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Paper Citation Record · LEDGER

Large Language Model Instruction Following: A Survey of Progresses and Challenges

As of 21 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 8 inbound Pith citation observations for arXiv:2303.10475.

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

pith.paper-citation-record.v1
2303.10475 v8

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 2026-08-21T06:32:19.484+00:00

measured 8 of 8 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T23:16:36.523196Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-28T23:22:47.135682Z

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 331b42d5-add0-4b3c-97b6-95c491e6c5b6 · inbound

Unveiling Language-Specific Features in Large Language Models via Sparse Autoencoders cites this paper.

Unveiling Language-Specific Features in Large Language Models via Sparse Autoencoders Large Language Model Instruction Following: A Survey of Progresses and Challenges

Reference 20

Resolution
unresolved
no resolver link, observed 2026-08-15T23:16:36.523196Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T23:16:36.523196Z digest=sha256:2bf23d59118d099fb1e4042883e5426b402f2483f7b2f254336dc32d4ab0d705

Observation c97fe7e4-7e51-47c6-9df7-1b3c0cff747c · inbound

Tests as Prompt: A Test-Driven-Development Benchmark for LLM Code Generation cites this paper.

Tests as Prompt: A Test-Driven-Development Benchmark for LLM Code Generation Large Language Model Instruction Following: A Survey of Progresses and Challenges

Reference 27

Resolution
unresolved
no resolver link, observed 2026-08-15T21:47:07.697713Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T21:47:07.697713Z digest=sha256:81db1c149d78f60013dc98b9e31a0bf8467bdeda723667146221078886eb82a9

Observation fc4f6e34-12a8-44ce-9398-c15217a2c169 · inbound

EVADE-Bench: Multimodal Benchmark for Evaluating and Enhancing Evasive Content Detection cites this paper.

EVADE-Bench: Multimodal Benchmark for Evaluating and Enhancing Evasive Content Detection Large Language Model Instruction Following: A Survey of Progresses and Challenges

Reference 24

Resolution
unresolved
no resolver link, observed 2026-08-07T14:46:04.112849Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:46:04.112849Z digest=sha256:91a45962dfa93ba505249410b4e3efb7fb81c54b0b820a4616cb69288d0b2ab8

Observation c821294f-a17b-4bb5-8330-4cc950f2fed9 · inbound

How Many Instructions Can LLMs Follow at Once? cites this paper.

How Many Instructions Can LLMs Follow at Once? Large Language Model Instruction Following: A Survey of Progresses and Challenges

Reference 17

Resolution
unresolved
no resolver link, observed 2026-08-06T17:13:47.512000Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T17:13:47.512000Z digest=sha256:18f1155ba96f770daba7169fb86ca805368fb9dc55e006c55db3c0e35fc60a39

Observation 5a978203-37e9-46d0-9f07-23cd0cb94c97 · inbound

Steerable Instruction Following Coding Data Synthesis with Actor-Parametric Schema Co-Evolution cites this paper.

Steerable Instruction Following Coding Data Synthesis with Actor-Parametric Schema Co-Evolution Large Language Model Instruction Following: A Survey of Progresses and Challenges

Reference 18

Resolution
verified exact
arxiv_id, observed 2026-05-15T18:01:25.371641Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-05-15T18:00:39.404711Z digest=sha256:09ceb1194ef584954a2b6fbd421163788ddfb9126c0102690604f76e71395346

Observation 09a72a00-3373-4278-8936-ec61dc40bc83 · inbound

PnP-Corrector: A Universal Correction Framework for Coupled Spatiotemporal Forecasting cites this paper.

PnP-Corrector: A Universal Correction Framework for Coupled Spatiotemporal Forecasting Large Language Model Instruction Following: A Survey of Progresses and Challenges

Reference 2

Resolution
verified exact
arxiv_id, observed 2026-05-12T07:21:26.200682Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=arxiv_source observed=2026-05-12T03:27:14.464987Z digest=sha256:c570cf869e1f97bd061606276388a58eb636e5432e7966ac6afafe716704711f

Observation 446e0933-d9f9-490a-9b27-ec2dc8c1b308 · inbound

PnP-Corrector: A Universal Correction Framework for Coupled Spatiotemporal Forecasting cites this paper.

PnP-Corrector: A Universal Correction Framework for Coupled Spatiotemporal Forecasting Large Language Model Instruction Following: A Survey of Progresses and Challenges

Reference 2

Resolution
verified exact
arxiv_id, observed 2026-05-14T21:22:59.177775Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=arxiv_source observed=2026-05-14T21:21:32.256476Z digest=sha256:0701ca59628b3dd95efecd3d53b1774f70ac98ff2d845dfaa0ea3c7f64d82604

Observation f2c512ba-9770-49ca-8b25-3b8e4f74ace7 · inbound

Text-guided Feature Disentanglement for Cross-modal Gait Recognition cites this paper.

Text-guided Feature Disentanglement for Cross-modal Gait Recognition Large Language Model Instruction Following: A Survey of Progresses and Challenges

Reference 25

Resolution
verified exact
arxiv_id, observed 2026-06-28T23:22:47.137128Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-06-28T23:18:45.919821Z digest=sha256:72cb119be348caeec09088fff4646cd5b37be87a47d982a520743c1b29c5fdf6