LAQuant: A Simple Overhead-free Large Reasoning Model Quantization by Layer-wise Lookahead Loss | Paper Citation Record

Observation 416d8a42-57ff-4b24-8f4a-7a5082aade3b · outbound

This paper cites HadaCore: Tensor Core Accelerated Hadamard Transform Kernel.

LAQuant: A Simple Overhead-free Large Reasoning Model Quantization by Layer-wise Lookahead Loss HadaCore: Tensor Core Accelerated Hadamard Transform Kernel

Reference 1

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This paper cites Quarot: Outlier-free 4-bit inference in rotated llms.Advances in Neural Information Processing Systems, 37:100213– 100240.

LAQuant: A Simple Overhead-free Large Reasoning Model Quantization by Layer-wise Lookahead Loss Quarot: Outlier-free 4-bit inference in rotated llms.Advances in Neural Information Processing Systems, 37:100213– 100240

Reference 2

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This paper cites Db-llm: Accurate dual-binarization for efficient llms.

LAQuant: A Simple Overhead-free Large Reasoning Model Quantization by Layer-wise Lookahead Loss Db-llm: Accurate dual-binarization for efficient llms

Reference 3

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Observation b7072eaf-39c8-4678-b4bb-030efa9365a9 · outbound

This paper cites EfficientQAT: Efficient Quantization-Aware Training for Large Language Models.

LAQuant: A Simple Overhead-free Large Reasoning Model Quantization by Layer-wise Lookahead Loss EfficientQAT: Efficient Quantization-Aware Training for Large Language Models

Reference 4

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Observation c93c1c47-7603-47d4-afe9-84c9cd4bd152 · outbound

This paper cites Boolq: Exploring the surprising difficulty of natural yes/no questions.

LAQuant: A Simple Overhead-free Large Reasoning Model Quantization by Layer-wise Lookahead Loss Boolq: Exploring the surprising difficulty of natural yes/no questions

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T19:21:47.008826Z

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Observation 81e10119-ed18-4bf5-a61e-a227a2595932 · outbound

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

LAQuant: A Simple Overhead-free Large Reasoning Model Quantization by Layer-wise Lookahead Loss Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge

Reference 6

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Observation be64087b-5180-4d0e-8244-3ee25e5f89c6 · outbound

This paper cites Fast Hadamard Transform in CUDA, with a PyTorch Interface.

LAQuant: A Simple Overhead-free Large Reasoning Model Quantization by Layer-wise Lookahead Loss Fast Hadamard Transform in CUDA, with a PyTorch Interface

Reference 7

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Observation 767708a3-1afd-4d44-b915-4d91bb4eb3f4 · outbound

This paper cites Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning.

LAQuant: A Simple Overhead-free Large Reasoning Model Quantization by Layer-wise Lookahead Loss Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T19:21:46.997989Z

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Observation dd69a17c-eaa2-4bae-a95e-7d4e881637ff · outbound

This paper cites Bitdistiller: Unleashing the potential of sub-4-bit llms via self-distillation.

LAQuant: A Simple Overhead-free Large Reasoning Model Quantization by Layer-wise Lookahead Loss Bitdistiller: Unleashing the potential of sub-4-bit llms via self-distillation

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T19:21:46.994293Z

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Observation 7d625e96-cec0-4548-8c99-7989d0e513d3 · outbound

This paper cites Layer-Wise Quantization: A Pragmatic and Effective Method for Quantizing LLMs Beyond Integer Bit-Levels.

LAQuant: A Simple Overhead-free Large Reasoning Model Quantization by Layer-wise Lookahead Loss Layer-Wise Quantization: A Pragmatic and Effective Method for Quantizing LLMs Beyond Integer Bit-Levels

Reference 10

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Observation 850f2a4c-7c95-4acb-93ac-3378aedfa772 · outbound

This paper cites Optimal brain compression: A framework for accurate post- training quantization and pruning.Advances in Neural Information Processing Systems, 35:4475– 4488.

