Paper Citation Record · LEDGER

Rethinking the Intermediate Features in Adversarial Attacks: Misleading Robotic Models via Adversarial Distillation

As of 20 August 2026, this Paper Citation Record lists 67 of 67 outbound references and 2 inbound Pith citation observations for arXiv:2411.15222.

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pith.paper-citation-record.v1
2411.15222 v1

Coverage vector

measured 67 of 67 reference resolution

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measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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

Source: arxiv_reference, observed 2026-05-23T03:32:28.013690Z

Reference resolution

67 of 67 outbound references displayed

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External citation measurements

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Outbound references

Observation efced63b-3513-48e6-bdc4-2a45c7ab29a1 · outbound

This paper cites Engelmore and A.

Rethinking the Intermediate Features in Adversarial Attacks: Misleading Robotic Models via Adversarial Distillation Engelmore and A

Reference 1

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Observation 7dce21de-c185-4656-853c-30e9b832d727 · outbound

This paper cites Communication, Simulation, and Intelligent Agents: Implications of Personal Intelligent Machines for Medical Education,.

Rethinking the Intermediate Features in Adversarial Attacks: Misleading Robotic Models via Adversarial Distillation Communication, Simulation, and Intelligent Agents: Implications of Personal Intelligent Machines for Medical Education,

Reference 2

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This paper cites Classification Problem Solving,.

Rethinking the Intermediate Features in Adversarial Attacks: Misleading Robotic Models via Adversarial Distillation Classification Problem Solving,

Reference 3

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This paper cites New ways to make microcircuits smaller,.

Rethinking the Intermediate Features in Adversarial Attacks: Misleading Robotic Models via Adversarial Distillation New ways to make microcircuits smaller,

Reference 4

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This paper cites New Ways to Make Microcircuits Smaller—Duplicate Entry,.

Rethinking the Intermediate Features in Adversarial Attacks: Misleading Robotic Models via Adversarial Distillation New Ways to Make Microcircuits Smaller—Duplicate Entry,

Reference 5

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Observation 3edc5f06-09e6-49b0-b901-acc0707ed522 · outbound

This paper cites Strategic explanations for a diagnostic consultation system,.

Rethinking the Intermediate Features in Adversarial Attacks: Misleading Robotic Models via Adversarial Distillation Strategic explanations for a diagnostic consultation system,

Reference 6

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This paper cites Strategic Explanations in Consultation—Duplicate,.

Rethinking the Intermediate Features in Adversarial Attacks: Misleading Robotic Models via Adversarial Distillation Strategic Explanations in Consultation—Duplicate,

Reference 7

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Observation cfc70159-49a9-4419-a889-ef3b4776040b · outbound

This paper cites Poligon: A System for Parallel Problem Solving,.

Rethinking the Intermediate Features in Adversarial Attacks: Misleading Robotic Models via Adversarial Distillation Poligon: A System for Parallel Problem Solving,

Reference 8

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Observation 634eb83f-f011-45e8-990a-c330206b18df · outbound

This paper cites Transfer of Rule-Based Expertise through a Tutorial Dialogue,.

Rethinking the Intermediate Features in Adversarial Attacks: Misleading Robotic Models via Adversarial Distillation Transfer of Rule-Based Expertise through a Tutorial Dialogue,

Reference 9

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Observation ab7904a7-aa33-4297-8cf3-641304e5d900 · outbound

This paper cites The Engineering of Qualitative Models,.

Rethinking the Intermediate Features in Adversarial Attacks: Misleading Robotic Models via Adversarial Distillation The Engineering of Qualitative Models,

Reference 10

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Observation dbed94ce-5f12-4c6c-8abd-017ed3c4736b · outbound

This paper cites Attention is all you need,.

Rethinking the Intermediate Features in Adversarial Attacks: Misleading Robotic Models via Adversarial Distillation Attention is all you need,

Reference 11

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This paper cites Pluto: The ’other’ red planet,.

Rethinking the Intermediate Features in Adversarial Attacks: Misleading Robotic Models via Adversarial Distillation Pluto: The ’other’ red planet,

Reference 12

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This paper cites Vima: General robot manipula- tion with multimodal prompts,.

Rethinking the Intermediate Features in Adversarial Attacks: Misleading Robotic Models via Adversarial Distillation Vima: General robot manipula- tion with multimodal prompts,

Reference 13

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Observation a151e780-f3fe-40f3-9dc9-9f4dc04bc86a · outbound

This paper cites Diffusion Policy Attacker: Crafting Adversarial Attacks for Diffusion-based Policies.

Rethinking the Intermediate Features in Adversarial Attacks: Misleading Robotic Models via Adversarial Distillation Diffusion Policy Attacker: Crafting Adversarial Attacks for Diffusion-based Policies

Reference 14

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Observation ac3051a3-2d55-41a0-aec2-411f076defd8 · outbound

This paper cites Diffusion Policy: Visuomotor Policy Learning via Action Diffusion.

