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Paper Citation Record · LEDGER

Understanding Is Done Early: A Depth Division of Labor in Large Language Models and Its Use for Unbounded-Context Memory

As of 14 August 2026, this Paper Citation Record lists 26 of 26 outbound references and 0 inbound Pith citation observations for arXiv:2607.28263.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2607.28263 v1

Coverage vector

measured 26 of 26 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-07-31T12:56:46.806841Z

measured 26 of 26 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

26 of 26 outbound references displayed

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  • malformed identifier1
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 5b257e32-d26d-40ff-9450-4edbb1cfdd63 · outbound

This paper cites The Llama 3 Herd of Models.

Understanding Is Done Early: A Depth Division of Labor in Large Language Models and Its Use for Unbounded-Context Memory The Llama 3 Herd of Models

Reference 4

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Observation c72022f2-797a-4c62-8851-a4c96ce608b8 · outbound

This paper cites RULER: What's the Real Context Size of Your Long-Context Language Models?.

Understanding Is Done Early: A Depth Division of Labor in Large Language Models and Its Use for Unbounded-Context Memory RULER: What's the Real Context Size of Your Long-Context Language Models?

Reference 6

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source=pdf_text observed=2026-07-31T12:56:44.892297Z digest=sha256:5a81a114fbad043affc40198974ece751d9e21e0341c0910f9736475dece8051

Observation 14ff5225-b6c4-4a1c-948b-b7bfa0922871 · outbound

This paper cites RAGCache: Efficient Knowledge Caching for Retrieval-Augmented Generation.

Understanding Is Done Early: A Depth Division of Labor in Large Language Models and Its Use for Unbounded-Context Memory RAGCache: Efficient Knowledge Caching for Retrieval-Augmented Generation

Reference 7

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source=pdf_text observed=2026-07-31T12:56:44.993564Z digest=sha256:d0fcf33c7a18079f7724dcf8b7d0cbb2cadb3effbac24a4f8d994d32fe7881a1

Observation bf6cf282-fc70-4d4e-94e3-7cd519c5a8df · outbound

This paper cites The Remarkable Robustness of LLMs: Stages of Inference?.

Understanding Is Done Early: A Depth Division of Labor in Large Language Models and Its Use for Unbounded-Context Memory The Remarkable Robustness of LLMs: Stages of Inference?

Reference 8

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source=pdf_text observed=2026-07-31T12:56:45.090058Z digest=sha256:91835b96d8a4e0873d0224626932b8b99d3abf5f4944a71f87f96dff7c0eb74a

Observation dd146709-5cf9-4f84-98d1-c9e0529c2a2f · outbound

This paper cites SnapKV: LLM Knows What You are Looking for Before Generation.

Understanding Is Done Early: A Depth Division of Labor in Large Language Models and Its Use for Unbounded-Context Memory SnapKV: LLM Knows What You are Looking for Before Generation

Reference 9

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source=pdf_text observed=2026-07-31T12:56:45.197106Z digest=sha256:0bd90c579068e8149e9a6a766b27ec3e8ec7eb06ca85aa92417184552d5cdb98

Observation 22bdc162-20b6-4d40-8af9-6cef3be9f150 · outbound

This paper cites CompressKV: Semantic-Retrieval-Guided KV-Cache Compression for Resource-Efficient Long-Context LLM Inference.

Understanding Is Done Early: A Depth Division of Labor in Large Language Models and Its Use for Unbounded-Context Memory CompressKV: Semantic-Retrieval-Guided KV-Cache Compression for Resource-Efficient Long-Context LLM Inference

Reference 10

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source=pdf_text observed=2026-07-31T12:56:45.291959Z digest=sha256:b209f68e3257639fb31ddff5232012617ab4df283a659d8068f376188110b4d9

Observation a10ddebc-b201-4562-84f4-d81c5d510e62 · outbound

This paper cites Evaluating Very Long-Term Conversational Memory of LLM Agents.

Understanding Is Done Early: A Depth Division of Labor in Large Language Models and Its Use for Unbounded-Context Memory Evaluating Very Long-Term Conversational Memory of LLM Agents

Reference 11

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source=pdf_text observed=2026-07-31T12:56:45.432364Z digest=sha256:1c802a60a1fdb9c1d63de4025ef8433e2ab33b399f1a45150a24a556fb02ab7e

Observation d26e740b-b28b-4095-be6d-f5b487faa1c7 · outbound

This paper cites Landmark Attention: Random-Access Infinite Context Length for Transformers.

Understanding Is Done Early: A Depth Division of Labor in Large Language Models and Its Use for Unbounded-Context Memory Landmark Attention: Random-Access Infinite Context Length for Transformers

Reference 12

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source=pdf_text observed=2026-07-31T12:56:45.516458Z digest=sha256:e2b67356e84e62c85d524ab9d3ef1c47ce00b7c0c040b9188dd787b89fdd3e1f

Observation 77902c20-8678-4c2e-9a89-59e4d2e6e36d · outbound

This paper cites YaRN: Efficient Context Window Extension of Large Language Models.

