MetaLA: Unified Optimal Linear Approximation to Softmax Attention Map | Paper Citation Record

Observation ca9ffb97-f30e-4a4a-ab97-0253f5b863e9 · outbound

This paper cites Attention is all you need.

MetaLA: Unified Optimal Linear Approximation to Softmax Attention Map Attention is all you need

Reference 1

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This paper cites Language models are few-shot learners.

MetaLA: Unified Optimal Linear Approximation to Softmax Attention Map Language models are few-shot learners

Reference 2

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Observation 3fec83d1-e2c6-4321-8cd4-0219d1992880 · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models.

MetaLA: Unified Optimal Linear Approximation to Softmax Attention Map LLaMA: Open and Efficient Foundation Language Models

Reference 3

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Observation f304b7d7-e324-49c7-a2fb-d7cdea813434 · outbound

This paper cites Llama 2: Open Foundation and Fine-Tuned Chat Models.

MetaLA: Unified Optimal Linear Approximation to Softmax Attention Map Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 4

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This paper cites PaLM 2 Technical Report.

MetaLA: Unified Optimal Linear Approximation to Softmax Attention Map PaLM 2 Technical Report

Reference 5

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This paper cites Neural Codec Language Models are Zero-Shot Text to Speech Synthesizers.

MetaLA: Unified Optimal Linear Approximation to Softmax Attention Map Neural Codec Language Models are Zero-Shot Text to Speech Synthesizers

Reference 6

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Observation 81a79af0-9881-4332-a8ef-2afa57beffba · outbound

This paper cites Visual instruction tuning.Advances in Neural Information Processing Systems, 36, 2024.

MetaLA: Unified Optimal Linear Approximation to Softmax Attention Map Visual instruction tuning.Advances in Neural Information Processing Systems, 36, 2024

Reference 7

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Observation e3bd59c1-1ccb-478f-a8b3-b2cbbb5fc033 · outbound

This paper cites Efficient transformers: A survey.

MetaLA: Unified Optimal Linear Approximation to Softmax Attention Map Efficient transformers: A survey

Reference 8

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This paper cites Transformers are rnns: Fast autoregressive transformers with linear attention.

MetaLA: Unified Optimal Linear Approximation to Softmax Attention Map Transformers are rnns: Fast autoregressive transformers with linear attention

Reference 9

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Observation e7d4007c-8d9e-44ba-9931-f2451f752490 · outbound

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

MetaLA: Unified Optimal Linear Approximation to Softmax Attention Map Efficiently modeling long sequences with structured state spaces

Reference 10

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Observation 12f4dd21-8139-4370-a4f5-9d6c9b69ad92 · outbound

This paper cites Parallelizing linear recurrent neural nets over sequence length.

MetaLA: Unified Optimal Linear Approximation to Softmax Attention Map Parallelizing linear recurrent neural nets over sequence length

Reference 11

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This paper cites GoldFinch: High Performance RWKV/Transformer Hybrid with Linear Pre-Fill and Extreme KV-Cache Compression.

MetaLA: Unified Optimal Linear Approximation to Softmax Attention Map GoldFinch: High Performance RWKV/Transformer Hybrid with Linear Pre-Fill and Extreme KV-Cache Compression

Reference 12

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Observation 4395c9d1-6da2-4c1d-b620-1cef39dccee8 · outbound

This paper cites The devil in linear transformer.

MetaLA: Unified Optimal Linear Approximation to Softmax Attention Map The devil in linear transformer

Reference 13

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This paper cites Transnormerllm: A faster and better large language model with improved transnormer, 2024.

MetaLA: Unified Optimal Linear Approximation to Softmax Attention Map Transnormerllm: A faster and better large language model with improved transnormer, 2024

Reference 14

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Observation 4ca69ee4-203d-4ea8-919a-81e66270cd41 · outbound

This paper cites Retentive Network: A Successor to Transformer for Large Language Models.

MetaLA: Unified Optimal Linear Approximation to Softmax Attention Map Retentive Network: A Successor to Transformer for Large Language Models

Reference 15

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This paper cites Gated linear attention transformers with hardware-efficient training.

MetaLA: Unified Optimal Linear Approximation to Softmax Attention Map Gated linear attention transformers with hardware-efficient training

Reference 16

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

MetaLA: Unified Optimal Linear Approximation to Softmax Attention Map Mamba: Linear-time sequence modeling with selective state spaces

Reference 17

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This paper cites Rwkv: Reinventing rnns for the transformer era.

