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Paper Citation Record · LEDGER

SLOT: Sample-specific Language Model Optimization at Test-time

As of 16 August 2026, this Paper Citation Record lists 42 of 42 outbound references and 6 inbound Pith citation observations for arXiv:2505.12392.

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

pith.paper-citation-record.v1
2505.12392 v2

Coverage vector

measured 42 of 42 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T20:38:17.037405Z

measured 48 of 48 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-04T16:07:30.821966Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

42 of 42 outbound references displayed

  • verified exact0
  • verified fuzzy6
  • unresolved35
  • parse uncertain0
  • malformed identifier1
  • metadata mismatch0

External citation measurements

0
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

Observation 986b9a51-ba5a-442c-b056-75302ff2b13e · outbound

This paper cites The Surprising Effectiveness of Test-Time Training for Few-Shot Learning.

SLOT: Sample-specific Language Model Optimization at Test-time The Surprising Effectiveness of Test-Time Training for Few-Shot Learning

Reference 1

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-15T20:38:16.862667Z digest=sha256:7c42fe6aad5c352193d46c6e0b81a8b39bdb194f301c487c2c0535799ce887f6

Observation 7573b81f-ef2f-46f0-b8d6-928fc77b82b8 · outbound

This paper cites Qwen Technical Report.

SLOT: Sample-specific Language Model Optimization at Test-time Qwen Technical Report

Reference 2

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no resolver link, observed 2026-08-15T20:38:16.868055Z

Source-reported events for the cited work

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source=pdf_text observed=2026-08-15T20:38:16.868055Z digest=sha256:d3cecec784c1bde84dd34ec7bb8b5d0f15e6ad8dc3e7594b75bce941987740dd

Observation 38bd0204-7d45-41d6-a50e-f718c2400647 · outbound

This paper cites Evaluating Large Language Models Trained on Code.

SLOT: Sample-specific Language Model Optimization at Test-time Evaluating Large Language Models Trained on Code

Reference 3

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source=pdf_text observed=2026-08-15T20:38:16.872974Z digest=sha256:9a010e3b8808ea5f25992e2553332b446eeb583366eba15f4cb422fda6a66f3d

Observation c6cc15dd-cc75-419f-8a0a-0ca998016cba · outbound

This paper cites Training Verifiers to Solve Math Word Problems.

SLOT: Sample-specific Language Model Optimization at Test-time Training Verifiers to Solve Math Word Problems

Reference 4

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no resolver link, observed 2026-08-15T20:38:16.877288Z

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source=pdf_text observed=2026-08-15T20:38:16.877288Z digest=sha256:6db4f7471548e13c8c98867cfd9d0641f5f25feba9389f6cb5dbaacd52e6a0e1

Observation 73273f8b-7451-4c14-bdc0-6fc91be149bf · outbound

This paper cites Learning How Hard to Think: Input-Adaptive Allocation of LM Computation.

SLOT: Sample-specific Language Model Optimization at Test-time Learning How Hard to Think: Input-Adaptive Allocation of LM Computation

Reference 5

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source=pdf_text observed=2026-08-15T20:38:16.881661Z digest=sha256:3f0743ad3863cc4ac49a44878618ff890288e7f004f4c6b1272c64b96594ea68

Observation c3eef4c6-626b-4496-bec7-3d797728c6bc · outbound

This paper cites Test-time training can close the natural distribution shift performance gap in deep learning based compressed sensing.

SLOT: Sample-specific Language Model Optimization at Test-time Test-time training can close the natural distribution shift performance gap in deep learning based compressed sensing

Reference 6

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raw_fallback, observed 2026-08-15T20:38:17.510693Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-15T20:38:16.885975Z digest=sha256:17cc32e86731d6c69ff1d3b9875d99149175dbfe430d5a0ec3b9359f693ad984

Observation c13331ff-6287-4d59-8acc-3f7f42a7a7ec · outbound

This paper cites Test-time training with masked autoencoders.Advances in Neural Information Processing Systems, 35:29374–29385, 2022.

SLOT: Sample-specific Language Model Optimization at Test-time Test-time training with masked autoencoders.Advances in Neural Information Processing Systems, 35:29374–29385, 2022

Reference 7

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source=pdf_text observed=2026-08-15T20:38:16.890188Z digest=sha256:04ec588afac43b19fea7d6ac83d62096680cd0e6925f9717a70750eef10244c5

Observation de5708aa-45a5-40a7-87d8-b6d6520f7581 · outbound

This paper cites The Llama 3 Herd of Models.

