Paper Citation Record · LEDGER

Exploring Concept Depth: How Large Language Models Acquire Knowledge and Concept at Different Layers?

As of 24 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 12 inbound Pith citation observations for arXiv:2404.07066.

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

pith.paper-citation-record.v1
2404.07066 v7

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 12 of 12 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 12 of 12 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T05:58:46.304822Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-19T05:32:05.673035Z

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 de08d555-3a4d-4034-abf4-177451535a6f · inbound

Steering Language Model Refusal with Sparse Autoencoders cites this paper.

Steering Language Model Refusal with Sparse Autoencoders Exploring Concept Depth: How Large Language Models Acquire Knowledge and Concept at Different Layers?

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-12T18:45:48.438601Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 587812f3-9419-4d4f-a230-7593322c6bcb · inbound

Token Prepending: A Training-Free Approach for Eliciting Better Sentence Embeddings from LLMs cites this paper.

Token Prepending: A Training-Free Approach for Eliciting Better Sentence Embeddings from LLMs Exploring Concept Depth: How Large Language Models Acquire Knowledge and Concept at Different Layers?

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-11T14:53:05.931474Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d698ae59-f505-4d7e-8ce0-42502cfe4572 · inbound

Correctness Assessment of Code Generated by Large Language Models Using Internal Representations cites this paper.

Correctness Assessment of Code Generated by Large Language Models Using Internal Representations Exploring Concept Depth: How Large Language Models Acquire Knowledge and Concept at Different Layers?

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-10T16:41:30.992041Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d4480f36-b2cd-493f-bf97-3b06d174a7db · inbound

TruthFlow: Truthful LLM Generation via Representation Flow Correction cites this paper.

TruthFlow: Truthful LLM Generation via Representation Flow Correction Exploring Concept Depth: How Large Language Models Acquire Knowledge and Concept at Different Layers?

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-08T22:23:27.966500Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cd753e08-343b-4ef1-ab66-45c82fae1574 · inbound

Bi-directional Model Cascading with Proxy Confidence cites this paper.

Bi-directional Model Cascading with Proxy Confidence Exploring Concept Depth: How Large Language Models Acquire Knowledge and Concept at Different Layers?

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-16T05:58:46.304822Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 445961d5-2a60-45c4-9f88-d197aaa54fd4 · inbound

Contrastive Prompting Enhances Sentence Embeddings in LLMs through Inference-Time Steering cites this paper.

Contrastive Prompting Enhances Sentence Embeddings in LLMs through Inference-Time Steering Exploring Concept Depth: How Large Language Models Acquire Knowledge and Concept at Different Layers?

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-15T20:29:59.658151Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d9fd0454-15ef-4b1e-99d9-394bfe3acca9 · inbound

Computer Vision Models Show Human-Like Sensitivity to Geometric and Topological Concepts cites this paper.

Computer Vision Models Show Human-Like Sensitivity to Geometric and Topological Concepts Exploring Concept Depth: How Large Language Models Acquire Knowledge and Concept at Different Layers?

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-15T20:21:48.872456Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c5f99ce4-bf40-445a-88f4-8be739c36be6 · inbound

Void in Language Models cites this paper.

Void in Language Models Exploring Concept Depth: How Large Language Models Acquire Knowledge and Concept at Different Layers?

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-07T15:37:01.788971Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 995d0c83-1955-4ccb-939b-581fcdc64688 · inbound

The Birth of Knowledge: Emergent Features across Time, Space, and Scale in Large Language Models cites this paper.

The Birth of Knowledge: Emergent Features across Time, Space, and Scale in Large Language Models Exploring Concept Depth: How Large Language Models Acquire Knowledge and Concept at Different Layers?

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-07T14:19:00.528159Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f40ceb79-67d4-4f13-aab9-049b4ef2b259 · inbound

The Generalization Ridge: Information Flow in Natural Language Generation cites this paper.

The Generalization Ridge: Information Flow in Natural Language Generation Exploring Concept Depth: How Large Language Models Acquire Knowledge and Concept at Different Layers?

Reference 9

Resolution: verified exact; arxiv_id, observed 2026-05-19T05:32:05.674938Z

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 258533b7-67c7-4c2d-b1dc-91b407b6ebe1 · inbound

SATORI: Static Test Oracle Generation for REST APIs cites this paper.

SATORI: Static Test Oracle Generation for REST APIs Exploring Concept Depth: How Large Language Models Acquire Knowledge and Concept at Different Layers?

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-05T17:26:48.659646Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 80b11fd2-a957-4a8a-9c82-4bd631996fce · inbound

Crown, Frame, Reverse: Layer-Wise Scaling Variants for LLM Pre-Training cites this paper.

Crown, Frame, Reverse: Layer-Wise Scaling Variants for LLM Pre-Training Exploring Concept Depth: How Large Language Models Acquire Knowledge and Concept at Different Layers?

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-04T23:33:33.839644Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.