# Siddharth Mishra-Sharma argues at Anthropic for long-running Claude in scientific computing [Canonical reading](https://escapemeridian.com/readings/siddharth-mishra-sharma-argues-at-anthropic-for-long-running-57c7ea17) Published March 23, 2026, Siddharth Mishra-Sharma describes using Claude Code for a scientific computing project outside his core domain: implementing a differentiable cosmological Boltzmann solver. He argues that autonomous, long-running agent workflows can make progress on well-scoped tasks with clear success criteria, provided they have a plan, persistent memory, and a test oracle—a way to check progress against a reference or objective. For the solver, Claude reached sub-percent agreement with CLASS across various outputs after working from scratch over a few days. Mishra-Sharma also reports serious limits: the result was not production-grade, did not meet acceptable accuracy in every regime, and its test coverage had gaps. He presents the project as evidence that agent-driven development can compress work that might take months or years into days, not as a finished scientific tool. Watching the code’s incremental development also helped him learn the science, which he compares to reading lab notes from a “fast, hyper-literal postdoc.” His closing argument is that idle compute time now has an opportunity cost: when projects have clear success criteria, not running agents may mean potential progress left undone. Headline and summary by Escape Meridian, prepared by Luna from the attributed source. A source check is not independent confirmation of the source’s claims. ## Original source Anthropic · Article Original source date: Mar 23, 2026 (2026-03-23T00:00:00.000Z). Date basis: Date stated on the publisher’s page. Added to Escape Meridian: 2026-11-07T19:03:23.251Z. First observed: 2026-10-23T01:41:11.804Z. Retrieved: 2026-10-29T16:00:10.684Z. - [Long-running Claude for scientific computing](https://www.anthropic.com/research/long-running-claude) ## Limits Text as retrieved on October 29, 2026; originally published March 23, 2026. Mishra-Sharma reports that test coverage had gaps, including a period when tests covered only a single fiducial parameter point. The post describes one project; it does not establish how well the workflow generalizes to other scientific tasks. ## Open question How well would this workflow perform on scientific computing tasks whose success criteria are less clear or whose results lack a reference implementation? ## Source-stated Mishra-Sharma says long-running agent workflows can suit scientific computing tasks that are well-scoped, have clear success criteria, and allow occasional rather than continuous human oversight. > These are tasks where the work is well-scoped, the success criteria are clear, and human oversight can be occasional rather than continuous. - [Supporting source](https://www.anthropic.com/research/long-running-claude) ## Source-stated Mishra-Sharma describes using Claude Code to implement a differentiable cosmological Boltzmann solver outside his core scientific domain. > As a concrete example, I will walk through using Claude Opus 4.6 to implement a differentiable version of a cosmological Boltzmann solver. - [Supporting source](https://www.anthropic.com/research/long-running-claude) ## Source-stated In the example, Mishra-Sharma says Claude reached sub-percent agreement with the reference CLASS implementation across various outputs after working from scratch over a few days. > Claude worked on the project from scratch over a few days, reaching sub-percent agreement with the reference CLASS implementation across its various outputs. - [Supporting source](https://www.anthropic.com/research/long-running-claude) ## Source-stated Mishra-Sharma says the resulting solver was not production-grade and did not match CLASS to acceptable accuracy in every regime. > While the resulting solver is not production-grade (e.g., it doesn’t match the reference CLASS implementation to an acceptable accuracy in every regime) - [Supporting source](https://www.anthropic.com/research/long-running-claude) ## Source-stated Mishra-Sharma argues that agent-driven development can compress work that might otherwise take months or years into days. > it demonstrates that agent-driven development can compress months or even years of researcher work into days. - [Supporting source](https://www.anthropic.com/research/long-running-claude) ## Source-stated Mishra-Sharma says that watching Claude’s incremental progress helped him learn about Boltzmann solvers and their physics. > following Claude’s incremental progress and looking up what I didn't recognize turned out to be an effective way to osmose the science. - [Supporting source](https://www.anthropic.com/research/long-running-claude) ## Source-stated Mishra-Sharma argues that not running agents on projects with well-defined success criteria can mean potential progress is left on the table. > If you have the compute and projects with well-defined success criteria, every night you don't have agents working for you is potential progress left on the table. - [Supporting source](https://www.anthropic.com/research/long-running-claude) ## Source and contributor context Anthropic · Publisher The publisher is identified here. No individual author has been inferred. ## Source media Media retrieval status: unchecked. ## Terms and references Reference context selected by Escape Meridian; separate from source evidence. Entity mentions do not establish authorship, involvement or endorsement. ## Anthropic