Paper Citation Record · LEDGER

Precise Length Control in Large Language Models

As of 18 August 2026, this Paper Citation Record lists 33 of 33 outbound references and 4 inbound Pith citation observations for arXiv:2412.11937.

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

pith.paper-citation-record.v1
2412.11937 v1

Coverage vector

measured 33 of 33 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T14:30:11.229601Z

measured 37 of 37 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T11:53:39.878520Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-18T00:19:22.185293Z

Reference resolution

33 of 33 outbound references displayed

verified exact0
verified fuzzy11
unresolved21
parse uncertain1
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 1fe13d49-1a96-4d23-ab02-8ed31606ef44 · outbound

This paper cites Curriculum learning.

Precise Length Control in Large Language Models Curriculum learning

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:30:11.861554Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 005f8fb4-ef26-4351-8fc9-36df4114d542 · outbound

This paper cites PIQA: Reasoning about Physical Commonsense in Natural Language.

Precise Length Control in Large Language Models PIQA: Reasoning about Physical Commonsense in Natural Language

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-11T14:30:11.065764Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bc0bcf3c-ec53-499a-bfcf-608d8577178d · outbound

This paper cites Language Models are Few-Shot Learners.

Precise Length Control in Large Language Models Language Models are Few-Shot Learners

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-11T14:30:11.071055Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7c43caf8-70e3-47a1-9ce1-2673f0a6287a · outbound

This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

Precise Length Control in Large Language Models BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-11T14:30:11.076456Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 040b5aa3-ef55-4712-9891-352bb4682239 · outbound

This paper cites Recent automatic text summarization techniques: a survey.

Precise Length Control in Large Language Models Recent automatic text summarization techniques: a survey

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:30:11.842518Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation f856eaa7-7d13-4602-a34c-9e2e30cae6be · outbound

This paper cites A framework for few-shot language model evaluation, 12 2023.

Precise Length Control in Large Language Models A framework for few-shot language model evaluation, 12 2023

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-11T14:30:11.086867Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8fe7196a-2244-4919-948e-23ffe2d7c2cc · outbound

This paper cites Contextual Position Encoding: Learning to Count What's Important.

Precise Length Control in Large Language Models Contextual Position Encoding: Learning to Count What's Important

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-11T14:30:11.092718Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6d485d08-8e08-4d2e-bd89-32db6246eafd · outbound

This paper cites News Summarization and Evaluation in the Era of GPT-3.

Precise Length Control in Large Language Models News Summarization and Evaluation in the Era of GPT-3

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-11T14:30:11.098176Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d5e61448-b386-4d2b-998a-25418c9ef0c3 · outbound

This paper cites Measuring massive multitask language understanding, 2021.

Precise Length Control in Large Language Models Measuring massive multitask language understanding, 2021

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-11T14:30:11.103427Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 51d7c75e-0d1f-4cfa-ac82-1f49d0769ecd · outbound

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

Precise Length Control in Large Language Models LoRA: Low-Rank Adaptation of Large Language Models

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-11T14:30:11.108536Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0453bdb8-822f-4cc8-9a3c-3f49207d527a · outbound

This paper cites an unresolved cited work.

Precise Length Control in Large Language Models Unresolved cited work

Reference 11

Resolution: unresolved; raw_fallback, observed 2026-08-11T14:30:11.800860Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 0d89dc39-0edc-4ebc-ae3b-9aef84c99a60 · outbound

This paper cites Prompt-Based Length Controlled Generation with Reinforcement Learning.

Precise Length Control in Large Language Models Prompt-Based Length Controlled Generation with Reinforcement Learning

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-11T14:30:11.118796Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b67ceb12-c817-49bd-bff7-7d40901bf2c9 · outbound

This paper cites Controlling output length in neural encoder-decoders.

Precise Length Control in Large Language Models Controlling output length in neural encoder-decoders

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:30:11.778938Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation e156535f-2781-4c9f-a1dc-27de22bc98a2 · outbound

This paper cites an unresolved cited work.

Precise Length Control in Large Language Models Unresolved cited work

Reference 14

Resolution: unresolved; raw_fallback, observed 2026-08-11T14:30:11.761268Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 0e2dc7e3-749b-431f-99b4-0025946d5d34 · outbound

This paper cites Openorca: An open dataset of gpt augmented flan reasoning traces.

Precise Length Control in Large Language Models Openorca: An open dataset of gpt augmented flan reasoning traces

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:30:11.744251Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 0b10495e-6bff-41dd-8d99-6d13b05140fd · outbound

This paper cites Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing.

Precise Length Control in Large Language Models Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:30:11.725365Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 8b1ff94d-f755-44bd-9679-62fd477ffe17 · outbound

This paper cites Length control in abstractive summa- rization by pretraining information selection.

Precise Length Control in Large Language Models Length control in abstractive summa- rization by pretraining information selection

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:30:11.706349Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 504a18cd-6adc-480c-9d21-fa2815a949f7 · outbound

This paper cites Decoupled weight decay regularization, 2019.

Precise Length Control in Large Language Models Decoupled weight decay regularization, 2019

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-11T14:30:11.149661Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1f5789a1-d224-40e1-a5d8-e8bc5b1e7f6f · outbound

This paper cites Transformers Can Do Arithmetic with the Right Embeddings.