MetaLA: Unified Optimal Linear Approximation to Softmax Attention Map Rwkv: Reinventing rnns for the transformer era

Reference 18

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This paper cites Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models.

MetaLA: Unified Optimal Linear Approximation to Softmax Attention Map Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models

Reference 19

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Observation 4beb0c1f-cd9b-41d9-9260-6d0c875132ae · outbound

This paper cites Orvieto, S.

MetaLA: Unified Optimal Linear Approximation to Softmax Attention Map Orvieto, S

Reference 20

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Observation c4143991-4aef-4b32-8be2-ef5eb2a876bc · outbound

This paper cites Hierarchically gated recurrent neural network for sequence modeling.

MetaLA: Unified Optimal Linear Approximation to Softmax Attention Map Hierarchically gated recurrent neural network for sequence modeling

Reference 21

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Observation b8b45666-82b2-4cf1-8a45-2b6399e13efb · outbound

This paper cites Fu, Tri Dao, Khaled Kamal Saab, Armin W.

MetaLA: Unified Optimal Linear Approximation to Softmax Attention Map Fu, Tri Dao, Khaled Kamal Saab, Armin W

Reference 22

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This paper cites Simplified state space layers for sequence modeling.

MetaLA: Unified Optimal Linear Approximation to Softmax Attention Map Simplified state space layers for sequence modeling

Reference 23

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Observation 5df85860-a92d-4c4b-8102-d092493a42c7 · outbound

This paper cites Various lengths, constant speed: Efficient language modeling with lightning attention.

MetaLA: Unified Optimal Linear Approximation to Softmax Attention Map Various lengths, constant speed: Efficient language modeling with lightning attention

Reference 24

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Observation a6d3f49f-6ab7-4069-8ce1-f8aa318c24a2 · outbound

This paper cites Modeling Sequences with Structured State Spaces.

MetaLA: Unified Optimal Linear Approximation to Softmax Attention Map Modeling Sequences with Structured State Spaces

Reference 25

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This paper cites On the parameterization and initialization of diagonal state space models.

MetaLA: Unified Optimal Linear Approximation to Softmax Attention Map On the parameterization and initialization of diagonal state space models

Reference 26

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This paper cites Diagonal state spaces are as effective as structured state spaces.

MetaLA: Unified Optimal Linear Approximation to Softmax Attention Map Diagonal state spaces are as effective as structured state spaces

Reference 27

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This paper cites Hippo: Recurrent memory with optimal polynomial projections.

MetaLA: Unified Optimal Linear Approximation to Softmax Attention Map Hippo: Recurrent memory with optimal polynomial projections

Reference 28

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This paper cites Wang, Hui Dai, and Yoav Artzi.

MetaLA: Unified Optimal Linear Approximation to Softmax Attention Map Wang, Hui Dai, and Yoav Artzi

Reference 29

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This paper cites HGRN2: Gated linear RNNs with state expansion.

MetaLA: Unified Optimal Linear Approximation to Softmax Attention Map HGRN2: Gated linear RNNs with state expansion

Reference 30

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This paper cites Rethinking attention with performers.

MetaLA: Unified Optimal Linear Approximation to Softmax Attention Map Rethinking attention with performers

Reference 31

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This paper cites Random feature attention.

MetaLA: Unified Optimal Linear Approximation to Softmax Attention Map Random feature attention

Reference 32

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This paper cites cosformer: Rethinking softmax in attention.

MetaLA: Unified Optimal Linear Approximation to Softmax Attention Map cosformer: Rethinking softmax in attention

Reference 33

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This paper cites Strongly-typed recurrent neural networks.

MetaLA: Unified Optimal Linear Approximation to Softmax Attention Map Strongly-typed recurrent neural networks

Reference 34

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MetaLA: Unified Optimal Linear Approximation to Softmax Attention Map Mlp-mixer: An all-mlp architecture for vision

Reference 35

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This paper cites Metaformer baselines for vision.

MetaLA: Unified Optimal Linear Approximation to Softmax Attention Map Metaformer baselines for vision

Reference 36

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This paper cites Zoology: Measuring and Improving Recall in Efficient Language Models.