Rethinking the Intermediate Features in Adversarial Attacks: Misleading Robotic Models via Adversarial Distillation Diffusion Policy: Visuomotor Policy Learning via Action Diffusion

Reference 15

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Observation 549430ec-b36f-4a8f-a4ca-90dc049a6932 · outbound

This paper cites Universal and Transferable Adversarial Attacks on Aligned Language Models.

Rethinking the Intermediate Features in Adversarial Attacks: Misleading Robotic Models via Adversarial Distillation Universal and Transferable Adversarial Attacks on Aligned Language Models

Reference 16

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Observation ef2f0b32-4b69-4ef6-808f-72850371a16e · outbound

This paper cites Explaining and Harnessing Adversarial Examples.

Rethinking the Intermediate Features in Adversarial Attacks: Misleading Robotic Models via Adversarial Distillation Explaining and Harnessing Adversarial Examples

Reference 17

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Observation 6f98d910-91ad-4d6f-8fe9-8bf39e9b3f4e · outbound

This paper cites Towards deep learning models resistant to adversarial attacks,.

Rethinking the Intermediate Features in Adversarial Attacks: Misleading Robotic Models via Adversarial Distillation Towards deep learning models resistant to adversarial attacks,

Reference 18

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Observation 238d49f6-7db5-4cfb-8c91-eacf6dbd9f7d · outbound

This paper cites Adversarial examples are not easily detected: Bypassing ten detection methods,.

Rethinking the Intermediate Features in Adversarial Attacks: Misleading Robotic Models via Adversarial Distillation Adversarial examples are not easily detected: Bypassing ten detection methods,

Reference 19

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Observation 7191bc25-e2e7-485d-9d2c-114d8e6ea6d8 · outbound

This paper cites Jailbreaking Black Box Large Language Models in Twenty Queries.

Rethinking the Intermediate Features in Adversarial Attacks: Misleading Robotic Models via Adversarial Distillation Jailbreaking Black Box Large Language Models in Twenty Queries

Reference 20

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This paper cites Jailbreaking Leading Safety-Aligned LLMs with Simple Adaptive Attacks.

Rethinking the Intermediate Features in Adversarial Attacks: Misleading Robotic Models via Adversarial Distillation Jailbreaking Leading Safety-Aligned LLMs with Simple Adaptive Attacks

Reference 21

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This paper cites Guiding multi-step rearrangement tasks with natural language instructions,.

Rethinking the Intermediate Features in Adversarial Attacks: Misleading Robotic Models via Adversarial Distillation Guiding multi-step rearrangement tasks with natural language instructions,

Reference 22

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Observation b61b8549-83f8-4d66-a1f9-3f982ee19066 · outbound

This paper cites Language conditioned imitation learning over unstructured data,.

Rethinking the Intermediate Features in Adversarial Attacks: Misleading Robotic Models via Adversarial Distillation Language conditioned imitation learning over unstructured data,

Reference 23

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This paper cites Attention is all you need,.

Rethinking the Intermediate Features in Adversarial Attacks: Misleading Robotic Models via Adversarial Distillation Attention is all you need,

Reference 24

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This paper cites Safe learning in robotics: From learning-based control to safe reinforcement learning,.

Rethinking the Intermediate Features in Adversarial Attacks: Misleading Robotic Models via Adversarial Distillation Safe learning in robotics: From learning-based control to safe reinforcement learning,

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Rethinking the Intermediate Features in Adversarial Attacks: Misleading Robotic Models via Adversarial Distillation RT-1: Robotics Transformer for Real-World Control at Scale

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Rethinking the Intermediate Features in Adversarial Attacks: Misleading Robotic Models via Adversarial Distillation Adaptive dis- cretization for model-based reinforcement learning,

Reference 27

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This paper cites Action- quantized offline reinforcement learning for robotic skill learning,.

Rethinking the Intermediate Features in Adversarial Attacks: Misleading Robotic Models via Adversarial Distillation Action- quantized offline reinforcement learning for robotic skill learning,

Reference 28

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Rethinking the Intermediate Features in Adversarial Attacks: Misleading Robotic Models via Adversarial Distillation Dota 2 with Large Scale Deep Reinforcement Learning

Reference 29

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Rethinking the Intermediate Features in Adversarial Attacks: Misleading Robotic Models via Adversarial Distillation Bc-z: Zero-shot task generalization with robotic imitation learning,

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Rethinking the Intermediate Features in Adversarial Attacks: Misleading Robotic Models via Adversarial Distillation Language-conditioned imitation learning for robot ma- nipulation tasks,

Reference 31

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Rethinking the Intermediate Features in Adversarial Attacks: Misleading Robotic Models via Adversarial Distillation Deep imitation learning for complex manipulation tasks from virtual reality teleoperation,

Reference 32

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Rethinking the Intermediate Features in Adversarial Attacks: Misleading Robotic Models via Adversarial Distillation Energy-Based Imitation Learning

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This paper cites Intriguing properties of neural networks,.

Rethinking the Intermediate Features in Adversarial Attacks: Misleading Robotic Models via Adversarial Distillation Intriguing properties of neural networks,

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Rethinking the Intermediate Features in Adversarial Attacks: Misleading Robotic Models via Adversarial Distillation Attacking Large Language Models with Projected Gradient Descent

Reference 35

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This paper cites AmpleGCG: Learning a Universal and Transferable Generative Model of Adversarial Suffixes for Jailbreaking Both Open and Closed LLMs.