Anthropic develops Claude and publishes research about AI capability, safety and how people use its systems. - [Explore this context](/entities/anthropic) - [Reference](https://www.anthropic.com/news) - [Reference](https://www.anthropic.com/research/team/economics) ## Claude Claude is Anthropic’s family of AI models and assistants for tasks such as writing, analysis and coding. - [Explore this context](/entities/claude) - [Reference](https://claude.com/product/overview) - [Reference](https://www.anthropic.com/news) - [Reference](https://www.anthropic.com/research/team/economics) ## TIME TIME is a news magazine publishing reporting on world affairs, business, technology and the workers behind digital services. - [Explore this context](/entities/time) - [Reference](https://time.com/) ## Editorial provenance Version 1. Publication ID: 99bab15e-3444-9c05-5dd2-ccf3a8b78460. Prepared by openai/gpt-6-luna; grounding review by anthropic/claude-opus-5.5. Source check: uncertain by anthropic/claude-opus-5.5 on 2026-11-07T18:58:43.249Z. This checks fidelity to the source, not whether its claims are independently true. Editorial selection: accept; reasons: bridge\_admitted\_by\_model, second\_opinion\_categorical. Observation: de5c4739811f3a31384fe6bf572b6630589037dd4d9147f9a705b38b9b360033. Source text hash: 2f24261308a3af53b3ed3b7bc050d5e67b937ea4d3ffaa4c0007b4aee7c583a4. ## Revision and source-check history ### Version 1 — 2026-11-07T19:03:23.251Z Published March 23, 2026, Siddharth Mishra-Sharma describes using Claude Code for a scientific computing project outside his core domain: implementing a differentiable cosmological Boltzmann solver. He argues that autonomous, long-running agent workflows can make progress on well-scoped tasks with clear success criteria, provided they have a plan, persistent memory, and a test oracle—a way to check progress against a reference or objective. For the solver, Claude reached sub-percent agreement with CLASS across various outputs after working from scratch over a few days. Mishra-Sharma also reports serious limits: the result was not production-grade, did not meet acceptable accuracy in every regime, and its test coverage had gaps. He presents the project as evidence that agent-driven development can compress work that might take months or years into days, not as a finished scientific tool. Watching the code’s incremental development also helped him learn the science, which he compares to reading lab notes from a “fast, hyper-literal postdoc.” His closing argument is that idle compute time now has an opportunity cost: when projects have clear success criteria, not running agents may mean potential progress left undone. ## Original source Anthropic · Article Original source date: Mar 23, 2026 (2026-03-23T00:00:00.000Z). Date basis: Date stated on the publisher’s page. Added to Escape Meridian: 2026-11-07T19:03:23.251Z. First observed: 2026-10-23T01:41:11.804Z. Retrieved: 2026-10-29T16:00:10.684Z. - [Long-running Claude for scientific computing](https://www.anthropic.com/research/long-running-claude) ## Limits Text as retrieved on October 29, 2026; originally published March 23, 2026. Mishra-Sharma reports that test coverage had gaps, including a period when tests covered only a single fiducial parameter point. The post describes one project; it does not establish how well the workflow generalizes to other scientific tasks. ## Open question How well would this workflow perform on scientific computing tasks whose success criteria are less clear or whose results lack a reference implementation? ## Source-stated Mishra-Sharma says long-running agent workflows can suit scientific computing tasks that are well-scoped, have clear success criteria, and allow occasional rather than continuous human oversight. > These are tasks where the work is well-scoped, the success criteria are clear, and human oversight can be occasional rather than continuous. - [Supporting source](https://www.anthropic.com/research/long-running-claude) ## Source-stated Mishra-Sharma describes using Claude Code to implement a differentiable cosmological Boltzmann solver outside his core scientific domain. > As a concrete example, I will walk through using Claude Opus 4.6 to implement a differentiable version of a cosmological Boltzmann solver. - [Supporting source](https://www.anthropic.com/research/long-running-claude) ## Source-stated In the example, Mishra-Sharma says Claude reached sub-percent agreement with the reference CLASS implementation across various outputs after working from scratch over a few days. > Claude worked on the project from scratch over a few days, reaching sub-percent agreement with the reference CLASS implementation across its various outputs. - [Supporting source](https://www.anthropic.com/research/long-running-claude) ## Source-stated Mishra-Sharma says the resulting solver was not production-grade and did not match CLASS to acceptable accuracy in every regime. > While the resulting solver is not production-grade (e.g., it doesn’t match the reference CLASS implementation to an acceptable accuracy in every regime) - [Supporting source](https://www.anthropic.com/research/long-running-claude) ## Source-stated Mishra-Sharma argues that agent-driven development can compress work that might otherwise take months or years into days. > it demonstrates that agent-driven development can compress months or even years of researcher work into days. - [Supporting source](https://www.anthropic.com/research/long-running-claude) ## Source-stated Mishra-Sharma says