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Paper Citation Record · LEDGER

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

As of 8 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 2026-08-08T06:32:00.761636+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=sha256:124bd05e08b1273f6f38b5acae6883697557d9f31e12bd4a5e1079b59f0450d9

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=sha256:8464ea5950e8d3ed859fd0b7883080ddfe027d58dae41668624b61a12d03732c

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=sha256:549fd52d16a98df1dc3a0e10b26581effaffd748034d61f621d9ea958310cdfe

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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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T11:04:47.182925Z digest=sha256:41a174b9a4448b43bb5d810b9756e2518b7094c75ecb730bc44f22dcb7ce5d53

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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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T11:04:47.188628Z digest=sha256:d1bf5cf646302dd83d94249e9bf70b1324e6a2a55414d9a559ff34029d465294

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:b3df7e31d0ab1731fba55e5feb2bb33d76c30b49cb648a5a466052f917380c4b

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=sha256:02dbcf8d597d5079ad59275ea25c419e1a05744c611f8c12b9407523b1944d6f

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=sha256:5eaacef7e35067657008bc573b26ff7b24cca37fa53839f5ccf4405065c4ef15

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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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T11:04:47.203037Z digest=sha256:4d555e75a1f5973fd41fbc0bbfe0c20e5ea75bb6b2b911185aeabd4c9b8281be

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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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T11:04:47.205691Z digest=sha256:c509b1ad13e215a2973b0d40241b94ec06fc2e21a235a623f33440f5550be0c8

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=sha256:43e8e08447c35ef34de9d160eb27ae172f7d384ad71f78aec6f90d84d169bf8c

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=sha256:771ebb18e052a3124523443d93554c8ba097092d7a42fba46a33b1f9c614ec2c

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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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T11:04:47.217354Z digest=sha256:aa2115653bcb60ec3d443d2bfae0f5058a264f10fb3d72a23fa266c2d700a019

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=sha256:39a46c3b1719c8f4a326f3627e92c13f9ea28583e059c56fd27bf3ceda78e10a

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=sha256:40b3fa4ab589b7333d82bc635dc87df121943bbb248eb7f8dcb9c153c1cb9ca4

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=sha256:2414436beb857da42b2353e98516a1d32d4d8852bdbed149f0b11d17f818b154

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:f7e8943cd967043b9b1940f3e8f91839bb9a266c3e1c9024d3670c0ee330e965

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=sha256:96e758495f2c660ab50483df6fe2973574f7d827b1496bf063ced379217ef7b0

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:bef21150ee2a5200b100c839135378d1473e9622d2205fbbe46269b668392970

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=sha256:5685b35f3d41dccb871a888ce38e12191891bca8dfe60975a25c32bc4332106a

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=sha256:051713defd2ec91efc6ef791b7bc2d03bcc7c0a69069117cf8acf9a62b0edeec

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=sha256:93df3482d494ee72d6050710d0d058314225ae5bd775941ecd4a88f17b3f6a39

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=sha256:475108eb9c38aa957f3bd986f019e425e88ad3e85f4967851b9634092c3cf346

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=sha256:951fe11f346308515e8876536d1e006dc67f809baa77baa56dd6caff338ae0ab

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:d06fb58c6639ec6e2a5d56a3460fdea5308a51f1d755dc51e4aed627dc3a436c

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

Resolution
metadata mismatch
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 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T11:04:47.174191Z digest=sha256:b70bc9c4705402960fa064cced6ee2bd75fb24e04fd0858ecc277f45016f3b02

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:bffe19bdf760cbbd368f0c6cf69d5feeab7714dc36951669368aed17d3aa7162

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=sha256:365abd45338e9c25024ea00cdd21419d2a099ac4e3583781b47a5e0970226f63

Pith citing papers

No inbound Pith citation observations are available.