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Paper Citation Record · LEDGER

BAR Conjecture: the Feasibility of Inference Budget-Constrained LLM Services with Authenticity and Reasoning

As of 14 August 2026, this Paper Citation Record lists 28 of 28 outbound references and 0 inbound Pith citation observations for arXiv:2507.23170.

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

pith.paper-citation-record.v1
2507.23170 v2

Coverage vector

measured 28 of 28 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T11:04:47.246210Z

measured 28 of 28 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

28 of 28 outbound references displayed

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  • verified fuzzy0
  • unresolved27
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  • malformed identifier0
  • metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 1b66c952-fc3c-4b18-8ae3-1d5d50e4646b · outbound

This paper cites Towards an AI co-scientist.

BAR Conjecture: the Feasibility of Inference Budget-Constrained LLM Services with Authenticity and Reasoning Towards an AI co-scientist

Reference 5

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source=pdf_text observed=2026-08-06T11:04:47.167562Z digest=sha256:f5510d963eaa7068c0cc493361fb351ce13b18b3602f7bb16146f279aaef4ac3

Observation 32c64fbd-6714-4625-a5f0-fb410a6bb187 · outbound

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

BAR Conjecture: the Feasibility of Inference Budget-Constrained LLM Services with Authenticity and Reasoning LoRA: Low-Rank Adaptation of Large Language Models

Reference 6

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source=pdf_text observed=2026-08-06T11:04:47.170707Z digest=sha256:2aa4e7c8752719ec55aa87cfdf0b6078d5318bab8c1ef7cc4ff0561db29dbc46

Observation 5ff1b3a9-c17c-4e7d-893b-798985b812c3 · outbound

This paper cites Efficient Memory Management for Large Language Model Serving with PagedAttention.

BAR Conjecture: the Feasibility of Inference Budget-Constrained LLM Services with Authenticity and Reasoning Efficient Memory Management for Large Language Model Serving with PagedAttention

Reference 9

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source=pdf_text observed=2026-08-06T11:04:47.180028Z digest=sha256:0336d8f7cdf7d8ed1e3597ca31812da9b3ce9c81e9012242b23a704ade7266cb

Observation 9af56362-bc4d-44ef-8328-722b371e73a2 · outbound

This paper cites Chain of Thought Empowers Transformers to Solve Inherently Serial Problems.

BAR Conjecture: the Feasibility of Inference Budget-Constrained LLM Services with Authenticity and Reasoning Chain of Thought Empowers Transformers to Solve Inherently Serial Problems

Reference 10

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T11:04:47.182925Z digest=sha256:fbf6515df25e74aeceb982bed86b269aa6dd65af3cb6775785efa999dee11759

Observation 1a89ef34-cc05-4813-8e29-b9c91dc2d336 · outbound

This paper cites William Merrill, Ashish Sabharwal, and Noah A.

BAR Conjecture: the Feasibility of Inference Budget-Constrained LLM Services with Authenticity and Reasoning William Merrill, Ashish Sabharwal, and Noah A

Reference 12

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source=pdf_text observed=2026-08-06T11:04:47.188628Z digest=sha256:cf91955a8de2b620a56bc14976d3f208a3e5df66919a361cb797659f170a5823

Observation 5f32f2fb-def0-4023-bfd7-cf6fd8924e2f · outbound

This paper cites WebGPT: Browser-assisted question-answering with human feedback.

BAR Conjecture: the Feasibility of Inference Budget-Constrained LLM Services with Authenticity and Reasoning WebGPT: Browser-assisted question-answering with human feedback

Reference 14

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T11:04:47.194442Z digest=sha256:5b4a5e0a2fdab53d21d54f39064a5029f72f20ab93ea6281bac1462c8c297839

Observation 74490c47-90ef-481e-87b2-ecae77f8fcb7 · outbound

This paper cites AlphaEvolve: A coding agent for scientific and algorithmic discovery.

