Paper Citation Record · LEDGER

Fine-tuning on simulated data outperforms prompting for agent tone of voice

As of 13 August 2026, this Paper Citation Record lists 20 of 20 outbound references and 1 inbound Pith citation observation for arXiv:2507.04889.

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

pith.paper-citation-record.v1
2507.04889 v1

Coverage vector

measured 20 of 20 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T19:42:02.778812Z

measured 21 of 21 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-01T08:17:10.481202Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-01T08:25:33.443431Z

Reference resolution

20 of 20 outbound references displayed

verified exact3
verified fuzzy0
unresolved17
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 95c57692-4605-4118-8f34-aaaddd32e8b6 · outbound

This paper cites Low-Rank Quantization-Aware Training for LLMs.

Fine-tuning on simulated data outperforms prompting for agent tone of voice Low-Rank Quantization-Aware Training for LLMs

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-06T19:42:00.899573Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9fee9a89-6bc7-4aef-a5ac-882a88bba002 · outbound

This paper cites The Llama 3 Herd of Models.

Fine-tuning on simulated data outperforms prompting for agent tone of voice The Llama 3 Herd of Models

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-06T19:42:01.265854Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c900cb8f-bf9c-487d-ac78-c3ada66d128d · outbound

This paper cites Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey.

Fine-tuning on simulated data outperforms prompting for agent tone of voice Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-06T19:42:01.398780Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 073b46af-19c6-4efd-b97c-a8f5e8cf3719 · outbound

This paper cites Are Longer Prompts Always Better? Prompt Selection in Large Language Models for Recommendation Systems.

Fine-tuning on simulated data outperforms prompting for agent tone of voice Are Longer Prompts Always Better? Prompt Selection in Large Language Models for Recommendation Systems

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-06T19:42:01.551848Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 36376b3a-063f-4a71-a1a2-87009f16dc0b · outbound

This paper cites Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity.

Fine-tuning on simulated data outperforms prompting for agent tone of voice Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-06T19:42:01.929370Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a469524d-016a-4e72-9611-2a7be1fb31cf · outbound

This paper cites An Empirical Study of Catastrophic Forgetting in Large Language Models During Continual Fine-tuning.

Fine-tuning on simulated data outperforms prompting for agent tone of voice An Empirical Study of Catastrophic Forgetting in Large Language Models During Continual Fine-tuning

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-06T19:42:02.024680Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 69e2730f-bc46-4a05-9023-03ba0a6846b2 · outbound

This paper cites Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?.

Fine-tuning on simulated data outperforms prompting for agent tone of voice Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-06T19:42:02.154504Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8417e108-98b4-4b51-be67-6c537a14c9f7 · outbound

This paper cites Efficient multi-prompt evaluation of LLMs.

Fine-tuning on simulated data outperforms prompting for agent tone of voice Efficient multi-prompt evaluation of LLMs

Reference 15

Resolution: verified exact; local_arxiv, observed 2026-08-06T19:42:03.030966Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 7ae0f467-f322-4e49-9d98-7ef88b704a61 · outbound

This paper cites Robust Speech Recognition via Large-Scale Weak Supervision.

Fine-tuning on simulated data outperforms prompting for agent tone of voice Robust Speech Recognition via Large-Scale Weak Supervision

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-06T19:42:02.363973Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 995f63ee-7523-4efb-96d6-a74b3eba5be9 · outbound

This paper cites Smarter, Better, Faster, Longer: A Modern Bidirectional Encoder for Fast, Memory Efficient, and Long Context Finetuning and Inference.

Fine-tuning on simulated data outperforms prompting for agent tone of voice Smarter, Better, Faster, Longer: A Modern Bidirectional Encoder for Fast, Memory Efficient, and Long Context Finetuning and Inference

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-06T19:42:02.475877Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 97dde9ae-c4f5-4963-9e88-17599217f8c8 · outbound

This paper cites Benchmarking Complex Instruction-Following with Multiple Constraints Composition.

Fine-tuning on simulated data outperforms prompting for agent tone of voice Benchmarking Complex Instruction-Following with Multiple Constraints Composition

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-06T19:42:02.568438Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 23f6ae84-93c2-48c6-bc61-4786f43fd5f9 · outbound

This paper cites Parameter-Efficient Fine-Tuning Methods for Pretrained Language Models: A Critical Review and Assessment.

Fine-tuning on simulated data outperforms prompting for agent tone of voice Parameter-Efficient Fine-Tuning Methods for Pretrained Language Models: A Critical Review and Assessment

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-06T19:42:02.683151Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d87a4944-d290-4699-bcfd-913853402e8d · outbound

This paper cites Calibrate Before Use: Improving Few-Shot Performance of Language Models.

Fine-tuning on simulated data outperforms prompting for agent tone of voice Calibrate Before Use: Improving Few-Shot Performance of Language Models

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-06T19:42:02.778812Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 633e7da5-cf2d-42fb-ad50-d9cae5d1cfd0 · outbound

This paper cites Decoupled Weight Decay Regularization.

Fine-tuning on simulated data outperforms prompting for agent tone of voice Decoupled Weight Decay Regularization

Reference 2019

Resolution: unresolved; no resolver link, observed 2026-08-06T19:42:01.855677Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c70cfd6a-5409-43d3-9a29-d860807c5f7c · outbound

This paper cites Language Models are Few-Shot Learners.

Fine-tuning on simulated data outperforms prompting for agent tone of voice Language Models are Few-Shot Learners

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-06T19:42:01.080533Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 538a01a4-9ce6-404a-b3e9-8498d320a442 · outbound

This paper cites LoRA: Low-Rank Adaptation of Large Language Models.

Fine-tuning on simulated data outperforms prompting for agent tone of voice LoRA: Low-Rank Adaptation of Large Language Models

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-06T19:42:01.487042Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cbe224bd-eba5-4b79-980d-f1448be9ce6d · outbound

This paper cites LLM.Int8(): 8-Bit Matrix Multiplication for Transformers at Scale.

Fine-tuning on simulated data outperforms prompting for agent tone of voice LLM.Int8(): 8-Bit Matrix Multiplication for Transformers at Scale

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-06T19:42:01.180762Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dbbaf3a8-13bf-4de0-8558-39ac713cebb7 · outbound

This paper cites Towards General Text Embeddings with Multi-stage Contrastive Learning.

Fine-tuning on simulated data outperforms prompting for agent tone of voice Towards General Text Embeddings with Multi-stage Contrastive Learning

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-06T19:42:01.752008Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7db66d8c-8b3e-40a0-a89d-83266853d2cc · outbound

This paper cites Quantization Avoids Saddle Points in Distributed Optimization.

Fine-tuning on simulated data outperforms prompting for agent tone of voice Quantization Avoids Saddle Points in Distributed Optimization

Reference 2024

Resolution: verified exact; doi, observed 2026-08-06T19:42:03.474538Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 7596ce54-f459-4ad0-bf55-96897282b28d · outbound

This paper cites Predictive Prompt Analysis.

Fine-tuning on simulated data outperforms prompting for agent tone of voice Predictive Prompt Analysis

Reference 2025

Resolution: verified exact; doi, observed 2026-08-06T19:42:03.280211Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Pith citing papers

Observation 21dc31cb-63d4-4e91-ab82-79ac74b6af8a · inbound

Prompt Governance? On Governing Technologies Governed by Natural Language cites this paper.

Prompt Governance? On Governing Technologies Governed by Natural Language Fine-tuning on simulated data outperforms prompting for agent tone of voice

Reference 220

Resolution: malformed identifier; arxiv_id, observed 2026-07-01T08:25:33.445168Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.