Paper Citation Record · LEDGER

AutoNeural: Co-Designing Vision-Language Models for NPU Inference

As of 22 August 2026, this Paper Citation Record lists 56 of 56 outbound references and 14 inbound Pith citation observations for arXiv:2512.02924.

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

pith.paper-citation-record.v1
2512.02924 v3

Coverage vector

measured 56 of 56 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-03T18:57:01.474744Z

measured 70 of 70 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00

measured 14 of 14 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-02T11:08:50.198349Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T01:39:23.983217Z

Reference resolution

56 of 56 outbound references displayed

verified exact0
verified fuzzy0
unresolved56
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malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 77ac84e2-5936-4016-a7af-df25475e5e91 · outbound

This paper cites OmniVLM: A Token-Compressed, Sub-Billion-Parameter Vision-Language Model for Efficient On-Device Inference.

AutoNeural: Co-Designing Vision-Language Models for NPU Inference OmniVLM: A Token-Compressed, Sub-Billion-Parameter Vision-Language Model for Efficient On-Device Inference

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-03T18:56:56.607192Z

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Observation 2ff689b0-f582-497f-a1a3-eedbd47f972b · outbound

This paper cites SmolVLM: Redefining small and efficient multimodal models.

AutoNeural: Co-Designing Vision-Language Models for NPU Inference SmolVLM: Redefining small and efficient multimodal models

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-03T18:56:56.759878Z

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Observation b638c712-ced3-4fb1-8798-01424a173b3b · outbound

This paper cites InternVL3: Exploring Advanced Training and Test-Time Recipes for Open-Source Multimodal Models.

AutoNeural: Co-Designing Vision-Language Models for NPU Inference InternVL3: Exploring Advanced Training and Test-Time Recipes for Open-Source Multimodal Models

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-03T18:56:56.861742Z

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Observation 8d0277b5-62ef-4b2e-827b-b460d45f71b9 · outbound

This paper cites Vision-language models for vision tasks: A survey.IEEE transactions on pattern analysis and machine intelligence, 46(8):5625–5644, 2024.

AutoNeural: Co-Designing Vision-Language Models for NPU Inference Vision-language models for vision tasks: A survey.IEEE transactions on pattern analysis and machine intelligence, 46(8):5625–5644, 2024

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-03T18:56:56.925070Z

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Observation 5981f1b3-5d7e-486a-8142-38ecd79ce7b4 · outbound

This paper cites Exploring the frontier of vision-language models: A survey of current methodologies and future directions.arXiv preprint arXiv:2404.07214, 2024.

AutoNeural: Co-Designing Vision-Language Models for NPU Inference Exploring the frontier of vision-language models: A survey of current methodologies and future directions.arXiv preprint arXiv:2404.07214, 2024

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-03T18:56:57.020196Z

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Observation 73f17bc5-82b1-47ad-b161-6e87229b5bb9 · outbound

This paper cites Efficient execution of deep neural networks on mobile devices with npu.

AutoNeural: Co-Designing Vision-Language Models for NPU Inference Efficient execution of deep neural networks on mobile devices with npu

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-03T18:56:57.087593Z

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Observation 1e1888a2-ba15-4154-acbe-4c0214dda1ec · outbound

This paper cites A survey on neural network hardware accelerators.IEEE Transac- tions on Artificial Intelligence, 5(8):3801–3822, 2024.

AutoNeural: Co-Designing Vision-Language Models for NPU Inference A survey on neural network hardware accelerators.IEEE Transac- tions on Artificial Intelligence, 5(8):3801–3822, 2024

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-03T18:56:57.161966Z

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Observation e882f080-dca6-4490-b72e-cbbdefc7a946 · outbound

This paper cites Architecture of neural processing unit for deep neural networks.

AutoNeural: Co-Designing Vision-Language Models for NPU Inference Architecture of neural processing unit for deep neural networks

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-03T18:56:57.243560Z

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Observation e3fa1a44-1735-48b3-8174-4ac2aa123766 · outbound

This paper cites Transformers in vision: A survey.ACM computing surveys (CSUR), 54(10s):1–41, 2022.

