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Paper Citation Record · LEDGER

Recursive transformers for semiconductor thermo-mechanical reliability

As of 17 August 2026, this Paper Citation Record lists 25 of 25 outbound references and 0 inbound Pith citation observations for arXiv:2607.27251.

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

pith.paper-citation-record.v1
2607.27251 v1

Coverage vector

measured 25 of 25 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-01T01:40:35.589014Z

measured 25 of 25 standing notices

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

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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Source: cited_works

Reference resolution

25 of 25 outbound references displayed

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Outbound references

Observation bee97df0-eafe-451e-8997-3902c28b6e75 · outbound

This paper cites Graph- transformer-based surrogate model for accelerated converter circuit topology design,.

Recursive transformers for semiconductor thermo-mechanical reliability Graph- transformer-based surrogate model for accelerated converter circuit topology design,

Reference 1

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Observation 1fc4f562-e575-4302-b780-28dc2210756e · outbound

This paper cites Transformer for partial differ- ential equations’ operator learning,.

Recursive transformers for semiconductor thermo-mechanical reliability Transformer for partial differ- ential equations’ operator learning,

Reference 2

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Observation ae87756c-37a7-45fe-9168-48471d5be9b9 · outbound

This paper cites Transformer self-attention encoder–decoder with mul- timodal deep learning for response time series forecasting and digital twin support in wind structural health monitoring,.

Recursive transformers for semiconductor thermo-mechanical reliability Transformer self-attention encoder–decoder with mul- timodal deep learning for response time series forecasting and digital twin support in wind structural health monitoring,

Reference 3

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Observation c94813b9-f159-4d2e-a088-eafa2fc2eff9 · outbound

This paper cites Vision Transformers on the Edge: A Comprehensive Survey of Model Compression and Acceleration Strategies.

Recursive transformers for semiconductor thermo-mechanical reliability Vision Transformers on the Edge: A Comprehensive Survey of Model Compression and Acceleration Strategies

Reference 4

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Observation 17ad0fae-e27d-4055-8559-ff1b7e85a0d8 · outbound

This paper cites Distributed Inference with Minimal Off-Chip Traffic for Transformers on Low-Power MCUs.

Recursive transformers for semiconductor thermo-mechanical reliability Distributed Inference with Minimal Off-Chip Traffic for Transformers on Low-Power MCUs

Reference 5

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Observation 4742e89f-8c88-4da2-9979-3e4d4620bf74 · outbound

This paper cites Flashattention: Fast and memory-efficient exact attention with io-awareness,.

Recursive transformers for semiconductor thermo-mechanical reliability Flashattention: Fast and memory-efficient exact attention with io-awareness,

Reference 6

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Observation b2f9f678-1fa0-4cf9-bbde-31890c96db21 · outbound

This paper cites Switch transformers: Scaling to trillion parameter models with simple and efficient sparsity,.

Recursive transformers for semiconductor thermo-mechanical reliability Switch transformers: Scaling to trillion parameter models with simple and efficient sparsity,

Reference 7

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Observation 945a2ba9-d4e5-414b-874e-0f644efec5e6 · outbound

This paper cites Universal transformers,.

Recursive transformers for semiconductor thermo-mechanical reliability Universal transformers,

Reference 8

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Observation 542e06d5-9bb7-4490-ab46-9d9b60168b21 · outbound

This paper cites Learning both weights and connections for efficient neural networks,.

Recursive transformers for semiconductor thermo-mechanical reliability Learning both weights and connections for efficient neural networks,

Reference 9

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Observation e5bca91d-8549-4341-a4e2-81686c004cd7 · outbound

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

Recursive transformers for semiconductor thermo-mechanical reliability Transformers are rnns: Fast autoregressive transformers with linear attention,

Reference 10

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Observation cd673d35-f257-4139-96cc-b4d1250e9ad2 · outbound

This paper cites Dynamicvit: Efficient vision transformers with dynamic token sparsification,.

