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Nav: Discover > Feed | URL: /papers Staying current with AI research is part of the job, but rarely the main job. You have models to ship, pipelines to maintain, and stakeholders asking if you’ve tried the latest technique they saw online. The result is a persistent backlog of ideas you’ll get to “eventually” and relevant work you discover weeks after it would have been useful. The Feed delivers a curated stream of resources (papers, repos, models) matched to what your team is building. You create focused Research Interests that each track a different area of your work, with their own recommendation cadence.

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Research Interests

Research Interests are the context that drives your recommendations. Each interest has its own name, context description, and recommendation cadence.

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Creating an Interest

Click + New Interest at the top of the Feed view.
FieldDescriptionExample
NameShort label (appears as a pill tab)“RAG & Retrieval”
ContextNatural language description, or URLs to HuggingFace models, GitHub repos, arXiv papers, blog postshttps://huggingface.co/your-org/your-model
Daily RecommendationsResources per day (1-10)3
Richer context = better recommendations. Link GitHub repos with detailed READMEs, HuggingFace models with comprehensive cards, or specific arXiv papers that define your area. Resources that cite or benchmark against your work get boosted.

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Interest Pill Tabs

Your interests appear as pill tabs at the top of the Feed:
  • All — Combined feed from all interests
  • [Interest Name] — Filtered to that interest only, with badge count showing how many new recommendations are available

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Managing Interests

Click the gear icon (⚙️) next to an interest name to:
  • Edit the name, context, or daily count
  • Toggle active/inactive (inactive interests stop generating recommendations but preserve history)
  • Delete the interest

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Research Interests from a repo

Instead of a free-text context, you can create an interest from a GitHub repo (pick one from your connected GitHub, or paste a public URL). Remyx extracts the repo’s merge history into a structured shipping record in the background, then ranks recommendations against the work your team has actually shipped — not just a topic description. This is the same extracted history that powers a Project; a repo-driven interest and a project that links the same repo converge on one history. When you create an interest from a repo, the dialog also offers an auto-PR setting that can provision automated discovery PRs for that repo:
ChoiceWhat happens
Set it up for meRemyx installs the recommendation GitHub Action, opens and merges the setup PR, and fires the first run automatically once recommendations exist.
Let me review the setup PRSame provisioning, but the setup PR is left open for you to review and merge first.
Not nowNo provisioning — turn it on later from the interest’s settings.
Editing a repo-sourced interest lets you curate its extracted history (include/exclude individual changes), the same way you curate a project’s experiment list. Keeping direction-setting work and dropping pure refactors sharpens what gets recommended.

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Recommendation Cards

Each recommendation shows:
ElementDescription
TitleResource title with link to detail viewer
Source typeBadge indicating arxiv_paper, github_repo, etc.
Relevance scoreHow well this matches your interest context
ReasoningOne-line explanation of why this was recommended
Interest tagWhich interest surfaced this resource
Cards are sorted by recency by default. Use the Sort control to change ordering.

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Refreshing Recommendations

Each interest has a Refresh button in the toolbar. Click it to trigger an immediate re-ranking — don’t wait until the next scheduled run. Refresh runs asynchronously. A progress indicator shows the status. Navigate away and come back; the job continues in the background.
Scheduled recommendations refresh daily at 9:30 AM UTC for all active interests. Use Refresh for instant updates, or update your context sources before the daily run.

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How recommendations are ranked

For interests backed by a shipping history (any repo-sourced interest or a project), ranking goes beyond matching your context text:
  • Your team’s extracted shipping history is fed into the ranker, so candidates that align with the methodological direction you’ve actually shipped rank above ones that are merely topically related.
  • A learned preference model, fit over your past experiments, scores candidates and breaks ties behind relevance.
Both make the feed sharper as your history grows.

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Resource Detail & Chat

Click View on any recommendation to open the detail page with:
  • Details tab — Full abstract, resource links, citations, Docker availability
  • Chat tab — Ask questions about the resource in natural language with streaming responses
See Search > Resource Detail Viewer for full details.

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From Feed to Experiment

From any resource in the feed, click Create Experiment to:
  1. Link the resource as the experiment source with source_type: paper
  2. Pre-fill the resource metadata in source_ref
  3. Redirect to the Outcomes detail page where a launch context generates automatically
This is the natural entry point for the discovery-to-experiment loop: your feed surfaces a relevant technique, you create an experiment to test it, and the results inform future recommendations.

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CLI & API

You can also access your feed programmatically: 
# Today's digest grouped by interest
remyxai papers digest

# Recommendations for one interest
remyxai papers list --interest "RAG & Retrieval" --period today

# Trigger a refresh
remyxai papers refresh --interest "RAG & Retrieval" --wait

Automated discovery PRs

Let the feed open draft PRs into your repo on a schedule (Outrider)

Search

On-demand semantic search across all resources

CLI: Interests & Papers

Manage interests and recommendations from the terminal