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Sprint projectJun 22, 2026Delhi

Arclight

Shivangi Gupta, Saurabh Gupta, Shruti kumari, Shruti Gupta, Siddhi Garg · Team Debuggers

Submitted to Global South AI Safety Hackathon. Sprint projects are early-stage work by participants, not Apart Research publications.

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Report: Arclight

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Arclight is an AI Dependency Intelligence platform that helps organizations map AI ecosystems, assess resilience risks, simulate disruptions, and generate strategies to reduce dependency-related failures.

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How much would this matter for AI safety if it worked? How innovative is it? For scores of 4-5: is this actually new to the field, or replicating recent work?

Scoring guide
  1. 1Negligible. No clear problem addressed, or no meaningful novelty.
  2. 2Limited. Addresses a real problem but with a generic or well-trodden approach. Incremental at best.
  3. 3Moderate. Clear problem with a reasonable approach; some novelty in framing or method beyond routine application of existing tools.
  4. 4Significant. Important problem with an original approach, or identifies a neglected problem area. A valuable contribution others could build on.
  5. 5Exceptional. Tackles a critical AI safety problem with a genuinely novel approach, or opens a new research direction. Clear theory of change. You'd be excited to share this with researchers in the area.

How sound are methodology, implementation, and findings?

Scoring guide
  1. 1Seriously flawed. Methodology broken, results uninterpretable, or implementation doesn't work.
  2. 2Weak. Approach has significant gaps: missing validation, flawed experimental design, or incomplete implementation.
  3. 3Competent. Technically solid given the short duration. Methodology makes sense, results are interpretable, limitations acknowledged, work builds toward clear conclusions.
  4. 4Strong. Thorough methodology with convincing validation. Results clearly support conclusions. Immediately useful for future work.
  5. 5Exceptional. Ambitious scope executed rigorously. Surprising findings, novel methods, or unusually robust validation.

How clearly are work, findings, and impact potential communicated?

Scoring guide
  1. 1Incomprehensible. Cannot determine what the project is actually claiming or doing.
  2. 2Hard to follow. Key information buried, missing, or diluted by excessive length. Significant effort to extract main points.
  3. 3Clear enough. Can understand the problem, approach, and results without undue effort. Core content clearly present: problem, method, findings, limitations.
  4. 4Well presented. Easy to follow, well-structured, appropriate level of detail. Target audience would get it quickly.
  5. 5Exceptionally clear. A pleasure to read. Complex ideas made accessible. Could serve as a model for how to present this type of work.

  1. Arclight presents a thoughtful approach to AI dependency intelligence by combining dependency mapping, sovereignty assessment, shock simulation, and resilience planning into a cohesive platform. The project goes beyond a UI mockup by implementing graph-based dependency traversal, node criticality analysis, redundancy detection, explainable sovereignty scoring, and actionable resilience recommendations, demonstrating a solid understanding of enterprise AI infrastructure. The architecture is modular and the application is polished, making the workflow easy to understand and explore. To further strengthen the project, future iterations could integrate live enterprise data sources such as cloud platforms, service catalogs, observability tools, or API gateways to automatically discover dependencies and validate the scoring model against real-world infrastructure. Demonstrating the platform on a realistic production-scale environment would further showcase its practical value and strengthen confidence in the proposed methodology.

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  2. Your safety thinking is correct. Answer only from trusted sources, show the source, and refuse when there is no good evidence. After you shared the code I can see there is a working app now, a chat screen connected to Gemini, so it is not only a plan and I raised the execution score for that. But the app is not the system the report describes. There is no real retrieval, no source database, and no refusal step. It is a normal Gemini chatbot with a small hardcoded list of political records, plus a lot of Google permissions (full Gmail, Drive, Sheets) that have nothing to do with the safety idea. I also see in the repository that the same project was sent to other hackathons under the name ElectAI, so it looks reused. Please build the retrieval and the refuse parts that you describe, remove the leftover cite tags in the report, and remove the permissions you do not need.

  3. There is a solid path towards a working product.

    To improve:

    1. Make the paper match what is actually built. The described stack doesn't match the project's.

    2. Alter the scoring naming as it's not AI-powered. Similarly, “continuously crawled” should be replaced with “static seed data.”

    3. Source the market statistics. Several claims are cited without references that the reader can verify.

    4. Add a findings and limitations section. The scoring weights are an unvalidated heuristic, so explain how they were chosen and what evidence would change them.

    5. Add a clearer AI safety and Global South connection, since both are currently underdeveloped for this venue.

Cite this project

@misc{gupta2026arclight,
  title = {{Arclight}},
  author = {Shivangi Gupta and Saurabh Gupta and Shruti kumari and Shruti Gupta and Siddhi Garg},
  year = {2026},
  month = jun,
  note = {Submitted to Global South AI Safety Hackathon, an Apart Research Sprint},
  howpublished = {\url{https://apartresearch.com/sprints/projects/arclight-xtkx}},
  url = {https://apartresearch.com/sprints/projects/arclight-xtkx}
}

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