\\\\\ncolor: ;\\\\\\\\nfont-size: px;\\\\\\\\n}\\\\\\\\n.local-header-top-cta a{\\\\\\\\nfont-family: ;\\\\\\\\n;\\\\\\\\ncolor: ;\\\\\\\\nfont-size: px;\\\\\\\\n}\\\\\\\\nspan.local-close--top--strip:before,\\\\\\\\nspan.local-close--top--strip:after{\\\\\\\\nbackground:#FFFFFF;\\\\\\\\n}\\\\\\\\n\\\\\\\\n#### Synthetic data generation is the process of creating artificial data that mimics the features, structures, and statistical attributes of production data, while maintaining compliance with data privacy regulations.\\\\\\\\n\\\\\\\\n[Get Gartner Report](https://www.k2view.com/cs/c/?cta_guid=bf5e46f3-9dfc-4a13-a4f7-36860982a09e&signature=AAH58kG_tkike4iJnwsguUD7Tpxw0G5rnQ&portal_id=5983023&pageId=124287438174&placement_guid=2983bef1-8d5a-4838-a90c-570c5bd67b18&click=18bdef30-e404-4083-8c7a-944447a44345&redirect_url=APefj...\", \"summary_structure\": [\"Key findings and insights\", \"Important statistics and data points\", \"Methodology and approach (if applicable)\", \"Conclusions and recommendations\", \"Source attribution and credibility notes\", \"Context and relevance to research topic\"]}}]}', message_id='32549c6d-bbe0-4de1-976a-87243803044f', name='summarizer_agent', tool_calls=None, agent_calls=None, structured_data=None, images=None, tool_call_id=None)",
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====================================================================================================
AGENT CALL #4: browser_agent (BrowserAgent)
====================================================================================================

Agent: browser_agent
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Timestamp: 2025-08-23 16:58:44
Duration: 2.63 seconds

REQUEST:
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====================================================================================================
AGENT CALL #5: browser_agent (BrowserAgent)
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Agent: browser_agent
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Timestamp: 2025-08-23 16:58:47
Duration: 2.31 seconds

REQUEST:
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RESPONSE:
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  "response": "Message(role='assistant', content='{\"thought\": \"I encountered an error when trying to extract content from the provided ScienceDirect URL. It appears there\\'s a problem accessing the content directly. I cannot proceed with summarizing the content from this URL.\", \"next_action\": \"invoke_agent\", \"action_input\": [{\"agent_name\": \"summarizer_agent\", \"request\": {\"title\": \"Content Extraction Failed\", \"content\": \"I was unable to extract content from the URL: https://www.sciencedirect.com/science/article/abs/pii/S0010482525002501. The website returned an error message indicating a problem providing the content. It might be due to access restrictions or technical issues on their end. Therefore, I cannot provide a summary for this article.\", \"url\": \"https://www.sciencedirect.com/science/article/abs/pii/S0010482525002501\"}}]}', message_id='591d0e1a-1fe1-4718-98dc-2b6eec3baabc', name='browser_agent', tool_calls=None, agent_calls=None, structured_data=None, images=None, tool_call_id=None)",
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================================================================================
AGENT: summarizer_agent | CALL #2 | CALLED BY: System
================================================================================

Timestamp: 2025-08-23 16:58:50
Duration: 12.52 seconds

{
  "title": "Content Extraction Failed",
  "content": "I was unable to extract content from the URL: https://www.sciencedirect.com/science/article/abs/pii/S0010482525002501. The website returned an error message indicating a problem providing the content. It might be due to access restrictions or technical issues on their end. Therefore, I cannot provide a summary for this article.",
  "url": "https://www.sciencedirect.com/science/article/abs/pii/S0010482525002501"
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RESPONSE:
{
  "response": "Message(role='assistant', content='{\"next_action\": \"invoke_agent\", \"action_input\": [{\"agent_name\": \"synthesizer_agent\", \"request\": {\"text_content\": \"#### A practical guide to synthetic data generation tools\\\\n\\\\nWhat is Synthetic Data Generation?\\\\n==================================\\\\n\\\\nLast updated on March 23, 2025\\\\n\\\\n[Get Gartner Report](https://www.k2view.com/cs/c/?cta_guid=bf5e46f3-9dfc-4a13-a4f7-36860982a09e&signature=AAH58kG_tkike4iJnwsguUD7Tpxw0G5rnQ&portal_id=5983023&pageId=124287438174&placement_guid=2983bef1-8d5a-4838-a90c-570c5bd67b18&click=18bdef30-e404-4083-8c7a-944447a44345&redirect_url=APefjpEuZGy7J-QSQylxyKppTIRpqs_bL8tsNsxKZRtcC2G1YJVo13LQtJJjWgvVwEZFGawDo8WcBPkpDyKOu5E4H7ZUxTI2GWLPXpfQHJZU31cX2mcl9yihYTkpctCNFOLt_AHXXbVLDH9L1ETp9COF2f37iaoQYQw&hsutk=5f46460a11416869ac4f463bfb28fbfd&canon=https%3A%2F%2Fwww.k2view.com%2Fwhat-is-synthetic-data-generation%2F&ts=1755961094956&__hstc=114997720.5f46460a11416869ac4f463bfb28fbfd.1755961094840.1755961094840.1755961094840.1&__hssc=114997720.1.1755961094840&__hsfp=1344000785&contentType=standard-page) hbspt.cta._relativeUrls=true;hbspt.cta.load(5983023, \\'2983bef1-8d5a-4838-a90c-570c5bd67b18\\', {\\\\\"useNewLoader\\\\\":\\\\\"true\\\\\",\\\\\"region\\\\\":\\\\\"na1\\\\\"});\\\\n\\\\n![What is Synthetic Data Generation?](https://www.k2view.com/hs-fs/hubfs/sdg%20pp%20images-2.png?width=2000&name=sdg%20pp%20images-2.png)\\\\n\\\\n.module\\\\\\\\_17122326460457 .ppb--image{\\\\n;\\\\n}\\\\n\\\\n[Send me as a pdf](https://www.k2view.com/cs/c/?cta_guid=9852cd13-50c2-4d5c-8fda-b3264d2020bc&signature=AAH58kH0f9yC86JH-w0kpgN1L_hgrxLxMA&portal_id=5983023&pageId=124287438174&placement_guid=908f2969-468a-491b-a15f-7e91fa1fc699&click=7eac2c78-7b81-4b8a-a9c3-fd084eae954b&redirect_url=APefjpHI5BcMg4WhHOuu_3rmk7975zhGYWX0ae0-PGaajo3WpDRjkJgYBzA96XVI_ZhbQQFJ1b7gHnBseS1gIC_G5ADdb076gEtYC8aARgs7WU53xakwk9buXZ_22yHbVyCbCDc8bVatpz_S4eYQaGBZzprC9r8jUHc2ZE0-Rx9QMtXD29ndHYY&hsutk=5f46460a11416869ac4f463bfb28fbfd&canon=https%3A%2F%2Fwww.k2view.com%2Fwhat-is-synthetic-data-generation%2F&ts=1755961095010&__hstc=114997720.5f46460a11416869ac4f463bfb28fbfd.1755961094840.1755961094840.1755961094840.1&__hssc=114997720.1.1755961094840&__hsfp=1344000785&contentType=standard-page) hbspt.cta._relativeUrls=true;hbspt.cta.load(5983023, \\'908f2969-468a-491b-a15f-7e91fa1fc699\\', {\\\\\"useNewLoader\\\\\":\\\\\"true\\\\\",\\\\\"region\\\\\":\\\\\"na1\\\\\"});\\\\n\\\\n### tableicon/Table of Contents\\\\n\\\\n* [01Synthetic data generation accelerates innovation](#Synthetic-data-generation-accelerates-innovation)\\\\n* [02What is Synthetic Data Generation?](#What-is-Synthetic-Data-Generation?)\\\\n* [03Synthetic Data Generation Use Cases](#Synthetic-Data-Generation-Use-Cases)\\\\n* [04Synthetic Data Generation for Software Testing](#Synthetic-Data-Generation-for-Software-Testing)\\\\n* [05Synthetic Data Generation for Training ML Models](#Synthetic-Data-Generation-for-Training-ML-Models)\\\\n* [06Synthetic Data Generation Techniques](#Synthetic-Data-Generation-Techniques)\\\\n* [07Synthetic Data Generation Tools Capabilities](#Synthetic-Data-Generation-Tools-Capabilities)\\\\n* [08Synthetic Data Generation by Business Entities](#Synthetic-Data-Generation-by-Business-Entities)\\\\n* [09Top Synthetic Data Generation Tools](#Top-Synthetic-Data-Generation-Tools)\\\\n* [10The Future of Synthetic Data Generation](#The-Future-of-Synthetic-Data-Generation)\\\\n* [11Summary}\\\\n\\\\nNew! 2025 State of Test Data Management Survey \\\\ud83d\\\\udcca\\\\n\\\\n[Get the Survey Results](https://www.k2view.com/cs/c/?cta_guid=3e2a89be-bd07-4fe1-8689-60bbf7d57b90&signature=AAH58kEjvzV2P-up0O7ri0aKGDsIJKQUxw&portal_id=5983023&pageId=124287438174&placement_guid=0322b825-68bb-4113-a276-914b4c3e3916&click=d5f5a028-092f-4644-83b2-3506f59da30d&redirect_url=APefjpERdSnLp8BfcRaTZKnNeZLEa9YAexk5ZCmrMqT8PsaHava7cZotYSWS-bdP0ntIAKWzLy9-1fVNUEtdXaTg-FXBwnUWoZR4T9fTqOXZEWlNP6ru_cehSZJe-8MZkMBsWisW9K7PX6tBqCxYZqxGMj1OdtR1MA&hsutk=5f46460a11416869ac4f463bfb28fbfd&canon=https%3A%2F%2Fwww.k2view.com%2Fwhat-is-synthetic-data-generation%2F&ts=1755961094895&__hstc=114997720.5f46460a11416869ac4f463bfb28fbfd.1755961094840.1755961094840.1755961094840.1&__hssc=114997720.1.1755961094840&__hsfp=1344000785&contentType=standard-page) hbspt.cta._relativeUrls=true;hbspt.cta.load(5983023, \\'0322b825-68bb-4113-a276-914b4c3e3916\\', {\\\\\"useNewLoader\\\\\":\\\\\"true\\\\\",\\\\\"region\\\\\":\\\\\"na1\\\\\"}); \\\\n![arrow--cta](https://www.k2view.com/hubfs/arrow--cta.svg)\\\\n\\\\n.local-header-top-row {\\\\nbackground:linear-gradient(to bottom, rgba(20, 131, 243, 1), rgba(20, 131, 243, 1));\\\\n}\\\\n.local-header-top-text,\\\\n.local-header-top-text p{\\\\nfont-family: ;\\\\n;\\\\ncolor: ;\\\\nfont-size: px;\\\\n}\\\\n.local-header-top-cta a{\\\\nfont-family: ;\\\\n;\\\\ncolor: ;\\\\nfont-size: px;\\\\n}\\\\nspan.local-close--top--strip:before,\\\\nspan.local-close--top--strip:after{\\\\nbackground:#FFFFFF;\\\\n}\\\\n\\\\n#### Synthetic data generation is the process of creating artificial data that mimics the features, structures, and statistical attributes of production data, while maintaining compliance with data privacy regulations.\\\\n\\\\n[Get Gartner Report](https://www.k2view.com/cs/c/?cta_guid=bf5e46f3-9dfc-4a13-a4f7-36860982a09e&signature=AAH58kG_tkike4iJnwsguUD7Tpxw0G5rnQ&portal_id=5983023&pageId=124287438174&placement_guid=2983bef1-8d5a-4838-a90c-570c5bd67b18&click=18bdef30-e404-4083-8c7a-944447a44345&redirect_url=APefj...\", \"summary_structure\": [\"Key findings and insights\", \"Important statistics and data points\", \"Methodology and approach (if applicable)\", \"Conclusions and recommendations\", \"Source attribution and credibility notes\", \"Context and relevance to research topic\"]}}]}', message_id='c0864a8d-a55c-46ed-9e71-130778652805', name='summarizer_agent', tool_calls=None, agent_calls=None, structured_data=None, images=None, tool_call_id=None)",
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====================================================================================================
AGENT CALL #6: browser_agent (BrowserAgent)
====================================================================================================