MetaLA: Unified Optimal Linear Approximation to Softmax Attention Map Zoology: Measuring and Improving Recall in Efficient Language Models

Reference 37

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This paper cites The Pile: An 800GB Dataset of Diverse Text for Language Modeling.

MetaLA: Unified Optimal Linear Approximation to Softmax Attention Map The Pile: An 800GB Dataset of Diverse Text for Language Modeling

Reference 38

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This paper cites Superglue: A stickier benchmark for general-purpose language understanding systems.

MetaLA: Unified Optimal Linear Approximation to Softmax Attention Map Superglue: A stickier benchmark for general-purpose language understanding systems

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MetaLA: Unified Optimal Linear Approximation to Softmax Attention Map Long range arena: A benchmark for efficient transformers

Reference 40

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This paper cites Imagenet: A large-scale hierarchical image database.

MetaLA: Unified Optimal Linear Approximation to Softmax Attention Map Imagenet: A large-scale hierarchical image database

Reference 41

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MetaLA: Unified Optimal Linear Approximation to Softmax Attention Map Mechanistic design and scaling of hybrid architectures

Reference 42

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MetaLA: Unified Optimal Linear Approximation to Softmax Attention Map Scaling Laws for Linear Complexity Language Models

Reference 43

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MetaLA: Unified Optimal Linear Approximation to Softmax Attention Map Simple linear attention language models balance the recall-throughput tradeoff

Reference 44

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MetaLA: Unified Optimal Linear Approximation to Softmax Attention Map Pythia: A suite for analyzing large language models across training and scaling

Reference 45

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MetaLA: Unified Optimal Linear Approximation to Softmax Attention Map GPT-NeoX: Large Scale Autoregressive Language Modeling in PyTorch

Reference 46

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MetaLA: Unified Optimal Linear Approximation to Softmax Attention Map Toeplitz neural network for sequence modeling

Reference 47

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MetaLA: Unified Optimal Linear Approximation to Softmax Attention Map Mega: Moving average equipped gated attention

Reference 48

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This paper cites What makes convolu- tional models great on long sequence modeling? In The Eleventh International Conference on Learning Representations, 2022.

MetaLA: Unified Optimal Linear Approximation to Softmax Attention Map What makes convolu- tional models great on long sequence modeling? In The Eleventh International Conference on Learning Representations, 2022

Reference 49

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MetaLA: Unified Optimal Linear Approximation to Softmax Attention Map Training data-efficient image transformers & distillation through attention

Reference 50

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This paper cites The hedgehog & the porcupine: Expressive linear attentions with softmax mimicry.

MetaLA: Unified Optimal Linear Approximation to Softmax Attention Map The hedgehog & the porcupine: Expressive linear attentions with softmax mimicry

Reference 51

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MetaLA: Unified Optimal Linear Approximation to Softmax Attention Map Never train from scratch: Fair comparison of long-sequence models requires data-driven priors

Reference 52

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MetaLA: Unified Optimal Linear Approximation to Softmax Attention Map Finetuning pretrained transformers into rnns

Reference 53

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MetaLA: Unified Optimal Linear Approximation to Softmax Attention Map Hybrid random features

Reference 54

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This paper cites Nyströmformer: A nyström-based algorithm for approximating self-attention.

MetaLA: Unified Optimal Linear Approximation to Softmax Attention Map Nyströmformer: A nyström-based algorithm for approximating self-attention

Reference 55

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MetaLA: Unified Optimal Linear Approximation to Softmax Attention Map Linear transformers are secretly fast weight programmers

Reference 56

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MetaLA: Unified Optimal Linear Approximation to Softmax Attention Map Networks of spiking neurons: the third generation of neural network models

Reference 57

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MetaLA: Unified Optimal Linear Approximation to Softmax Attention Map Attention spiking neural networks

Reference 58

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MetaLA: Unified Optimal Linear Approximation to Softmax Attention Map Spike-based dynamic computing with asynchronous sensing-computing neuromorphic chip

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-12T19:28:29.696900Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1aeb4176-1f56-4091-a411-5b82156c1599 · outbound

This paper cites Brain-inspired computing: A systematic survey and future trends.

MetaLA: Unified Optimal Linear Approximation to Softmax Attention Map Brain-inspired computing: A systematic survey and future trends

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:28:32.921467Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 45aa2243-e590-4d17-9450-abb6016aee68 · outbound

This paper cites Spike- driven transformer.