SLOT: Sample-specific Language Model Optimization at Test-time The Llama 3 Herd of Models

Reference 8

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source=pdf_text observed=2026-08-15T20:38:16.894534Z digest=sha256:eb667512bcc0844b5a5462afa5399ab7b22b5d1de52c8951d1c1f38b567bcff2

Observation d08dbc8e-943e-4908-a970-5bbd07da5d4c · outbound

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

SLOT: Sample-specific Language Model Optimization at Test-time DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 9

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source=pdf_text observed=2026-08-15T20:38:16.898650Z digest=sha256:045fb4202352cb0e306d2da49ef418e9ebdf3c37d9ad46ba9b33f0605936c568

Observation 04b713be-57c2-414f-bd5c-c2d525023b2a · outbound

This paper cites Measuring Mathematical Problem Solving With the MATH Dataset.

SLOT: Sample-specific Language Model Optimization at Test-time Measuring Mathematical Problem Solving With the MATH Dataset

Reference 10

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source=pdf_text observed=2026-08-15T20:38:16.903127Z digest=sha256:2a9855ebbc506ae2118825125436817c1b9e8451d8acc37313578453f8f18bf6

Observation 050b98c3-b7a7-4a8b-bada-11457ac3ce3e · outbound

This paper cites Parameter-efficient transfer learning for nlp.

SLOT: Sample-specific Language Model Optimization at Test-time Parameter-efficient transfer learning for nlp

Reference 11

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source=pdf_text observed=2026-08-15T20:38:16.907495Z digest=sha256:71934d159fc7be10324716c5353a4c5267f516526dcd12b55bd88ce23aba4590

Observation 341ac7c5-d098-4b84-b1aa-7a438fe5725d · outbound

This paper cites Lora: Low-rank adaptation of large language models.ICLR, 1(2):3, 2022.

SLOT: Sample-specific Language Model Optimization at Test-time Lora: Low-rank adaptation of large language models.ICLR, 1(2):3, 2022

Reference 12

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source=pdf_text observed=2026-08-15T20:38:16.911973Z digest=sha256:ba24502adf37dce9eb7057e9c02f4e5558dd980ad787eb1953f2c157736b9d66

Observation d3980604-bdb2-4681-9c7d-0dc3fdbac35e · outbound

This paper cites C-eval: A multi-level multi-discipline chinese evaluation suite for foundation models.Advances in Neural Information Processing Systems, 36:62991–63010, 2023.

SLOT: Sample-specific Language Model Optimization at Test-time C-eval: A multi-level multi-discipline chinese evaluation suite for foundation models.Advances in Neural Information Processing Systems, 36:62991–63010, 2023

Reference 13

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source=pdf_text observed=2026-08-15T20:38:16.916224Z digest=sha256:ed19475023d3d4ab2153a4ae725fdd87112fcec8ba58af8eb58830219cda55bb

Observation e7d67827-d582-48df-b1f9-7d6f4ef54b2c · outbound

This paper cites A Survey of Test-Time Compute: From Intuitive Inference to Deliberate Reasoning.

SLOT: Sample-specific Language Model Optimization at Test-time A Survey of Test-Time Compute: From Intuitive Inference to Deliberate Reasoning

Reference 14

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source=pdf_text observed=2026-08-15T20:38:16.920727Z digest=sha256:2243367368b6fc81ac6d342a855c716296e83b0926fa736e736edffeed58c5a8

Observation fe6deb32-b237-471c-a812-1e017f76f086 · outbound

This paper cites Gonzalez, Hao Zhang, and Ion Stoica.

SLOT: Sample-specific Language Model Optimization at Test-time Gonzalez, Hao Zhang, and Ion Stoica

Reference 15

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raw_fallback, observed 2026-08-15T20:38:17.466594Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-15T20:38:16.925003Z digest=sha256:89c7e22ec3b601ee99d1a7a10105cdeea4f1f3962240a69fb473c22fbb922b10

Observation 52d4ba75-e817-4553-ad86-78f8dd9a3b45 · outbound

This paper cites The Power of Scale for Parameter-Efficient Prompt Tuning.