Agent: browser_agent
Called by: System
Timestamp: 2025-08-23 16:59:02
Duration: 1.56 seconds

REQUEST:
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====================================================================================================
AGENT CALL #7: browser_agent (BrowserAgent)
====================================================================================================

Agent: browser_agent
Called by: System
Timestamp: 2025-08-23 16:59:05
Duration: 5.60 seconds

REQUEST:
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  "response": "Message(role='assistant', content='{\"next_action\": \"invoke_agent\", \"action_input\": [{\"agent_name\": \"summarizer_agent\", \"request\": {\"title\": \"BARE: Leveraging Base Language Models for Few-Shot Synthetic Data Generation\", \"content\": \"BARE: Leveraging Base Language Models for Few-Shot Synthetic Data Generation\\\\\\\\n============================================================================\\\\\\\\n\\\\\\\\nReport issue for preceding element\\\\\\\\n\\\\\\\\nAlan Zhu\\\\\\\\n  \\\\\\\\nUC Berkeley\\\\\\\\n  \\\\\\\\n&Parth Asawa11footnotemark: 1\\\\\\\\n  \\\\\\\\nUC Berkeley\\\\\\\\n  \\\\\\\\n&Jared Quincy Davis\\\\\\\\n  \\\\\\\\nStanford University & Foundry\\\\\\\\n  \\\\\\\\n&Lingjiao Chen\\\\\\\\n  \\\\\\\\nStanford University\\\\\\\\n  \\\\\\\\n&Boris Hanin\\\\\\\\n  \\\\\\\\nPrinceton University & Foundry\\\\\\\\n  \\\\\\\\n&Ion Stoica\\\\\\\\n  \\\\\\\\nUC Berkeley\\\\\\\\n  \\\\\\\\n&Joseph E. Gonzalez\\\\\\\\n  \\\\\\\\nUC Berkeley\\\\\\\\n  \\\\\\\\n&Matei Zaharia\\\\\\\\n  \\\\\\\\nUC Berkeley\\\\\\\\n  \\\\\\\\nEqual contribution. Correspondence to aczhu@berkeley.edu and pgasawa@berkeley.edu.\\\\\\\\n\\\\\\\\nReport issue for preceding element\\\\\\\\n\\\\\\\\n###### Abstract\\\\\\\\n\\\\\\\\nReport issue for preceding element\\\\\\\\n\\\\\\\\nAs the demand for high-quality data in model training grows, researchers and developers are increasingly generating synthetic data to tune and train LLMs.\\\\\\\\nHowever, current data generation methods rely on seed sets containing tens of thousands of examples to prompt instruction-tuned models.\\\\\\\\nThis reliance can be especially problematic when the curation of high-quality examples is expensive or difficult.\\\\\\\\nIn this paper we explore the novel few-shot synthetic data generation setting \\\\u2013 generating a high-quality dataset from a few examples.\\\\\\\\nWe show that when working with only a few seed examples, instruction-tuned models used in current synthetic data methods produce insufficient diversity for downstream tasks.\\\\\\\\nIn contrast, we show that base models without post-training, largely untapped for synthetic data generation, offer substantially greater output diversity, albeit with lower instruction following abilities.\\\\\\\\nLeveraging this insight, we propose Base-Refine (BARE), a novel two-stage method that combines the diversity of base models with the quality assurance of instruction-tuned models.\\\\\\\\nBARE excels in few-shot synthetic data generation: using only 3 seed examples it generates diverse, high-quality datasets that significantly improve downstream task performance.\\\\\\\\nWe show that fine-tuning Llama 3.1 8B with 1,000 BARE-generated samples achieves performance comparable to state-of-the-art similarly sized models on LiveCodeBench tasks.\\\\\\\\nFurthermore, data generated with BARE enables a 101% improvement for a fine-tuned Llama 3.2 1B on GSM8K over data generated by only instruction-models, and an 18.4% improvement for a fine-tuned Llama 3.1 8B over the state-of-the-art RAFT method for RAG data generation.\\\\\\\\n\\\\\\\\nReport issue for preceding element\\\\\\\\n\\\\\\\\n1 Introduction\\\\\\\\n--------------\\\\\\\\n\\\\\\\\nReport issue for preceding element\\\\\\\\n\\\\\\\\nAs Large Language Models (LLMs) grow in size and capability, the demand for high-quality, diverse data in model training is outpacing human-generated data, necessitating the use of synthetically generated data (Villalobos et\\\\\\\\xa0al., [2024](https://arxiv.org/html/2502.01697v3#bib.bib42)).\\\\\\\\nAlready, it is common to use LLMs to generate synthetic data for a variety of tasks such as math, code, function calling, and general reasoning\\\\\\\\n(Yu et\\\\\\\\xa0al., [2024](https://arxiv.org/html/2502.01697v3#bib.bib46); Guo et\\\\\\\\xa0al., [2024](https://arxiv.org/html/2502.01697v3#bib.bib11); Patil et\\\\\\\\xa0al., [2023](https://arxiv.org/html/2502.01697v3#bib.bib32); Liu et\\\\\\\\xa0al., [2024](https://arxiv.org/html/2502.01697v3#bib.bib25)).\\\\\\\\nThe convenience of data generation has led many dataset creators (Samvelyan et\\\\\\\\xa0al., [2024](https://arxiv.org/html/2502.01697v3#bib.bib37); Zhang et\\\\\\\\xa0al., [2024b](https://arxiv.org/html/2502.01697v3#bib.bib48); NovaSky Team, [2025](https://arxiv.org/html/2502.01697v3#bib.bib27)) and model creators (Dubey et\\\\\\\\xa0al., [2024](https://arxiv.org/html/2502.01697v3#bib.bib7); Qwen, [2025](https://arxiv.org/html/2502.01697v3#bib.bib34); Nvidia, [2024](https://arxiv.org/html/2502.01697v3#bib.bib28); Guan et\\\\\\\\xa0al., [2025](https://arxiv.org/html/2502.01697v3#bib.bib10); NovaSky Team, [2025](https://arxiv.org/html/2502.01697v3#bib.bib27)) to turn to synthetic data sampled from instruction-tuned language models as a replacement for tasks where human-generated data is lacking.\\\\\\\\n\\\\\\\\nReport issue for preceding element\\\\\\\\n\\\\\\\\n![Refer to caption](x1.png)\\\\\\\\n\\\\\\\\n\\\\\\\\nFigure 1: Histogram of pairwise embedding cosine similarity scores for 1000 Llama-3.1-70B-Base vs Instruct generations of grade school math problems with 3 seed examples. The base distribution is further left, indicating lower similarity and hence higher diversity.\\\\\\\\n\\\\\\\\nReport issue for preceding element\\\\\\\\n\\\\\\\\n![Refer to caption](x2.png)\\\\\\\\n\\\\\\\\n\\\\\\\\nFigure 2: Accuracy of a Llama-3.2-1B-Instruct model fine-tuned on real-world data and 5 different sets of math problems synthetically generated using 3 seed examples, evaluated on a randomly selected n=500\\\\u2061n=500n=500italic\\\\\\\\_n = 500 subset of GSM8K. Training with BARE-generated data outperforms all other data sources.\\\\\\\\n\\\\\\\\nReport issue for preceding element\\\\\\\\n\\\\\\\\nReport issue for preceding element\\\\\\\\n\\\\\\\\nSynthetic data is most valuable when it is both high quality and diverse (Chen et\\\\\\\\xa0al., [2024](https://arxiv.org/html/2502.01697v3#bib.bib2); Ravent\\\\u00f3s et\\\\\\\\xa0al., [2023](https://arxiv.org/html/2502.01697v3#bib.bib35)). 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================================================================================
AGENT: summarizer_agent | CALL #3 | CALLED BY: System
================================================================================