Precise Length Control in Large Language Models Transformers Can Do Arithmetic with the Right Embeddings

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-11T14:30:11.155463Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 92223724-112e-440a-8617-a39ae85de1a4 · outbound

This paper cites Introducing Meta Llama 3: The most capable openly available LLM to date — ai.meta.com.

Precise Length Control in Large Language Models Introducing Meta Llama 3: The most capable openly available LLM to date — ai.meta.com

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:30:11.668495Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 2df6c2f2-aa6f-4efa-93e1-1c0f339aa3f0 · outbound

This paper cites Abstractive text summarization using sequence-to- sequence rnns and beyond, 2016.

Precise Length Control in Large Language Models Abstractive text summarization using sequence-to- sequence rnns and beyond, 2016

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:30:11.632549Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 0b8eb228-3bb9-4bdb-b7b4-a91437be1ec8 · outbound

This paper cites GPT3.5 Turbo-0125, 2024.

Precise Length Control in Large Language Models GPT3.5 Turbo-0125, 2024

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:30:11.614417Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation c51b05f2-1672-4135-9bf0-f4054d19fe39 · outbound

This paper cites Recipes for building an open-domain chatbot.

Precise Length Control in Large Language Models Recipes for building an open-domain chatbot

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-11T14:30:11.182410Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 66401f5c-6fd1-4b3b-8e7b-fcae248d6c3d · outbound

This paper cites WinoGrande: An Adversarial Winograd Schema Challenge at Scale.

Precise Length Control in Large Language Models WinoGrande: An Adversarial Winograd Schema Challenge at Scale

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-11T14:30:11.187633Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 918c84ab-9322-4264-91f4-961c34e23f57 · outbound

This paper cites Self-Attention with Relative Position Representations.

Precise Length Control in Large Language Models Self-Attention with Relative Position Representations

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-11T14:30:11.192876Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4f2a943c-69a2-4e22-9ab6-526c20ce5a24 · outbound

This paper cites Roformer: Enhanced transformer with rotary position embedding.

Precise Length Control in Large Language Models Roformer: Enhanced transformer with rotary position embedding

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-11T14:30:11.197938Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 96f4db9f-e52b-4e09-95d2-51355d2e0437 · outbound

This paper cites Positional encoding to control output se- quence length.

Precise Length Control in Large Language Models Positional encoding to control output se- quence length

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:30:11.575211Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 9fd88900-2ca6-4207-9eab-d9013051f52f · outbound

This paper cites Attention is all you need.

Precise Length Control in Large Language Models Attention is all you need

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:30:11.555395Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 97c1e578-64d4-47ce-b75b-47191ef379aa · outbound

This paper cites HuggingFace's Transformers: State-of-the-art Natural Language Processing.

Precise Length Control in Large Language Models HuggingFace's Transformers: State-of-the-art Natural Language Processing

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-11T14:30:11.212821Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b6672b31-870b-42ac-bac5-939cf684e975 · outbound

This paper cites LenAtten: An Effective Length Controlling Unit For Text Summarization.

Precise Length Control in Large Language Models LenAtten: An Effective Length Controlling Unit For Text Summarization

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-11T14:30:11.217684Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b138c20a-e4f2-4b42-a2bc-a0c9ef030d71 · outbound

This paper cites HellaSwag: Can a Machine Really Finish Your Sentence?.

Precise Length Control in Large Language Models HellaSwag: Can a Machine Really Finish Your Sentence?

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-11T14:30:11.223640Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 35814e7d-c7fa-4dfd-8b7c-f79778fd9003 · outbound

This paper cites Latent Prompt Tuning for Text Summarization.

Precise Length Control in Large Language Models Latent Prompt Tuning for Text Summarization

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-11T14:30:11.229601Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 69aae9b4-f6b6-4ae8-abe2-641c80c6dead · outbound

This paper cites an unresolved cited work.

Precise Length Control in Large Language Models Unresolved cited work

Reference 2024

Resolution: parse uncertain; raw_fallback, observed 2026-08-11T14:30:11.650119Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Pith citing papers

Observation 8457cd01-76f5-4779-8d85-06dfbc2b3392 · inbound

L1: Controlling How Long A Reasoning Model Thinks With Reinforcement Learning cites this paper.

L1: Controlling How Long A Reasoning Model Thinks With Reinforcement Learning Precise Length Control in Large Language Models

Reference 4

Resolution: verified exact; arxiv_id, observed 2026-05-18T00:19:22.190910Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation ec1326e4-3a25-4972-b835-fd708f1bd9d3 · inbound

An Empirical Study of LLM Reasoning Ability Under Strict Output Length Constraint cites this paper.

An Empirical Study of LLM Reasoning Ability Under Strict Output Length Constraint Precise Length Control in Large Language Models

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-16T11:53:39.878520Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1b61773e-cd8c-4b32-ba7c-0445eb7be52a · inbound

Scalable Chain of Thoughts via Elastic Reasoning cites this paper.

Scalable Chain of Thoughts via Elastic Reasoning Precise Length Control in Large Language Models

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-15T23:13:32.964111Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 50380b6f-a70b-4f9a-94cc-0d5ef0ec6303 · inbound

Making Small Language Models Efficient Reasoners: Intervention, Supervision, Reinforcement cites this paper.

Making Small Language Models Efficient Reasoners: Intervention, Supervision, Reinforcement Precise Length Control in Large Language Models

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-15T22:11:33.887907Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.