SLOT: Sample-specific Language Model Optimization at Test-time The Power of Scale for Parameter-Efficient Prompt Tuning

Reference 16

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no resolver link, observed 2026-08-15T20:38:16.929184Z

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source=pdf_text observed=2026-08-15T20:38:16.929184Z digest=sha256:3c6cdce48fe0fbe042caccfc16549f5f794e6204b589ac1f80daed8e7cb4b46d

Observation 8a7e0f14-ebde-48ff-be69-7d0b1c2def29 · outbound

This paper cites Prefix-Tuning: Optimizing Continuous Prompts for Generation.

SLOT: Sample-specific Language Model Optimization at Test-time Prefix-Tuning: Optimizing Continuous Prompts for Generation

Reference 17

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source=pdf_text observed=2026-08-15T20:38:16.933344Z digest=sha256:71158e4fd70ee0fd74eef32789f267509656dd79c20fbe9c501f15a80ba84014

Observation 02772e5a-6f2f-4b64-95a9-e3ef28d7b969 · outbound

This paper cites A Survey on LLM Test-Time Compute via Search: Tasks, LLM Profiling, Search Algorithms, and Relevant Frameworks.

SLOT: Sample-specific Language Model Optimization at Test-time A Survey on LLM Test-Time Compute via Search: Tasks, LLM Profiling, Search Algorithms, and Relevant Frameworks

Reference 18

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source=pdf_text observed=2026-08-15T20:38:16.937355Z digest=sha256:c6cabd34a2c8423d637155c33fdf1e9139b1d0562deab60941cdc32cb84b1819

Observation 7ca01e10-82d6-4c5b-bc2c-171388286475 · outbound

This paper cites Can 1B LLM Surpass 405B LLM? Rethinking Compute-Optimal Test-Time Scaling.

SLOT: Sample-specific Language Model Optimization at Test-time Can 1B LLM Surpass 405B LLM? Rethinking Compute-Optimal Test-Time Scaling

Reference 19

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source=pdf_text observed=2026-08-15T20:38:16.941819Z digest=sha256:eb9ff00573db9e4b8863e5f710be6c65a2d66843c667cf041801e98bed944aab

Observation e84261a0-1bfe-46af-906c-ab656e6e042d · outbound

This paper cites P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks.

SLOT: Sample-specific Language Model Optimization at Test-time P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks

Reference 20

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no resolver link, observed 2026-08-15T20:38:16.946128Z

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source=pdf_text observed=2026-08-15T20:38:16.946128Z digest=sha256:0bce50612a4202baccffd2aba619a6184c48dd977e264934edc6431210c598a4

Observation feedad1f-a017-45ea-b62b-ffe4e02c4fe2 · outbound

This paper cites Ttt++: When does self-supervised test-time training fail or thrive?Advances in Neural Information Processing Systems, 34:21808–21820, 2021.

SLOT: Sample-specific Language Model Optimization at Test-time Ttt++: When does self-supervised test-time training fail or thrive?Advances in Neural Information Processing Systems, 34:21808–21820, 2021

Reference 21

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source=pdf_text observed=2026-08-15T20:38:16.950336Z digest=sha256:142cf5b9bdcce53942a68c64cd4cf25461ce2c0b2d63c4206bdcfce7f90f72d4

Observation 3bdb711e-f310-4e16-8474-8d909e164ffe · outbound

This paper cites Decoupled Weight Decay Regularization.

SLOT: Sample-specific Language Model Optimization at Test-time Decoupled Weight Decay Regularization

Reference 22

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source=pdf_text observed=2026-08-15T20:38:16.954211Z digest=sha256:a4e1f43e7990957ff914019122e9591d7d47938412c9e91d720643f0fc6256fa

Observation 33c4cd93-3ea7-403c-8d5c-fc70557544d2 · outbound

This paper cites Language Models are Few-Shot Learners.

SLOT: Sample-specific Language Model Optimization at Test-time Language Models are Few-Shot Learners

Reference 23

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source=pdf_text observed=2026-08-15T20:38:16.958199Z digest=sha256:b8acec623dc2a0e6790675a5770b0d8be34d869b5ccae44aa62b9cf0e3a1e77f

Observation e752f4b3-7b39-4de7-b8c4-3264b7c11ae7 · outbound

This paper cites Adaptive Inference-Time Compute: LLMs Can Predict if They Can Do Better, Even Mid-Generation.