MetaLA: Unified Optimal Linear Approximation to Softmax Attention Map Spike- driven transformer

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:28:32.851746Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 7c796d07-ee9f-4355-a2d0-d1e2f10fe6fa · outbound

This paper cites Spike-driven transformer v2: Meta spiking neural network architecture inspiring the design of next-generation neuromorphic chips.

MetaLA: Unified Optimal Linear Approximation to Softmax Attention Map Spike-driven transformer v2: Meta spiking neural network architecture inspiring the design of next-generation neuromorphic chips

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:28:32.783164Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 7062e33d-3b11-46e4-8155-64bd65e2678b · outbound

This paper cites Transformer quality in linear time.

MetaLA: Unified Optimal Linear Approximation to Softmax Attention Map Transformer quality in linear time

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:28:32.703980Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation ecfd5c89-1280-4d5a-bbe7-286b1598b5b6 · outbound

This paper cites Fine-tuning pre-trained transformers into decaying fast weights.

MetaLA: Unified Optimal Linear Approximation to Softmax Attention Map Fine-tuning pre-trained transformers into decaying fast weights

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:28:32.666128Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 161f766f-59aa-42d7-83b3-44d076c0b5f1 · outbound

This paper cites GateLoop: Fully Data-Controlled Linear Recurrence for Sequence Modeling.

MetaLA: Unified Optimal Linear Approximation to Softmax Attention Map GateLoop: Fully Data-Controlled Linear Recurrence for Sequence Modeling

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-12T19:28:29.729282Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2b86f549-cd77-4078-8023-a76d1ef724a6 · outbound

This paper cites Combining recurrent, convolutional, and continuous-time models with linear state space layers.

MetaLA: Unified Optimal Linear Approximation to Softmax Attention Map Combining recurrent, convolutional, and continuous-time models with linear state space layers

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:28:32.577892Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 11a28ceb-7edd-4bf9-a9c5-fca84435ff0b · outbound

This paper cites Long range language modeling via gated state spaces.

MetaLA: Unified Optimal Linear Approximation to Softmax Attention Map Long range language modeling via gated state spaces

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-12T19:28:29.887185Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 00b6d505-2381-4d56-925d-01fc6955dc1a · outbound

This paper cites Pretraining without attention.

MetaLA: Unified Optimal Linear Approximation to Softmax Attention Map Pretraining without attention

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:28:32.497604Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation fe7a2b06-cc62-4fc0-94ba-d319cf7afda1 · outbound

This paper cites Hyena hierarchy: Towards larger convolutional language models.

MetaLA: Unified Optimal Linear Approximation to Softmax Attention Map Hyena hierarchy: Towards larger convolutional language models

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-12T19:28:29.901270Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c4c2a0be-1caf-4629-90ed-b758caa25858 · outbound

This paper cites S4nd: Modeling images and videos as multidimensional signals with state spaces.

MetaLA: Unified Optimal Linear Approximation to Softmax Attention Map S4nd: Modeling images and videos as multidimensional signals with state spaces

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:28:32.415127Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 03c088f3-5464-4068-b9db-ed894d9c8f2d · outbound

This paper cites Liquid structural state-space models.

MetaLA: Unified Optimal Linear Approximation to Softmax Attention Map Liquid structural state-space models

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:28:32.352376Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 4a1b4dfa-a0a0-4314-b565-98af334ad950 · outbound

This paper cites Empirical evaluation of gated recurrent neural networks on sequence modeling.

MetaLA: Unified Optimal Linear Approximation to Softmax Attention Map Empirical evaluation of gated recurrent neural networks on sequence modeling

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:28:32.272974Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation a6a0b545-d8b4-44b9-821d-fc5350428c61 · outbound

This paper cites Graves and A.

MetaLA: Unified Optimal Linear Approximation to Softmax Attention Map Graves and A

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:28:32.188557Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 13ea700b-ad21-46f6-ab0e-17dd5785d4b2 · outbound

This paper cites Learning to forget: Continual prediction with lstm.

MetaLA: Unified Optimal Linear Approximation to Softmax Attention Map Learning to forget: Continual prediction with lstm

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:28:32.127726Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 9ae0ea9b-a6a7-4814-8fe4-ad346a6eea2b · outbound

This paper cites Linear dynamical systems as a core computational primitive.

