LoopUS: Recasting Pretrained LLMs into Looped Latent Refinement Models | Paper Citation Record

Observation 3421bad1-130b-4ccc-a8d3-e497bad46291 · outbound

This paper cites Mamba-3: Improved sequence modeling using state space principles.

LoopUS: Recasting Pretrained LLMs into Looped Latent Refinement Models Mamba-3: Improved sequence modeling using state space principles

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T08:27:32.552446Z

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Observation 040eba49-57b8-472e-b2a1-9185aa487959 · outbound

This paper cites Inference scaling laws: An empirical analysis of compute-optimal inference for LLM problem-solving.

LoopUS: Recasting Pretrained LLMs into Looped Latent Refinement Models Inference scaling laws: An empirical analysis of compute-optimal inference for LLM problem-solving

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T08:27:32.560982Z

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This paper cites Scaling LLM test- time compute optimally can be more effective than scaling parameters for reasoning.

LoopUS: Recasting Pretrained LLMs into Looped Latent Refinement Models Scaling LLM test- time compute optimally can be more effective than scaling parameters for reasoning

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T08:27:32.556982Z

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Observation 1d884d67-139f-46f9-84a5-8a76aa1b582c · outbound

This paper cites Scaling Latent Reasoning via Looped Language Models.

LoopUS: Recasting Pretrained LLMs into Looped Latent Refinement Models Scaling Latent Reasoning via Looped Language Models

Reference 4

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Observation f1120ad0-6cf4-4486-bed2-1d04ad526b5b · outbound

This paper cites Mixture-of- recursions: Learning dynamic recursive depths for adaptive token-level computation.

LoopUS: Recasting Pretrained LLMs into Looped Latent Refinement Models Mixture-of- recursions: Learning dynamic recursive depths for adaptive token-level computation

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T08:27:32.545074Z

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Observation 08756bd9-1f19-45c7-a70e-32ebf9bc0b76 · outbound

This paper cites Scaling up Test-Time Compute with Latent Reasoning: A Recurrent Depth Approach.

LoopUS: Recasting Pretrained LLMs into Looped Latent Refinement Models Scaling up Test-Time Compute with Latent Reasoning: A Recurrent Depth Approach

Reference 6

Resolution: metadata mismatch; local_arxiv, observed 2026-05-13T07:22:28.708278Z

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Observation fada187a-6f0e-495e-886a-9f04501a2281 · outbound

This paper cites Energy-based transformers are scalable learners and thinkers.

LoopUS: Recasting Pretrained LLMs into Looped Latent Refinement Models Energy-based transformers are scalable learners and thinkers

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T08:27:32.498325Z

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Observation 25c36a4f-712d-40ed-98fe-79bae7f27733 · outbound

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LoopUS: Recasting Pretrained LLMs into Looped Latent Refinement Models Unresolved cited work

Reference 8

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Observation 0428d386-5acb-4e1f-ad6d-d7d5a067e1b4 · outbound

This paper cites Hierarchical Reasoning Model.

LoopUS: Recasting Pretrained LLMs into Looped Latent Refinement Models Hierarchical Reasoning Model

Reference 9

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Observation f81b21cb-f33a-4027-a722-b074fc7e1fff · outbound

This paper cites Large language diffusion models.

LoopUS: Recasting Pretrained LLMs into Looped Latent Refinement Models Large language diffusion models

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T08:27:32.540675Z

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Observation b7c9b9aa-cfc0-49c6-870a-385d7c79f959 · outbound

This paper cites Bartoldson, Bhavya Kailkhura, Avi Schwarzschild, Jonas Geiping, Tom Goldstein, and Micah Goldblum.

LoopUS: Recasting Pretrained LLMs into Looped Latent Refinement Models Bartoldson, Bhavya Kailkhura, Avi Schwarzschild, Jonas Geiping, Tom Goldstein, and Micah Goldblum

Reference 11

Resolution: verified exact; arxiv_id, observed 2026-05-13T07:22:28.739485Z

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Observation c99bd131-6173-46bf-bc37-f886daf96ce9 · outbound

This paper cites Relaxed recursive transformers: Effective parameter sharing with layer-wise loRA.

LoopUS: Recasting Pretrained LLMs into Looped Latent Refinement Models Relaxed recursive transformers: Effective parameter sharing with layer-wise loRA

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T08:27:32.482965Z

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Observation 5c8ad26f-5e79-4cde-9a93-35946c53e57d · outbound

This paper cites Loop as a bridge: Can looped transformers truly link representation space and natural language outputs?CoRR, abs/2601.10242.