LAQuant: A Simple Overhead-free Large Reasoning Model Quantization by Layer-wise Lookahead Loss Optimal brain compression: A framework for accurate post- training quantization and pruning.Advances in Neural Information Processing Systems, 35:4475– 4488

Reference 11

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Observation 8349e49b-3da0-4837-92f7-d5d3648439d9 · outbound

This paper cites OPTQ: Accurate quantization for generative pre-trained transformers.

LAQuant: A Simple Overhead-free Large Reasoning Model Quantization by Layer-wise Lookahead Loss OPTQ: Accurate quantization for generative pre-trained transformers

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T19:21:46.872238Z

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Observation cdb52f0b-ced6-4d97-afbc-c82d4b07ea23 · outbound

This paper cites The language model evaluation harness, 07 2024.

LAQuant: A Simple Overhead-free Large Reasoning Model Quantization by Layer-wise Lookahead Loss The language model evaluation harness, 07 2024

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T19:21:46.958714Z

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Observation 5acf8d37-9ef8-42f7-b471-30b617186120 · outbound

This paper cites Fast r-cnn.

LAQuant: A Simple Overhead-free Large Reasoning Model Quantization by Layer-wise Lookahead Loss Fast r-cnn

Reference 14

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Observation a484f3d1-7fa8-46cc-9bc7-a1f3dddd6869 · outbound

This paper cites The Llama 3 Herd of Models.

LAQuant: A Simple Overhead-free Large Reasoning Model Quantization by Layer-wise Lookahead Loss The Llama 3 Herd of Models

Reference 15

Resolution: verified exact; local_arxiv, observed 2026-05-12T07:16:30.033811Z

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Observation d2e2f417-fee2-42bd-b462-35420fda727a · outbound

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

LAQuant: A Simple Overhead-free Large Reasoning Model Quantization by Layer-wise Lookahead Loss DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 16

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Observation 9a8b7780-1629-4236-b8ce-3daba0fa435e · outbound

This paper cites Lighteval: A lightweight framework for llm evaluation.

LAQuant: A Simple Overhead-free Large Reasoning Model Quantization by Layer-wise Lookahead Loss Lighteval: A lightweight framework for llm evaluation

Reference 17

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Observation 3ca35901-a173-4d54-bf68-3b1a0b39d895 · outbound

This paper cites Second order derivatives for network pruning: Optimal brain surgeon.Advances in neural information processing systems, 5.

LAQuant: A Simple Overhead-free Large Reasoning Model Quantization by Layer-wise Lookahead Loss Second order derivatives for network pruning: Optimal brain surgeon.Advances in neural information processing systems, 5

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T19:21:46.812501Z

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Observation a2e6b270-069d-487d-8769-23115ee29ab5 · outbound

This paper cites Billm: pushing the limit of post-training quantization for llms.

LAQuant: A Simple Overhead-free Large Reasoning Model Quantization by Layer-wise Lookahead Loss Billm: pushing the limit of post-training quantization for llms

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T19:21:46.822276Z

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Observation f63fef00-b76e-4b7b-96fa-cd81c81930b1 · outbound

This paper cites Robust estimation of a location parameter.

LAQuant: A Simple Overhead-free Large Reasoning Model Quantization by Layer-wise Lookahead Loss Robust estimation of a location parameter

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T19:21:46.840229Z

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Observation 8260486d-5f8f-44b5-b05b-40ed53c57f00 · outbound

This paper cites Open r1: A fully open reproduction of deepseek-r1, January 2025.

LAQuant: A Simple Overhead-free Large Reasoning Model Quantization by Layer-wise Lookahead Loss Open r1: A fully open reproduction of deepseek-r1, January 2025

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T19:21:46.867958Z

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Observation 635fc5d0-97da-4419-af2a-ec7f0b8e8bf0 · outbound

This paper cites Livecodebench: Holistic and contamination free evaluation of large language models for code.