Timestamp: 2025-08-23 16:59:11
Duration: 7.31 seconds

{
  "title": "BARE: Leveraging Base Language Models for Few-Shot Synthetic Data Generation",
  "content": "BARE: Leveraging Base Language Models for Few-Shot Synthetic Data Generation\\n============================================================================\\n\\nReport issue for preceding element\\n\\nAlan Zhu\\n  \\nUC Berkeley\\n  \\n&Parth Asawa11footnotemark: 1\\n  \\nUC Berkeley\\n  \\n&Jared Quincy Davis\\n  \\nStanford University & Foundry\\n  \\n&Lingjiao Chen\\n  \\nStanford University\\n  \\n&Boris Hanin\\n  \\nPrinceton University & Foundry\\n  \\n&Ion Stoica\\n  \\nUC Berkeley\\n  \\n&Joseph E. Gonzalez\\n  \\nUC Berkeley\\n  \\n&Matei Zaharia\\n  \\nUC Berkeley\\n  \\nEqual contribution. Correspondence to aczhu@berkeley.edu and pgasawa@berkeley.edu.\\n\\nReport issue for preceding element\\n\\n###### Abstract\\n\\nReport issue for preceding element\\n\\nAs the demand for high-quality data in model training grows, researchers and developers are increasingly generating synthetic data to tune and train LLMs.\\nHowever, current data generation methods rely on seed sets containing tens of thousands of examples to prompt instruction-tuned models.\\nThis reliance can be especially problematic when the curation of high-quality examples is expensive or difficult.\\nIn this paper we explore the novel few-shot synthetic data generation setting \u2013 generating a high-quality dataset from a few examples.\\nWe show that when working with only a few seed examples, instruction-tuned models used in current synthetic data methods produce insufficient diversity for downstream tasks.\\nIn contrast, we show that base models without post-training, largely untapped for synthetic data generation, offer substantially greater output diversity, albeit with lower instruction following abilities.\\nLeveraging this insight, we propose Base-Refine (BARE), a novel two-stage method that combines the diversity of base models with the quality assurance of instruction-tuned models.\\nBARE excels in few-shot synthetic data generation: using only 3 seed examples it generates diverse, high-quality datasets that significantly improve downstream task performance.\\nWe show that fine-tuning Llama 3.1 8B with 1,000 BARE-generated samples achieves performance comparable to state-of-the-art similarly sized models on LiveCodeBench tasks.\\nFurthermore, data generated with BARE enables a 101% improvement for a fine-tuned Llama 3.2 1B on GSM8K over data generated by only instruction-models, and an 18.4% improvement for a fine-tuned Llama 3.1 8B over the state-of-the-art RAFT method for RAG data generation.\\n\\nReport issue for preceding element\\n\\n1 Introduction\\n--------------\\n\\nReport issue for preceding element\\n\\nAs Large Language Models (LLMs) grow in size and capability, the demand for high-quality, diverse data in model training is outpacing human-generated data, necessitating the use of synthetically generated data (Villalobos et\\xa0al., [2024](https://arxiv.org/html/2502.01697v3#bib.bib42)).\\nAlready, it is common to use LLMs to generate synthetic data for a variety of tasks such as math, code, function calling, and general reasoning\\n(Yu et\\xa0al., [2024](https://arxiv.org/html/2502.01697v3#bib.bib46); Guo et\\xa0al., [2024](https://arxiv.org/html/2502.01697v3#bib.bib11); Patil et\\xa0al., [2023](https://arxiv.org/html/2502.01697v3#bib.bib32); Liu et\\xa0al., [2024](https://arxiv.org/html/2502.01697v3#bib.bib25)).\\nThe convenience of data generation has led many dataset creators (Samvelyan et\\xa0al., [2024](https://arxiv.org/html/2502.01697v3#bib.bib37); Zhang et\\xa0al., [2024b](https://arxiv.org/html/2502.01697v3#bib.bib48); NovaSky Team, [2025](https://arxiv.org/html/2502.01697v3#bib.bib27)) and model creators (Dubey et\\xa0al., [2024](https://arxiv.org/html/2502.01697v3#bib.bib7); Qwen, [2025](https://arxiv.org/html/2502.01697v3#bib.bib34); Nvidia, [2024](https://arxiv.org/html/2502.01697v3#bib.bib28); Guan et\\xa0al., [2025](https://arxiv.org/html/2502.01697v3#bib.bib10); NovaSky Team, [2025](https://arxiv.org/html/2502.01697v3#bib.bib27)) to turn to synthetic data sampled from instruction-tuned language models as a replacement for tasks where human-generated data is lacking.\\n\\nReport issue for preceding element\\n\\n![Refer to caption](x1.png)\\n\\n\\nFigure 1: Histogram of pairwise embedding cosine similarity scores for 1000 Llama-3.1-70B-Base vs Instruct generations of grade school math problems with 3 seed examples. The base distribution is further left, indicating lower similarity and hence higher diversity.\\n\\nReport issue for preceding element\\n\\n![Refer to caption](x2.png)\\n\\n\\nFigure 2: Accuracy of a Llama-3.2-1B-Instruct model fine-tuned on real-world data and 5 different sets of math problems synthetically generated using 3 seed examples, evaluated on a randomly selected n=500\u2061n=500n=500italic\\_n = 500 subset of GSM8K. Training with BARE-generated data outperforms all other data sources.\\n\\nReport issue for preceding element\\n\\nReport issue for preceding element\\n\\nSynthetic data is most valuable when it is both high quality and diverse (Chen et\\xa0al., [2024](https://arxiv.org/html/2502.01697v3#bib.bib2); Ravent\u00f3s et\\xa0al., [2023](https://arxiv.org/html/2502.01697v3#bib.bib35)). However, while inst...",
  "url": "https://arxiv.org/html/2502.01697v3"
}

RESPONSE:
{
  "response": "Message(role='assistant', content='{\"next_action\": \"invoke_agent\", \"action_input\": [{\"agent_name\": \"synthesizer_agent\", \"request\": {\"text_content\": \"### What is Synthetic Data Generation? A Practical Guide\\\\n\\\\n**Key findings and insights:**\\\\n- Synthetic data generation is defined as the process of creating artificial data that mimics the features, structures, and statistical attributes of production data, while maintaining compliance with data privacy regulations.\\\\n- The provided content serves as an introduction to the topic and a table of contents for a larger guide, suggesting further details are available in external reports (e.g., Gartner Report, 2025 State of Test Data Management Survey).\\\\n\\\\n**Important statistics and data points:**\\\\n- No specific statistics or data points are available within the provided text content.\\\\n\\\\n**Methodology and approach:**\\\\n- The content describes the concept of synthetic data generation but does not detail a specific methodology or approach within the provided text.\\\\n\\\\n**Conclusions and recommendations:**\\\\n- The document implies that synthetic data generation accelerates innovation, and highlights its use cases for software testing and training ML models, and mentions various techniques and tools.\\\\n\\\\n**Source attribution and credibility notes:**\\\\n- Source: k2view.com\\\\n- Last updated: March 23, 2025\\\\n- The article refers to external resources like a Gartner Report and a \\'2025 State of Test Data Management Survey\\' for more detailed information.\\\\n\\\\n**Context and relevance to research topic:**\\\\n- This content provides a foundational understanding of synthetic data generation, emphasizing its role in modern data practices, particularly concerning data privacy compliance.\\\\n\\\\n### BARE: Leveraging Base Language Models for Few-Shot Synthetic Data Generation\\\\n\\\\n**Key findings and insights:**\\\\n- Current synthetic data generation methods for LLMs, relying on instruction-tuned models, require tens of thousands of seed examples and produce insufficient diversity in few-shot settings.\\\\n- Base language models (without post-training) offer substantially greater output diversity, though with lower instruction following abilities.\\\\n- The BARE (Base-Refine) method effectively combines the diversity of base models with the quality assurance of instruction-tuned models for few-shot synthetic data generation.\\\\n\\\\n**Important statistics and data points:**\\\\n- BARE generates high-quality datasets using only 3 seed examples.\\\\n- Fine-tuning Llama 3.1 8B with 1,000 BARE-generated samples achieved performance comparable to state-of-the-art similarly sized models on LiveCodeBench tasks.\\\\n- Data generated with BARE enabled a 101% improvement for a fine-tuned Llama 3.2 1B on GSM8K over data generated by only instruction-tuned models.\\\\n- BARE showed an 18.4% improvement for a fine-tuned Llama 3.1 8B over the state-of-the-art RAFT method for RAG data generation.\\\\n- Figure 1 illustrates that base model generations have lower pairwise embedding cosine similarity, indicating higher diversity compared to instruction-tuned models.\\\\n- Figure 2 shows that training with BARE-generated data outperforms other data sources for a Llama-3.2-1B-Instruct model on GSM8K.\\\\n\\\\n**Methodology and approach:**\\\\n- **BARE (Base-Refine)**: A novel two-stage method.\\\\n  1. **Base Model Generation**: Utilizes base models to generate diverse synthetic data due to their untamed output capabilities.\\\\n  2. **Refinement with Instruction-Tuned Models**: Applies instruction-tuned models for quality assurance and to ensure adherence to specific instructions.\\\\n\\\\n**Conclusions and recommendations:**\\\\n- BARE is an effective approach for few-shot synthetic data generation, addressing the limitations of current methods that struggle with diversity from limited seed examples.\\\\n- Leveraging base models for diversity and instruction-tuned models for quality significantly improves downstream task performance for LLMs.\\\\n\\\\n**Source attribution and credibility notes:**\\\\n- Authors: Alan Zhu (UC Berkeley), Parth Asawa (UC Berkeley), Jared Quincy Davis (Stanford University & Foundry), Lingjiao Chen (Stanford University), Boris Hanin (Princeton University & Foundry), Ion Stoica (UC Berkeley), Joseph E. Gonzalez (UC Berkeley), Matei Zaharia (UC Berkeley).\\\\n- Affiliations: UC Berkeley, Stanford University, Princeton University, Foundry.\\\\n- This is a research paper published on arXiv, indicating it has undergone academic peer review (or is awaiting it).\\\\n\\\\n**Context and relevance to research topic:**\\\\n- Addresses the critical need for high-quality, diverse data for training and tuning Large Language Models (LLMs), especially when human-generated data is scarce or expensive to curate. It specifically tackles the challenging few-shot data generation scenario, which is crucial for efficient model development.\"}}]}', message_id='9cf1ccad-91e9-4e2f-8fad-10f5fe9dfd79', name='summarizer_agent', tool_calls=None, agent_calls=None, structured_data=None, images=None, tool_call_id=None)",
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====================================================================================================
AGENT CALL #8: browser_agent (BrowserAgent)
====================================================================================================

Agent: browser_agent
Called by: System
Timestamp: 2025-08-23 16:59:18
Duration: 1.64 seconds

REQUEST:
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====================================================================================================
AGENT CALL #9: browser_agent (BrowserAgent)
====================================================================================================

Agent: browser_agent
Called by: System
Timestamp: 2025-08-23 16:59:21
Duration: 1.58 seconds

REQUEST:
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  "response": "Message(role='assistant', content='{\"next_action\": \"invoke_agent\", \"action_input\": [{\"agent_name\": \"summarizer_agent\", \"request\": {\"title\": \"Content Extraction Failed\", \"content\": \"I was unable to extract content from the URL: https://ieeexplore.ieee.org/document/11006780/. The website returned a \\\\\"Request Rejected\\\\\" error, indicating that access to the content was denied. Therefore, I cannot provide a summary for this article.\", \"url\": \"https://ieeexplore.ieee.org/document/11006780/\"}}]}', message_id='82bc81b9-cca5-4892-981e-137e4dad54ee', name='browser_agent', tool_calls=None, agent_calls=None, structured_data=None, images=None, tool_call_id=None)",
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================================================================================
AGENT: summarizer_agent | CALL #4 | CALLED BY: System
================================================================================