LoopUS: Recasting Pretrained LLMs into Looped Latent Refinement Models Loop as a bridge: Can looped transformers truly link representation space and natural language outputs?CoRR, abs/2601.10242

Reference 13

Resolution: verified exact; arxiv_id, observed 2026-05-13T07:22:28.723394Z

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Observation bea98e34-b94c-4782-ac5b-131ca71193a1 · outbound

This paper cites an unresolved cited work.

LoopUS: Recasting Pretrained LLMs into Looped Latent Refinement Models Unresolved cited work

Reference 14

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Observation a665e8f8-3835-4d0d-8b3a-a2dd31400029 · outbound

This paper cites On the difficulty of training recurrent neural networks.

LoopUS: Recasting Pretrained LLMs into Looped Latent Refinement Models On the difficulty of training recurrent neural networks

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T08:27:32.478289Z

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Observation d4338d14-893e-4fde-a32d-98c033a3f949 · outbound

This paper cites A Survey on Latent Reasoning.

LoopUS: Recasting Pretrained LLMs into Looped Latent Refinement Models A Survey on Latent Reasoning

Reference 16

Resolution: verified exact; arxiv_id, observed 2026-05-13T07:22:28.637973Z

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Observation d79fba7c-99bd-4d12-a8b6-b769b3047be2 · outbound

This paper cites Suppressing final layer hidden state jumps in transformer pretraining.

LoopUS: Recasting Pretrained LLMs into Looped Latent Refinement Models Suppressing final layer hidden state jumps in transformer pretraining

Reference 17

Resolution: verified exact; arxiv_id, observed 2026-05-13T07:22:28.718174Z

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Observation 993e7959-f69f-4e3d-9d04-813aa1df706b · outbound

This paper cites Frozen in the middle: Hidden states remain unchanged across intermediate layers of language models.

LoopUS: Recasting Pretrained LLMs into Looped Latent Refinement Models Frozen in the middle: Hidden states remain unchanged across intermediate layers of language models

Reference 18

Resolution: verified exact; arxiv_id, observed 2026-05-13T07:22:27.807334Z

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Observation 2730d4dd-a99e-4bcb-a7bb-f273f3e0feae · outbound

This paper cites Llm neuroanatomy: How i topped the llm leaderboard without changing a single weight.

LoopUS: Recasting Pretrained LLMs into Looped Latent Refinement Models Llm neuroanatomy: How i topped the llm leaderboard without changing a single weight

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T08:27:32.530351Z

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Observation 3ac37841-0db5-4aa0-93b5-2c850749b453 · outbound

This paper cites Mapping the mind of a large language model.

LoopUS: Recasting Pretrained LLMs into Looped Latent Refinement Models Mapping the mind of a large language model

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T08:27:32.501977Z

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Observation 55981505-e12b-4537-b326-e4631f43eca9 · outbound

This paper cites On the biology of a large language model.

LoopUS: Recasting Pretrained LLMs into Looped Latent Refinement Models On the biology of a large language model

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T08:27:32.535704Z

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Observation c75fe7a4-7e9e-46ec-a3b3-e9592830389c · outbound

This paper cites Interpreting GPT: The logit lens.

LoopUS: Recasting Pretrained LLMs into Looped Latent Refinement Models Interpreting GPT: The logit lens

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T08:27:32.486642Z

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Observation 8d1e4cd8-c689-43e2-a7d6-4d28f08ac8b9 · outbound

This paper cites The bottom-up evolution of representations in the transformer: A study with machine translation and language modeling objectives.

LoopUS: Recasting Pretrained LLMs into Looped Latent Refinement Models The bottom-up evolution of representations in the transformer: A study with machine translation and language modeling objectives

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T08:27:32.521523Z

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Observation f11d3c81-1de9-4858-9229-428bc8166933 · outbound

This paper cites SOLAR 10.7B: Scaling large language models with simple yet effective depth up-scaling.

LoopUS: Recasting Pretrained LLMs into Looped Latent Refinement Models SOLAR 10.7B: Scaling large language models with simple yet effective depth up-scaling

Reference 24

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Observation 05d5c018-48c0-4629-8b02-7b7c211d64ab · outbound

This paper cites doi: 10.18653/v1/2024.naacl-industry.3.