LAQuant: A Simple Overhead-free Large Reasoning Model Quantization by Layer-wise Lookahead Loss Livecodebench: Holistic and contamination free evaluation of large language models for code

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T19:21:46.848162Z

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Observation c093f77f-07fb-4e9c-ac15-2c88107159ec · outbound

This paper cites Aime problem set 2024.

LAQuant: A Simple Overhead-free Large Reasoning Model Quantization by Layer-wise Lookahead Loss Aime problem set 2024

Reference 23

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Observation 56d0a82e-fe2c-482e-9ffb-9453f590b0ce · outbound

This paper cites Mahoney, and Kurt Keutzer.

LAQuant: A Simple Overhead-free Large Reasoning Model Quantization by Layer-wise Lookahead Loss Mahoney, and Kurt Keutzer

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T19:21:46.855245Z

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Observation 084903f5-e809-4814-82e6-af41c9bcec6e · outbound

This paper cites The impact of quantization on large reasoning model reinforcement learning.

LAQuant: A Simple Overhead-free Large Reasoning Model Quantization by Layer-wise Lookahead Loss The impact of quantization on large reasoning model reinforcement learning

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T19:21:46.804128Z

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Observation 37770ace-c8f0-41df-b0be-7d507803538e · outbound

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

LAQuant: A Simple Overhead-free Large Reasoning Model Quantization by Layer-wise Lookahead Loss Gonzalez, Hao Zhang, and Ion Stoica

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T19:21:46.896035Z

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Observation 8deccc73-cd4a-4ce6-b779-70d498532257 · outbound

This paper cites Optimal brain damage.Advances in neural information processing systems, 2.

LAQuant: A Simple Overhead-free Large Reasoning Model Quantization by Layer-wise Lookahead Loss Optimal brain damage.Advances in neural information processing systems, 2

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T19:21:46.928941Z

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Observation 7a5d26fd-038f-4210-bed2-19ee9794d1f8 · outbound

This paper cites Rilq: Rank-insensitive lora-based quantization error compensation for boosting 2-bit large language model accuracy.

LAQuant: A Simple Overhead-free Large Reasoning Model Quantization by Layer-wise Lookahead Loss Rilq: Rank-insensitive lora-based quantization error compensation for boosting 2-bit large language model accuracy

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T19:21:46.921373Z

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Observation 0e22c51e-8cab-4013-a3fd-59a651e419fb · outbound

This paper cites Quantization Meets Reasoning: Exploring LLM Low-Bit Quantization Degradation for Mathematical Reasoning.

LAQuant: A Simple Overhead-free Large Reasoning Model Quantization by Layer-wise Lookahead Loss Quantization Meets Reasoning: Exploring LLM Low-Bit Quantization Degradation for Mathematical Reasoning

Reference 29

Resolution: verified exact; arxiv_id, observed 2026-05-12T07:16:30.052044Z

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Observation 577cb0ff-7ac7-47af-aae6-d85182a55bcd · outbound

This paper cites ParoQuant: Pairwise Rotation Quantization for Efficient Reasoning LLM Inference.

LAQuant: A Simple Overhead-free Large Reasoning Model Quantization by Layer-wise Lookahead Loss ParoQuant: Pairwise Rotation Quantization for Efficient Reasoning LLM Inference

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T19:21:46.982130Z

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Observation 205064f0-efca-4ded-a2e7-11c077851e85 · outbound

This paper cites Duquant: Distributing outliers via dual transformation makes stronger quantized llms.Advances in Neural Information Processing Systems, 37:87766–87800.

LAQuant: A Simple Overhead-free Large Reasoning Model Quantization by Layer-wise Lookahead Loss Duquant: Distributing outliers via dual transformation makes stronger quantized llms.Advances in Neural Information Processing Systems, 37:87766–87800

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T19:21:46.881121Z

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Observation 1642ad78-6658-465e-912c-2a2adb6571b6 · outbound

This paper cites Awq: Activation-aware weight quantization for on-device llm compression and acceleration.Proceedings of machine learning and systems, 6:87–100.