MetaLA: Unified Optimal Linear Approximation to Softmax Attention Map Linear dynamical systems as a core computational primitive

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:28:32.100352Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation b97ea4b9-9621-493a-921d-69a8cc72d283 · outbound

This paper cites Universality of Linear Recurrences Followed by Non-linear Projections: Finite-Width Guarantees and Benefits of Complex Eigenvalues.

MetaLA: Unified Optimal Linear Approximation to Softmax Attention Map Universality of Linear Recurrences Followed by Non-linear Projections: Finite-Width Guarantees and Benefits of Complex Eigenvalues

Reference 76

Resolution: unresolved; no resolver link, observed 2026-08-12T19:28:30.017336Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9656a8fd-668a-4800-ada8-3e366323b9da · outbound

This paper cites Quasi-recurrent neural networks.

MetaLA: Unified Optimal Linear Approximation to Softmax Attention Map Quasi-recurrent neural networks

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:28:31.997326Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 89904bcf-c5b6-4d08-b38b-4d3a7c3baa6e · outbound

This paper cites An Attention Free Transformer.

MetaLA: Unified Optimal Linear Approximation to Softmax Attention Map An Attention Free Transformer

Reference 78

Resolution: unresolved; no resolver link, observed 2026-08-12T19:28:30.076777Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a10137d4-8ff8-4aed-a6f8-8c3d9ac90e6e · outbound

This paper cites Efficient attention via control variates.

MetaLA: Unified Optimal Linear Approximation to Softmax Attention Map Efficient attention via control variates

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:28:31.971488Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation f1cc5f84-c765-4849-a0bd-4f5a0802e4d8 · outbound

This paper cites Fla: A triton-based library for hardware-efficient im- plementations of linear attention mechanism.

MetaLA: Unified Optimal Linear Approximation to Softmax Attention Map Fla: A triton-based library for hardware-efficient im- plementations of linear attention mechanism

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:28:31.946743Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 3a32fdaf-acfd-4003-bb1f-d01591e68b47 · outbound

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

MetaLA: Unified Optimal Linear Approximation to Softmax Attention Map The LAMBADA dataset: Word prediction requiring a broad discourse context

Reference 81

Resolution: unresolved; no resolver link, observed 2026-08-12T19:28:30.106724Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 63fa2bbc-1e30-4a29-82d8-38f5838316e8 · outbound

This paper cites Logiqa: a challenge dataset for machine reading comprehension with logical reasoning.

MetaLA: Unified Optimal Linear Approximation to Softmax Attention Map Logiqa: a challenge dataset for machine reading comprehension with logical reasoning

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:28:31.817955Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation abbb2e70-cf63-451f-94af-17053891d733 · outbound

This paper cites The winograd schema challenge.

MetaLA: Unified Optimal Linear Approximation to Softmax Attention Map The winograd schema challenge

Reference 83

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:28:31.795150Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation d6c63930-d8bb-4221-bc25-9962c180bbc5 · outbound

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

MetaLA: Unified Optimal Linear Approximation to Softmax Attention Map Boolq: Exploring the surprising difficulty of natural yes/no questions

Reference 84

Resolution: unresolved; no resolver link, observed 2026-08-12T19:28:30.137612Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5279b2e6-3b2f-4eba-a98f-967999f1c9a4 · outbound

This paper cites Piqa: Reasoning about physical commonsense in natural language.

MetaLA: Unified Optimal Linear Approximation to Softmax Attention Map Piqa: Reasoning about physical commonsense in natural language

Reference 85

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:28:31.763124Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation af29ae7e-d588-4ee3-a1ec-bb34dbc4c893 · outbound

This paper cites Hellaswag: Can a machine really finish your sentence? In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics.

MetaLA: Unified Optimal Linear Approximation to Softmax Attention Map Hellaswag: Can a machine really finish your sentence? In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Reference 86

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:28:31.740551Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation efff8d06-af15-498a-ad01-bae114feb876 · outbound

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

MetaLA: Unified Optimal Linear Approximation to Softmax Attention Map Winogrande: An adversarial winograd schema challenge at scale

Reference 87

Resolution: unresolved; no resolver link, observed 2026-08-12T19:28:30.157019Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7629280a-2d8e-4ee9-824a-1bdc1116f11d · outbound

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

MetaLA: Unified Optimal Linear Approximation to Softmax Attention Map Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge

Reference 88

Resolution: unresolved; no resolver link, observed 2026-08-12T19:28:30.162921Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 29f4efde-e11d-4c34-af11-5a56e899fe03 · outbound

This paper cites Can a suit of armor conduct electricity? a new dataset for open book question answering.