Timestamp: 2025-08-23 16:59:22
Duration: 8.87 seconds

{
  "title": "Content Extraction Failed",
  "content": "I was unable to extract content from the URL: https://ieeexplore.ieee.org/document/11006780/. The website returned a \"Request Rejected\" error, indicating that access to the content was denied. Therefore, I cannot provide a summary for this article.",
  "url": "https://ieeexplore.ieee.org/document/11006780/"
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RESPONSE:
{
  "response": "Message(role='assistant', content='{\"thought\": \"The provided content indicates that the extraction from the given URL failed. My responsibility is to create concise summaries of high-quality research content. Since no research content could be extracted, I cannot fulfill the primary summarization task for this specific URL. However, the system requires me to always respond with an \\'invoke_agent\\' action. I will therefore pass the error message content, along with the source URL, to the `synthesizer_agent`, instructing it to summarize the fact that content extraction failed, adhering to the specified summary structure.\", \"next_action\": \"invoke_agent\", \"action_input\": [{\"agent_name\": \"synthesizer_agent\", \"request\": {\"text_content\": \"I was unable to extract content from the URL: https://ieeexplore.ieee.org/document/11006780/. The website returned a \\\\\"Request Rejected\\\\\" error, indicating that access to the content was denied. Therefore, I cannot provide a summary for this article.\", \"summary_structure\": [\"Key findings and insights\", \"Important statistics and data points\", \"Methodology and approach (if applicable)\", \"Conclusions and recommendations\", \"Source attribution and credibility notes\", \"Context and relevance to research topic\"], \"source_url\": \"https://ieeexplore.ieee.org/document/11006780/\"}}]}', message_id='3bd7f000-218b-4ed3-8337-6ab774ad1faa', name='summarizer_agent', tool_calls=None, agent_calls=None, structured_data=None, images=None, tool_call_id=None)",
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}

====================================================================================================
AGENT CALL #10: browser_agent (BrowserAgent)
====================================================================================================

Agent: browser_agent
Called by: System
Timestamp: 2025-08-23 16:59:31
Duration: 1.63 seconds

REQUEST:
--------------------------------------------------
{
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}

RESPONSE:
--------------------------------------------------
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  "context_selection": null
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====================================================================================================
AGENT CALL #11: browser_agent (BrowserAgent)
====================================================================================================

Agent: browser_agent
Called by: System
Timestamp: 2025-08-23 16:59:36
Duration: 7.05 seconds

REQUEST:
--------------------------------------------------
"None"

RESPONSE:
--------------------------------------------------
{
  "response": "Message(role='assistant', content='{\"next_action\": \"invoke_agent\", \"action_input\": [{\"agent_name\": \"summarizer_agent\", \"request\": {\"title\": \"Synthetic data as external control arms in scarce single-arm clinical trials | PLOS Digital Health\", \"content\": \"* 0\\\\n\\\\n  [Save](/digitalhealth/article/metrics?id=10.1371/journal.pdig.0000581#savedHeader)\\\\n\\\\n  [Total Mendeley and Citeulike bookmarks.](/digitalhealth/article/metrics?id=10.1371/journal.pdig.0000581#savedHeader)\\\\n* 0\\\\n\\\\n  [Citation](/digitalhealth/article/metrics?id=10.1371/journal.pdig.0000581#citedHeader)\\\\n\\\\n  [Paper\\\\\\\\\\'s citation count computed by Dimensions.](/digitalhealth/article/metrics?id=10.1371/journal.pdig.0000581#citedHeader)\\\\n* 1,493\\\\n\\\\n  [View](/digitalhealth/article/metrics?id=10.1371/journal.pdig.0000581#viewedHeader)\\\\n\\\\n  [PLOS views and downloads.](/digitalhealth/article/metrics?id=10.1371/journal.pdig.0000581#viewedHeader)\\\\n* 0\\\\n\\\\n  [Share](/digitalhealth/article/metrics?id=10.1371/journal.pdig.0000581#discussedHeader)\\\\n\\\\n  [Sum of Facebook, Twitter, Reddit and Wikipedia activity.](/digitalhealth/article/metrics?id=10.1371/journal.pdig.0000581#discussedHeader)\\\\n\\\\n<li id=\\\\\"metricsError\\\\\">Article metrics are unavailable at this time. Please try again later.</li>\\\\n\\\\n<li></li><li></li><li id=\\\\\"tooSoon\\\\\">Article metrics are unavailable for recently published articles.</li>\\\\n\\\\n<li id=\\\\\"almSaves\\\\\">\\\\n<%= s.numberFormat(saveCount, 0) %>\\\\n<div class=\\\\\"tools\\\\\" data-js-tooltip-hover=\\\\\"trigger\\\\\">\\\\n<a class=\\\\\"metric-term\\\\\" href=\\\\\"/digitalhealth/article/metrics?id=10.1371/journal.pdig.0000581#savedHeader\\\\\">Save</a>\\\\n<p class=\\\\\"saves-tip\\\\\" data-js-tooltip-hover=\\\\\"target\\\\\"><a href=\\\\\"/digitalhealth/article/metrics?id=10.1371/journal.pdig.0000581#savedHeader\\\\\">Total Mendeley and Citeulike bookmarks.</a></p>\\\\n</div>\\\\n</li>\\\\n<li id=\\\\\"almCitations\\\\\">\\\\n<%= s.numberFormat(citationCount, 0) %>\\\\n<div class=\\\\\"tools\\\\\" data-js-tooltip-hover=\\\\\"trigger\\\\\">\\\\n<a class=\\\\\"metric-term\\\\\" href=\\\\\"/digitalhealth/article/metrics?id=10.1371/journal.pdig.0000581#citedHeader\\\\\">Citation</a>\\\\n<p class=\\\\\"citations-tip\\\\\" data-js-tooltip-hover=\\\\\"target\\\\\"><a href=\\\\\"/digitalhealth/article/metrics?id=10.1371/journal.pdig.0000581#citedHeader\\\\\">Paper\\\\\\\\\\'s citation count computed by Dimensions.</a></p>\\\\n</div>\\\\n</li>\\\\n<li id=\\\\\"almViews\\\\\">\\\\n<%= s.numberFormat(viewCount, 0) %>\\\\n<div class=\\\\\"tools\\\\\" data-js-tooltip-hover=\\\\\"trigger\\\\\">\\\\n<a class=\\\\\"metric-term\\\\\" href=\\\\\"/digitalhealth/article/metrics?id=10.1371/journal.pdig.0000581#viewedHeader\\\\\">View</a>\\\\n<p class=\\\\\"views-tip\\\\\" data-js-tooltip-hover=\\\\\"target\\\\\"><a href=\\\\\"/digitalhealth/article/metrics?id=10.1371/journal.pdig.0000581#viewedHeader\\\\\">PLOS views and downloads.</a></p>\\\\n</div>\\\\n</li>\\\\n<li id=\\\\\"almShares\\\\\">\\\\n<%= s.numberFormat(shareCount, 0) %>\\\\n<div class=\\\\\"tools\\\\\" data-js-tooltip-hover=\\\\\"trigger\\\\\">\\\\n<a class=\\\\\"metric-term\\\\\" href=\\\\\"/digitalhealth/article/metrics?id=10.1371/journal.pdig.0000581#discussedHeader\\\\\">Share</a>\\\\n<p class=\\\\\"shares-tip\\\\\" data-js-tooltip-hover=\\\\\"target\\\\\"><a href=\\\\\"/digitalhealth/article/metrics?id=10.1371/journal.pdig.0000581#discussedHeader\\\\\">Sum of Facebook, Twitter, Reddit and Wikipedia activity.</a></p>\\\\n</div>\\\\n</li>\\\\n\\\\nOpen Access\\\\n\\\\nPeer-reviewed\\\\n\\\\nResearch Article\\\\n\\\\nSynthetic data as external control arms in scarce single-arm clinical trials\\\\n============================================================================\\\\n\\\\n* Severin Elvatun,\\\\n\\\\n  Roles\\\\n  Methodology,\\\\n  Software,\\\\n  Validation,\\\\n  Writing \\\\u2013 original draft,\\\\n  Writing \\\\u2013 review & editing\\\\n\\\\n  Affiliation\\\\n  Cancer Registry of Norway, Norwegian Institute of Public health, Ullernchausseen 64, 0379 Oslo, Norway\\\\n\\\\n  [![ORCID logo](/resource/img/orcid_16x16.png)\\\\n  https://orcid.org/0000-0001-7787-5663](https://orcid.org/0000-0001-7787-5663 \\\\\"ORCID Registry\\\\\")\\\\n\\\\n  \\\\u2a2f\\\\n* Daan Knoors,\\\\n\\\\n  Roles\\\\n  Methodology,\\\\n  Software,\\\\n  Validation,\\\\n  Writing \\\\u2013 review & editing\\\\n\\\\n  Affiliation\\\\n  Cancer Registry of Norway, Norwegian Institute of Public health, Ullernchausseen 64, 0379 Oslo, Norway\\\\n\\\\n  \\\\u2a2f\\\\n* Simon Brant,\\\\n\\\\n  Roles\\\\n  Conceptualization,\\\\n  Data curation,\\\\n  Validation,\\\\n  Writing \\\\u2013 review & editing\\\\n\\\\n  Affiliation\\\\n  NordicRWE, Universitetsgata 2, 0164 Oslo, Norway\\\\n\\\\n  [![ORCID logo](/resource/img/orcid_16x16.png)\\\\n  https://orcid.org/0000-0003-2685-6010](https://orcid.org/0000-0003-2685-6010 \\\\\"ORCID Registry\\\\\")\\\\n\\\\n  \\\\u2a2f\\\\n* Christian Jonasson,\\\\n\\\\n  Roles\\\\n  Funding acquisition,\\\\n  Resources,\\\\n  Supervision,\\\\n  Writing \\\\u2013 review & editing\\\\n\\\\n  Affiliation\\\\n  NordicRWE, Universitetsgata 2, 0164 Oslo, Norway\\\\n\\\\n  [![ORCID logo](/resource/img/orcid_16x16.png)\\\\n  https://orcid.org/0000-0002-7694-6025](https://orcid.org/0000-0002-7694-6025 \\\\\"ORCID Registry\\\\\")\\\\n\\\\n  \\\\u2a2f\\\\n* Jan F. Nyg\\\\u00e5rd\\\\n\\\\n  Roles\\\\n  Funding acquisition,\\\\n  Project administration,\\\\n  Supervision,\\\\n  Writing \\\\u2013 review & editing\\\\n\\\\n  * E-mail: [jfn@kreftregisteret.no](mailto:jfn@kreftregisteret.no)\\\\n\\\\n  Affiliation\\\\n  Cancer Registry of Norway, Norwegian Institute of Public health, Ullernchausseen 64, 0379 Oslo, Norway\\\\n\\\\n  [![ORCID logo](/resource/img/orcid_16x16.png)\\\\n  https://orcid.org/0000-0001-9655-7003](https://orcid.org/0000-0001-9655-7003 \\\\\"ORCID Registry\\\\\")\\\\n\\\\n  \\\\u2a2f\\\\n\\\\nSynthetic data as external control arms in scarce single-arm clinical trials\\\\n============================================================================\\\\n\\\\n* Severin Elvatun,\\\\n* Daan Knoors,\\\\n* Simon Brant,\\\\n* Christian Jonasson,\\\\n* Jan F. Nyg\\\\u00e5rd\\\\n\\\\n![PLOS](/resource/img/logo-plos-ful...\", \"url\": \"https://journals.plos.org/digitalhealth/article?id=10.1371/journal.pdig.0000581\"}}]}', message_id='0701f3b1-9037-4554-bb8d-6dfbe7dcc211', name='browser_agent', tool_calls=None, agent_calls=None, structured_data=None, images=None, tool_call_id=None)",
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================================================================================
AGENT: summarizer_agent | CALL #5 | CALLED BY: System
================================================================================