AutoNeural: Co-Designing Vision-Language Models for NPU Inference Transformers in vision: A survey.ACM computing surveys (CSUR), 54(10s):1–41, 2022

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-03T18:56:57.373502Z

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Observation f9e98a47-1e23-4861-881c-5482f1b716cc · outbound

This paper cites Comprehensive Survey of Model Compression and Speed up for Vision Transformers.

AutoNeural: Co-Designing Vision-Language Models for NPU Inference Comprehensive Survey of Model Compression and Speed up for Vision Transformers

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-03T18:56:57.460747Z

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Observation a8319bc8-299b-425b-b181-0f7fe8d80464 · outbound

This paper cites Understanding and improving layer normalization.Advances in neural information processing systems, 32, 2019.

AutoNeural: Co-Designing Vision-Language Models for NPU Inference Understanding and improving layer normalization.Advances in neural information processing systems, 32, 2019

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-03T18:56:57.538815Z

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Observation bd700577-3454-4994-b342-9aed805def73 · outbound

This paper cites LoftQ: LoRA-Fine-Tuning-Aware Quantization for Large Language Models.

AutoNeural: Co-Designing Vision-Language Models for NPU Inference LoftQ: LoRA-Fine-Tuning-Aware Quantization for Large Language Models

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-03T18:56:57.627233Z

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Observation 989058f6-8f66-45a9-b492-b020fc034f65 · outbound

This paper cites Low-Rank Quantization-Aware Training for LLMs.

AutoNeural: Co-Designing Vision-Language Models for NPU Inference Low-Rank Quantization-Aware Training for LLMs

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-03T18:56:57.732475Z

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Observation cd507273-49c2-4f55-8eb1-f991f19a42c6 · outbound

This paper cites Efficien- tqat: Efficient quantization-aware training for large language models.

AutoNeural: Co-Designing Vision-Language Models for NPU Inference Efficien- tqat: Efficient quantization-aware training for large language models

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-03T18:56:57.838469Z

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Observation 6313c0a1-ee1f-4d92-9c44-6ee558386ae3 · outbound

This paper cites Vlm-auto: Vlm-based autonomous driving assistant with human-like behavior and understanding for complex road scenes.

AutoNeural: Co-Designing Vision-Language Models for NPU Inference Vlm-auto: Vlm-based autonomous driving assistant with human-like behavior and understanding for complex road scenes

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-03T18:56:57.962432Z

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Observation b57e86bd-7545-46b9-8b81-d17fe70ea8f7 · outbound

This paper cites VLM-RL: A Unified Vision Language Models and Reinforcement Learning Framework for Safe Autonomous Driving.

AutoNeural: Co-Designing Vision-Language Models for NPU Inference VLM-RL: A Unified Vision Language Models and Reinforcement Learning Framework for Safe Autonomous Driving

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-03T18:56:58.078201Z

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Observation 6c4b1558-51df-459a-b4c9-adf5e45530d9 · outbound

This paper cites Octopus v3: Technical report for on-device sub-billion multimodal ai agent,.

AutoNeural: Co-Designing Vision-Language Models for NPU Inference Octopus v3: Technical report for on-device sub-billion multimodal ai agent,

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-03T18:56:58.209786Z

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Observation 67f3cbb7-37e9-4d32-8331-1eb6bb017893 · outbound

This paper cites Octopus: On-device language model for function calling of software APIs.

AutoNeural: Co-Designing Vision-Language Models for NPU Inference Octopus: On-device language model for function calling of software APIs

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-03T18:56:58.468278Z

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Observation bba0c969-6aba-4658-8e76-95ef6b6a8474 · outbound

This paper cites Octopus v2: On-device language model for super agent.