BAR Conjecture: the Feasibility of Inference Budget-Constrained LLM Services with Authenticity and Reasoning AlphaEvolve: A coding agent for scientific and algorithmic discovery

Reference 15

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source=pdf_text observed=2026-08-06T11:04:47.197109Z digest=sha256:ced689b7bd8c4a9af44eae637a60845143c753ecc97a9939dcbaf595f8261353

Observation 20883236-95d0-459f-9721-f577d66837f0 · outbound

This paper cites Training language models to follow instructions with human feedback.

BAR Conjecture: the Feasibility of Inference Budget-Constrained LLM Services with Authenticity and Reasoning Training language models to follow instructions with human feedback

Reference 16

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source=pdf_text observed=2026-08-06T11:04:47.200093Z digest=sha256:d84d542c43a41d12921f3ae0e5d6b42e48459bd5ab9764836b46ddf2d092fb9b

Observation 772bb140-c975-474e-aad6-ae0cbcc5fb76 · outbound

This paper cites Rewon Child Pope and Scott Gray.

BAR Conjecture: the Feasibility of Inference Budget-Constrained LLM Services with Authenticity and Reasoning Rewon Child Pope and Scott Gray

Reference 17

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source=pdf_text observed=2026-08-06T11:04:47.203037Z digest=sha256:e1ced0c9c034c6fe7c39a37aa21175db5ba29facb6ae8b695447980d0923ef3f

Observation 2df02792-4455-477c-9e32-7cc8770fd046 · outbound

This paper cites Direct Preference Optimization: Your Language Model is Secretly a Reward Model.

BAR Conjecture: the Feasibility of Inference Budget-Constrained LLM Services with Authenticity and Reasoning Direct Preference Optimization: Your Language Model is Secretly a Reward Model

Reference 18

Resolution
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source=pdf_text observed=2026-08-06T11:04:47.205691Z digest=sha256:84fe1e76321de0b124c6aa095f8ed4317f1e0074c17efc40cc7a8fe56d602792

Observation ad9eef2a-6b9b-4d55-b8e9-907e947fa65a · outbound

This paper cites GPQA: A Graduate-Level Google-Proof Q&A Benchmark.

BAR Conjecture: the Feasibility of Inference Budget-Constrained LLM Services with Authenticity and Reasoning GPQA: A Graduate-Level Google-Proof Q&A Benchmark

Reference 19

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source=pdf_text observed=2026-08-06T11:04:47.208540Z digest=sha256:302a1b8d56852451895d6085707df2a936a007f1663fe27b03c43df0b15d84a6

Observation 3a8320f0-f3eb-4b85-ae25-8c320865a0cf · outbound

This paper cites Towards Understanding Sycophancy in Language Models.

BAR Conjecture: the Feasibility of Inference Budget-Constrained LLM Services with Authenticity and Reasoning Towards Understanding Sycophancy in Language Models

Reference 20

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source=pdf_text observed=2026-08-06T11:04:47.211412Z digest=sha256:0e1db82e2818bbe9fa7ce5fa8ee6a1a156a94d905bb83387a5bba38a3bb0bf06

Observation 66f2a7a2-8b7e-44b3-a144-d8d36f377565 · outbound

This paper cites Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism.

BAR Conjecture: the Feasibility of Inference Budget-Constrained LLM Services with Authenticity and Reasoning Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism

Reference 22

Resolution
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source=pdf_text observed=2026-08-06T11:04:47.217354Z digest=sha256:6b6623999ee38a7d73e338d7dd8ecd5175feddafba6d34d9c754f35ecf86da04

Observation f125b8d1-327f-4444-a71b-e6f3b46b29a1 · outbound

This paper cites Average-Hard Attention Transformers are Constant-Depth Uniform Threshold Circuits.