LoopUS: Recasting Pretrained LLMs into Looped Latent Refinement Models doi: 10.18653/v1/2024.naacl-industry.3

Reference 25

Resolution: verified exact; doi, observed 2026-05-13T07:22:27.821116Z

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Observation 1ae1d1b6-3bcc-45fb-af4e-edc5dc043731 · outbound

This paper cites Chi, Quoc V Le, and Denny Zhou.

LoopUS: Recasting Pretrained LLMs into Looped Latent Refinement Models Chi, Quoc V Le, and Denny Zhou

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T08:27:32.548500Z

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Observation 9375839c-8964-49ba-ac9f-6398d7d74848 · outbound

This paper cites Pretraining language models to ponder in continuous space.CoRR, abs/2505.20674.

LoopUS: Recasting Pretrained LLMs into Looped Latent Refinement Models Pretraining language models to ponder in continuous space.CoRR, abs/2505.20674

Reference 27

Resolution: verified exact; arxiv_id, observed 2026-05-13T07:22:28.745632Z

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Observation e51dbe41-8333-4790-93e0-c0b34c50ab42 · outbound

This paper cites Think-at-hard: Teaching small language models to think on hard problems.

LoopUS: Recasting Pretrained LLMs into Looped Latent Refinement Models Think-at-hard: Teaching small language models to think on hard problems

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T07:22:30.261253Z

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Observation 32a12520-5d16-4ab0-949d-d765e921df3c · outbound

This paper cites Universal Transformers.

LoopUS: Recasting Pretrained LLMs into Looped Latent Refinement Models Universal Transformers

Reference 29

Resolution: verified exact; arxiv_id, observed 2026-05-13T08:04:31.534239Z

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Observation 0a39a41e-de51-47e1-a6c4-11e4cc0f4de4 · outbound

This paper cites Ouro: A latent reasoning model with adaptive depth via gated recurrence.

LoopUS: Recasting Pretrained LLMs into Looped Latent Refinement Models Ouro: A latent reasoning model with adaptive depth via gated recurrence

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T07:22:30.257480Z

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Observation 5737f43c-c4cd-4398-a9f4-357b24120bcb · outbound

This paper cites Plausible Counterfactual Explanations of Recommendations.

LoopUS: Recasting Pretrained LLMs into Looped Latent Refinement Models Plausible Counterfactual Explanations of Recommendations

Reference 31

Resolution: verified exact; arxiv_id, observed 2026-05-13T07:22:28.647538Z

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Observation 591cc8be-554d-485d-9a4c-e83aeabee43d · outbound

This paper cites Long short-term memory.Neural computation, 9(8): 1735–1780.

LoopUS: Recasting Pretrained LLMs into Looped Latent Refinement Models Long short-term memory.Neural computation, 9(8): 1735–1780

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T08:27:32.514011Z

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Observation 90655ed0-af7c-40a0-a2e3-692831fbfef7 · outbound

This paper cites In: Moschitti, A., Pang, B., Daelemans, W.

LoopUS: Recasting Pretrained LLMs into Looped Latent Refinement Models In: Moschitti, A., Pang, B., Daelemans, W

Reference 33

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Observation 7ea19e20-259d-4e76-bdbc-d337dce2f8dd · outbound

This paper cites Training Very Deep Networks.

LoopUS: Recasting Pretrained LLMs into Looped Latent Refinement Models Training Very Deep Networks

Reference 34

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Observation 4b63ec44-f19a-4527-8f0d-93180c1eb354 · outbound

This paper cites Kristianto, G.

LoopUS: Recasting Pretrained LLMs into Looped Latent Refinement Models Kristianto, G

Reference 35

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Observation 420e3b4c-4fdc-4a86-8f02-5d3e7fc006ac · outbound

This paper cites Mamba: Linear-time sequence modeling with selective state spaces.

LoopUS: Recasting Pretrained LLMs into Looped Latent Refinement Models Mamba: Linear-time sequence modeling with selective state spaces

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T07:22:30.253109Z

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No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 891c4040-f5f4-4d0a-a9db-c6f42730e202 · outbound

This paper cites Mamba: Linear-Time Sequence Modeling with Selective State Spaces.

LoopUS: Recasting Pretrained LLMs into Looped Latent Refinement Models Mamba: Linear-Time Sequence Modeling with Selective State Spaces

Reference 37

Resolution: verified exact; local_arxiv, observed 2026-05-13T07:22:28.652734Z

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Observation 42cf399d-f09c-4356-9ca3-e505cf5679e3 · outbound

This paper cites Efficiently modeling long sequences with structured state spaces.