AutoNeural: Co-Designing Vision-Language Models for NPU Inference Octopus v2: On-device language model for super agent

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-03T18:56:58.585038Z

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Observation 210c1b66-a807-4b58-8c79-b1bf46766a4d · outbound

This paper cites Octopus v4: Graph of language models.

AutoNeural: Co-Designing Vision-Language Models for NPU Inference Octopus v4: Graph of language models

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-03T18:56:58.678379Z

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Observation 93d10921-7a66-433d-8241-035b209138a5 · outbound

This paper cites Octo-planner: On-device Language Model for Planner-Action Agents.

AutoNeural: Co-Designing Vision-Language Models for NPU Inference Octo-planner: On-device Language Model for Planner-Action Agents

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-03T18:56:58.756838Z

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Observation ea30d657-4fb8-4e19-8228-4ce659a998fd · outbound

This paper cites Squid: Long Context as a New Modality for Energy-Efficient On-Device Language Models.

AutoNeural: Co-Designing Vision-Language Models for NPU Inference Squid: Long Context as a New Modality for Energy-Efficient On-Device Language Models

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-03T18:56:58.816760Z

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Observation df68b037-a5a3-4393-abe3-f668962bf51b · outbound

This paper cites Edge-side npu inference optimization: Adaptation research of multimodal large models on qualcomm platforms.Intelligent Data Analysis, page 1088467X251342172, 2025.

AutoNeural: Co-Designing Vision-Language Models for NPU Inference Edge-side npu inference optimization: Adaptation research of multimodal large models on qualcomm platforms.Intelligent Data Analysis, page 1088467X251342172, 2025

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-03T18:56:58.910460Z

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Observation 3d3ce413-9f09-4c4d-a4ca-f71e254c7d1c · outbound

This paper cites Fast on- device llm inference with npus.

AutoNeural: Co-Designing Vision-Language Models for NPU Inference Fast on- device llm inference with npus

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-03T18:56:58.978803Z

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Observation b3f0b065-7e84-4aff-b08d-0fcfc0d8a2ac · outbound

This paper cites Overviewing ai-dedicated hardware for on-device ai in smartphones.

AutoNeural: Co-Designing Vision-Language Models for NPU Inference Overviewing ai-dedicated hardware for on-device ai in smartphones

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-03T18:56:59.072110Z

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Observation 533f2c81-fd93-4db0-9853-7d183a0ce250 · outbound

This paper cites Advances in the neural network quantization: A comprehensive review.Applied Sciences, 14(17):7445, 2024.

AutoNeural: Co-Designing Vision-Language Models for NPU Inference Advances in the neural network quantization: A comprehensive review.Applied Sciences, 14(17):7445, 2024

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-03T18:56:59.153925Z

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Observation 626eb763-fabb-43d0-8f1a-1ef795a8cb55 · outbound

This paper cites Pruning deep neural networks for green energy-efficient models: A survey.Cognitive Computation, 16(6):2931–2952, 2024.

AutoNeural: Co-Designing Vision-Language Models for NPU Inference Pruning deep neural networks for green energy-efficient models: A survey.Cognitive Computation, 16(6):2931–2952, 2024

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-03T18:56:59.239533Z

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Observation dee8fbc4-f1aa-4cb6-83d1-6b4cab2ebb4e · outbound

This paper cites A comprehensive review of model compression techniques in machine learning.Applied Intelligence, 54(22): 11804–11844, 2024.

AutoNeural: Co-Designing Vision-Language Models for NPU Inference A comprehensive review of model compression techniques in machine learning.Applied Intelligence, 54(22): 11804–11844, 2024

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-03T18:56:59.303455Z

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Observation 3263cffe-dceb-4770-9a3c-64e553bb318b · outbound

This paper cites LLM Inference Acceleration via Efficient Operation Fusion.

AutoNeural: Co-Designing Vision-Language Models for NPU Inference LLM Inference Acceleration via Efficient Operation Fusion

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-03T18:56:59.405172Z

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Observation b1490825-f9dd-4591-a84c-a20a207d8aab · outbound

This paper cites Efficient LLM Inference using Dynamic Input Pruning and Cache-Aware Masking.

