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Paper Citation Record · LEDGER

Unlocking General Long Chain-of-Thought Reasoning Capabilities of Large Language Models via Representation Engineering

As of 14 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 8 inbound Pith citation observations for arXiv:2503.11314.

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

pith.paper-citation-record.v1
2503.11314 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 8 of 8 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 8 of 8 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T13:02:23.592574Z

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

0
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 e01e48f6-a94f-49db-87f0-a0ca6bb51e23 · inbound

LongReD: Mitigating Short-Text Degradation of Long-Context Large Language Models via Restoration Distillation cites this paper.

LongReD: Mitigating Short-Text Degradation of Long-Context Large Language Models via Restoration Distillation Unlocking General Long Chain-of-Thought Reasoning Capabilities of Large Language Models via Representation Engineering

Reference 64

Resolution
unresolved
no resolver link, observed 2026-08-08T13:02:23.592574Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-08T13:02:23.592574Z digest=sha256:53ba482dd79da8105e523c27ff742a5eab4804ee0109d2242099e51202b478c4

Observation 9d3f7704-a80b-4415-81ab-03d1b3823450 · inbound

Amplify Adjacent Token Differences: Enhancing Long Chain-of-Thought Reasoning with Shift-FFN cites this paper.

Amplify Adjacent Token Differences: Enhancing Long Chain-of-Thought Reasoning with Shift-FFN Unlocking General Long Chain-of-Thought Reasoning Capabilities of Large Language Models via Representation Engineering

Reference 31

Resolution
unresolved
no resolver link, observed 2026-08-07T15:05:18.186011Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T15:05:18.186011Z digest=sha256:0cb0770f26e9d80447931967f56f32dd9da025c423f3189e7d9a8459080e026b

Observation 5a9080bb-fe0a-4a60-b29a-2bdaf9a9141c · inbound

Activation Control for Efficiently Eliciting Long Chain-of-thought Ability of Language Models cites this paper.

Activation Control for Efficiently Eliciting Long Chain-of-thought Ability of Language Models Unlocking General Long Chain-of-Thought Reasoning Capabilities of Large Language Models via Representation Engineering

Reference 43

Resolution
unresolved
no resolver link, observed 2026-08-07T14:46:17.456419Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:46:17.456419Z digest=sha256:e93422e8d7f533d45b2c491ed433e4b88d4f9d6d6fd10f7e95fba71dee0658ee

Observation 52fc4979-7cf7-4065-99ee-5faba847a214 · inbound

Logit Arithmetic Elicits Long Reasoning Capabilities Without Training cites this paper.

Logit Arithmetic Elicits Long Reasoning Capabilities Without Training Unlocking General Long Chain-of-Thought Reasoning Capabilities of Large Language Models via Representation Engineering

Reference 37

Resolution
unresolved
no resolver link, observed 2026-08-06T16:45:59.645017Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T16:45:59.645017Z digest=sha256:482479e3658bd3e32bd9d32b9bc5b8d90c63b03741d4046912cc2c3260f1765f

Observation 34ec79ad-94b0-41b6-8ca0-aacc6ccaf376 · inbound

Enhancing Cross-task Transfer of Large Language Models via Activation Steering cites this paper.

Enhancing Cross-task Transfer of Large Language Models via Activation Steering Unlocking General Long Chain-of-Thought Reasoning Capabilities of Large Language Models via Representation Engineering

Reference 30

Resolution
unresolved
no resolver link, observed 2026-08-06T16:31:51.350436Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T16:31:51.350436Z digest=sha256:379b7009ac66ab1ec30495cff4b97e7a883f8490e23fd50e96715e4f680295b2

Observation 44664614-d825-4fc8-8455-6fe5e4bea3fe · inbound

Can Aha Moments Be Fake? Towards Quantifying Decorative and True Thinking in Chain-of-Thought cites this paper.

Can Aha Moments Be Fake? Towards Quantifying Decorative and True Thinking in Chain-of-Thought Unlocking General Long Chain-of-Thought Reasoning Capabilities of Large Language Models via Representation Engineering

Reference 25

Resolution
verified exact
arxiv_id, observed 2026-05-18T02:45:46.080045Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-05-18T02:44:48.729794Z digest=sha256:4ef15b5a30383f3d9a4534e3f0b98dc3a88dc5b2fde33d10885931e3787098c8

Observation 5052dfba-e5e8-402a-8bcf-9454afe32956 · inbound

Can Aha Moments Be Fake? Towards Quantifying Decorative and True Thinking in Chain-of-Thought cites this paper.

Can Aha Moments Be Fake? Towards Quantifying Decorative and True Thinking in Chain-of-Thought Unlocking General Long Chain-of-Thought Reasoning Capabilities of Large Language Models via Representation Engineering

Reference 25

Resolution
unresolved
no resolver link, observed 2026-08-04T07:43:11.054073Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T07:43:11.054073Z digest=sha256:7a316be2395d48eb83a5e4957c9cd86e67d11f91ce8417f447b80daca3d1e2b3

Observation f1083680-137e-4169-b8f9-addc06fcf258 · inbound

The Tell-Tale Norm: $\ell_2$ Magnitude as a Signal for Reasoning Dynamics in Large Language Models cites this paper.

The Tell-Tale Norm: $\ell_2$ Magnitude as a Signal for Reasoning Dynamics in Large Language Models Unlocking General Long Chain-of-Thought Reasoning Capabilities of Large Language Models via Representation Engineering

Reference 7

Resolution
verified exact
arxiv_id, observed 2026-07-02T12:26:56.915789Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=arxiv_source observed=2026-06-28T02:07:49.501480Z digest=sha256:cb1ebbce01f51a59c733c1c18882d5ebbd4ea60dded15c2c974938d2509daa51