Paper Citation Record · LEDGER

Eliciting Fine-Tuned Transformer Capabilities via Inference-Time Techniques

As of 9 August 2026, this Paper Citation Record lists 38 of 38 outbound references and 1 inbound Pith citation observation for arXiv:2506.08060.

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

pith.paper-citation-record.v1
2506.08060 v1

Coverage vector

measured 38 of 38 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T05:38:37.134674Z

measured 39 of 39 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 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-12T00:52:51.509014Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-12T08:36:26.806213Z

Reference resolution

38 of 38 outbound references displayed

verified exact2
verified fuzzy6
unresolved26
parse uncertain0
malformed identifier0
metadata mismatch4

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 462ba245-4802-4f0f-af19-bca856a0f78c · outbound

This paper cites Attention Is All You Need.

Eliciting Fine-Tuned Transformer Capabilities via Inference-Time Techniques Attention Is All You Need

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-07T05:38:34.137344Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f7b1667b-9d5d-4fe2-941b-6cd2ac4243da · outbound

This paper cites On the Computational Power of Transformers and its Implications in Sequence Modeling.

Eliciting Fine-Tuned Transformer Capabilities via Inference-Time Techniques On the Computational Power of Transformers and its Implications in Sequence Modeling

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-07T05:38:34.215539Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fde1f927-c147-4a02-adf9-ae1b8164b69a · outbound

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

Eliciting Fine-Tuned Transformer Capabilities via Inference-Time Techniques DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-07T05:38:34.305444Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 79ae7b2b-497f-4202-9d9e-ed7d9a05f340 · outbound

This paper cites an unresolved cited work.

Eliciting Fine-Tuned Transformer Capabilities via Inference-Time Techniques Unresolved cited work

Reference 4

Resolution: unresolved; raw_fallback, observed 2026-08-07T05:38:41.383955Z

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.

Observation bfd91ce2-f9ac-4b4a-bbf2-89c908dace72 · outbound

This paper cites Large Language Models are Zero-Shot Reasoners.

Eliciting Fine-Tuned Transformer Capabilities via Inference-Time Techniques Large Language Models are Zero-Shot Reasoners

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-07T05:38:34.489476Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a23ce5df-3401-4fef-86c9-900490a8d71e · outbound

This paper cites Finetuned Language Models Are Zero-Shot Learners.

Eliciting Fine-Tuned Transformer Capabilities via Inference-Time Techniques Finetuned Language Models Are Zero-Shot Learners

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-07T05:38:34.546048Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bf28f25e-3998-4813-90ba-e62b3fc2e942 · outbound

This paper cites an unresolved cited work.

Eliciting Fine-Tuned Transformer Capabilities via Inference-Time Techniques Unresolved cited work

Reference 7

Resolution: unresolved; raw_fallback, observed 2026-08-07T05:38:41.160354Z

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.

Observation 63ea0ed6-9565-4e66-9323-8146c41347a1 · outbound

This paper cites K., Ginsberg, E.

Eliciting Fine-Tuned Transformer Capabilities via Inference-Time Techniques K., Ginsberg, E

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:38:41.015404Z

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.

Observation 2e46f2de-d363-4175-9b36-a05d56940c1f · outbound

This paper cites An Explanation of In-context Learning as Implicit Bayesian Inference.

Eliciting Fine-Tuned Transformer Capabilities via Inference-Time Techniques An Explanation of In-context Learning as Implicit Bayesian Inference

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-07T05:38:34.748484Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7ce40b1d-4168-4b2b-a4f1-c7cb8c548470 · outbound

This paper cites Fine-Tuned Transformers Show Clusters of Similar Representations Across Layers.

Eliciting Fine-Tuned Transformer Capabilities via Inference-Time Techniques Fine-Tuned Transformers Show Clusters of Similar Representations Across Layers

Reference 10

Resolution: verified exact; local_arxiv, observed 2026-08-07T05:38:38.534612Z

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.

Observation 12891052-5e46-4f88-8ef0-1e497c35a52f · outbound

This paper cites Eliciting Latent Predictions from Transformers with the Tuned Lens.

Eliciting Fine-Tuned Transformer Capabilities via Inference-Time Techniques Eliciting Latent Predictions from Transformers with the Tuned Lens

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-07T05:38:34.910116Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 565b6225-4cbb-4bad-b428-b6c40f3aa216 · outbound

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

Eliciting Fine-Tuned Transformer Capabilities via Inference-Time Techniques Chain-of-Thought Prompting Elicits Reasoning in Large Language Models

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-07T05:38:35.025542Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0ae773d6-c2a8-4f19-b2da-d8677023a547 · outbound

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

Eliciting Fine-Tuned Transformer Capabilities via Inference-Time Techniques LoRA: Low-Rank Adaptation of Large Language Models

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-07T05:38:35.108517Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0cff4e16-ad2a-46d4-ba3d-61f462485682 · outbound

This paper cites an unresolved cited work.