AutoNeural: Co-Designing Vision-Language Models for NPU Inference Efficient LLM Inference using Dynamic Input Pruning and Cache-Aware Masking

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-03T18:56:59.496372Z

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Observation 4bcc1189-7c2b-44cb-98a4-cfd2ee78baeb · outbound

This paper cites Local Look-Ahead Guidance via Verifier-in-the-Loop for Automated Theorem Proving.

AutoNeural: Co-Designing Vision-Language Models for NPU Inference Local Look-Ahead Guidance via Verifier-in-the-Loop for Automated Theorem Proving

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-03T18:56:59.559898Z

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Observation c4c8cd31-f2d0-482c-ab21-0f27388bbf1f · outbound

This paper cites Distilling Multi-modal Large Language Models for Autonomous Driving.

AutoNeural: Co-Designing Vision-Language Models for NPU Inference Distilling Multi-modal Large Language Models for Autonomous Driving

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-03T18:56:59.636047Z

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Observation e5d4ff49-69e9-4013-8ed0-ed941efdbf52 · outbound

This paper cites LoRA-X: Bridging Foundation Models with Training-Free Cross-Model Adaptation.

AutoNeural: Co-Designing Vision-Language Models for NPU Inference LoRA-X: Bridging Foundation Models with Training-Free Cross-Model Adaptation

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Resolution: unresolved; no resolver link, observed 2026-08-03T18:56:59.730522Z

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Observation 63fa62b0-2155-4afb-b4d4-6de5a50a2b0d · outbound

This paper cites Mindvl: Towards efficient and effective training of multimodal large language models on ascend npus.

AutoNeural: Co-Designing Vision-Language Models for NPU Inference Mindvl: Towards efficient and effective training of multimodal large language models on ascend npus

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Resolution: unresolved; no resolver link, observed 2026-08-03T18:56:59.816556Z

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This paper cites MiniCPM-V 4.5: Cooking Efficient MLLMs via Architecture, Data, and Training Recipe.

AutoNeural: Co-Designing Vision-Language Models for NPU Inference MiniCPM-V 4.5: Cooking Efficient MLLMs via Architecture, Data, and Training Recipe

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-03T18:56:59.896367Z

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Observation ebab57cc-375c-462e-8a20-ca90b0af6cfe · outbound

This paper cites MiniCPM-V: A GPT-4V Level MLLM on Your Phone.

AutoNeural: Co-Designing Vision-Language Models for NPU Inference MiniCPM-V: A GPT-4V Level MLLM on Your Phone

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-03T18:57:00.023571Z

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AutoNeural: Co-Designing Vision-Language Models for NPU Inference An enhanced hybrid mobilenet

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-03T18:57:00.098640Z

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Observation dd1cf042-347c-42ba-858f-3759d68ccec1 · outbound

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AutoNeural: Co-Designing Vision-Language Models for NPU Inference Thin mobilenet: An enhanced mobilenet architecture

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Resolution: unresolved; no resolver link, observed 2026-08-03T18:57:00.168195Z

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Observation 1b7a7a2f-c8c6-4bf0-98ce-90acc1a1b11e · outbound

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AutoNeural: Co-Designing Vision-Language Models for NPU Inference Fd-mobilenet: Improved mobilenet with a fast downsampling strategy

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-03T18:57:00.269254Z

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Observation a81d5007-9dd2-4935-9d35-f9d452a5592b · outbound

This paper cites Efficient mobilenet architecture as image recognition on mobile and embedded devices.Indonesian Journal of Electrical Engineering and Computer Science, 16(1):389–394, 2019.