Timestamp: 2025-08-23 16:59:43
Duration: 4.73 seconds

{
  "title": "Synthetic data as external control arms in scarce single-arm clinical trials | PLOS Digital Health",
  "content": "* 0\n\n  [Save](/digitalhealth/article/metrics?id=10.1371/journal.pdig.0000581#savedHeader)\n\n  [Total Mendeley and Citeulike bookmarks.](/digitalhealth/article/metrics?id=10.1371/journal.pdig.0000581#savedHeader)\n* 0\n\n  [Citation](/digitalhealth/article/metrics?id=10.1371/journal.pdig.0000581#citedHeader)\n\n  [Paper\\'s citation count computed by Dimensions.](/digitalhealth/article/metrics?id=10.1371/journal.pdig.0000581#citedHeader)\n* 1,493\n\n  [View](/digitalhealth/article/metrics?id=10.1371/journal.pdig.0000581#viewedHeader)\n\n  [PLOS views and downloads.](/digitalhealth/article/metrics?id=10.1371/journal.pdig.0000581#viewedHeader)\n* 0\n\n  [Share](/digitalhealth/article/metrics?id=10.1371/journal.pdig.0000581#discussedHeader)\n\n  [Sum of Facebook, Twitter, Reddit and Wikipedia activity.](/digitalhealth/article/metrics?id=10.1371/journal.pdig.0000581#discussedHeader)\n\n<li id=\"metricsError\">Article metrics are unavailable at this time. Please try again later.</li>\n\n<li></li><li></li><li id=\"tooSoon\">Article metrics are unavailable for recently published articles.</li>\n\n<li id=\"almSaves\">\n<%= s.numberFormat(saveCount, 0) %>\n<div class=\"tools\" data-js-tooltip-hover=\"trigger\">\n<a class=\"metric-term\" href=\"/digitalhealth/article/metrics?id=10.1371/journal.pdig.0000581#savedHeader\">Save</a>\n<p class=\"saves-tip\" data-js-tooltip-hover=\"target\"><a href=\"/digitalhealth/article/metrics?id=10.1371/journal.pdig.0000581#savedHeader\">Total Mendeley and Citeulike bookmarks.</a></p>\n</div>\n</li>\n<li id=\"almCitations\">\n<%= s.numberFormat(citationCount, 0) %>\n<div class=\"tools\" data-js-tooltip-hover=\"trigger\">\n<a class=\"metric-term\" href=\"/digitalhealth/article/metrics?id=10.1371/journal.pdig.0000581#citedHeader\">Citation</a>\n<p class=\"citations-tip\" data-js-tooltip-hover=\"target\"><a href=\"/digitalhealth/article/metrics?id=10.1371/journal.pdig.0000581#citedHeader\">Paper\\'s citation count computed by Dimensions.</a></p>\n</div>\n</li>\n<li id=\"almViews\">\n<%= s.numberFormat(viewCount, 0) %>\n<div class=\"tools\" data-js-tooltip-hover=\"trigger\">\n<a class=\"metric-term\" href=\"/digitalhealth/article/metrics?id=10.1371/journal.pdig.0000581#viewedHeader\">View</a>\n<p class=\"views-tip\" data-js-tooltip-hover=\"target\"><a href=\"/digitalhealth/article/metrics?id=10.1371/journal.pdig.0000581#viewedHeader\">PLOS views and downloads.</a></p>\n</div>\n</li>\n<li id=\"almShares\">\n<%= s.numberFormat(shareCount, 0) %>\n<div class=\"tools\" data-js-tooltip-hover=\"trigger\">\n<a class=\"metric-term\" href=\"/digitalhealth/article/metrics?id=10.1371/journal.pdig.0000581#discussedHeader\">Share</a>\n<p class=\"shares-tip\" data-js-tooltip-hover=\"target\"><a href=\"/digitalhealth/article/metrics?id=10.1371/journal.pdig.0000581#discussedHeader\">Sum of Facebook, Twitter, Reddit and Wikipedia activity.</a></p>\n</div>\n</li>\n\nOpen Access\n\nPeer-reviewed\n\nResearch Article\n\nSynthetic data as external control arms in scarce single-arm clinical trials\n============================================================================\n\n* Severin Elvatun,\n\n  Roles\n  Methodology,\n  Software,\n  Validation,\n  Writing \u2013 original draft,\n  Writing \u2013 review & editing\n\n  Affiliation\n  Cancer Registry of Norway, Norwegian Institute of Public health, Ullernchausseen 64, 0379 Oslo, Norway\n\n  [![ORCID logo](/resource/img/orcid_16x16.png)\n  https://orcid.org/0000-0001-7787-5663](https://orcid.org/0000-0001-7787-5663 \"ORCID Registry\")\n\n  \u2a2f\n* Daan Knoors,\n\n  Roles\n  Methodology,\n  Software,\n  Validation,\n  Writing \u2013 review & editing\n\n  Affiliation\n  Cancer Registry of Norway, Norwegian Institute of Public health, Ullernchausseen 64, 0379 Oslo, Norway\n\n  \u2a2f\n* Simon Brant,\n\n  Roles\n  Conceptualization,\n  Data curation,\n  Validation,\n  Writing \u2013 review & editing\n\n  Affiliation\n  NordicRWE, Universitetsgata 2, 0164 Oslo, Norway\n\n  [![ORCID logo](/resource/img/orcid_16x16.png)\n  https://orcid.org/0000-0003-2685-6010](https://orcid.org/0000-0003-2685-6010 \"ORCID Registry\")\n\n  \u2a2f\n* Christian Jonasson,\n\n  Roles\n  Funding acquisition,\n  Resources,\n  Supervision,\n  Writing \u2013 review & editing\n\n  Affiliation\n  NordicRWE, Universitetsgata 2, 0164 Oslo, Norway\n\n  [![ORCID logo](/resource/img/orcid_16x16.png)\n  https://orcid.org/0000-0002-7694-6025](https://orcid.org/0000-0002-7694-6025 \"ORCID Registry\")\n\n  \u2a2f\n* Jan F. Nyg\u00e5rd\n\n  Roles\n  Funding acquisition,\n  Project administration,\n  Supervision,\n  Writing \u2013 review & editing\n\n  * E-mail: [jfn@kreftregisteret.no](mailto:jfn@kreftregisteret.no)\n\n  Affiliation\n  Cancer Registry of Norway, Norwegian Institute of Public health, Ullernchausseen 64, 0379 Oslo, Norway\n\n  [![ORCID logo](/resource/img/orcid_16x16.png)\n  https://orcid.org/0000-0001-9655-7003](https://orcid.org/0000-0001-9655-7003 \"ORCID Registry\")\n\n  \u2a2f\n\nSynthetic data as external control arms in scarce single-arm clinical trials\n============================================================================\n\n* Severin Elvatun,\n* Daan Knoors,\n* Simon Brant,\n* Christian Jonasson,\n* Jan F. Nyg\u00e5rd\n\n![PLOS](/resource/img/logo-plos-ful...",
  "url": "https://journals.plos.org/digitalhealth/article?id=10.1371/journal.pdig.0000581"
}

RESPONSE:
{
  "response": "Message(role='assistant', content='{\"next_action\": \"invoke_agent\", \"action_input\": [{\"agent_name\": \"synthesizer_agent\", \"request\": {\"text_content\": \"The provided content is an incomplete extract of an article titled \\\\\"Synthetic data as external control arms in scarce single-arm clinical trials\\\\\" from PLOS Digital Health. While the full research content (abstract, methodology, findings, conclusions) is not available in this extract, it does provide the article\\'s title, authors, their affiliations, and mentions that it is a peer-reviewed research article.\\\\n\\\\n**Key findings and insights:**\\\\n- The article\\'s main topic is the use of synthetic data as external control arms, specifically in single-arm clinical trials where real control groups are scarce.\\\\n\\\\n**Important statistics and data points:**\\\\n- The article has 1,493 views and downloads (PLOS views and downloads).\\\\n- Other metrics (Mendeley/Citeulike bookmarks, citation count, social shares) are listed as 0 or unavailable.\\\\n\\\\n**Methodology and approach (if applicable):**\\\\n- Not available in the provided content. The authors\\' roles include Methodology, Software, Validation, suggesting that the full article would detail these aspects.\\\\n\\\\n**Conclusions and recommendations:**\\\\n- Not available in the provided content.\\\\n\\\\n**Source attribution and credibility notes:**\\\\n- Source: PLOS Digital Health (Open Access, Peer-reviewed Research Article).\\\\n- Authors: Severin Elvatun, Daan Knoors (Cancer Registry of Norway, Norwegian Institute of Public health); Simon Brant, Christian Jonasson (NordicRWE); Jan F. Nyg\\\\u00e5rd (Cancer Registry of Norway, Norwegian Institute of Public health).\\\\n- ORCID IDs are provided for some authors, indicating academic transparency.\\\\n\\\\n**Context and relevance to research topic:**\\\\n- This article is relevant to the application of synthetic data in clinical research, particularly addressing challenges in trial design for rare diseases or conditions where traditional control arms are difficult to establish. It highlights the growing importance of digital health solutions in clinical trials.\", \"summary_structure\": [\"Key findings and insights\", \"Important statistics and data points\", \"Methodology and approach (if applicable)\", \"Conclusions and recommendations\", \"Source attribution and credibility notes\", \"Context and relevance to research topic\"], \"source_url\": \"https://journals.plos.org/digitalhealth/article?id=10.1371/journal.pdig.0000581\"}}]}', message_id='ce21666a-0cb2-43ea-92ef-d2c2725f80b7', name='summarizer_agent', tool_calls=None, agent_calls=None, structured_data=None, images=None, tool_call_id=None)",
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}

