Paper Citation Record · LEDGER

Compression Represents Intelligence Linearly

As of 23 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 20 inbound Pith citation observations for arXiv:2404.09937.

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

pith.paper-citation-record.v1
2404.09937 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 20 of 20 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+00:00

measured 20 of 20 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T10:12:43.030615Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T22:17:25.634233Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation ea5e771a-a465-4f89-aa24-c0f145297032 · inbound

Large Language Models for Lossless Image Compression: Next-Pixel Prediction in Language Space is All You Need cites this paper.

Large Language Models for Lossless Image Compression: Next-Pixel Prediction in Language Space is All You Need Compression Represents Intelligence Linearly

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-12T17:35:57.488924Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 97cb7e55-a901-4691-b164-15a24fb8b279 · inbound

Predicting Emergent Capabilities by Finetuning cites this paper.

Predicting Emergent Capabilities by Finetuning Compression Represents Intelligence Linearly

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-12T13:41:46.095518Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 65a1651a-47d6-4144-aa58-cd76025fae78 · inbound

Predictable Emergent Abilities of LLMs: Proxy Tasks Are All You Need cites this paper.

Predictable Emergent Abilities of LLMs: Proxy Tasks Are All You Need Compression Represents Intelligence Linearly

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-11T19:11:48.466876Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation df95277d-80a4-4d91-a0ea-320ca6af8b25 · inbound

L3TC: Leveraging RWKV for Learned Lossless Low-Complexity Text Compression cites this paper.

L3TC: Leveraging RWKV for Learned Lossless Low-Complexity Text Compression Compression Represents Intelligence Linearly

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-11T10:27:43.188827Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 36927574-e937-4c2b-8335-99f35095fd31 · inbound

Separate Source Channel Coding Is Still What You Need: An LLM-based Rethinking cites this paper.

Separate Source Channel Coding Is Still What You Need: An LLM-based Rethinking Compression Represents Intelligence Linearly

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-10T21:43:04.651402Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 68e345dc-6f9a-48c6-8a28-902a195b61d6 · inbound

Large Language Diffusion Models cites this paper.

Large Language Diffusion Models Compression Represents Intelligence Linearly

Reference 14

Resolution: verified exact; arxiv_id, observed 2026-05-11T01:42:55.024824Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation e2f41cbf-f7d6-4c1c-9f37-123a4a019e72 · inbound

Revisiting Transformers through the Lens of Low Entropy and Dynamic Sparsity cites this paper.

Revisiting Transformers through the Lens of Low Entropy and Dynamic Sparsity Compression Represents Intelligence Linearly

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-16T10:12:43.030615Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f4158d59-3deb-4cd4-93dd-7e5267b9983d · inbound

Semantic-aided Parallel Image Transmission Compatible with Practical System cites this paper.

Semantic-aided Parallel Image Transmission Compatible with Practical System Compression Represents Intelligence Linearly

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-16T05:08:28.060098Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 970e6076-637d-4644-85e0-4b899f5fc9c2 · inbound

Lossless Compression of Large Language Model-Generated Text via Next-Token Prediction cites this paper.

Lossless Compression of Large Language Model-Generated Text via Next-Token Prediction Compression Represents Intelligence Linearly

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-15T23:29:39.701104Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b1cc2334-1a52-4202-9562-d6522531ad34 · inbound

Prioritizing Image-Related Tokens Enhances Vision-Language Pre-Training cites this paper.

Prioritizing Image-Related Tokens Enhances Vision-Language Pre-Training Compression Represents Intelligence Linearly

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-15T21:49:27.814220Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6924b2d3-ed6f-43e5-85cc-65d1ff5de7b5 · inbound

Thinking Beyond Tokens: From Brain-Inspired Intelligence to Cognitive Foundations for Artificial General Intelligence and its Societal Impact cites this paper.

Thinking Beyond Tokens: From Brain-Inspired Intelligence to Cognitive Foundations for Artificial General Intelligence and its Societal Impact Compression Represents Intelligence Linearly

Reference 171

Resolution: unresolved; no resolver link, observed 2026-08-06T21:07:11.309517Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3ec98d88-b742-4d4f-9079-7c7957c05bc7 · inbound

Joint Lossless Compression and Steganography for Medical Images via Large Language Models cites this paper.

Joint Lossless Compression and Steganography for Medical Images via Large Language Models Compression Represents Intelligence Linearly

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-06T05:31:33.660247Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 759da69e-87db-4833-8e14-b6a9b1819c95 · inbound

Signal and Noise: A Framework for Reducing Uncertainty in Language Model Evaluation cites this paper.

Signal and Noise: A Framework for Reducing Uncertainty in Language Model Evaluation Compression Represents Intelligence Linearly

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-15T17:21:06.694837Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9da05335-fa85-42b3-a6b4-8628e40c8c32 · inbound

HE-SNR: Uncovering Latent Logic via Entropy for Guiding Mid-Training on SWE-bench cites this paper.

HE-SNR: Uncovering Latent Logic via Entropy for Guiding Mid-Training on SWE-bench Compression Represents Intelligence Linearly

Reference 10

Resolution: verified exact; arxiv_id, observed 2026-05-16T11:02:47.163405Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation e6f15f46-3336-4482-b5d2-a7f030838bb6 · inbound

HE-SNR: Uncovering Latent Logic via Entropy for Guiding Mid-Training on SWE-bench cites this paper.

HE-SNR: Uncovering Latent Logic via Entropy for Guiding Mid-Training on SWE-bench Compression Represents Intelligence Linearly

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-15T15:44:36.062807Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation db2701da-163d-450d-8c6f-af9b17aa4353 · inbound

Sema: Semantic Transport for Real-Time Multimodal Agents cites this paper.

Sema: Semantic Transport for Real-Time Multimodal Agents Compression Represents Intelligence Linearly

Reference 19

Resolution: verified exact; arxiv_id, observed 2026-05-09T22:49:15.628040Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 660565f1-2b8a-4cc0-a4ca-1a8ebffe6391 · inbound

Continuous Latent Diffusion Language Model cites this paper.

Continuous Latent Diffusion Language Model Compression Represents Intelligence Linearly

Reference 37

Resolution: verified exact; arxiv_id, observed 2026-05-11T20:11:10.742843Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation d0fb99f8-c3fe-4559-b17e-7ab8e1ef1c37 · inbound

Model Capacity Determines Grokking through Competing Memorisation and Generalisation Speeds cites this paper.

Model Capacity Determines Grokking through Competing Memorisation and Generalisation Speeds Compression Represents Intelligence Linearly

Reference 9

Resolution: verified exact; arxiv_id, observed 2026-05-12T03:56:21.728791Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation e98a9ef4-92f4-4aec-9f5b-9624aead304b · inbound

Adapting Diffusion Language Models for Lossless Pixel-Level Image Transmission cites this paper.

Adapting Diffusion Language Models for Lossless Pixel-Level Image Transmission Compression Represents Intelligence Linearly

Reference 6

Resolution: verified exact; arxiv_id, observed 2026-07-02T15:47:06.427612Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation a6fe28f4-7c45-4f99-b097-ba36b18f1d4e · inbound

Ling and Ring 2.6 Technical Report: Efficient and Instant Agentic Intelligence at Trillion-Parameter Scale cites this paper.

Ling and Ring 2.6 Technical Report: Efficient and Instant Agentic Intelligence at Trillion-Parameter Scale Compression Represents Intelligence Linearly

Reference 69

Resolution: verified exact; arxiv_id, observed 2026-07-02T22:17:25.636005Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.