AutoNeural: Co-Designing Vision-Language Models for NPU Inference Efficient mobilenet architecture as image recognition on mobile and embedded devices.Indonesian Journal of Electrical Engineering and Computer Science, 16(1):389–394, 2019

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Resolution: unresolved; no resolver link, observed 2026-08-03T18:57:00.342046Z

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AutoNeural: Co-Designing Vision-Language Models for NPU Inference PaliGemma: A versatile 3B VLM for transfer

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Resolution: unresolved; no resolver link, observed 2026-08-03T18:57:00.416763Z

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Observation 47ed50b3-1551-463d-8fc0-b22dcec8868c · outbound

This paper cites PaliGemma 2: A Family of Versatile VLMs for Transfer.

AutoNeural: Co-Designing Vision-Language Models for NPU Inference PaliGemma 2: A Family of Versatile VLMs for Transfer

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Resolution: unresolved; no resolver link, observed 2026-08-03T18:57:00.517405Z

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Observation c48870d2-7544-4ea4-bf63-4824afba5456 · outbound

This paper cites Efficiently Modeling Long Sequences with Structured State Spaces.

AutoNeural: Co-Designing Vision-Language Models for NPU Inference Efficiently Modeling Long Sequences with Structured State Spaces

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Resolution: unresolved; no resolver link, observed 2026-08-03T18:57:00.589191Z

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Observation 6cf6510e-9b2b-4414-90dd-c398dc03c7ea · outbound

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AutoNeural: Co-Designing Vision-Language Models for NPU Inference Mamba: Linear-time sequence modeling with selective state spaces

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Resolution: unresolved; no resolver link, observed 2026-08-03T18:57:00.660924Z

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Observation aa5338cd-fa44-42b1-8639-9f7c5d18858a · outbound

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AutoNeural: Co-Designing Vision-Language Models for NPU Inference Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality

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Resolution: unresolved; no resolver link, observed 2026-08-03T18:57:00.734153Z

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Observation a1a007b2-7aa4-4dc6-891e-7630ce474161 · outbound

This paper cites Combining recurrent, convolutional, and continuous-time models with linear state space layers.Advances in neural information processing systems, 34:572–585, 2021.

AutoNeural: Co-Designing Vision-Language Models for NPU Inference Combining recurrent, convolutional, and continuous-time models with linear state space layers.Advances in neural information processing systems, 34:572–585, 2021

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Resolution: unresolved; no resolver link, observed 2026-08-03T18:57:00.805245Z

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Observation b540e1e8-a5c9-400e-a37f-a14975165cb7 · outbound

This paper cites Liquid: Language Models are Scalable and Unified Multi-modal Generators.

AutoNeural: Co-Designing Vision-Language Models for NPU Inference Liquid: Language Models are Scalable and Unified Multi-modal Generators

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-03T18:57:00.890684Z

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Observation a231f0db-76d4-4f2d-a1b5-f1eba368e3b7 · outbound

This paper cites Towards a theory of learning dynamics in deep state space models.

AutoNeural: Co-Designing Vision-Language Models for NPU Inference Towards a theory of learning dynamics in deep state space models

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Resolution: unresolved; no resolver link, observed 2026-08-03T18:57:00.986982Z

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Observation cac9283b-b3a5-4e0a-9411-0f24fcb3eceb · outbound

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AutoNeural: Co-Designing Vision-Language Models for NPU Inference Mechanistic Design and Scaling of Hybrid Architectures

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-03T18:57:01.034847Z

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Observation d311223f-6cfd-4ff3-89fd-3f75314dd85d · outbound

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AutoNeural: Co-Designing Vision-Language Models for NPU Inference Gemma 3n

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-03T18:57:01.110311Z

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Observation 0768fe1d-6cd5-46bf-8e7d-cfc5bb47a331 · outbound

This paper cites Gemma 3 Technical Report.

AutoNeural: Co-Designing Vision-Language Models for NPU Inference Gemma 3 Technical Report

Reference 51

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Observation a80b0535-469d-4189-8529-667e368d5008 · outbound

This paper cites Pilot attitudes toward ai in the cockpit: implications for design.

AutoNeural: Co-Designing Vision-Language Models for NPU Inference Pilot attitudes toward ai in the cockpit: implications for design

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-03T18:57:01.255206Z

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Observation 8e42b685-9dc4-43fd-a610-007318431213 · outbound

This paper cites Intelligent multimodal human- machine collaboration system for safety and security in the cockpit.