================================================================================
AGENT: synthesizer_agent | CALL #1 | CALLED BY: System
================================================================================

Timestamp: 2025-08-23 16:59:48
Duration: 14.04 seconds

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      "content": "{\"next_action\": \"invoke_agent\", \"action_input\": [{\"agent_name\": \"synthesizer_agent\", \"request\": {\"text_content\": \"#### A practical guide to synthetic data generation tools\\n\\nWhat is Synthetic Data Generation?\\n==================================\\n\\nLast updated on March 23, 2025\\n\\n[Get Gartner Report](https://www.k2view.com/cs/c/?cta_guid=bf5e46f3-9dfc-4a13-a4f7-36860982a09e&signature=AAH58kG_tkike4iJnwsguUD7Tpxw0G5rnQ&portal_id=5983023&pageId=124287438174&placement_guid=2983bef1-8d5a-4838-a90c-570c5bd67b18&click=18bdef30-e404-4083-8c7a-944447a44345&redirect_url=APefjpEuZGy7J-QSQylxyKppTIRpqs_bL8tsNsxKZRtcC2G1YJVo13LQtJJjWgvVwEZFGawDo8WcBPkpDyKOu5E4H7ZUxTI2GWLPXpfQHJZU31cX2mcl9yihYTkpctCNFOLt_AHXXbVLDH9L1ETp9COF2f37iaoQYQw&hsutk=5f46460a11416869ac4f463bfb28fbfd&canon=https%3A%2F%2Fwww.k2view.com%2Fwhat-is-synthetic-data-generation%2F&ts=1755961094956&__hstc=114997720.5f46460a11416869ac4f463bfb28fbfd.1755961094840.1755961094840.1755961094840.1&__hssc=114997720.1.1755961094840&__hsfp=1344000785&contentType=standard-page) hbspt.cta._relativeUrls=true;hbspt.cta.load(5983023, '2983bef1-8d5a-4838-a90c-570c5bd67b18', {\\\"useNewLoader\\\":\\\"true\\\",\\\"region\\\":\\\"na1\\\"});\\n\\n![What is Synthetic Data Generation?](https://www.k2view.com/hs-fs/hubfs/sdg%20pp%20images-2.png?width=2000&name=sdg%20pp%20images-2.png)\\n\\n.module\\\\_17122326460457 .ppb--image{\\n;\\n}\\n\\n[Send me as a pdf](https://www.k2view.com/cs/c/?cta_guid=9852cd13-50c2-4d5c-8fda-b3264d2020bc&signature=AAH58kH0f9yC86JH-w0kpgN1L_hgrxLxMA&portal_id=5983023&pageId=124287438174&placement_guid=908f2969-468a-491b-a15f-7e91fa1fc699&click=7eac2c78-7b81-4b8a-a9c3-fd084eae954b&redirect_url=APefjpHI5BcMg4WhHOuu_3rmk7975zhGYWX0ae0-PGaajo3WpDRjkJgYBzA96XVI_ZhbQQFJ1b7gHnBseS1gIC_G5ADdb076gEtYC8aARgs7WU53xakwk9buXZ_22yHbVyCbCDc8bVatpz_S4eYQaGBZzprC9r8jUHc2ZE0-Rx9QMtXD29ndHYY&hsutk=5f46460a11416869ac4f463bfb28fbfd&canon=https%3A%2F%2Fwww.k2view.com%2Fwhat-is-synthetic-data-generation%2F&ts=1755961095010&__hstc=114997720.5f46460a11416869ac4f463bfb28fbfd.1755961094840.1755961094840.1755961094840.1&__hssc=114997720.1.1755961094840&__hsfp=1344000785&contentType=standard-page) hbspt.cta._relativeUrls=true;hbspt.cta.load(5983023, '908f2969-468a-491b-a15f-7e91fa1fc699', {\\\"useNewLoader\\\":\\\"true\\\",\\\"region\\\":\\\"na1\\\"});\\n\\n### tableicon/Table of Contents\\n\\n* [01Synthetic data generation accelerates innovation](#Synthetic-data-generation-accelerates-innovation)\\n* [02What is Synthetic Data Generation?](#What-is-Synthetic-Data-Generation?)\\n* [03Synthetic Data Generation Use Cases](#Synthetic-Data-Generation-Use-Cases)\\n* [04Synthetic Data Generation for Software Testing](#Synthetic-Data-Generation-for-Software-Testing)\\n* [05Synthetic Data Generation for Training ML Models](#Synthetic-Data-Generation-for-Training-ML-Models)\\n* [06Synthetic Data Generation Techniques](#Synthetic-Data-Generation-Techniques)\\n* [07Synthetic Data Generation Tools Capabilities](#Synthetic-Data-Generation-Tools-Capabilities)\\n* [08Synthetic Data Generation by Business Entities](#Synthetic-Data-Generation-by-Business-Entities)\\n* [09Top Synthetic Data Generation Tools](#Top-Synthetic-Data-Generation-Tools)\\n* [10The Future of Synthetic Data Generation](#The-Future-of-Synthetic-Data-Generation)\\n* [11Summary}\\n\\nNew! 2025 State of Test Data Management Survey \\ud83d\\udcca\\n\\n[Get the Survey Results](https://www.k2view.com/cs/c/?cta_guid=3e2a89be-bd07-4fe1-8689-60bbf7d57b90&signature=AAH58kEjvzV2P-up0O7ri0aKGDsIJKQUxw&portal_id=5983023&pageId=124287438174&placement_guid=0322b825-68bb-4113-a276-914b4c3e3916&click=d5f5a028-092f-4644-83b2-3506f59da30d&redirect_url=APefjpERdSnLp8BfcRaTZKnNeZLEa9YAexk5ZCmrMqT8PsaHava7cZotYSWS-bdP0ntIAKWzLy9-1fVNUEtdXaTg-FXBwnUWoZR4T9fTqOXZEWlNP6ru_cehSZJe-8MZkMBsWisW9K7PX6tBqCxYZqxGMj1OdtR1MA&hsutk=5f46460a11416869ac4f463bfb28fbfd&canon=https%3A%2F%2Fwww.k2view.com%2Fwhat-is-synthetic-data-generation%2F&ts=1755961094895&__hstc=114997720.5f46460a11416869ac4f463bfb28fbfd.1755961094840.1755961094840.1755961094840.1&__hssc=114997720.1.1755961094840&__hsfp=1344000785&contentType=standard-page) hbspt.cta._relativeUrls=true;hbspt.cta.load(5983023, '0322b825-68bb-4113-a276-914b4c3e3916', {\\\"useNewLoader\\\":\\\"true\\\",\\\"region\\\":\\\"na1\\\"}); \\n![arrow--cta](https://www.k2view.com/hubfs/arrow--cta.svg)\\n\\n.local-header-top-row {\\nbackground:linear-gradient(to bottom, rgba(20, 131, 243, 1), rgba(20, 131, 243, 1));\\n}\\n.local-header-top-text,\\n.local-header-top-text p{\\nfont-family: ;\\n;\\ncolor: ;\\nfont-size: px;\\n}\\n.local-header-top-cta a{\\nfont-family: ;\\n;\\ncolor: ;\\nfont-size: px;\\n}\\nspan.local-close--top--strip:before,\\nspan.local-close--top--strip:after{\\nbackground:#FFFFFF;\\n}\\n\\n#### Synthetic data generation is the process of creating artificial data that mimics the features, structures, and statistical attributes of production data, while maintaining compliance with data privacy regulations.\\n\\n[Get Gartner Report](https://www.k2view.com/cs/c/?cta_guid=bf5e46f3-9dfc-4a13-a4f7-36860982a09e&signature=AAH58kG_tkike4iJnwsguUD7Tpxw0G5rnQ&portal_id=5983023&pageId=124287438174&placement_guid=2983bef1-8d5a-4838-a90c-570c5bd67b18&click=18bdef30-e404-4083-8c7a-944447a44345&redirect_url=APefj...\", \"summary_structure\": [\"Key findings and insights\", \"Important statistics and data points\", \"Methodology and approach (if applicable)\", \"Conclusions and recommendations\", \"Source attribution and credibility notes\", \"Context and relevance to research topic\"]}}]}",
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      "content": "{\"next_action\": \"invoke_agent\", \"action_input\": [{\"agent_name\": \"synthesizer_agent\", \"request\": {\"text_content\": \"The provided content is an incomplete extract of an article titled \\\"Synthetic data as external control arms in scarce single-arm clinical trials\\\" from PLOS Digital Health. While the full research content (abstract, methodology, findings, conclusions) is not available in this extract, it does provide the article's title, authors, their affiliations, and mentions that it is a peer-reviewed research article.\\n\\n**Key findings and insights:**\\n- The article's main topic is the use of synthetic data as external control arms, specifically in single-arm clinical trials where real control groups are scarce.\\n\\n**Important statistics and data points:**\\n- The article has 1,493 views and downloads (PLOS views and downloads).\\n- Other metrics (Mendeley/Citeulike bookmarks, citation count, social shares) are listed as 0 or unavailable.\\n\\n**Methodology and approach (if applicable):**\\n- Not available in the provided content. The authors' roles include Methodology, Software, Validation, suggesting that the full article would detail these aspects.\\n\\n**Conclusions and recommendations:**\\n- Not available in the provided content.\\n\\n**Source attribution and credibility notes:**\\n- Source: PLOS Digital Health (Open Access, Peer-reviewed Research Article).\\n- Authors: Severin Elvatun, Daan Knoors (Cancer Registry of Norway, Norwegian Institute of Public health); Simon Brant, Christian Jonasson (NordicRWE); Jan F. Nyg\\u00e5rd (Cancer Registry of Norway, Norwegian Institute of Public health).\\n- ORCID IDs are provided for some authors, indicating academic transparency.\\n\\n**Context and relevance to research topic:**\\n- This article is relevant to the application of synthetic data in clinical research, particularly addressing challenges in trial design for rare diseases or conditions where traditional control arms are difficult to establish. It highlights the growing importance of digital health solutions in clinical trials.\", \"summary_structure\": [\"Key findings and insights\", \"Important statistics and data points\", \"Methodology and approach (if applicable)\", \"Conclusions and recommendations\", \"Source attribution and credibility notes\", \"Context and relevance to research topic\"], \"source_url\": \"https://journals.plos.org/digitalhealth/article?id=10.1371/journal.pdig.0000581\"}}]}",
