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/research-method

Researches about a new method (scalarizer or aggregator) from the scientific literature and how to integrate it to TorchJD. Use when a contributor is interested into adding a new aggregator, scalarizer, or a new method that could be either, that is not already listed in the

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torchjd
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$ npx -y skills add simplexlab/torchjd --skill research-method --agent claude-code

How it fires

How this skill gets triggered: by you, by Claude, or both.

  • Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
  • You can call itInvoke it directly when you want it.
  • Slash command/research-method

Context preview

The summary Claude sees to decide when to auto-load this skill.

Researches about a new method (scalarizer or aggregator) from the scientific literature and how to integrate it to TorchJD. Use when a contributor is interested into adding a new aggregator, scalarizer, or a new method that could be either, that is not already listed in the

SKILL.md

research-method.SKILL.md
name: research-method
description: Researches about a new method (scalarizer or aggregator) from the scientific literature and how to integrate it to TorchJD. Use when a contributor is interested into adding a new aggregator, scalarizer, or a new method that could be either, that is not already listed in the tracking issues.

Research new method

This skill researches about a new method by reading the paper and the existing implementations, proposing a plan for integrating an implementation in TorchJD.

**For agents:** invoke as `/implement-method method-name (paper-name)` (e.g. `/implement-method upgrad (Jacobian Descent for Multi-objective Optimization)`). If no method name is provided, ask the user for the name of the method and the title of the paper.

**For humans:** follow the numbered steps below to guide you in your development.

---

Instructions

Step 1: Generate a tracking summary

On GitHub, we have a tracking issue for scalarizers (https://github.com/SimplexLab/TorchJD/issues/667) and one for aggregators (https://github.com/SimplexLab/TorchJD/issues/665). The first goal is to generate a new row for the table in the appropriate tracking issue. You will also have to find the official implementation (if any), that could be linked in the paper, and the most known non-official implementations (search in particular in LibMTL, libmoon and pymoo, and search for more repos online). You will have to find the exact files and lines in which the method is implemented. You can also write special remarks if anything deserves further attention / if you're not sure about something. Do not bother trying to find citation count or the exact venue in which the paper was published, unless you directly see this information from your initial search.

Step 2: Analyze the method

Based on the paper and the existing implementations, find the things in the interface of the new method that are non-standard compared to existing methods. For example, is the method stateful (and in which way), random, does it require some warm-up period, other stats than those provided through the forward method (like losses for aggregators), and so on. Report all of those non-standard things.

Step 3: Produce summary row

Produce and output a row that can be directly copy-pasted to this table, but do not modify the issue yourself. Propose to post a comment on the issue with the row.

---

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Ships withtorchjd

Library for Jacobian descent with PyTorch. It enables the optimization of neural networks with multiple losses (e.g. multi-task learning).

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