Recursive transformers for semiconductor thermo-mechanical reliability Dynamicvit: Efficient vision transformers with dynamic token sparsification,

Reference 11

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Observation 659803c7-0531-4ef4-ba02-ac00639a4396 · outbound

This paper cites Token merging: Your vit but faster,.

Recursive transformers for semiconductor thermo-mechanical reliability Token merging: Your vit but faster,

Reference 12

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Observation 66521b6d-6f86-4e83-bd1d-302acb32bc8d · outbound

This paper cites Glam: Efficient scaling of language models with mixture- of-experts,.

Recursive transformers for semiconductor thermo-mechanical reliability Glam: Efficient scaling of language models with mixture- of-experts,

Reference 13

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Observation 65b1d6bb-5761-4ec6-a019-29ea8c02359e · outbound

This paper cites Albert: A lite bert for self-supervised learning of language representa- tions,.

Recursive transformers for semiconductor thermo-mechanical reliability Albert: A lite bert for self-supervised learning of language representa- tions,

Reference 14

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Observation 50d24429-d807-430e-94dc-126abb4cf15a · outbound

This paper cites Looped transformers are better at learning learning algorithms,.

Recursive transformers for semiconductor thermo-mechanical reliability Looped transformers are better at learning learning algorithms,

Reference 15

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Observation 8cf7525b-0ec9-40dc-a6fc-5e28ef29e3ef · outbound

This paper cites Tiny recursive models,.

Recursive transformers for semiconductor thermo-mechanical reliability Tiny recursive models,

Reference 16

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Observation e2badded-6a0b-4b8e-8cd1-201b3423c16f · outbound

This paper cites Distilling the knowledge in a neural network,.

Recursive transformers for semiconductor thermo-mechanical reliability Distilling the knowledge in a neural network,

Reference 17

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Observation 3a05d6fe-a253-40e8-b729-04b324ede22d · outbound

This paper cites Lora: Low-rank adaptation of large language models,.

Recursive transformers for semiconductor thermo-mechanical reliability Lora: Low-rank adaptation of large language models,

Reference 18

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Observation edbce6f5-7102-42eb-b0b8-066a76f885bb · outbound

This paper cites Flashattention-2: Faster attention with better parallelism,.

Recursive transformers for semiconductor thermo-mechanical reliability Flashattention-2: Faster attention with better parallelism,

Reference 19

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Observation 8489a90f-d4d0-4693-af5b-a513d2cf8ffc · outbound

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Recursive transformers for semiconductor thermo-mechanical reliability Tensorrt-llm,

Reference 20

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Observation 31756b30-8315-4520-92c4-fc38f3456875 · outbound

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

Recursive transformers for semiconductor thermo-mechanical reliability Efficiently modeling long sequences with structured state spaces,

Reference 21

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Observation fed19c49-d5ee-474b-8a5a-651080776d7f · outbound

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

Recursive transformers for semiconductor thermo-mechanical reliability Mamba: Linear-time sequence modeling with selective state spaces,

Reference 22

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Observation e523f8d5-7d2a-4fb4-8a8d-afd7cc36e37c · outbound

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Recursive transformers for semiconductor thermo-mechanical reliability Mixtral of experts,

Reference 23

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Observation 290db4a9-ba58-4b28-86a6-f69ea3e2e708 · outbound

This paper cites Deep clustering based boundary-decoder net for inter and intra layer stress prediction of heterogeneous integrated ic chip,.

Recursive transformers for semiconductor thermo-mechanical reliability Deep clustering based boundary-decoder net for inter and intra layer stress prediction of heterogeneous integrated ic chip,

Reference 24

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Observation 7135ae85-14cb-4f4a-a162-b1f4cc4c4517 · outbound

This paper cites Inverse prediction of capacitor multiphysics dynamic parameters using deep generative model,.

Recursive transformers for semiconductor thermo-mechanical reliability Inverse prediction of capacitor multiphysics dynamic parameters using deep generative model,

Reference 25

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