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Paper Citation Record · LEDGER

GenQA: Generating Millions of Instructions from a Handful of Prompts

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 12 inbound Pith citation observations for arXiv:2406.10323.

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

pith.paper-citation-record.v1
2406.10323 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 12 of 12 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 12 of 12 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T13:20:27.547714Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-30T22:05:05.834308Z

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 217d6053-41cf-44db-9aa0-61c61ed15de9 · inbound

Magpie: Alignment Data Synthesis from Scratch by Prompting Aligned LLMs with Nothing cites this paper.

Magpie: Alignment Data Synthesis from Scratch by Prompting Aligned LLMs with Nothing GenQA: Generating Millions of Instructions from a Handful of Prompts

Reference 95

Resolution
verified exact
arxiv_id, observed 2026-05-16T06:58:36.802882Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-05-16T06:58:36.684583Z digest=sha256:a3a6c0144d5e6f7a7a8b1073c683729280d800953a0f91c2df72095e6d2b03ec

Observation 363d877d-4e92-41a7-ac51-4bbce35aff6f · inbound

RAD: Redundancy-Aware Distillation for Hybrid Models via Self-Speculative Decoding cites this paper.

RAD: Redundancy-Aware Distillation for Hybrid Models via Self-Speculative Decoding GenQA: Generating Millions of Instructions from a Handful of Prompts

Reference 7

Resolution
unresolved
no resolver link, observed 2026-08-07T13:20:27.547714Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:20:27.547714Z digest=sha256:bf84beb52ba728b8bd8bf2e29e806ba2f2b265e18224b4670467d081ddcd56d9

Observation 0038ccda-7977-4d22-928b-75fbc3ad31fe · inbound

Zero-Shot Vision Encoder Grafting via LLM Surrogates cites this paper.

Zero-Shot Vision Encoder Grafting via LLM Surrogates GenQA: Generating Millions of Instructions from a Handful of Prompts

Reference 5

Resolution
unresolved
no resolver link, observed 2026-08-07T13:10:04.258432Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:10:04.258432Z digest=sha256:712b1e14be8e112df6c28e6249f0be59caa4efb4ede26737b3ff4a0a16442201

Observation 2fda7eb7-1f61-4008-b791-ef709c12bf72 · inbound

Improved Supervised Fine-Tuning for Large Language Models to Mitigate Catastrophic Forgetting cites this paper.

Improved Supervised Fine-Tuning for Large Language Models to Mitigate Catastrophic Forgetting GenQA: Generating Millions of Instructions from a Handful of Prompts

Reference 2024

Resolution
unresolved
no resolver link, observed 2026-08-07T04:52:55.994940Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T04:52:55.994940Z digest=sha256:1e8db2dea8084f7e43bcdf2c379d600ece3ce8839bc88a221c304e1704d88588

Observation 60f0f2e2-d63f-4d8e-a6fa-28d0a5c28c45 · inbound

FarSkip-Collective: Unhobbling Blocking Communication in Mixture of Experts Models cites this paper.

FarSkip-Collective: Unhobbling Blocking Communication in Mixture of Experts Models GenQA: Generating Millions of Instructions from a Handful of Prompts

Reference 43

Resolution
unresolved
no resolver link, observed 2026-08-03T22:13:59.176576Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T22:13:59.176576Z digest=sha256:0ba77e059f1f162900ddd556f9d54bebca5adb50fa648625ae6ef2a73b21496b

Observation 2b7bf7da-6f08-461d-b49b-abab39771e78 · inbound

One Prompt, Many Sounds: Modeling Listener Variability in LLM-Based Equalization cites this paper.

One Prompt, Many Sounds: Modeling Listener Variability in LLM-Based Equalization GenQA: Generating Millions of Instructions from a Handful of Prompts

Reference 30

Resolution
verified exact
arxiv_id, observed 2026-05-16T14:27:59.201417Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-16T14:26:36.236424Z digest=sha256:e84550e7c797a5a569e4c0cb668e3005236413ab5a5fed1021bb2e4eb47f4bf3

Observation 8755820b-9c77-4f2e-844b-f77d5e61fe87 · inbound

One Prompt, Many Sounds: Modeling Listener Variability in LLM-Based Equalization cites this paper.

One Prompt, Many Sounds: Modeling Listener Variability in LLM-Based Equalization GenQA: Generating Millions of Instructions from a Handful of Prompts

Reference 30

Resolution
unresolved
no resolver link, observed 2026-08-03T10:39:37.483039Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T10:39:37.483039Z digest=sha256:e93336ae7c199d0761186529e6ee05339f2116f445e764da740b454f5fdbeb91

Observation 777ae68e-6444-4b2c-9c84-9c16349c07ef · inbound

MAR: Efficient Large Language Models via Module-aware Architecture Refinement cites this paper.

MAR: Efficient Large Language Models via Module-aware Architecture Refinement GenQA: Generating Millions of Instructions from a Handful of Prompts

Reference 24

Resolution
verified exact
arxiv_id, observed 2026-05-16T10:12:43.041776Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-16T10:12:35.951089Z digest=sha256:a251a179f4c7f536a048a578bf86f665c1892d7839f0c994bc2ae0d1b85ed538

Observation 76be6c74-3573-4ea1-bcfc-ed0354b6ece3 · inbound

Less is Enough: Synthesizing Diverse Data in LLM Feature Space with Sparse Autoencoders cites this paper.

Less is Enough: Synthesizing Diverse Data in LLM Feature Space with Sparse Autoencoders GenQA: Generating Millions of Instructions from a Handful of Prompts

Reference 74

Resolution
unresolved
no resolver link, observed 2026-08-03T01:17:12.055643Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T01:17:12.055643Z digest=sha256:b4d62dfeba09d5e11d98f2d97600c93024af01e664db16561e2b2fb18144fd55

Observation d25d730f-4533-4455-94da-848824cef838 · inbound

Multi-Token Residual Prediction cites this paper.

Multi-Token Residual Prediction GenQA: Generating Millions of Instructions from a Handful of Prompts

Reference 8

Resolution
verified exact
arxiv_id, observed 2026-05-20T22:44:09.769757Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-20T22:43:59.588152Z digest=sha256:fd43d79f5ae6678a31e7f45d1d0cf31cb605ca172aea39777f097b9d2ff026dd

Observation 4ee3e06b-fee1-4258-817f-c3ca77f05b15 · inbound

Multi-Token Residual Prediction cites this paper.

Multi-Token Residual Prediction GenQA: Generating Millions of Instructions from a Handful of Prompts

Reference 8

Resolution
verified exact
arxiv_id, observed 2026-06-30T22:05:05.835816Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-06-30T22:00:45.427111Z digest=sha256:fc8828aedaad43c473b7fd939de06f8d362a51eaa1d4f15b41cd0b9152caac11

Observation 27522345-587d-4c15-a6e2-51a5c70d6338 · inbound

Adaptive Depth Sparse Framework: Similarity-Driven Resource Allocation for Pre-Trained LLMs cites this paper.

Adaptive Depth Sparse Framework: Similarity-Driven Resource Allocation for Pre-Trained LLMs GenQA: Generating Millions of Instructions from a Handful of Prompts

Reference 4

Resolution
unresolved
no resolver link, observed 2026-08-01T07:57:56.074171Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T07:57:56.074171Z digest=sha256:72a74ad01af93438866f4455c0fd409ea8b81d04f3b41b0da3cdd99edb5e8768