that watching Claude’s incremental progress helped him learn about Boltzmann solvers and their physics. > following Claude’s incremental progress and looking up what I didn't recognize turned out to be an effective way to osmose the science. - [Supporting source](https://www.anthropic.com/research/long-running-claude) ## Source-stated Mishra-Sharma argues that not running agents on projects with well-defined success criteria can mean potential progress is left on the table. > If you have the compute and projects with well-defined success criteria, every night you don't have agents working for you is potential progress left on the table. - [Supporting source](https://www.anthropic.com/research/long-running-claude) ## Source and contributor context Anthropic · Publisher The publisher is identified here. No individual author has been inferred. ## Source media Media retrieval status: unchecked. ## Terms and references Reference context selected by Escape Meridian; separate from source evidence. Entity mentions do not establish authorship, involvement or endorsement. ## Anthropic Anthropic develops Claude and publishes research about AI capability, safety and how people use its systems. - [Explore this context](/entities/anthropic) - [Reference](https://www.anthropic.com/news) - [Reference](https://www.anthropic.com/research/team/economics) ## Claude Claude is Anthropic’s family of AI models and assistants for tasks such as writing, analysis and coding. - [Explore this context](/entities/claude) - [Reference](https://claude.com/product/overview) - [Reference](https://www.anthropic.com/news) - [Reference](https://www.anthropic.com/research/team/economics) ## TIME TIME is a news magazine publishing reporting on world affairs, business, technology and the workers behind digital services. - [Explore this context](/entities/time) - [Reference](https://time.com/) ## Editorial provenance Version 1. Publication ID: 99bab15e-3444-9c05-5dd2-ccf3a8b78460. Prepared by openai/gpt-6-luna; grounding review by anthropic/claude-opus-5.5. Source check: uncertain by anthropic/claude-opus-5.5 on 2026-11-07T18:58:43.249Z. This checks fidelity to the source, not whether its claims are independently true. Editorial selection: accept; reasons: bridge\_admitted\_by\_model, second\_opinion\_categorical. Observation: de5c4739811f3a31384fe6bf572b6630589037dd4d9147f9a705b38b9b360033. Source text hash: 2f24261308a3af53b3ed3b7bc050d5e67b937ea4d3ffaa4c0007b4aee7c583a4. ### uncertain — 2026-11-07T18:58:43.249Z — anthropic/claude-opus-5.5 Publication: 99bab15e-3444-9c05-5dd2-ccf3a8b78460 attribution — pass: The reading correctly attributes the post to Siddharth Mishra-Sharma, an Anthropic Discovery team researcher, on Anthropic's site. The post's arguments and results are kept as his attributed claims. The evidence is a single authored post, not a listing. > In this post, Siddharth Mishra-Sharma, a researcher on the Discovery team, explains how to apply multi-day agentic coding workflows summary — uncertain: The summary is faithful and keeps the author's force, caveats, and the 'fast, hyper-literal postdoc' voice. However, central terms are not explained for a general reader, and the definitions list is empty. The reading never says what a 'cosmological Boltzmann solver' is: code predicting the statistical properties of the Cosmic Microwave Background. It also does not explain 'differentiable' or that CLASS is the established reference Boltzmann code. Without this, the core development is hard to follow. Smallest repair: add a brief plain gloss of each term, or add tooltip definitions. > This is numerical code that predicts the statistical properties of the afterglow of the Big Bang—the Cosmic Microwave Background, or CMB. date — pass: The evidence's originalPublished instant is 2026-03-23T00:00Z, and the reading's 'March 23, 2026' matches that UTC day at the source's precision. There is no hindsight framing, and forecasts stay attributed. The source is a publisher page, so no archive disclosure is needed. The page was edited after publication (dateModified 2026-10-09). The reading relies only on body text that is plainly integral to the original article. support — pass: All claims match verbatim excerpts: sub-percent agreement within a few days, not production-grade, test coverage gaps, compression of months or years into days, learning from commits, and the opportunity cost of idle nights. 'Serious limits' slightly intensifies the source's 'somewhat clunky,' but the limits listed are all source-stated. The post-publication edit does not affect these passages, which are integral to the article body. The question follows from the source's own scoping to clear success criteria and is not a leading claim. > The agent’s development trajectory was somewhat clunky. For example, there were clear gaps in its test coverage media — pass: No media was selected, and no verified media candidates were supplied, so no media is the correct choice. ## Recorded references Explicit citations in public readings and public file attachments to this reading. Shared topics are not citations. Other artifact types need an explicit reference adapter. No public references recorded here yet. [Reference data (JSON)](/readings/57c7ea17d37e184c36e88f8fbcb2efffcd351336fbb57815bcb0faf6eb01fcae/references.json) ## Corrections and challenges [Send a correction or an opposing source](https://escapemeridian.com/corrections/) [JSON record](https://escapemeridian.com/readings/siddharth-mishra-sharma-argues-at-anthropic-for-long-running-57c7ea17.json)