Eliciting Fine-Tuned Transformer Capabilities via Inference-Time Techniques Unresolved cited work

Reference 14

Resolution: unresolved; raw_fallback, observed 2026-08-07T05:38:40.849753Z

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.

Observation 036772fa-2944-4984-b8d5-206c295f6300 · outbound

This paper cites Are Transformers universal approximators of sequence-to-sequence functions?.

Eliciting Fine-Tuned Transformer Capabilities via Inference-Time Techniques Are Transformers universal approximators of sequence-to-sequence functions?

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-07T05:38:35.295805Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 024cab8f-b061-4b05-92cd-9b2032aa1505 · outbound

This paper cites What Can Transformers Learn In-Context? A Case Study of Simple Function Classes.

Eliciting Fine-Tuned Transformer Capabilities via Inference-Time Techniques What Can Transformers Learn In-Context? A Case Study of Simple Function Classes

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-07T05:38:35.365797Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f53505b2-cede-4056-8040-acfc15085046 · outbound

This paper cites Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters.

Eliciting Fine-Tuned Transformer Capabilities via Inference-Time Techniques Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-07T05:38:35.457268Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 444aab0f-4dd9-49b9-882d-5d583580fb56 · outbound

This paper cites A nested MLMC framework for efficient simulations on FPGAs.

Eliciting Fine-Tuned Transformer Capabilities via Inference-Time Techniques A nested MLMC framework for efficient simulations on FPGAs

Reference 18

Resolution: metadata mismatch; local_arxiv, observed 2026-08-07T05:38:38.162661Z

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.

Observation dfecb220-7198-4b08-9bd0-d258255cd90c · outbound

This paper cites an unresolved cited work.

Eliciting Fine-Tuned Transformer Capabilities via Inference-Time Techniques Unresolved cited work

Reference 19

Resolution: unresolved; raw_fallback, observed 2026-08-07T05:38:40.672051Z

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.

Observation e977dde5-7213-4110-aa3e-5d92ad9e6efc · outbound

This paper cites SSRN 5253327.http://dx.doi.org/10.2139/ssrn.5253327.

Eliciting Fine-Tuned Transformer Capabilities via Inference-Time Techniques SSRN 5253327.http://dx.doi.org/10.2139/ssrn.5253327

Reference 20

Resolution: verified exact; doi, observed 2026-08-07T05:38:37.342593Z

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.

Observation 129666af-5872-474d-8876-81c115be9ded · outbound

This paper cites Patched MOA: optimizing inference for diverse software development tasks.

Eliciting Fine-Tuned Transformer Capabilities via Inference-Time Techniques Patched MOA: optimizing inference for diverse software development tasks

Reference 21

Resolution: metadata mismatch; local_arxiv, observed 2026-08-07T05:38:37.921589Z

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.

Observation 0bbe7218-344e-4926-9150-bf5cdb7b65e4 · outbound

This paper cites Patched RTC: evaluating LLMs for diverse software development tasks.

Eliciting Fine-Tuned Transformer Capabilities via Inference-Time Techniques Patched RTC: evaluating LLMs for diverse software development tasks

Reference 22

Resolution: metadata mismatch; local_arxiv, observed 2026-08-07T05:38:37.767944Z

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.

Observation 7790cbe4-a170-44a8-b46e-ec2b6eb966f5 · outbound

This paper cites GitHub.https://github.com/codelion/adaptive-classifier.

Eliciting Fine-Tuned Transformer Capabilities via Inference-Time Techniques GitHub.https://github.com/codelion/adaptive-classifier

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:38:40.542395Z

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.

Observation 4463206d-8f44-4170-acbb-d11e74173876 · outbound

This paper cites an unresolved cited work.

Eliciting Fine-Tuned Transformer Capabilities via Inference-Time Techniques Unresolved cited work

Reference 24

Resolution: unresolved; raw_fallback, observed 2026-08-07T05:38:40.400743Z

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

Observation f0bc58ca-3b4f-4f89-b79a-670ced7faca0 · outbound

This paper cites arXiv:2410.12345.https://arxiv.org/abs/2410.12345.

Eliciting Fine-Tuned Transformer Capabilities via Inference-Time Techniques arXiv:2410.12345.https://arxiv.org/abs/2410.12345

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-07T05:38:36.102091Z

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Unavailable: canonical work link unavailable.