SLOT: Sample-specific Language Model Optimization at Test-time Adaptive Inference-Time Compute: LLMs Can Predict if They Can Do Better, Even Mid-Generation

Reference 24

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source=pdf_text observed=2026-08-15T20:38:16.962058Z digest=sha256:83ab241a4e466b2ae2b9f7b3a480e6fe85ce42d1e5fe46dc1d3f8d482c2829e2

Observation c6303c76-ab54-481f-a432-3ae39a21d958 · outbound

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

SLOT: Sample-specific Language Model Optimization at Test-time Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?

Reference 25

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source=pdf_text observed=2026-08-15T20:38:16.965914Z digest=sha256:70f93454a9ceafd74b6b8059ab4a7122598195a667df14679f8bcfe8c805881f

Observation 09d81cfd-dc9a-43bd-baa1-17059074609d · outbound

This paper cites Improving Black-box Robustness with In-Context Rewriting.

SLOT: Sample-specific Language Model Optimization at Test-time Improving Black-box Robustness with In-Context Rewriting

Reference 26

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source=pdf_text observed=2026-08-15T20:38:16.970305Z digest=sha256:44c2b07a0efa2eba7868614eda7740af3621fbe97643f28524de44fef29c8ec6

Observation 088ed9d4-eccb-4cf5-b1f0-49a5e60867f7 · outbound

This paper cites Tttflow: Unsupervised test-time training with normalizing flow.

SLOT: Sample-specific Language Model Optimization at Test-time Tttflow: Unsupervised test-time training with normalizing flow

Reference 27

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raw_fallback, observed 2026-08-15T20:38:17.447277Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-15T20:38:16.974689Z digest=sha256:3413928512e8bb8ff0350db76889e6a07a5e2125179a02b5001f3cd7422f0f52

Observation 4653fa01-6f0f-4a58-89f0-457f52d14f08 · outbound

This paper cites Gpqa: A graduate-level google-proof q&a benchmark.

SLOT: Sample-specific Language Model Optimization at Test-time Gpqa: A graduate-level google-proof q&a benchmark

Reference 28

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source=pdf_text observed=2026-08-15T20:38:16.978638Z digest=sha256:873d3d654a2a7d7b070236e2e025fe32db872452a7551ff9fa1ec3582d9e69ae

Observation 60f8ee74-291c-4823-9140-bfc16300c2c4 · outbound

This paper cites Towards real-world test-time adaptation: Tri-net self-training with balanced normalization.

SLOT: Sample-specific Language Model Optimization at Test-time Towards real-world test-time adaptation: Tri-net self-training with balanced normalization

Reference 29

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raw_fallback, observed 2026-08-15T20:38:17.427328Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-15T20:38:16.982354Z digest=sha256:12407b8e79521349110f70e43e700063b63902ffe42b184aab1b1ef4303ebeb1

Observation 434269cb-6a58-4f86-ae91-c49652ab7c27 · outbound

This paper cites Stop Overthinking: A Survey on Efficient Reasoning for Large Language Models.

SLOT: Sample-specific Language Model Optimization at Test-time Stop Overthinking: A Survey on Efficient Reasoning for Large Language Models

Reference 30

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source=pdf_text observed=2026-08-15T20:38:16.986205Z digest=sha256:30dbb7554b8c87054ca13817e02a2be96cce349a552fdf0547b49a1602f580de

Observation 5dd43cf7-598d-4a59-8e54-d04d7a48a18f · outbound

This paper cites Learning to (Learn at Test Time).

SLOT: Sample-specific Language Model Optimization at Test-time Learning to (Learn at Test Time)

Reference 31

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source=pdf_text observed=2026-08-15T20:38:16.990116Z digest=sha256:c5f445450f6580715c923483908f17d243a372048d6668c35ba7307800ffb485

Observation 2985d54d-e242-4666-bfa3-b984451a513b · outbound

This paper cites Test- time training with self-supervision for generalization under distribution shifts.

SLOT: Sample-specific Language Model Optimization at Test-time Test- time training with self-supervision for generalization under distribution shifts

Reference 32

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raw_fallback, observed 2026-08-15T20:38:17.413863Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-15T20:38:16.994116Z digest=sha256:e7ad252784e139c54003e50c880773f841137583b6c3f389f1c2c81a2e87c687

Observation 4045d2b4-f3dc-4d6e-a4cf-40acf894e090 · outbound

This paper cites Tent: Fully Test-time Adaptation by Entropy Minimization.