MetaLA: Unified Optimal Linear Approximation to Softmax Attention Map Can a suit of armor conduct electricity? a new dataset for open book question answering

Reference 89

Resolution: unresolved; no resolver link, observed 2026-08-12T19:28:30.169129Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2c6d6a41-f094-45cc-a756-8e5ddeca5f8d · outbound

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

MetaLA: Unified Optimal Linear Approximation to Softmax Attention Map A framework for few-shot language model evaluation

Reference 90

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:28:31.669207Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 060d97d7-5ec3-44c9-b785-a85fda3ff9d2 · outbound

This paper cites Flashattention: Fast and memory-efficient exact attention with io-awareness.Advances in Neural Information Processing Systems, 35:16344–16359, 2022.

MetaLA: Unified Optimal Linear Approximation to Softmax Attention Map Flashattention: Fast and memory-efficient exact attention with io-awareness.Advances in Neural Information Processing Systems, 35:16344–16359, 2022

Reference 91

Resolution: malformed identifier; raw_fallback, observed 2026-08-12T19:28:31.642916Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 02220aff-416d-41b8-b39b-bd1c065ece27 · outbound

This paper cites including recall, memorization, and compression.

MetaLA: Unified Optimal Linear Approximation to Softmax Attention Map including recall, memorization, and compression

Reference 92

Resolution: malformed identifier; raw_fallback, observed 2026-08-12T19:28:31.575640Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation d0702585-005b-4b63-b85c-569a522ec130 · outbound

This paper cites Guidelines: • The answer NA means that the abstract and introduction do not include the claims made in the paper.

MetaLA: Unified Optimal Linear Approximation to Softmax Attention Map Guidelines: • The answer NA means that the abstract and introduction do not include the claims made in the paper

Reference 93

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:28:31.479910Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation e8d313b8-d128-41b8-80c2-d5501e060859 · outbound

This paper cites Limitations.

MetaLA: Unified Optimal Linear Approximation to Softmax Attention Map Limitations

Reference 94

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:28:31.459561Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation e33f776e-cf2c-4ebf-a77f-e5dbd772bab8 · outbound

This paper cites 4 and appendix A2, we provided the definitions and detailed proofs of our theory.

MetaLA: Unified Optimal Linear Approximation to Softmax Attention Map 4 and appendix A2, we provided the definitions and detailed proofs of our theory

Reference 95

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:28:31.441915Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 9a06316d-7323-497c-a8ce-df87ca9eabdb · outbound

This paper cites 6, we showcased our experimental setup and the results, and in appendix A4, we further elaborated on the details of all experimental setups.

MetaLA: Unified Optimal Linear Approximation to Softmax Attention Map 6, we showcased our experimental setup and the results, and in appendix A4, we further elaborated on the details of all experimental setups

Reference 96

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:28:31.423376Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 608ea057-ce4e-488b-97a7-45f7797d3b7e · outbound

This paper cites 6 and appendix A4 to ensure the reproducibility of the results.

MetaLA: Unified Optimal Linear Approximation to Softmax Attention Map 6 and appendix A4 to ensure the reproducibility of the results

Reference 97

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:28:31.356628Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation f6550875-6b7e-496e-9cbe-5012a707947e · outbound

This paper cites 6 and ap- pendix A4.

MetaLA: Unified Optimal Linear Approximation to Softmax Attention Map 6 and ap- pendix A4

Reference 98

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:28:31.287000Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 2efa4890-71c8-4c0e-a45e-3d930f84f314 · outbound

This paper cites Guidelines: • The answer NA means that the paper does not include experiments.

MetaLA: Unified Optimal Linear Approximation to Softmax Attention Map Guidelines: • The answer NA means that the paper does not include experiments

Reference 99

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:28:31.187468Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 346dfad7-c842-4997-96a9-6c1b695e912b · outbound

This paper cites 6 and appendix A4, we provided details about the computer resources used for the experiments.

MetaLA: Unified Optimal Linear Approximation to Softmax Attention Map 6 and appendix A4, we provided details about the computer resources used for the experiments

Reference 100

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:28:31.095865Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Reference resolution

Outbound references

Pith citing papers