AutoNeural: Co-Designing Vision-Language Models for NPU Inference Intelligent multimodal human- machine collaboration system for safety and security in the cockpit

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Resolution: unresolved; no resolver link, observed 2026-08-03T18:57:01.339379Z

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Observation 26c0b9b7-d9d2-4ae6-ac04-737f0737d4fe · outbound

This paper cites Infinity-MM: Scaling Multimodal Performance with Large-Scale and High-Quality Instruction Data.

AutoNeural: Co-Designing Vision-Language Models for NPU Inference Infinity-MM: Scaling Multimodal Performance with Large-Scale and High-Quality Instruction Data

Reference 54

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Observation 205fea20-c3da-446a-a2f7-240801743957 · outbound

This paper cites Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution.

AutoNeural: Co-Designing Vision-Language Models for NPU Inference Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-03T18:57:01.474744Z

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Observation 4fe77338-d475-4221-9eca-8114986dde4a · outbound

This paper cites Octopus v3: Technical Report for On-device Sub-billion Multimodal AI Agent.

AutoNeural: Co-Designing Vision-Language Models for NPU Inference Octopus v3: Technical Report for On-device Sub-billion Multimodal AI Agent

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-03T18:56:58.340919Z

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Pith citing papers

Observation 3341d6c6-0ef6-4ef8-8e24-9eebefe90515 · inbound

Seeing is Coding: On the Effectiveness of Vision Language Models in Code Understanding cites this paper.

Seeing is Coding: On the Effectiveness of Vision Language Models in Code Understanding AutoNeural: Co-Designing Vision-Language Models for NPU Inference

Reference 22

Resolution: verified exact; arxiv_id, observed 2026-07-21T02:20:32.885266Z

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Observation fa9d14d3-cefb-4de9-b63e-dc87cb83375b · inbound

Enhancing Financial Report Question-Answering: A Retrieval-Augmented Generation System with Reranking Analysis cites this paper.

Enhancing Financial Report Question-Answering: A Retrieval-Augmented Generation System with Reranking Analysis AutoNeural: Co-Designing Vision-Language Models for NPU Inference

Reference 16

Resolution: verified exact; arxiv_id, observed 2026-07-21T02:20:32.885266Z

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Observation 842d82f3-b450-4671-aea3-64053c5a86f1 · inbound

FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction cites this paper.

FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction AutoNeural: Co-Designing Vision-Language Models for NPU Inference

Reference 28

Resolution: verified exact; arxiv_id, observed 2026-07-21T02:20:32.885266Z

Source-reported events for the cited work

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Observation 6fb7a056-658b-4c7c-a1c9-0be73d2b56c9 · inbound

ReTrack: Evidence-Driven Dual-Stream Directional Anchor Calibration Network for Composed Video Retrieval cites this paper.

ReTrack: Evidence-Driven Dual-Stream Directional Anchor Calibration Network for Composed Video Retrieval AutoNeural: Co-Designing Vision-Language Models for NPU Inference

Reference 10

Resolution: verified exact; arxiv_id, observed 2026-07-21T02:20:32.885266Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation a5f3fcdf-542b-4100-a9d4-55a83510e920 · inbound

HABIT: Chrono-Synergia Robust Progressive Learning Framework for Composed Image Retrieval cites this paper.

HABIT: Chrono-Synergia Robust Progressive Learning Framework for Composed Image Retrieval AutoNeural: Co-Designing Vision-Language Models for NPU Inference

Reference 19

Resolution: verified exact; arxiv_id, observed 2026-07-21T02:20:32.885266Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 94bda9df-d700-40c7-aa4b-259c3dcef9e9 · inbound

INTENT: Invariance and Discrimination-aware Noise Mitigation for Robust Composed Image Retrieval cites this paper.