LAQuant: A Simple Overhead-free Large Reasoning Model Quantization by Layer-wise Lookahead Loss Awq: Activation-aware weight quantization for on-device llm compression and acceleration.Proceedings of machine learning and systems, 6:87–100

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T19:21:46.969878Z

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Observation d0a073f3-6d58-4f65-b97a-ff42589ba22f · outbound

This paper cites Qserve: W4a8kv4 quantization and system co-design for efficient llm serving.

LAQuant: A Simple Overhead-free Large Reasoning Model Quantization by Layer-wise Lookahead Loss Qserve: W4a8kv4 quantization and system co-design for efficient llm serving

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T19:21:46.808506Z

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Observation 1e569c06-81f9-446d-9952-1cb8784451a1 · outbound

This paper cites Quantization Hurts Reasoning? An Empirical Study on Quantized Reasoning Models.

LAQuant: A Simple Overhead-free Large Reasoning Model Quantization by Layer-wise Lookahead Loss Quantization Hurts Reasoning? An Empirical Study on Quantized Reasoning Models

Reference 34

Resolution: verified exact; arxiv_id, observed 2026-05-12T07:16:29.962602Z

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Observation 15b2df96-56ab-4af2-a6c9-f20c32dcd6a6 · outbound

This paper cites Vptq: Extreme low-bit vector post-training quantization for large language models.

LAQuant: A Simple Overhead-free Large Reasoning Model Quantization by Layer-wise Lookahead Loss Vptq: Extreme low-bit vector post-training quantization for large language models

Reference 35

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Observation db1ed53a-cc76-467e-8519-67d13ed52c6a · outbound

This paper cites Spinquant: LLM quantization with learned rotations.

LAQuant: A Simple Overhead-free Large Reasoning Model Quantization by Layer-wise Lookahead Loss Spinquant: LLM quantization with learned rotations

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T19:21:46.836236Z

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Observation 7eb6fa0e-b309-48c2-8dfc-d9f2e0b94b38 · outbound

This paper cites Decoupled weight decay regularization.

LAQuant: A Simple Overhead-free Large Reasoning Model Quantization by Layer-wise Lookahead Loss Decoupled weight decay regularization

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T19:21:46.966442Z

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Observation f37acbb0-2e06-459b-807f-6116eb63c0aa · outbound

This paper cites What makes low-bit quantization-aware training work for reasoning llms? a systematic study.

LAQuant: A Simple Overhead-free Large Reasoning Model Quantization by Layer-wise Lookahead Loss What makes low-bit quantization-aware training work for reasoning llms? a systematic study

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T19:21:46.884782Z

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Observation 35fcec6c-6459-41c2-a0a6-c4053fd0ac20 · outbound

This paper cites Aime problem set 2025.

LAQuant: A Simple Overhead-free Large Reasoning Model Quantization by Layer-wise Lookahead Loss Aime problem set 2025

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T19:21:46.908009Z

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Observation 3813e6ec-791b-427b-ad27-d04b7906c5fe · outbound

This paper cites Does quantization affect models’ performance on long-context tasks? InProceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pages 9433–9481.

LAQuant: A Simple Overhead-free Large Reasoning Model Quantization by Layer-wise Lookahead Loss Does quantization affect models’ performance on long-context tasks? InProceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pages 9433–9481

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T19:21:46.986011Z

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Observation 29195419-5c4a-4515-a8b2-97bf141193fb · outbound

This paper cites Pointer sentinel mixture models.

LAQuant: A Simple Overhead-free Large Reasoning Model Quantization by Layer-wise Lookahead Loss Pointer sentinel mixture models

Reference 41

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Observation 6b80ced4-80a5-44cd-a46a-01421f172752 · outbound

This paper cites s1: Simple test-time scaling.

LAQuant: A Simple Overhead-free Large Reasoning Model Quantization by Layer-wise Lookahead Loss s1: Simple test-time scaling

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T19:21:46.949755Z

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Observation 3ff0e282-fefd-4a5d-ba3d-7914a30d7a65 · outbound

This paper cites Towards quantization- aware training for ultra-low-bit reasoning LLMs.

