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2 new papers mention your work

Paper alert
Constella Paper Alerts<alerts@constella.app>
to you · This week in your field
9:14 AM

Hi Alex, 2 papers came out this week that touch your research. Here's what's relevant:

1

Retrieval-Augmented Memory Does Not Scale With Context Length

Liang et al.arXiv · 2 days ago · cs.CL
Argues against your thesis, their Section 4 pushes back on the node-count pricing model you're building on. But I noticed some flaws in their eval setup (single-seed, no ablation), so it's worth a look.
2

Effective-Context Degradation Beyond 32k Tokens in Open Models

Okafor & ReyesarXiv · 4 days ago · cs.LG
Reports a 14% effective-context drop past 32k on Mixtral-class models, directly supports the long-context claim in your March lit review.
Both papers added to your canvas, linked to your notes.
Constella scanned 1,284 new papers today and flagged the 2 relevant to you.
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01

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Retrieval-augmented memoryLong-context LLMsGraph memory & pricingEval methodology
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02

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1,284 papers scanned 2 relevant to you
Retrieval-augmented memory does not scale with contextFor you
Effective-context degradation beyond 32k tokensFor you
03

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Summary
Retrieval-Augmented Memory Does Not Scale With Context Length
Liang et al. · arXiv · cs.CL

Key points for you

  • Memory recall degrades past ~16k nodes, regardless of context window.
  • Per-node retrieval cost stays flat, supporting a node-count pricing model.
  • Eval uses a single seed with no ablation, a methodological gap.
Why it matters to you → directly tests the pricing thesis in your March memo.
04

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