INTENT: Invariance and Discrimination-aware Noise Mitigation for Robust Composed Image Retrieval AutoNeural: Co-Designing Vision-Language Models for NPU Inference

Reference 45

Resolution: verified exact; arxiv_id, observed 2026-07-21T02:20:32.885266Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 9b5e565e-09ca-4e43-8f43-e116f6e625ad · inbound

ConeSep: Cone-based Robust Noise-Unlearning Compositional Network for Composed Image Retrieval cites this paper.

ConeSep: Cone-based Robust Noise-Unlearning Compositional Network for Composed Image Retrieval AutoNeural: Co-Designing Vision-Language Models for NPU Inference

Reference 60

Resolution: verified exact; arxiv_id, observed 2026-07-21T02:20:32.885266Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation d06e6fc3-dc44-4168-a7a0-eec8e89e4998 · inbound

MedSynapse-V: Bridging Visual Perception and Clinical Intuition via Latent Memory Evolution cites this paper.

MedSynapse-V: Bridging Visual Perception and Clinical Intuition via Latent Memory Evolution AutoNeural: Co-Designing Vision-Language Models for NPU Inference

Reference 9

Resolution: verified exact; arxiv_id, observed 2026-07-21T02:20:32.885266Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 8e7782ad-0235-49b8-8477-de925962f1e1 · inbound

MedSynapse-V: Bridging Visual Perception and Clinical Intuition via Latent Memory Evolution cites this paper.

MedSynapse-V: Bridging Visual Perception and Clinical Intuition via Latent Memory Evolution AutoNeural: Co-Designing Vision-Language Models for NPU Inference

Reference 9

Resolution: verified exact; arxiv_id, observed 2026-07-21T02:20:32.885266Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 8d95c99b-52ae-4004-95e9-75f6ab9a4915 · inbound

Cornerstones or Stumbling Blocks? Deciphering the Rock Tokens in On-Policy Distillation cites this paper.

Cornerstones or Stumbling Blocks? Deciphering the Rock Tokens in On-Policy Distillation AutoNeural: Co-Designing Vision-Language Models for NPU Inference

Reference 4

Resolution: verified exact; arxiv_id, observed 2026-07-21T02:20:32.885266Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation ffa351cb-2c2f-40dd-9279-6eff311d369d · inbound

Cornerstones or Stumbling Blocks? Deciphering the Rock Tokens in On-Policy Distillation cites this paper.

Cornerstones or Stumbling Blocks? Deciphering the Rock Tokens in On-Policy Distillation AutoNeural: Co-Designing Vision-Language Models for NPU Inference

Reference 4

Resolution: verified exact; arxiv_id, observed 2026-07-21T02:20:32.885266Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation b2e09a40-803a-4e26-98ac-4a95023b124e · inbound

Detail Consistent Stage-Wise Distillation for Efficient 3D MRI Segmentation cites this paper.

Detail Consistent Stage-Wise Distillation for Efficient 3D MRI Segmentation AutoNeural: Co-Designing Vision-Language Models for NPU Inference

Reference 2

Resolution: verified exact; arxiv_id, observed 2026-07-21T02:20:32.885266Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation fc40c1a1-6a4f-4334-b250-952f65128a35 · inbound

Energy-Efficient On-Device RAG on a Mobile NPU: System Design and Benchmark on Snapdragon X Elite cites this paper.

Energy-Efficient On-Device RAG on a Mobile NPU: System Design and Benchmark on Snapdragon X Elite AutoNeural: Co-Designing Vision-Language Models for NPU Inference

Reference 42

Resolution: verified exact; arxiv_id, observed 2026-07-21T02:20:32.885266Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 5794d1a3-4a67-4308-a9f3-0faeab962db2 · inbound

Estimating Treatment Effects for Depression in Longitudinal Therapy Switching Settings cites this paper.

Estimating Treatment Effects for Depression in Longitudinal Therapy Switching Settings AutoNeural: Co-Designing Vision-Language Models for NPU Inference

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-02T11:08:50.198349Z

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