LoopUS: Recasting Pretrained LLMs into Looped Latent Refinement Models Efficiently modeling long sequences with structured state spaces

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T07:22:30.244018Z

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Observation d7896773-aaa3-4980-906a-6c8845d18397 · outbound

This paper cites xLSTM: Ex- tended long short-term memory.

LoopUS: Recasting Pretrained LLMs into Looped Latent Refinement Models xLSTM: Ex- tended long short-term memory

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T07:22:30.247985Z

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Observation abbcfcd5-b09e-4e6f-8dea-80cfba8cd598 · outbound

This paper cites Titans: Learning to Memorize at Test Time.

LoopUS: Recasting Pretrained LLMs into Looped Latent Refinement Models Titans: Learning to Memorize at Test Time

Reference 40

Resolution: verified exact; arxiv_id, observed 2026-05-14T22:08:15.778565Z

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Observation 88a66319-3abd-4e18-b125-a8cb9bcb3ce2 · outbound

This paper cites Gated delta networks: Improving mamba2 with delta rule.

LoopUS: Recasting Pretrained LLMs into Looped Latent Refinement Models Gated delta networks: Improving mamba2 with delta rule

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T07:22:30.240617Z

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No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 5ba8bd36-e333-41c0-b130-1ea9cbd260e3 · outbound

This paper cites Mi: dm 2.0 korea-centric bilingual language models.

LoopUS: Recasting Pretrained LLMs into Looped Latent Refinement Models Mi: dm 2.0 korea-centric bilingual language models

Reference 42

Resolution: verified exact; arxiv_id, observed 2026-05-13T07:22:28.663987Z

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Observation 1c97b03e-1244-4ca9-b891-55cc2465fc8e · outbound

This paper cites Less is More: Recursive Reasoning with Tiny Networks.

LoopUS: Recasting Pretrained LLMs into Looped Latent Refinement Models Less is More: Recursive Reasoning with Tiny Networks

Reference 43

Resolution: verified exact; arxiv_id, observed 2026-05-15T04:53:09.592494Z

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No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 5e537ecd-73bd-401f-bc9f-e2e4bc826126 · outbound

This paper cites Scalable Diffusion Models with Transformers.

LoopUS: Recasting Pretrained LLMs into Looped Latent Refinement Models Scalable Diffusion Models with Transformers

Reference 44

Resolution: verified exact; local_arxiv, observed 2026-05-13T07:22:28.750772Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 558b79fe-3d3a-44c9-a342-1142fc0d5ed0 · outbound

This paper cites Sigmoid-weighted linear units for neural network function approximation in reinforcement learning.Neural networks, 107:3–11.

LoopUS: Recasting Pretrained LLMs into Looped Latent Refinement Models Sigmoid-weighted linear units for neural network function approximation in reinforcement learning.Neural networks, 107:3–11

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T07:22:30.237050Z

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No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation ababb8be-2fe7-48e8-bb06-072aa1db7082 · outbound

This paper cites Rectified linear units improve restricted boltzmann machines.

LoopUS: Recasting Pretrained LLMs into Looped Latent Refinement Models Rectified linear units improve restricted boltzmann machines

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T07:22:30.217870Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 7ace8159-5ae7-49e1-81af-975f6456b977 · outbound

This paper cites Self-normalizing neural networks.Advances in neural information processing systems, 30.

LoopUS: Recasting Pretrained LLMs into Looped Latent Refinement Models Self-normalizing neural networks.Advances in neural information processing systems, 30

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T07:22:30.221611Z

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Observation 6eac191f-fa38-4a52-803f-41599818e07e · outbound

This paper cites Qwen3 Technical Report.

LoopUS: Recasting Pretrained LLMs into Looped Latent Refinement Models Qwen3 Technical Report

Reference 48

Resolution: verified exact; local_arxiv, observed 2026-05-13T07:22:28.627087Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 666ca1f5-9904-47b3-8910-b263b75869fe · outbound

This paper cites Tinyllama: An open-source small language model.

LoopUS: Recasting Pretrained LLMs into Looped Latent Refinement Models Tinyllama: An open-source small language model

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T07:22:30.197659Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 4cc8242d-a1ba-4818-9550-fc91bd61b4dc · outbound

This paper cites Phi-4 Technical Report.

LoopUS: Recasting Pretrained LLMs into Looped Latent Refinement Models Phi-4 Technical Report

Reference 50

Resolution: metadata mismatch; local_arxiv, observed 2026-05-13T07:22:28.692481Z

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No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 4a5be956-75c2-44ba-aed2-59054b7233dc · outbound

This paper cites The fineweb datasets: Decanting the web for the finest text data at scale.