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      "content": "{\"next_action\": \"invoke_agent\", \"action_input\": [{\"agent_name\": \"synthesizer_agent\", \"request\": {\"text_content\": \"### What is Synthetic Data Generation? A Practical Guide\\n\\n**Key findings and insights:**\\n- Synthetic data generation is defined as the process of creating artificial data that mimics the features, structures, and statistical attributes of production data, while maintaining compliance with data privacy regulations.\\n- The provided content serves as an introduction to the topic and a table of contents for a larger guide, suggesting further details are available in external reports (e.g., Gartner Report, 2025 State of Test Data Management Survey).\\n\\n**Important statistics and data points:**\\n- No specific statistics or data points are available within the provided text content.\\n\\n**Methodology and approach:**\\n- The content describes the concept of synthetic data generation but does not detail a specific methodology or approach within the provided text.\\n\\n**Conclusions and recommendations:**\\n- The document implies that synthetic data generation accelerates innovation, and highlights its use cases for software testing and training ML models, and mentions various techniques and tools.\\n\\n**Source attribution and credibility notes:**\\n- Source: k2view.com\\n- Last updated: March 23, 2025\\n- The article refers to external resources like a Gartner Report and a '2025 State of Test Data Management Survey' for more detailed information.\\n\\n**Context and relevance to research topic:**\\n- This content provides a foundational understanding of synthetic data generation, emphasizing its role in modern data practices, particularly concerning data privacy compliance.\\n\\n### BARE: Leveraging Base Language Models for Few-Shot Synthetic Data Generation\\n\\n**Key findings and insights:**\\n- Current synthetic data generation methods for LLMs, relying on instruction-tuned models, require tens of thousands of seed examples and produce insufficient diversity in few-shot settings.\\n- Base language models (without post-training) offer substantially greater output diversity, though with lower instruction following abilities.\\n- The BARE (Base-Refine) method effectively combines the diversity of base models with the quality assurance of instruction-tuned models for few-shot synthetic data generation.\\n\\n**Important statistics and data points:**\\n- BARE generates high-quality datasets using only 3 seed examples.\\n- Fine-tuning Llama 3.1 8B with 1,000 BARE-generated samples achieved performance comparable to state-of-the-art similarly sized models on LiveCodeBench tasks.\\n- Data generated with BARE enabled a 101% improvement for a fine-tuned Llama 3.2 1B on GSM8K over data generated by only instruction-tuned models.\\n- BARE showed an 18.4% improvement for a fine-tuned Llama 3.1 8B over the state-of-the-art RAFT method for RAG data generation.\\n- Figure 1 illustrates that base model generations have lower pairwise embedding cosine similarity, indicating higher diversity compared to instruction-tuned models.\\n- Figure 2 shows that training with BARE-generated data outperforms other data sources for a Llama-3.2-1B-Instruct model on GSM8K.\\n\\n**Methodology and approach:**\\n- **BARE (Base-Refine)**: A novel two-stage method.\\n  1. **Base Model Generation**: Utilizes base models to generate diverse synthetic data due to their untamed output capabilities.\\n  2. **Refinement with Instruction-Tuned Models**: Applies instruction-tuned models for quality assurance and to ensure adherence to specific instructions.\\n\\n**Conclusions and recommendations:**\\n- BARE is an effective approach for few-shot synthetic data generation, addressing the limitations of current methods that struggle with diversity from limited seed examples.\\n- Leveraging base models for diversity and instruction-tuned models for quality significantly improves downstream task performance for LLMs.\\n\\n**Source attribution and credibility notes:**\\n- Authors: Alan Zhu (UC Berkeley), Parth Asawa (UC Berkeley), Jared Quincy Davis (Stanford University & Foundry), Lingjiao Chen (Stanford University), Boris Hanin (Princeton University & Foundry), Ion Stoica (UC Berkeley), Joseph E. Gonzalez (UC Berkeley), Matei Zaharia (UC Berkeley).\\n- Affiliations: UC Berkeley, Stanford University, Princeton University, Foundry.\\n- This is a research paper published on arXiv, indicating it has undergone academic peer review (or is awaiting it).\\n\\n**Context and relevance to research topic:**\\n- Addresses the critical need for high-quality, diverse data for training and tuning Large Language Models (LLMs), especially when human-generated data is scarce or expensive to curate. It specifically tackles the challenging few-shot data generation scenario, which is crucial for efficient model development.\"}}]}",
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      "content": "{\"next_action\": \"invoke_agent\", \"action_input\": [{\"agent_name\": \"synthesizer_agent\", \"request\": {\"text_content\": \"#### A practical guide to synthetic data generation tools\\\\n\\\\nWhat is Synthetic Data Generation?\\\\n==================================\\\\n\\\\nLast updated on March 23, 2025\\\\n\\\\n[Get Gartner Report](https://www.k2view.com/cs/c/?cta_guid=bf5e46f3-9dfc-4a13-a4f7-36860982a09e&signature=AAH58kG_tkike4iJnwsguUD7Tpxw0G5rnQ&portal_id=5983023&pageId=124287438174&placement_guid=2983bef1-8d5a-4838-a90c-570c5bd67b18&click=18bdef30-e404-4083-8c7a-944447a44345&redirect_url=APefjpEuZGy7J-QSQylxyKppTIRpqs_bL8tsNsXKZRtC2G1YJVo13LQtJJjWgvVwEZFGawDo8WcBPkpDyKOu5E4H7ZUxTI2GWLPXpfQHJZU31cX2mcl9yihYTkpctCNFOLt_AHXXbVLDH9L1ETp9COF2f37iaoQYQw&hsutk=5f46460a11416869ac4f463bfb28fbfd&canon=https%3A%2F%2Fwww.k2view.com%2Fwhat-is-synthetic-data-generation%2F&ts=1755961094956&__hstc=114997720.5f46460a11416869ac4f463bfb28fbfd.1755961094840.1755961094840.1755961094840.1&__hssc=114997720.1.1755961094840&__hsfp=1344000785&contentType=standard-page) hbspt.cta.\\\\_relativeUrls=true;hbspt.cta.load(5983023, \\\\'2983bef1-8d5a-4838-a90c-570c5bd67b18\\\\', {\\\\\\\"useNewLoader\\\\\\\":\\\\\\\"true\\\\\\\",\\\\\\\"region\\\\\\\":\\\\\\\"na1\\\\\\\"});\\\\n\\\\n![What is Synthetic Data Generation?](https://www.k2view.com/hs-fs/hubfs/sdg%20pp%20images-2.png?width=2000&name=sdg%20pp%20images-2.png)\\\\n\\\\n.module\\\\\\\\_17122326460457 .ppb--image{\\\\n;\\\\n}\\\\n\\\\n[Send me as a pdf](https://www.k2view.com/cs/c/?cta_guid=9852cd13-50c2-4d5c-8fda-b3264d2020bc&signature=AAH58kH0f9yC86JH-w0kpgN1L_hgrxLxMA&portal_id=5983023&pageId=124287438174&placement_guid=908f2969-468a-491b-a15f-7e91fa1fc699&click=7eac2c78-7b81-4b8a-a9c3-fd084eae954b&redirect_url=APefjpHI5BcMg4WhHOuu_3rmk7975zhGYWX0ae0-PGaajo3WpDRjkJgYBzA96XVI_ZhbQQFJ1b7gHnBseS1gIC_G5ADdb076gEtYC8aARgs7WU53xakwk9buXZ_22yHbVyCbCDc8bVatpz_S4eYQaGBZzprC9r8jUHc2ZE0-Rx9QMtXD29ndHYY&hsutk=5f46460a11416869ac4f463bfb28fbfd&canon=https%3A%2F%2Fwww.k2view.com%2Fwhat-is-synthetic-data-generation%2F&ts=1755961095010&__hstc=114997720.5f46460a11416869ac4f463bfb28fbfd.1755961094840.1755961094840.1755961094840.1&__hssc=114997720.1.1755961094840&__hsfp=1344000785&contentType=standard-page) hbspt.cta.\\\\_relativeUrls=true;hbspt.cta.load(5983023, \\\\'908f2969-468a-491b-a15f-7e91fa1fc699\\\\', {\\\\\\\"useNewLoader\\\\\\\":\\\\\\\"true\\\\\\\",\\\\\\\"region\\\\\\\":\\\\\\\"na1\\\\\\\"});\\\\n\\\\n### tableicon/Table of Contents\\\\n\\\\n* [01Synthetic data generation accelerates innovation](#Synthetic-data-generation-accelerates-innovation)\\\\n* [02What is Synthetic Data Generation?](#What-is-Synthetic-Data-Generation?)\\\\n* [03Synthetic Data Generation Use Cases](#Synthetic-Data-Generation-Use-Cases)\\\\n* [04Synthetic Data Generation for Software Testing](#Synthetic-Data-Generation-for-Software-Testing)\\\\n* [05Synthetic Data Generation for Training ML Models](#Synthetic-Data-Generation-for-Training-ML-Models)\\\\n* [06Synthetic Data Generation Techniques](#Synthetic-Data-Generation-Techniques)\\\\n* [07Synthetic Data Generation Tools Capabilities](#Synthetic-Data-Generation-Tools-Capabilities)\\\\n* [08Synthetic Data Generation by Business Entities](#Synthetic-Data-Generation-by-Business-Entities)\\\\n* [09Top Synthetic Data Generation Tools](#Top-Synthetic-Data-Generation-Tools)\\\\n* [10The Future of Synthetic Data Generation](#The-Future-of-Synthetic-Data-Generation)\\\\n* [11Summary}\\\\n\\\\nNew! 2025 State of Test Data Management Survey \\ud83d\\udcca\\\\n\\\\n[Get the Survey Results](https://www.k2view.com/cs/c/?cta_guid=3e2a89be-bd07-4fe1-8689-60bbf7d57b90&signature=AAH58kEjvzV2P-up0O7ri0aKGDsIJKQUxw&portal_id=5983023&pageId=124287438174&placement_guid=0322b825-68bb-4113-a276-914b4c3e3916&click=d5f5a028-092f-4644-83b2-3506f59da30d&redirect_url=APefjpERdSnLp8BfcRaTZKnNeZLEa9YAexk5ZCmrMqT8PsaHava7cZotYSWS-bdP0ntIAKWzLy9-1fVNUEtdXaTg-FXBwnUWoZR4T9fTqOXZEWlNP6ru_cehSZJe-8MZkMBsWisW9K7PX6tBqCxYZqxGMj1OdtR1MA&hsutk=5f46460a11416869ac4f463bfb28fbfd&canon=https%3A%2F%2Fwww.k2view.com%2Fwhat-is-synthetic-data-generation%2F&ts=1755961094895&__hstc=114997720.5f46460a11416869ac4f463bfb28fbfd.1755961094840.1755961094840.1755961094840.1&__hssc=114997720.1.1755961094840&__hsfp=1344000785&contentType=standard-page) hbspt.cta.\\\\_relativeUrls=true;hbspt.cta.load(5983023, \\\\'0322b825-68bb-4113-a276-914b4c3e3916\\\\', {\\\\\\\"useNewLoader\\\\\\\":\\\\\\\"true\\\\\\\",\\\\\\\"region\\\\\\\":\\\\\\\"na1\\\\\\\"}); \\\\n![arrow--cta](https://www.k2view.com/hubfs/arrow--cta.svg)\\\\n\\\\n.local-header-top-row {\\\\nbackground:linear-gradient(to bottom, rgba(20, 131, 243, 1), rgba(20, 131, 243, 1));\\\\n}\\\\n.local-header-top-text,\\\\n.local-header-top-text p{\\\\nfont-family: ;\\\\n;\\\\ncolor: ;\\\\nfont-size: px;\\\\n}\\\\n.local-header-top-cta a{\\\\nfont-family: ;\\\\n;\\\\ncolor: ;\\\\nfont-size: px;\\\\n}\\\\nspan.local-close--top--strip:before,\\\\nspan.local-close--top--strip:after{\\\\nbackground:#FFFFFF;\\\\n}\\\\n\\\\n#### Synthetic data generation is the process of creating artificial data that mimics the features, structures, and statistical attributes of production data, while maintaining compliance with data privacy regulations.\\\\n\\\\n[Get Gartner Report](https://www.k2view.com/cs/c/?cta_guid=bf5e46f3-9dfc-4a13-a4f7-36860982a09e&signature=AAH58kG_tkike4iJnwsguUD7Tpxw0G5rnQ&portal_id=5983023&pageId=124287438174&placement_guid=2983bef1-8d5a-4838-a90c-570c5bd67b18&click=18bdef30-e404-4083-8c7a-944447a44345&redirect_url=APefj...\", \"summary_structure\": [\"Key findings and insights\", \"Important statistics and data points\", \"Methodology and approach (if applicable)\", \"Conclusions and recommendations\", \"Source attribution and credibility notes\", \"Context and relevance to research topic\"]}}]}",