LAQuant: A Simple Overhead-free Large Reasoning Model Quantization by Layer-wise Lookahead Loss Towards quantization- aware training for ultra-low-bit reasoning LLMs

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T19:21:46.978025Z

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Observation e897025d-99be-420d-ae4e-05ada09a669b · outbound

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LAQuant: A Simple Overhead-free Large Reasoning Model Quantization by Layer-wise Lookahead Loss Unresolved cited work

Reference 44

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Observation f5d5a291-04e0-4996-a89e-ae4312f30c3f · outbound

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LAQuant: A Simple Overhead-free Large Reasoning Model Quantization by Layer-wise Lookahead Loss Unresolved cited work

Reference 45

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Observation ee82e121-8ac0-4655-b31c-40b31547f0c4 · outbound

This paper cites Omniquant: Omnidirectionally calibrated quan- tization for large language models.

LAQuant: A Simple Overhead-free Large Reasoning Model Quantization by Layer-wise Lookahead Loss Omniquant: Omnidirectionally calibrated quan- tization for large language models

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T19:21:46.851693Z

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Observation ce4611e9-953f-4e15-975e-d13533add57f · outbound

This paper cites DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models.

LAQuant: A Simple Overhead-free Large Reasoning Model Quantization by Layer-wise Lookahead Loss DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

Reference 47

Resolution: verified exact; local_arxiv, observed 2026-05-12T07:16:30.073475Z

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Observation 2f946c59-8016-4323-ad3d-e42a0af64728 · outbound

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

LAQuant: A Simple Overhead-free Large Reasoning Model Quantization by Layer-wise Lookahead Loss Scaling LLM test-time com- pute optimally can be more effective than scaling parameters for reasoning

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T19:21:46.945625Z

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 0cf8ccc9-3e98-4843-8dae-5e4c6291a1c2 · outbound

This paper cites Qtip: Quantization with trellises and incoherence processing.Advances in Neural Information Processing Systems, 37:59597– 59620.

LAQuant: A Simple Overhead-free Large Reasoning Model Quantization by Layer-wise Lookahead Loss Qtip: Quantization with trellises and incoherence processing.Advances in Neural Information Processing Systems, 37:59597– 59620

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T19:21:46.932613Z

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 6bad0043-39d8-490c-99f5-d6acd3a216fe · outbound

This paper cites BitNet: Scaling 1-bit Transformers for Large Language Models.

LAQuant: A Simple Overhead-free Large Reasoning Model Quantization by Layer-wise Lookahead Loss BitNet: Scaling 1-bit Transformers for Large Language Models

Reference 50

Resolution: verified exact; arxiv_id, observed 2026-05-12T07:16:29.974116Z

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 dc62e25f-af31-48e3-a25c-3ed30305fe55 · outbound

This paper cites Mmlu-pro: A more robust and challenging multi-task language understanding benchmark.Advances in Neural Information Processing Systems, 37:95266–95290.

LAQuant: A Simple Overhead-free Large Reasoning Model Quantization by Layer-wise Lookahead Loss Mmlu-pro: A more robust and challenging multi-task language understanding benchmark.Advances in Neural Information Processing Systems, 37:95266–95290

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T19:21:46.974413Z

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 4b3574d1-5869-436b-b0b5-0344561afe73 · outbound

This paper cites an unresolved cited work.

LAQuant: A Simple Overhead-free Large Reasoning Model Quantization by Layer-wise Lookahead Loss Unresolved cited work

Reference 52

Resolution: unresolved; raw_fallback, observed 2026-05-12T19:21:46.962163Z

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 7bfd6899-44d6-43f4-9741-b58991ca6146 · outbound

This paper cites On the impact of calibration data in post-training quantization and pruning.