LoopUS: Recasting Pretrained LLMs into Looped Latent Refinement Models The fineweb datasets: Decanting the web for the finest text data at scale

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T07:22:30.210619Z

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Observation 6d16ed92-9565-42ab-a2b2-9481ea869e90 · outbound

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

LoopUS: Recasting Pretrained LLMs into Looped Latent Refinement Models A framework for few-shot language model evaluation, 12 2023

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T07:22:30.206380Z

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Observation 1ae4605e-22b4-4caa-baad-c203b4f1abe9 · outbound

This paper cites Pointer sentinel mixture models.

LoopUS: Recasting Pretrained LLMs into Looped Latent Refinement Models Pointer sentinel mixture models

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T07:22:30.214141Z

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Observation ed3ca835-b91c-435a-805c-ad338d318240 · outbound

This paper cites The LAMBADA dataset: Word prediction requiring a broad discourse context.

LoopUS: Recasting Pretrained LLMs into Looped Latent Refinement Models The LAMBADA dataset: Word prediction requiring a broad discourse context

Reference 54

Resolution: verified exact; doi, observed 2026-05-13T07:22:27.795951Z

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Observation c545078f-290c-4f78-9af5-663b27315c41 · outbound

This paper cites Measuring Massive Multitask Language Understanding.

LoopUS: Recasting Pretrained LLMs into Looped Latent Refinement Models Measuring Massive Multitask Language Understanding

Reference 55

Resolution: verified exact; local_arxiv, observed 2026-05-13T07:22:28.712797Z

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Observation d81046ca-537b-44b6-9394-f924e11a4581 · outbound

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

LoopUS: Recasting Pretrained LLMs into Looped Latent Refinement Models HellaSwag: Can a Machine Really Finish Your Sentence?

Reference 56

Resolution: verified exact; local_arxiv, observed 2026-05-13T07:22:28.608719Z

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Observation d3422f46-9757-45ae-af8c-75308181c636 · outbound

This paper cites Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge.

LoopUS: Recasting Pretrained LLMs into Looped Latent Refinement Models Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge

Reference 57

Resolution: verified exact; local_arxiv, observed 2026-05-13T07:22:28.604115Z

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Observation 1777d359-b573-4c32-a51e-c559d5d51554 · outbound

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

LoopUS: Recasting Pretrained LLMs into Looped Latent Refinement Models PIQA: Reasoning about Physical Commonsense in Natural Language

Reference 58

Resolution: verified exact; arxiv_id, observed 2026-05-17T14:55:18.098549Z

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No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 0bd4ec57-9c15-4a0b-90a2-053488de30b5 · outbound

This paper cites Winogrande: An adversarial winograd schema challenge at scale.

LoopUS: Recasting Pretrained LLMs into Looped Latent Refinement Models Winogrande: An adversarial winograd schema challenge at scale

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T07:22:30.193559Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation b200c77f-37a8-4a8e-9df6-c13e06bf425b · outbound

This paper cites Can a Suit of Armor Conduct Electricity? A New Dataset for Open Book Question Answering.

LoopUS: Recasting Pretrained LLMs into Looped Latent Refinement Models Can a Suit of Armor Conduct Electricity? A New Dataset for Open Book Question Answering

Reference 60

Resolution: verified exact; arxiv_id, observed 2026-05-13T08:14:48.923977Z

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No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 0bcd6925-ea18-4921-80b6-56ad952f616d · outbound

This paper cites Autoregressive Language Models are Secretly Energy-Based Models: Insights into the Lookahead Capabilities of Next-Token Prediction.

LoopUS: Recasting Pretrained LLMs into Looped Latent Refinement Models Autoregressive Language Models are Secretly Energy-Based Models: Insights into the Lookahead Capabilities of Next-Token Prediction

Reference 61

Resolution: metadata mismatch; local_arxiv, observed 2026-05-13T07:22:28.622080Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 61c4bbfa-b3cb-46e9-bbed-a7a796715c59 · outbound

This paper cites Incor- porating second-order functional knowledge for better option pricing.

LoopUS: Recasting Pretrained LLMs into Looped Latent Refinement Models Incor- porating second-order functional knowledge for better option pricing

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T07:22:30.202357Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation eaa8f018-2a46-4037-abc4-b0d2882726f4 · outbound

This paper cites and Schmidhuber, J.