SLOT: Sample-specific Language Model Optimization at Test-time Tent: Fully Test-time Adaptation by Entropy Minimization

Reference 33

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source=pdf_text observed=2026-08-15T20:38:16.998243Z digest=sha256:cb1c9bbbc1970047f3a67817c4a907dda01543a3adc6fd6d92ffde3890211cbc

Observation fbf0a411-64df-460b-acae-f9d75d19d280 · outbound

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

SLOT: Sample-specific Language Model Optimization at Test-time Self-Consistency Improves Chain of Thought Reasoning in Language Models

Reference 34

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source=pdf_text observed=2026-08-15T20:38:17.002426Z digest=sha256:4ba5b7d5e9b3e21d0f6e235e1e4f92feeee79609a27d24de0a24238a1deba539

Observation 4407432e-bcf3-4586-93c4-dda52f9fc7bd · outbound

This paper cites Beyond Model Adaptation at Test Time: A Survey.

SLOT: Sample-specific Language Model Optimization at Test-time Beyond Model Adaptation at Test Time: A Survey

Reference 35

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source=pdf_text observed=2026-08-15T20:38:17.006489Z digest=sha256:f36cc8adf8fbd63a12b2fe03406a3a6e634235c3090e94ba7324f3c494c5d1c9

Observation f7738cbe-275b-4833-83e5-26a53ddc0209 · outbound

This paper cites Stta: enhanced text classification via selective test-time augmentation.PeerJ Computer Science, 9:e1757, 2023.

SLOT: Sample-specific Language Model Optimization at Test-time Stta: enhanced text classification via selective test-time augmentation.PeerJ Computer Science, 9:e1757, 2023

Reference 36

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verified fuzzy
raw_fallback, observed 2026-08-15T20:38:17.399769Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-15T20:38:17.011778Z digest=sha256:6695d8900f1b5eb9fc4b5ac33d472b30c607065457b03919460c4d5aec06b0ba

Observation fc4348f3-88b0-4e4d-886f-8fc7fc2a73ca · outbound

This paper cites Qwen2.5 Technical Report.

SLOT: Sample-specific Language Model Optimization at Test-time Qwen2.5 Technical Report

Reference 37

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unresolved
no resolver link, observed 2026-08-15T20:38:17.016069Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T20:38:17.016069Z digest=sha256:31f34723aaa997ad106aeb3239c2349bd105eb1ee34b57a080003e3f77adc956

Observation e3b0f746-0e29-4d0a-9b41-948f4c0e9bd4 · outbound

This paper cites Qwen2.5-Math Technical Report: Toward Mathematical Expert Model via Self-Improvement.

SLOT: Sample-specific Language Model Optimization at Test-time Qwen2.5-Math Technical Report: Toward Mathematical Expert Model via Self-Improvement

Reference 38

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unresolved
no resolver link, observed 2026-08-15T20:38:17.020163Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T20:38:17.020163Z digest=sha256:ca791279bd49503b9b52a690722a1c0867b145e87b5bb43ecd4e91ae41b701a8

Observation a45991d6-62c8-4263-b08a-3f53a23d45be · outbound

This paper cites Tree of thoughts: Deliberate problem solving with large language models.Ad- vances in neural information processing systems, 36:11809–11822, 2023.

SLOT: Sample-specific Language Model Optimization at Test-time Tree of thoughts: Deliberate problem solving with large language models.Ad- vances in neural information processing systems, 36:11809–11822, 2023

Reference 39

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unresolved
no resolver link, observed 2026-08-15T20:38:17.024323Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T20:38:17.024323Z digest=sha256:b13b163602e4199db342a4b681efe8a2692a3fd96a96751e3a61b248b012cdb1

Observation 0c2265a9-7a01-4b92-a256-86071c0e2f5f · outbound

This paper cites Benchmarking Reasoning Robustness in Large Language Models.

SLOT: Sample-specific Language Model Optimization at Test-time Benchmarking Reasoning Robustness in Large Language Models

Reference 40

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no resolver link, observed 2026-08-15T20:38:17.028835Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T20:38:17.028835Z digest=sha256:736fac8f41f24cb2c9318dceaaa686ca8b1464f4b8e2d871c4a85239b590e691

Observation d4d6e143-130f-4034-b844-af8d5a89411e · outbound

This paper cites On Pitfalls of Test-Time Adaptation.