Observation eb843ab6-f906-4e05-82b0-c73a573b0673 · outbound

This paper cites Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks.

Eliciting Fine-Tuned Transformer Capabilities via Inference-Time Techniques Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-07T05:38:36.163579Z

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Observation b721c68a-ed7d-4ce3-93e9-a48db87c78b9 · outbound

This paper cites Language Models are Few-Shot Learners.

Eliciting Fine-Tuned Transformer Capabilities via Inference-Time Techniques Language Models are Few-Shot Learners

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-07T05:38:36.273599Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5130a06f-9d92-4ecd-8787-3eff8960d462 · outbound

This paper cites GitHub.https://github.com/codelion/ pts 10.

Eliciting Fine-Tuned Transformer Capabilities via Inference-Time Techniques GitHub.https://github.com/codelion/ pts 10

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:38:40.215236Z

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.

Observation 37c09233-0ebe-4b60-9ce9-bbf6b6b029db · outbound

This paper cites Coresets for Scalable Bayesian Logistic Regression.

Eliciting Fine-Tuned Transformer Capabilities via Inference-Time Techniques Coresets for Scalable Bayesian Logistic Regression

Reference 29

Resolution: metadata mismatch; local_arxiv, observed 2026-08-07T05:38:37.508249Z

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.

Observation ca015fff-a586-45dc-873c-7d30d75f4d02 · outbound

This paper cites Introduction to Core-sets: an Updated Survey.

Eliciting Fine-Tuned Transformer Capabilities via Inference-Time Techniques Introduction to Core-sets: an Updated Survey

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-07T05:38:36.501100Z

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Unavailable: canonical work link unavailable.

Observation eb39c7ea-34d7-46ec-9020-e8afdc45ea20 · outbound

This paper cites Springer.

Eliciting Fine-Tuned Transformer Capabilities via Inference-Time Techniques Springer

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-07T05:38:36.571427Z

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Unavailable: canonical work link unavailable.

Observation 1b50d77b-0a30-4f5b-845d-795734fca848 · outbound

This paper cites GLM: General Language Model Pretraining with Autoregressive Blank Infilling.

Eliciting Fine-Tuned Transformer Capabilities via Inference-Time Techniques GLM: General Language Model Pretraining with Autoregressive Blank Infilling

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-07T05:38:36.638733Z

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Observation 8e8e865d-b68c-4282-bafb-3da90f7e8e2c · outbound

This paper cites A short note on learning discrete distributions.

Eliciting Fine-Tuned Transformer Capabilities via Inference-Time Techniques A short note on learning discrete distributions

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-07T05:38:36.718814Z

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Unavailable: canonical work link unavailable.

Observation d12de946-04b3-4691-9a08-b456dae50d48 · outbound

This paper cites [SEP]",.

Eliciting Fine-Tuned Transformer Capabilities via Inference-Time Techniques [SEP]",

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:38:40.036286Z

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

Observation 211b9d19-3ec6-435b-8d94-8073e018b5f7 · outbound

This paper cites an unresolved cited work.

Eliciting Fine-Tuned Transformer Capabilities via Inference-Time Techniques Unresolved cited work

Reference 35

Resolution: unresolved; raw_fallback, observed 2026-08-07T05:38:39.680340Z

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.

Observation f279a3fd-4bd8-43e0-a6f1-e8f65e0c9be5 · outbound

This paper cites Append the input x_i to p b.

Eliciting Fine-Tuned Transformer Capabilities via Inference-Time Techniques Append the input x_i to p b

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:38:39.334583Z

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.

Observation 7056ff61-a9f3-4b4b-addc-9100ad918f75 · outbound

This paper cites an unresolved cited work.

Eliciting Fine-Tuned Transformer Capabilities via Inference-Time Techniques Unresolved cited work

Reference 37

Resolution: unresolved; raw_fallback, observed 2026-08-07T05:38:39.066573Z

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

Observation a364e8b8-3f78-42b4-b265-6356f3d2d12e · outbound

This paper cites Great␣movie!.

Eliciting Fine-Tuned Transformer Capabilities via Inference-Time Techniques Great␣movie!

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:38:38.835562Z

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.

Pith citing papers

Observation 551ac4eb-0ca3-4a46-b459-109590c23739 · inbound

On Distinguishing Capability Elicitation from Capability Creation in Post-Training: A Free-Energy Perspective cites this paper.

On Distinguishing Capability Elicitation from Capability Creation in Post-Training: A Free-Energy Perspective Eliciting Fine-Tuned Transformer Capabilities via Inference-Time Techniques

Reference 21

Resolution: verified exact; arxiv_id, observed 2026-05-12T08:36:26.808917Z

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.