LoopUS: Recasting Pretrained LLMs into Looped Latent Refinement Models and Schmidhuber, J

Reference 63

Resolution: verified exact; doi, observed 2026-05-13T07:22:27.811482Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 0d02b73a-7233-4edb-b287-6ba2add15d17 · outbound

This paper cites Flashattention-2: Faster attention with better parallelism and work partitioning.

LoopUS: Recasting Pretrained LLMs into Looped Latent Refinement Models Flashattention-2: Faster attention with better parallelism and work partitioning

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T07:22:30.226301Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation eace7f3e-3fa1-4d20-908f-3a2a622cd4e3 · outbound

This paper cites Denoising diffusion implicit models.

LoopUS: Recasting Pretrained LLMs into Looped Latent Refinement Models Denoising diffusion implicit models

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T08:27:32.517559Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation eb92a33d-6e8d-49a4-91ec-fc42a9c4f26f · outbound

This paper cites Looping back to move forward: Recursive transformers for efficient and flexible large multimodal models.

LoopUS: Recasting Pretrained LLMs into Looped Latent Refinement Models Looping back to move forward: Recursive transformers for efficient and flexible large multimodal models

Reference 66

Resolution: verified exact; arxiv_id, observed 2026-05-13T07:22:28.728002Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation c7fc4eb3-0b68-4fae-9b21-7fd1d85d6c6c · outbound

This paper cites Qwen3.6-27B: Flagship-level coding in a 27B dense model, April 2026.

LoopUS: Recasting Pretrained LLMs into Looped Latent Refinement Models Qwen3.6-27B: Flagship-level coding in a 27B dense model, April 2026

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T08:27:32.509670Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 7fa5de58-3d66-43f9-b38e-3d7a78770a5d · outbound

This paper cites Deepseek-v4: Towards highly efficient million-token context intelligence.

LoopUS: Recasting Pretrained LLMs into Looped Latent Refinement Models Deepseek-v4: Towards highly efficient million-token context intelligence

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T08:27:32.490970Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation baff6871-1e21-4b1f-8901-1245726a1102 · outbound

This paper cites K-exaone technical report.

LoopUS: Recasting Pretrained LLMs into Looped Latent Refinement Models K-exaone technical report

Reference 69

Resolution: verified exact; arxiv_id, observed 2026-05-13T07:22:28.756472Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 080c59ca-149e-41ad-8e1e-36671eedfe29 · outbound

This paper cites Solar open technical report.

LoopUS: Recasting Pretrained LLMs into Looped Latent Refinement Models Solar open technical report

Reference 70

Resolution: verified exact; arxiv_id, observed 2026-05-13T07:22:28.643185Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 7dcad8f6-c7b3-4be9-8dbb-47ec5c6ce1bb · outbound

This paper cites NVIDIA Nemotron 3: Efficient and Open Intelligence.

LoopUS: Recasting Pretrained LLMs into Looped Latent Refinement Models NVIDIA Nemotron 3: Efficient and Open Intelligence

Reference 71

Resolution: verified exact; arxiv_id, observed 2026-05-18T01:40:43.189893Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation c4251de7-1817-4176-80a0-0f497771c026 · outbound

This paper cites Attention Residuals.

LoopUS: Recasting Pretrained LLMs into Looped Latent Refinement Models Attention Residuals

Reference 72

Resolution: metadata mismatch; arxiv_id, observed 2026-05-21T06:39:04.582456Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation baa8338e-4d8c-4e59-b001-0520fc461a9e · outbound

This paper cites A Survey on Post-training of Large Language Models.

LoopUS: Recasting Pretrained LLMs into Looped Latent Refinement Models A Survey on Post-training of Large Language Models

Reference 73

Resolution: metadata mismatch; arxiv_id, observed 2026-05-13T07:22:28.687150Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation cfc109cf-6d20-407b-afd9-f4da74adab98 · outbound

This paper cites dllm: Simple diffusion language modeling.arXiv preprint arXiv:2602.22661.

LoopUS: Recasting Pretrained LLMs into Looped Latent Refinement Models dllm: Simple diffusion language modeling.arXiv preprint arXiv:2602.22661

Reference 74

Resolution: verified exact; arxiv_id, observed 2026-05-13T07:22:28.762889Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation fdd0b592-b977-4956-9732-6536fef7c75f · outbound

This paper cites all but 9 run away.

LoopUS: Recasting Pretrained LLMs into Looped Latent Refinement Models all but 9 run away

Reference 75

Resolution: malformed identifier; raw_fallback, observed 2026-05-13T07:22:30.233496Z

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No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

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