BAR Conjecture: the Feasibility of Inference Budget-Constrained LLM Services with Authenticity and Reasoning Average-Hard Attention Transformers are Constant-Depth Uniform Threshold Circuits

Reference 24

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source=pdf_text observed=2026-08-06T11:04:47.223422Z digest=sha256:62e69cc6eebe9664c47ea32d9d452d519da480d98a978b13f7753c92fb39f3c0

Observation 936f2e52-7692-491b-9a99-4fbbb9ef1ebe · outbound

This paper cites Hierarchical Reasoning Model.

BAR Conjecture: the Feasibility of Inference Budget-Constrained LLM Services with Authenticity and Reasoning Hierarchical Reasoning Model

Reference 25

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source=pdf_text observed=2026-08-06T11:04:47.225944Z digest=sha256:c8551495a43d52f9d4597a12a44bee50fa1c35d40e83912a23279a7a70673f32

Observation 4b4555b9-1f58-4aef-99fa-00a2286b083c · outbound

This paper cites Self-Consistency Improves Chain of Thought Reasoning in Language Models.

BAR Conjecture: the Feasibility of Inference Budget-Constrained LLM Services with Authenticity and Reasoning Self-Consistency Improves Chain of Thought Reasoning in Language Models

Reference 27

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source=pdf_text observed=2026-08-06T11:04:47.231963Z digest=sha256:63e6645e22e98b8849443712ee85c891f6914d4f6882c279b0515078eb4a6d83

Observation a9dee617-dfc4-42f7-8541-f64159a39746 · outbound

This paper cites Chain-of-Thought Prompting Elicits Reasoning in Large Language Models.

BAR Conjecture: the Feasibility of Inference Budget-Constrained LLM Services with Authenticity and Reasoning Chain-of-Thought Prompting Elicits Reasoning in Large Language Models

Reference 28

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source=pdf_text observed=2026-08-06T11:04:47.234406Z digest=sha256:db0b93d288c338f6332074c1b9588175848607d2e91b3bf3816c276376649350

Observation fa07a21d-7eb0-4d7e-ba4b-3a9766a99875 · outbound

This paper cites Large Language Models as Analogical Reasoners.

BAR Conjecture: the Feasibility of Inference Budget-Constrained LLM Services with Authenticity and Reasoning Large Language Models as Analogical Reasoners

Reference 30

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source=pdf_text observed=2026-08-06T11:04:47.240529Z digest=sha256:557b7528de1571bd28548c7b60529234d13d26837514087cef0ab99001d5bc82

Observation 2b23ebea-be3e-4d4c-a17f-77db0b109197 · outbound

This paper cites Least-to-Most Prompting Enables Complex Reasoning in Large Language Models.

BAR Conjecture: the Feasibility of Inference Budget-Constrained LLM Services with Authenticity and Reasoning Least-to-Most Prompting Enables Complex Reasoning in Large Language Models

Reference 32

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source=pdf_text observed=2026-08-06T11:04:47.246210Z digest=sha256:d9700ef264d41e9ec0968e432b7184043f690b0f45b1e3a2386a02aa03e76092

Observation 761e51e9-5093-4b72-8e07-b6339d36bef4 · outbound

This paper cites TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension.

BAR Conjecture: the Feasibility of Inference Budget-Constrained LLM Services with Authenticity and Reasoning TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension

Reference 2017

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source=pdf_text observed=2026-08-06T11:04:47.176983Z digest=sha256:362c48cce31774ad3d42d02c679cc0a1cf3db7a34a6b7955a64f1d5505684cab

Observation 53813fa4-b8c5-430f-86d1-f321aa31da0f · outbound

This paper cites LogicBench: Towards Systematic Evaluation of Logical Reasoning Ability of Large Language Models.

BAR Conjecture: the Feasibility of Inference Budget-Constrained LLM Services with Authenticity and Reasoning LogicBench: Towards Systematic Evaluation of Logical Reasoning Ability of Large Language Models

Reference 2018

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source=pdf_text observed=2026-08-06T11:04:47.237610Z digest=sha256:9ca5928fc7aedc7ff668410131e228cdd6e8c68da7b0c4e147b7cc5bcaa1b179

Observation d92c68b1-6eb0-4ad5-97ac-e268cab42e02 · outbound

This paper cites Towards Understanding Systems Trade-offs in Retrieval-Augmented Generation Model Inference.

