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Paper Citation Record · LEDGER

Circuit Component Reuse Across Tasks in Transformer Language Models

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 10 inbound Pith citation observations for arXiv:2310.08744.

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

pith.paper-citation-record.v1
2310.08744 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 10 of 10 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 10 of 10 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T15:00:56.044360Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

3
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 12a834ed-fb16-4038-ae8a-da41521b98df · inbound

How to use and interpret activation patching cites this paper.

How to use and interpret activation patching Circuit Component Reuse Across Tasks in Transformer Language Models

Reference 21

Resolution
verified exact
arxiv_id, observed 2026-05-16T20:34:06.993150Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-16T20:34:06.888683Z digest=sha256:c61b0a76a0b6034c7ea5ec2e276755365bdd95fe50933cd540b9b8ea7917cd11

Observation 440fc231-dc7f-413d-98aa-55ccb17809ff · inbound

Beyond Induction Heads: In-Context Meta Learning Induces Multi-Phase Circuit Emergence cites this paper.

Beyond Induction Heads: In-Context Meta Learning Induces Multi-Phase Circuit Emergence Circuit Component Reuse Across Tasks in Transformer Language Models

Reference 22

Resolution
unresolved
no resolver link, observed 2026-08-07T15:00:56.044360Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T15:00:56.044360Z digest=sha256:7e9527c1deac2f79b2c1db1ff4562812aacf405b75d0f0250c8bee20c7d59c5e

Observation 95ed4f4e-8328-4a17-a302-075f4d8e95bd · inbound

Paths Not Taken: Understanding and Mending the Multilingual Factual Recall Pipeline cites this paper.

Paths Not Taken: Understanding and Mending the Multilingual Factual Recall Pipeline Circuit Component Reuse Across Tasks in Transformer Language Models

Reference 25

Resolution
unresolved
no resolver link, observed 2026-08-07T13:58:18.368602Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T13:58:18.368602Z digest=sha256:f4efe5dc26e5eb927414e9801e0197e1801a49d56a2304da57491acb65bbb4c1

Observation 119eecba-5da6-40d7-a85f-6301fe42b66a · inbound

Unveiling Instruction-Specific Neurons & Experts: An Analytical Framework for LLM's Instruction-Following Capabilities cites this paper.

Unveiling Instruction-Specific Neurons & Experts: An Analytical Framework for LLM's Instruction-Following Capabilities Circuit Component Reuse Across Tasks in Transformer Language Models

Reference 29

Resolution
unresolved
no resolver link, observed 2026-08-07T13:43:22.212757Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T13:43:22.212757Z digest=sha256:06141b706a04526d3120cd90baa88322394e3d8e3a512884dec6a4f4a34745b1

Observation ab555f7a-9a92-4648-8d89-0f2ad3783b6b · inbound

Distinct Computations Emerge From Compositional Curricula in In-Context Learning cites this paper.

Distinct Computations Emerge From Compositional Curricula in In-Context Learning Circuit Component Reuse Across Tasks in Transformer Language Models

Reference 24

Resolution
unresolved
no resolver link, observed 2026-08-07T00:41:54.781639Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T00:41:54.781639Z digest=sha256:b65d958846fed94e6c95e4e67533f012f91eb96794dc8bf8e3e319103df8a3d1

Observation c9cf5a4d-96db-46cf-998b-79584ef2e639 · inbound

From Indirect Object Identification to Syllogisms: Exploring Binary Mechanisms in Transformer Circuits cites this paper.

From Indirect Object Identification to Syllogisms: Exploring Binary Mechanisms in Transformer Circuits Circuit Component Reuse Across Tasks in Transformer Language Models

Reference 3

Resolution
unresolved
no resolver link, observed 2026-08-05T17:37:53.915873Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T17:37:53.915873Z digest=sha256:282d86786cd82df35497de23f2b98b66a5686f7ea66545a5e248b3ba319b5d78

Observation ded8d3bc-c88f-4ca5-be5a-5b74b9b2070d · inbound

Inside-Out: Measuring Generalization in Vision Transformers Through Inner Workings cites this paper.

Inside-Out: Measuring Generalization in Vision Transformers Through Inner Workings Circuit Component Reuse Across Tasks in Transformer Language Models

Reference 48

Resolution
verified exact
arxiv_id, observed 2026-05-11T00:30:54.035354Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-10T18:28:49.855280Z digest=sha256:df7cfe865c6f32f42b1fce19a2be04c4e1c2d357b28388c23bf7ca46567cb82f

Observation 40f324c5-98aa-47c6-ad0e-19cfc18635ca · inbound

Localizing Anchoring Pathways in Language Models cites this paper.

Localizing Anchoring Pathways in Language Models Circuit Component Reuse Across Tasks in Transformer Language Models

Reference 9

Resolution
metadata mismatch
arxiv_id, observed 2026-07-03T14:28:31.355684Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-06-27T07:04:57.016552Z digest=sha256:36e27901a190f1c37e5946aae69abe6176f9b4e972fbcd83d78a2e17678cbe27

Observation c41c80af-45ea-4ff5-8023-5064c4e7d776 · inbound

Pretraining Curricula Enable Selective Fine-tuning cites this paper.

Pretraining Curricula Enable Selective Fine-tuning Circuit Component Reuse Across Tasks in Transformer Language Models

Reference 39

Resolution
unresolved
no resolver link, observed 2026-07-11T12:36:24.747752Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-11T12:36:24.747752Z digest=sha256:cc720932eca2055f133541fa9e7b9ab1e21b0ebea8bbcdfa14e89cfd3b9aec02

Observation 7b196349-e0b6-48c9-9ef2-8596fefa7fba · inbound

Explaining and Tuning Transformer-based LLMs in Arithmetic Tasks with Human Strategies cites this paper.

Explaining and Tuning Transformer-based LLMs in Arithmetic Tasks with Human Strategies Circuit Component Reuse Across Tasks in Transformer Language Models

Reference 8

Resolution
unresolved
no resolver link, observed 2026-08-01T18:53:09.982258Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T18:53:09.982258Z digest=sha256:9fe1b0bc03846a7f9e8f86354f7084dfa1cbcc8aab301395b94de9eded925d1a