Understanding Is Done Early: A Depth Division of Labor in Large Language Models and Its Use for Unbounded-Context Memory YaRN: Efficient Context Window Extension of Large Language Models

Reference 13

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source=pdf_text observed=2026-07-31T12:56:45.589864Z digest=sha256:d7ccd1e3cc37ae4a68c64b29552bed6a9e6718c59f4b0cba481cfb08897e149f

Observation 2166dd7d-26f5-4ead-8f8e-6888b228ecb0 · outbound

This paper cites arXiv preprint arXiv:2603.19664.

Understanding Is Done Early: A Depth Division of Labor in Large Language Models and Its Use for Unbounded-Context Memory arXiv preprint arXiv:2603.19664

Reference 14

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source=pdf_text observed=2026-07-31T12:56:45.699653Z digest=sha256:6d2f6766188e7d3fa4b2e09cb06baac58d28cce9c40e526bf838de87e1c91e4e

Observation eb06958c-9289-4e84-b333-3ac60a66a525 · outbound

This paper cites You Only Cache Once: Decoder-Decoder Architectures for Language Models.

Understanding Is Done Early: A Depth Division of Labor in Large Language Models and Its Use for Unbounded-Context Memory You Only Cache Once: Decoder-Decoder Architectures for Language Models

Reference 17

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source=pdf_text observed=2026-07-31T12:56:45.924478Z digest=sha256:357def1cea1b3396bc93c46a03d17e7af40b7112efe867f2e7a39ff60dfabde6

Observation d377100a-6afc-4bf7-acc6-d42127968a84 · outbound

This paper cites Focused Transformer: Contrastive Training for Context Scaling.

Understanding Is Done Early: A Depth Division of Labor in Large Language Models and Its Use for Unbounded-Context Memory Focused Transformer: Contrastive Training for Context Scaling

Reference 18

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source=pdf_text observed=2026-07-31T12:56:45.996091Z digest=sha256:60e5ceba82e60ca56397ee2b3126a7edc98fe8a7616a1202f6e0e02c56ea57d1

Observation 7642a1f2-9aac-4553-8f54-cffc0e58b46e · outbound

This paper cites MEMORYLLM: Towards Self-Updatable Large Language Models.

Understanding Is Done Early: A Depth Division of Labor in Large Language Models and Its Use for Unbounded-Context Memory MEMORYLLM: Towards Self-Updatable Large Language Models

Reference 20

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source=pdf_text observed=2026-07-31T12:56:46.177620Z digest=sha256:231e3ca26df3e96b51f9b09b5f852d2b750f46eadefee4839bbee1d9e6d30a12

Observation dc2c4eee-5961-41b9-800a-1342369c9d8b · outbound

This paper cites Layer-Condensed KV Cache for Efficient Inference of Large Language Models.

Understanding Is Done Early: A Depth Division of Labor in Large Language Models and Its Use for Unbounded-Context Memory Layer-Condensed KV Cache for Efficient Inference of Large Language Models

Reference 21

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source=pdf_text observed=2026-07-31T12:56:46.292098Z digest=sha256:b0a2901a43dd4ab8d3ece1b02cfc2d293763e76ddfa46cab56d26aeaf0686fb4

Observation 246bac0e-0583-4083-ae8d-d7f5e39f296f · outbound

This paper cites CacheBlend: Fast Large Language Model Serving for RAG with Cached Knowledge Fusion.

Understanding Is Done Early: A Depth Division of Labor in Large Language Models and Its Use for Unbounded-Context Memory CacheBlend: Fast Large Language Model Serving for RAG with Cached Knowledge Fusion

Reference 23

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source=pdf_text observed=2026-07-31T12:56:46.484066Z digest=sha256:3544d45b5863115f51a90c94e2805b451a90f8ec8984abc12c27a804ed463a32

Observation 56a026ec-e8b0-4b30-ada3-51ce465eab2a · outbound

This paper cites Long Context Compression with Activation Beacon.

Understanding Is Done Early: A Depth Division of Labor in Large Language Models and Its Use for Unbounded-Context Memory Long Context Compression with Activation Beacon

Reference 24

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source=pdf_text observed=2026-07-31T12:56:46.586819Z digest=sha256:1de61550c08658938456b48ab67be86ec9578be76f65b4711195ae6fab803fd5

Observation 56c6f3a7-c37f-45e0-ad71-8748f98dc2b8 · outbound

This paper cites InNeurIPS.

Understanding Is Done Early: A Depth Division of Labor in Large Language Models and Its Use for Unbounded-Context Memory InNeurIPS

Reference 25

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source=pdf_text observed=2026-07-31T12:56:46.698287Z digest=sha256:1e230d98ff6b6e6cfe4890a99a66b3cd4c9ed62deb0fb924b3b5d6cde32d68cf

Observation 2053ddf8-536e-4843-82b6-62932083f24e · outbound

This paper cites †MemoryLLM uses a different chat-tuned backbone.