LAQuant: A Simple Overhead-free Large Reasoning Model Quantization by Layer-wise Lookahead Loss On the impact of calibration data in post-training quantization and pruning

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T19:21:46.912090Z

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 20b68d8b-6395-4755-aca2-df1e9291591a · outbound

This paper cites Think before you prune: Selective self- generated calibration for pruning large reasoning models.

LAQuant: A Simple Overhead-free Large Reasoning Model Quantization by Layer-wise Lookahead Loss Think before you prune: Selective self- generated calibration for pruning large reasoning models

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T19:21:46.817089Z

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 559d3cfb-c238-4c1a-89e4-9255d7dca79d · outbound

This paper cites SmoothQuant: Accurate and efficient post-training quantization for large language models.

LAQuant: A Simple Overhead-free Large Reasoning Model Quantization by Layer-wise Lookahead Loss SmoothQuant: Accurate and efficient post-training quantization for large language models

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T19:21:46.916892Z

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 bc72d758-d03e-473d-8a66-19bf03986a8a · outbound

This paper cites Qwen3 Technical Report.

LAQuant: A Simple Overhead-free Large Reasoning Model Quantization by Layer-wise Lookahead Loss Qwen3 Technical Report

Reference 56

Resolution: verified exact; local_arxiv, observed 2026-05-12T07:16:30.001888Z

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 433f7aea-0753-4e9c-a9c0-88d8c15daf8a · outbound

This paper cites DAPO: An Open-Source LLM Reinforcement Learning System at Scale.

LAQuant: A Simple Overhead-free Large Reasoning Model Quantization by Layer-wise Lookahead Loss DAPO: An Open-Source LLM Reinforcement Learning System at Scale

Reference 57

Resolution: verified exact; local_arxiv, observed 2026-05-12T07:16:30.017359Z

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 0b87694a-f503-4f2e-b60c-43eada8f1be8 · outbound

This paper cites Hellaswag: Can a machine really finish your sentence? InProceedings of the 57th annual meeting of the association for computational linguistics, pages 4791–4800.

LAQuant: A Simple Overhead-free Large Reasoning Model Quantization by Layer-wise Lookahead Loss Hellaswag: Can a machine really finish your sentence? InProceedings of the 57th annual meeting of the association for computational linguistics, pages 4791–4800

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T19:21:46.831698Z

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 03272172-6638-4431-9b47-0dec6c77d9cc · outbound

This paper cites Quantlrm: Quantization of large reasoning models via fine-tuning signals.

LAQuant: A Simple Overhead-free Large Reasoning Model Quantization by Layer-wise Lookahead Loss Quantlrm: Quantization of large reasoning models via fine-tuning signals

Reference 59

Resolution: verified exact; arxiv_id, observed 2026-05-12T07:16:29.986985Z

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 4ff5550f-a2bd-4a9f-b64d-d07015dc1179 · outbound

This paper cites Atom: Low-bit quantization for efficient and accurate llm serving.

LAQuant: A Simple Overhead-free Large Reasoning Model Quantization by Layer-wise Lookahead Loss Atom: Low-bit quantization for efficient and accurate llm serving

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T19:21:46.890935Z

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 06471a8b-5d82-463f-ad44-b873e0d688a1 · outbound

This paper cites An empirical study of qwen3 quantiza- tion.Visual Intelligence, 4(1):11.

LAQuant: A Simple Overhead-free Large Reasoning Model Quantization by Layer-wise Lookahead Loss An empirical study of qwen3 quantiza- tion.Visual Intelligence, 4(1):11

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T19:21:46.844742Z

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 bde502e0-d0fd-43f2-b3b8-df72fd2e8827 · outbound

This paper cites AR-LSAT: Investigating Analytical Reasoning of Text.

LAQuant: A Simple Overhead-free Large Reasoning Model Quantization by Layer-wise Lookahead Loss AR-LSAT: Investigating Analytical Reasoning of Text

Reference 62

Resolution: malformed identifier; arxiv_id, observed 2026-05-12T07:16:29.996229Z

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.

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