# LinkedIn Post — arxiv Endorser Request

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**Looking for an arxiv endorser (cs.IR / cs.CL / cs.AI)**

I built a persistent memory system for Claude Code that just scored +33.4% on BEAM-10M — the hardest long-context memory benchmark in the field (Tavakoli et al., ICLR 2026). 10 million tokens per conversation, 196 probing questions, 10 memory abilities.

The result that matters: the architecture works **without oracle labels**. Using only timestamps — no ground-truth topic partitioning — it outperforms the oracle-label version (0.471 vs 0.429 MRR). Temporal proximity turns out to be a stronger retrieval signal than topic boundaries for conversational memory.

The architecture was designed in September 2025 for generating 9-page PRDs on Apple Intelligence's 4096-token context window. Publicly verifiable commit: github.com/cdeust/ai-prd-builder, commit 462de01 — one month before the BEAM paper existed.

**Paper:** "Stage-Aware Context Assembly for Long-Context Memory Retrieval"
**LaTeX source ready:** github.com/cdeust/Cortex/docs/arxiv-context-assembly/
**Repo (MIT, open source):** github.com/cdeust/Cortex

Other benchmark results (E1 v3 verification campaign, May 2026):
• 98.4% Recall@10 / 0.9124 MRR on LongMemEval (vs 78.4% paper best, n=500)
• 94.2% Recall@10 / 0.8278 MRR on LoCoMo (vs 92.6% / 0.794, n=1986)
• 45 row entries of per-mechanism ablation evidence (17 LME-S + 14 LoCoMo + 14 LoCoMo post-fix)
• 41 paper citations, 26 biological mechanisms with faithful implementations
• 2700+ tests passing
• Two production fixes shipped during verification (consolidation cadence, plasticity result-shape)

The paper was reviewed by three independent reasoning agents (Einstein operational-definition audit, Feynman cargo-cult detector, Shannon information-theoretic analysis) and revised based on their findings — including running the temporal-detection experiment they demanded. Every limitation is disclosed. Every constant traces to a paper or measured ablation.

If you have arxiv publishing history in **cs.IR**, **cs.CL**, **cs.AI**, **q-bio.NC**, **stat.ML**, or **math.OC** and would be willing to endorse, I'd be grateful. The paper is self-contained, reproducible, and honest.

DM me or comment below. Happy to share the PDF.

#InformationRetrieval #LongContextMemory #Neuroscience #arxiv #BEAM #MachineLearning #Research

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## Shorter version (if the above is too long)

Looking for an **arxiv endorser** for cs.IR.

Built a memory system that scores +33.4% on BEAM-10M (ICLR 2026) — without oracle labels, using only timestamps. The architecture was designed in Sept 2025, one month before BEAM existed.

Paper: "Stage-Aware Context Assembly for Long-Context Memory Retrieval"
Repo: github.com/cdeust/Cortex (MIT, LaTeX source in docs/arxiv-context-assembly/)

98.4% R@10 LongMemEval | 94.2% R@10 LoCoMo | +33.4% BEAM-10M

If you can endorse on cs.IR, cs.CL, or cs.AI — DM me. Paper is ready.

#arxiv #InformationRetrieval #LongContextMemory