SLOT: Sample-specific Language Model Optimization at Test-time On Pitfalls of Test-Time Adaptation

Reference 41

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unresolved
no resolver link, observed 2026-08-15T20:38:17.033229Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T20:38:17.033229Z digest=sha256:4a0b1b55d9bacd8f86d6dacf8d90c20f68806cb583cf1485b45629af4308933c

Observation 3bad336c-8c0b-465e-88e6-291adcbcccc7 · outbound

This paper cites TTRL: Test-Time Reinforcement Learning.

SLOT: Sample-specific Language Model Optimization at Test-time TTRL: Test-Time Reinforcement Learning

Reference 42

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malformed identifier
no resolver link, observed 2026-08-15T20:38:17.037405Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T20:38:17.037405Z digest=sha256:bfba372ff3c232788ee557d2580769323f3d7a7041806a4f9cd0c84ea6e07aba

Pith citing papers

Observation 8fb8a73f-287e-4ea6-a7d3-064619ab26af · inbound

Reinforcement Learning Meets Large Language Models: A Survey of Advancements and Applications Across the LLM Lifecycle cites this paper.

Reinforcement Learning Meets Large Language Models: A Survey of Advancements and Applications Across the LLM Lifecycle SLOT: Sample-specific Language Model Optimization at Test-time

Reference 64

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unresolved
no resolver link, observed 2026-08-04T16:07:30.821966Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T16:07:30.821966Z digest=sha256:a770c954464de88f18cff184d78a0b699d5212ca6012aa75f37f9c1a3a96c976

Observation eac35b3f-19ea-4b2f-b0b4-0324713cec3c · inbound

Self-Reflective Generation at Test Time cites this paper.

Self-Reflective Generation at Test Time SLOT: Sample-specific Language Model Optimization at Test-time

Reference 8

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unresolved
no resolver link, observed 2026-08-04T12:41:42.525028Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T12:41:42.525028Z digest=sha256:beb2a12727a01fb19d00e64c1eb5621d7bdf836f66aa2b9d5c0029a0ba969ef0

Observation b3c2337d-b6b2-40a8-8e46-7a20d768bf08 · inbound

From Exposure to Internalization: Dual-Stream Calibration for In-context Clinical Reasoning cites this paper.

From Exposure to Internalization: Dual-Stream Calibration for In-context Clinical Reasoning SLOT: Sample-specific Language Model Optimization at Test-time

Reference 23

Resolution
verified exact
arxiv_id, observed 2026-05-10T23:10:50.521245Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-05-10T19:19:15.053725Z digest=sha256:b9adf6ea0d518479bc5d0b4bfed1c17060afce763bf06645b3c259f455551ea8

Observation d68e740f-b228-4c32-bb1b-63e1ccb1f9e1 · inbound

Rethinking Entropy Minimization in Test-Time Adaptation for Autoregressive Models cites this paper.

Rethinking Entropy Minimization in Test-Time Adaptation for Autoregressive Models SLOT: Sample-specific Language Model Optimization at Test-time

Reference 16

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verified exact
arxiv_id, observed 2026-05-12T00:51:14.022947Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-05-12T00:48:47.213681Z digest=sha256:e1ad46c8011b02187fb2f11dd7519b1ec25ca60a3de8374be5b568be4ca72bfd

Observation dc5440c8-50dc-43f8-9f09-d48e2557360d · inbound

SOLAR: A Self-Optimizing Open-Ended Autonomous Agent for Lifelong Learning and Continual Adaptation cites this paper.

SOLAR: A Self-Optimizing Open-Ended Autonomous Agent for Lifelong Learning and Continual Adaptation SLOT: Sample-specific Language Model Optimization at Test-time

Reference 4

Resolution
verified exact
arxiv_id, observed 2026-05-21T11:24:08.699295Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-05-21T11:21:30.867480Z digest=sha256:66e55e2338578514c553808caef760f666c2a72a2ad723e287b9a38da44006ad

Observation e802226d-8ba5-45de-8ab2-7db931ba651c · inbound

From Reasoning Traces to Reusable Modules: Understanding Compositional Generalization in Language Model Reasoning cites this paper.

From Reasoning Traces to Reusable Modules: Understanding Compositional Generalization in Language Model Reasoning SLOT: Sample-specific Language Model Optimization at Test-time

Reference 78

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metadata mismatch
arxiv_id, observed 2026-07-03T20:38:55.966851Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-06-27T01:13:11.483599Z digest=sha256:43c8598baf1d9e6c1801ceca2f16f8a2ec0ee4044088b4b161e394e2fbd3700f