Paper Citation Record · LEDGER

I Have Covered All the Bases Here: Interpreting Reasoning Features in Large Language Models via Sparse Autoencoders

As of 23 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 11 inbound Pith citation observations for arXiv:2503.18878.

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

pith.paper-citation-record.v1
2503.18878 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 11 of 11 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00

measured 11 of 11 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:44:23.234479Z

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 7d9a689a-d348-4f41-ba2b-8d9e55855340 · inbound

Get Experience from Practice: LLM Agents with Record & Replay cites this paper.

Get Experience from Practice: LLM Agents with Record & Replay I Have Covered All the Bases Here: Interpreting Reasoning Features in Large Language Models via Sparse Autoencoders

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-07T14:44:23.234479Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f8bb119f-f6ac-4136-991c-45324ab7c699 · inbound

Reasoning-Finetuning Repurposes Latent Representations in Base Models cites this paper.

Reasoning-Finetuning Repurposes Latent Representations in Base Models I Have Covered All the Bases Here: Interpreting Reasoning Features in Large Language Models via Sparse Autoencoders

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-06T16:50:00.194759Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7fe71ad6-57ad-4266-8612-7fd0530e0210 · inbound

Layer-Wise Perturbations via Sparse Autoencoders for Adversarial Text Generation cites this paper.

Layer-Wise Perturbations via Sparse Autoencoders for Adversarial Text Generation I Have Covered All the Bases Here: Interpreting Reasoning Features in Large Language Models via Sparse Autoencoders

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-05T20:31:31.253466Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ed56be10-08bd-42c0-ae61-272eec8bacbf · inbound

Controllable LLM Reasoning via Sparse Autoencoder-Based Steering cites this paper.

Controllable LLM Reasoning via Sparse Autoencoder-Based Steering I Have Covered All the Bases Here: Interpreting Reasoning Features in Large Language Models via Sparse Autoencoders

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-03T12:20:58.883133Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 21391415-eafd-450f-be88-ba4f326f41e9 · inbound

Locate, Steer, and Improve: A Practical Survey of Actionable Mechanistic Interpretability in Large Language Models cites this paper.

Locate, Steer, and Improve: A Practical Survey of Actionable Mechanistic Interpretability in Large Language Models I Have Covered All the Bases Here: Interpreting Reasoning Features in Large Language Models via Sparse Autoencoders

Reference 76

Resolution: verified exact; arxiv_id, observed 2026-05-16T12:40:54.776198Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 12b9be53-fc88-488a-8a0c-fb6e1bab5bae · inbound

Contrastive Attribution in the Wild: An Interpretability Analysis of LLM Failures on Realistic Benchmarks cites this paper.

Contrastive Attribution in the Wild: An Interpretability Analysis of LLM Failures on Realistic Benchmarks I Have Covered All the Bases Here: Interpreting Reasoning Features in Large Language Models via Sparse Autoencoders

Reference 25

Resolution: metadata mismatch; arxiv_id, observed 2026-05-10T09:38:42.820595Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 9d0fb067-8708-464b-bc2a-b9fca23d470e · inbound

Why Does Reinforcement Learning Generalize? A Feature-Level Mechanistic Study of Post-Training in Large Language Models cites this paper.

Why Does Reinforcement Learning Generalize? A Feature-Level Mechanistic Study of Post-Training in Large Language Models I Have Covered All the Bases Here: Interpreting Reasoning Features in Large Language Models via Sparse Autoencoders

Reference 1

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T22:01:12.677498Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 75ff0ea4-2377-49e9-8aef-d16c827c1a5a · inbound

Steered Generation via Gradient-Based Optimization on Sparse Query Features cites this paper.

Steered Generation via Gradient-Based Optimization on Sparse Query Features I Have Covered All the Bases Here: Interpreting Reasoning Features in Large Language Models via Sparse Autoencoders

Reference 15

Resolution: verified exact; arxiv_id, observed 2026-05-25T05:36:40.429670Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation d912a75e-ec43-4732-bf63-4efa19771549 · 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 I Have Covered All the Bases Here: Interpreting Reasoning Features in Large Language Models via Sparse Autoencoders

Reference 8

Resolution: metadata mismatch; arxiv_id, observed 2026-07-02T12:26:56.913383Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 4adc838f-5e6d-4a91-aec8-e6c561786ac0 · inbound

Characterize Then Distill: Mechanistic Reasoning in Large Output Spaces cites this paper.

Characterize Then Distill: Mechanistic Reasoning in Large Output Spaces I Have Covered All the Bases Here: Interpreting Reasoning Features in Large Language Models via Sparse Autoencoders

Reference 51

Resolution: metadata mismatch; arxiv_id, observed 2026-06-27T22:31:21.460721Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 28a025ab-1516-4726-b01e-0f17440116ec · inbound

The Periodic Table of LLM Reasoning: A Structured Survey of Reasoning Paradigms, Methods, and Failure Modes cites this paper.

The Periodic Table of LLM Reasoning: A Structured Survey of Reasoning Paradigms, Methods, and Failure Modes I Have Covered All the Bases Here: Interpreting Reasoning Features in Large Language Models via Sparse Autoencoders

Reference 65

Resolution: verified exact; arxiv_id, observed 2026-07-03T05:57:41.597502Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.