BAR Conjecture: the Feasibility of Inference Budget-Constrained LLM Services with Authenticity and Reasoning Towards Understanding Systems Trade-offs in Retrieval-Augmented Generation Model Inference

Reference 2019

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source=pdf_text observed=2026-08-06T11:04:47.214527Z digest=sha256:1872900929453b45b9088e8c1d3cd5aed68f48b993bf9912043a7ed3b14f7de4

Observation fcb93ed2-2b4c-4e65-9a00-ac1ddcb29def · outbound

This paper cites PaLM: Scaling Language Modeling with Pathways.

BAR Conjecture: the Feasibility of Inference Budget-Constrained LLM Services with Authenticity and Reasoning PaLM: Scaling Language Modeling with Pathways

Reference 2020

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source=pdf_text observed=2026-08-06T11:04:47.154381Z digest=sha256:449b92689f720adbcded93bb8bc37406fb8456784b735f4d570e7211f3032ab7

Observation f3001182-f35c-4f0e-b9f6-f98a693ebbbd · outbound

This paper cites Training Verifiers to Solve Math Word Problems.

BAR Conjecture: the Feasibility of Inference Budget-Constrained LLM Services with Authenticity and Reasoning Training Verifiers to Solve Math Word Problems

Reference 2021

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source=pdf_text observed=2026-08-06T11:04:47.157860Z digest=sha256:37f935769f94bc7f7ea7d21eec49918065c1e5c6ff9bcda23d08204e43d67ba2

Observation 6d5eb9f8-df43-4981-93af-12c4f6aad751 · outbound

This paper cites TruthfulQA: Measuring How Models Mimic Human Falsehoods.

BAR Conjecture: the Feasibility of Inference Budget-Constrained LLM Services with Authenticity and Reasoning TruthfulQA: Measuring How Models Mimic Human Falsehoods

Reference 2022

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source=pdf_text observed=2026-08-06T11:04:47.185762Z digest=sha256:bbfe4f640fbe06e3f443d10dee319d90531f76820ea8fd45f05a22606704dc11

Observation efb6f31f-3f01-4b78-9b4c-7d974bd32fbf · outbound

This paper cites Back to Basics: A Simple Recipe for Improving Out-of-Domain Retrieval in Dense Encoders.

BAR Conjecture: the Feasibility of Inference Budget-Constrained LLM Services with Authenticity and Reasoning Back to Basics: A Simple Recipe for Improving Out-of-Domain Retrieval in Dense Encoders

Reference 2023

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local_arxiv, observed 2026-08-06T11:04:47.589792Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-06T11:04:47.174191Z digest=sha256:d1fef4feeda5d83684982d42ff7efc33e89e410d77efcddacb6699d03ad20f90

Observation 792e17eb-f0cc-4ee3-b883-4b592fa580b1 · outbound

This paper cites KTO: Model Alignment as Prospect Theoretic Optimization.

BAR Conjecture: the Feasibility of Inference Budget-Constrained LLM Services with Authenticity and Reasoning KTO: Model Alignment as Prospect Theoretic Optimization

Reference 2024

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source=pdf_text observed=2026-08-06T11:04:47.163937Z digest=sha256:96abac40af7abcfa7e1c45f483bcc7776cac2e4cc028f7eb008d1169feb8a6ea

Observation b1f68ccc-56c0-4974-8a44-17321f381b7b · outbound

This paper cites DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning.

BAR Conjecture: the Feasibility of Inference Budget-Constrained LLM Services with Authenticity and Reasoning DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 2025

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source=pdf_text observed=2026-08-06T11:04:47.160657Z digest=sha256:4eba321e5511d9a28472be8365b5a1c62369fddbd2b752f7a39b0c674d613f0d

Pith citing papers

No inbound Pith citation observations are available.