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RESPONSE:
{
  "response": "Message(role='assistant', content='{\"thought\": \"I have received multiple summaries regarding synthetic data generation. I need to synthesize this information into a comprehensive markdown report, following the specified structure, and then save it to a file. I will consolidate the key findings, statistics, methodologies, and conclusions from all valid summaries and list all sources.\", \"next_action\": \"call_tool\", \"action_input\": {\"tool_calls\": [{\"id\": \"call_save_report\", \"type\": \"function\", \"function\": {\"name\": \"save_report_to_file\", \"arguments\": {\"content\": \"# Research Report: Synthetic Data Generation\\\\n\\\\n## Executive Summary\\\\n\\\\nSynthetic data generation is an increasingly vital process that involves creating artificial datasets mimicking the statistical properties and structures of real-world production data, while strictly adhering to data privacy regulations. This capability significantly accelerates innovation across various domains by providing readily available, privacy-compliant data for critical tasks such as software testing, training sophisticated machine learning models, and even enabling novel approaches in clinical trials where real data is scarce. Recent advancements, particularly in the realm of Large Language Models (LLMs), highlight methods like BARE (Base-Refine) that overcome limitations of data diversity and quantity, demonstrating substantial improvements in model performance with minimal seed data.\\\\n\\\\n## Introduction\\\\n\\\\nThis report compiles insights from various sources to provide a comprehensive overview of synthetic data generation. The primary objective is to define synthetic data, explore its diverse applications, highlight key methodologies, and summarize its benefits and challenges, with a focus on its relevance in modern data-driven environments and research.\\\\n\\\\n## Main Findings\\\\n\\\\n### What is Synthetic Data Generation?\\\\nSynthetic data generation is defined as the process of creating artificial data that mirrors the features, structures, and statistical attributes of production data, ensuring compliance with data privacy regulations. This artificial data behaves statistically similarly to real data but does not contain any actual sensitive information, making it ideal for scenarios where privacy and data access are concerns.\\\\n\\\\n### Use Cases\\\\nSynthetic data finds application in several critical areas:\\\\n*   **Software Testing**: It provides a scalable and privacy-safe alternative to real production data for comprehensive testing of software applications.\\\\n*   **Training Machine Learning Models**: Synthetic data can augment or replace real datasets, especially when real data is limited, sensitive, or expensive to acquire. This is crucial for developing robust and unbiased AI models.\\\\n*   **Clinical Trials**: Synthetic data can serve as external control arms in single-arm clinical trials, particularly for rare diseases or conditions where establishing traditional control groups is challenging. This helps in overcoming data scarcity in medical research.\\\\n\\\\n### Techniques and Tools\\\\nWhile general techniques and tools for synthetic data generation exist, a notable recent advancement is the **BARE (Base-Refine) method** for few-shot synthetic data generation for Large Language Models (LLMs).\\\\n\\\\n*   **Limitations of Current Methods**: Traditional methods for LLM synthetic data generation, often relying on instruction-tuned models, require tens of thousands of seed examples and tend to produce limited diversity in few-shot settings.\\\\n*   **BARE Method**: This novel two-stage approach leverages:\\\\n    1.  **Base Model Generation**: Utilizes base language models (without post-training) to generate diverse synthetic data, capitalizing on their untamed output capabilities.\\\\n    2.  **Refinement with Instruction-Tuned Models**: Applies instruction-tuned models for quality assurance, ensuring the generated data adheres to specific instructions and quality standards.\\\\n\\\\n### Benefits and Challenges\\\\n*   **Privacy Compliance**: A core benefit is maintaining compliance with data privacy regulations while providing data for development and testing.\\\\n*   **Accelerated Innovation**: Access to readily available synthetic data can significantly speed up development cycles and innovation.\\\\n*   **Data Diversity (LLMs)**: The BARE method specifically addresses the challenge of generating diverse data, which is crucial for training high-performing LLMs, especially from limited initial examples.\\\\n\\\\n## Key Statistics and Data Points\\\\n*   An article on synthetic data in clinical trials received **1,493 views and downloads** on PLOS Digital Health.\\\\n*   The BARE method can generate high-quality datasets using **only 3 seed examples**.\\\\n*   Fine-tuning Llama 3.1 8B with **1,000 BARE-generated samples** achieved performance comparable to state-of-the-art similarly sized models on LiveCodeBench tasks.\\\\n*   BARE-generated data enabled a **101% improvement** for a fine-tuned Llama 3.2 1B on GSM8K over data generated by only instruction-tuned models.\\\\n*   BARE showed an **18.4% improvement** for a fine-tuned Llama 3.1 8B over the state-of-the-art RAFT method for RAG data generation.\\\\n\\\\n## Methodology Notes\\\\n\\\\nThe methodologies discussed primarily include:\\\\n*   The general concept of mimicking production data features and statistical attributes to create artificial data.\\\\n*   The specific **BARE (Base-Refine) method** for LLMs, which involves a two-stage process of initial diverse generation by base models followed by refinement using instruction-tuned models. This approach is designed to optimize for both diversity and quality in synthetic data, particularly in few-shot scenarios.\\\\n\\\\nIt is noted that for one source (PLOS Digital Health), the full methodology was not available in the provided extract, though author roles suggested detailed methodological aspects within the complete article.\\\\n\\\\n## Conclusions and Recommendations\\\\n\\\\nSynthetic data generation is a powerful and evolving field that addresses critical needs in data privacy, accessibility, and diversity. It is a cornerstone for accelerating innovation in software development, machine learning, and specialized research areas like clinical trials.\\\\n\\\\nThe BARE method represents a significant advancement for LLMs, demonstrating that high-quality, diverse synthetic data can be generated efficiently even from very limited seed examples, leading to substantial improvements in downstream model performance. Future research and development should continue to focus on enhancing the fidelity, diversity, and applicability of synthetic data across an even wider range of complex use cases.\\\\n\\\\n## References and Source URLs\\\\n\\\\n*   **k2view.com**: \\\\\"A practical guide to synthetic data generation tools\\\\\" / \\\\\"What is Synthetic Data Generation?\\\\\", Last updated on March 23, 2025. References external resources like a Gartner Report and a \\'2025 State of Test Data Management Survey.\\\\n*   **PLOS Digital Health**: \\\\\"Synthetic data as external control arms in scarce single-arm clinical trials\\\\\" by Severin Elvatun, Daan Knoors, Simon Brant, Christian Jonasson, Jan F. Nyg\\\\u00e5rd. Source: [https://journals.plos.org/digitalhealth/article?id=10.1371/journal.pdig.0000581](https://journals.plos.org/digitalhealth/article?id=10.1371/journal.pdig.0000581)\\\\n*   **arXiv**: \\\\\"BARE: Leveraging Base Language Models for Few-Shot Synthetic Data Generation\\\\\" by Alan Zhu, Parth Asawa, Jared Quincy Davis, Lingjiao Chen, Boris Hanin, Ion Stoica, Joseph E. Gonzalez, Matei Zaharia. Affiliations: UC Berkeley, Stanford University, Princeton University, Foundry.\\\\n*   **IEEE Xplore**: Attempted content extraction from https://ieeexplore.ieee.org/document/11006780/ failed due to a \\\\\"Request Rejected\\\\\" error, thus no summary could be provided for this source.\"}}}]}}', message_id='05cac69c-0699-4529-ba25-59903a1aa2a2', name='synthesizer_agent', tool_calls=None, agent_calls=None, structured_data=None, images=None, tool_call_id=None)",
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AGENT: synthesizer_agent | CALL #2 | CALLED BY: System
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Timestamp: 2025-08-23 17:00:02
Duration: 1.70 seconds

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