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Paper Citation Record · LEDGER

Position: An Inner Interpretability Framework for AI Inspired by Lessons from Cognitive Neuroscience

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2406.01352.

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

pith.paper-citation-record.v1
2406.01352 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 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 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T22:08:49.479518Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-07T12:35:43.101025Z

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 40c8db46-091a-44f9-a79e-514c8786c25a · inbound

Towards Unified Attribution in Explainable AI, Data-Centric AI, and Mechanistic Interpretability cites this paper.

Towards Unified Attribution in Explainable AI, Data-Centric AI, and Mechanistic Interpretability Position: An Inner Interpretability Framework for AI Inspired by Lessons from Cognitive Neuroscience

Reference 96

Resolution
unresolved
no resolver link, observed 2026-08-09T22:08:49.479518Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-09T22:08:49.479518Z digest=sha256:18c1a0f37838e704d2707623d3f5395c1ff1b2d9989ebbd2648cca1129af3e64

Observation ae35ad49-f82c-40b3-a740-616edf723077 · inbound

Circuit Stability Characterizes Language Model Generalization cites this paper.

Circuit Stability Characterizes Language Model Generalization Position: An Inner Interpretability Framework for AI Inspired by Lessons from Cognitive Neuroscience

Reference 57

Resolution
metadata mismatch
local_arxiv, observed 2026-08-07T12:35:43.211999Z

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=arxiv_source observed=2026-08-07T12:35:41.437977Z digest=sha256:5318507f75042cc3e740b59618c7c9eb1caf9090ff48486343e14b06ad89926f