Understanding Is Done Early: A Depth Division of Labor in Large Language Models and Its Use for Unbounded-Context Memory †MemoryLLM uses a different chat-tuned backbone

Reference 92

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source=pdf_text observed=2026-07-31T12:56:46.806841Z digest=sha256:f15f7c6124a7f376ea9bb22af389b0f3057d938f0a3e6e7897f864a2a97d48fb

Observation ebebcc82-b04e-4bc9-b2d1-8c3f2fd00856 · outbound

This paper cites Distilling the Knowledge in a Neural Network.

Understanding Is Done Early: A Depth Division of Labor in Large Language Models and Its Use for Unbounded-Context Memory Distilling the Knowledge in a Neural Network

Reference 2015

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source=pdf_text observed=2026-07-31T12:56:44.764763Z digest=sha256:abcae045cc9f82b5fb3e01259b1d8a10fa38d9aabdfeee825de834e82cafbbc2

Observation 5129d7ef-986f-49e1-9161-c36d44b6fc21 · outbound

This paper cites Recursively Summarizing Enables Long-Term Dialogue Memory in Large Language Models.

Understanding Is Done Early: A Depth Division of Labor in Large Language Models and Its Use for Unbounded-Context Memory Recursively Summarizing Enables Long-Term Dialogue Memory in Large Language Models

Reference 2017

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source=pdf_text observed=2026-07-31T12:56:46.074813Z digest=sha256:fa024a450ed6e890457314838caa4ba6e998c6baea6ef84f90683290a0f384d1

Observation 7fcdedf1-afc9-4a53-8d29-2ed1fc60cea6 · outbound

This paper cites Compressive Transformers for Long-Range Sequence Modelling.

Understanding Is Done Early: A Depth Division of Labor in Large Language Models and Its Use for Unbounded-Context Memory Compressive Transformers for Long-Range Sequence Modelling

Reference 2019

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source=pdf_text observed=2026-07-31T12:56:45.774990Z digest=sha256:57d141ce7ae5868c7b8b0f99eca373680c360950ee62def9a3a806b00a7b81d6

Observation 9b9b10e7-af59-439b-84b4-5bccc4e2235c · outbound

This paper cites RoFormer: Enhanced Transformer with Rotary Position Embedding.

Understanding Is Done Early: A Depth Division of Labor in Large Language Models and Its Use for Unbounded-Context Memory RoFormer: Enhanced Transformer with Rotary Position Embedding

Reference 2021

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source=pdf_text observed=2026-07-31T12:56:45.848263Z digest=sha256:4dfff811c74d55101ae6afb4e282cb08cad53c5e5b3019dc0e1816d1821554e4

Observation e3440b2f-b3f3-44d2-bf0a-e6d85d53dfda · outbound

This paper cites LongBench: A Bilingual, Multitask Benchmark for Long Context Understanding.

Understanding Is Done Early: A Depth Division of Labor in Large Language Models and Its Use for Unbounded-Context Memory LongBench: A Bilingual, Multitask Benchmark for Long Context Understanding

Reference 2023

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source=pdf_text observed=2026-07-31T12:56:44.371714Z digest=sha256:7b5a8f3faa565a0a5f16d0b16fa785035e3092339c27043c79dc76e44c3f509b

Observation 0f018553-5d42-4941-a98d-0fec93bc3a9b · outbound

This paper cites PyramidKV: Dynamic KV Cache Compression based on Pyramidal Information Funneling.

Understanding Is Done Early: A Depth Division of Labor in Large Language Models and Its Use for Unbounded-Context Memory PyramidKV: Dynamic KV Cache Compression based on Pyramidal Information Funneling

Reference 2024

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source=pdf_text observed=2026-07-31T12:56:44.434623Z digest=sha256:f7ee039a1adccf83bb387e32a256752c93d56eb661db93591cf393a0d41f408b

Observation 1591ab30-e8f3-4e69-b554-2d5d89ca6e6a · outbound

This paper cites Qwen3 Technical Report.

Understanding Is Done Early: A Depth Division of Labor in Large Language Models and Its Use for Unbounded-Context Memory Qwen3 Technical Report

Reference 2025

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source=pdf_text observed=2026-07-31T12:56:46.369683Z digest=sha256:c56c25191ff4cc21ce029c44c80e64e192832b7064d2deabe8349fd60862c4d7

Observation 910c87a8-5fa8-45c2-84b1-25cfb63079ee · outbound

This paper cites Training Transformers for KV Cache Compressibility.

Understanding Is Done Early: A Depth Division of Labor in Large Language Models and Its Use for Unbounded-Context Memory Training Transformers for KV Cache Compressibility

Reference 2026

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source=pdf_text observed=2026-07-31T12:56:44.516747Z digest=sha256:d920bd1cc20b5633241d028c9c091856ad7ce5a121e93ccd63518a7208bca70b

Pith citing papers

No inbound Pith citation observations are available.