Rethinking the Intermediate Features in Adversarial Attacks: Misleading Robotic Models via Adversarial Distillation AmpleGCG: Learning a Universal and Transferable Generative Model of Adversarial Suffixes for Jailbreaking Both Open and Closed LLMs

Reference 36

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This paper cites Adversarial example does good: Preventing painting imi- tation from diffusion models via adversarial examples,.

Rethinking the Intermediate Features in Adversarial Attacks: Misleading Robotic Models via Adversarial Distillation Adversarial example does good: Preventing painting imi- tation from diffusion models via adversarial examples,

Reference 37

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This paper cites On the adversarial robustness of multi- modal foundation models,.

Rethinking the Intermediate Features in Adversarial Attacks: Misleading Robotic Models via Adversarial Distillation On the adversarial robustness of multi- modal foundation models,

Reference 38

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This paper cites Attacking deep reinforcement learning with decoupled adversarial policy,.

Rethinking the Intermediate Features in Adversarial Attacks: Misleading Robotic Models via Adversarial Distillation Attacking deep reinforcement learning with decoupled adversarial policy,

Reference 39

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This paper cites Revisiting the adversarial robustness-accuracy tradeoff in robot learning,.

Rethinking the Intermediate Features in Adversarial Attacks: Misleading Robotic Models via Adversarial Distillation Revisiting the adversarial robustness-accuracy tradeoff in robot learning,

Reference 40

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This paper cites Studying adversarial attacks on behavioral cloning dynamics,.

Rethinking the Intermediate Features in Adversarial Attacks: Misleading Robotic Models via Adversarial Distillation Studying adversarial attacks on behavioral cloning dynamics,

Reference 41

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This paper cites Is deep learning safe for robot vision? adversarial examples against the icub humanoid,.

Rethinking the Intermediate Features in Adversarial Attacks: Misleading Robotic Models via Adversarial Distillation Is deep learning safe for robot vision? adversarial examples against the icub humanoid,

Reference 42

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This paper cites Analyzing adversarial attacks against deep learning for robot navigation.

Rethinking the Intermediate Features in Adversarial Attacks: Misleading Robotic Models via Adversarial Distillation Analyzing adversarial attacks against deep learning for robot navigation

Reference 43

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This paper cites Video pretraining (vpt): Learning to act by watching unlabeled online videos,.

Rethinking the Intermediate Features in Adversarial Attacks: Misleading Robotic Models via Adversarial Distillation Video pretraining (vpt): Learning to act by watching unlabeled online videos,

Reference 44

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This paper cites Is bert really robust? a strong baseline for natural language attack on text classification and entailment,.

Rethinking the Intermediate Features in Adversarial Attacks: Misleading Robotic Models via Adversarial Distillation Is bert really robust? a strong baseline for natural language attack on text classification and entailment,

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:57:08.001026Z

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Observation dfc20bae-f501-43bc-a936-d39e8d24d1b4 · outbound

This paper cites Hard prompts made easy: Gradient-based discrete optimization for prompt tuning and discovery,.

Rethinking the Intermediate Features in Adversarial Attacks: Misleading Robotic Models via Adversarial Distillation Hard prompts made easy: Gradient-based discrete optimization for prompt tuning and discovery,

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:57:07.939626Z

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This paper cites Automatically auditing large language models via discrete optimization,.

Rethinking the Intermediate Features in Adversarial Attacks: Misleading Robotic Models via Adversarial Distillation Automatically auditing large language models via discrete optimization,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:57:07.897804Z

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Observation c5be924d-da2f-4b6a-9c97-35914711a6b5 · outbound

This paper cites Character-level white-box adversarial attacks against transformers via attachable subwords substitution,.

Rethinking the Intermediate Features in Adversarial Attacks: Misleading Robotic Models via Adversarial Distillation Character-level white-box adversarial attacks against transformers via attachable subwords substitution,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:57:07.848410Z

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Observation 432feeb3-3d1a-4baa-a45a-6f8dba8c0ef6 · outbound

This paper cites Generating natural language adversarial examples,.

Rethinking the Intermediate Features in Adversarial Attacks: Misleading Robotic Models via Adversarial Distillation Generating natural language adversarial examples,

Reference 49

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Observation f1b9edac-9088-42cb-8bfe-d10f544f0174 · outbound

This paper cites FitNets: Hints for Thin Deep Nets.

Rethinking the Intermediate Features in Adversarial Attacks: Misleading Robotic Models via Adversarial Distillation FitNets: Hints for Thin Deep Nets

Reference 50

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This paper cites Knowledge transfer via distilla- tion of activation boundaries formed by hidden neurons,.

Rethinking the Intermediate Features in Adversarial Attacks: Misleading Robotic Models via Adversarial Distillation Knowledge transfer via distilla- tion of activation boundaries formed by hidden neurons,

Reference 51

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Observation e79bf631-224b-4e54-8aa2-37b43455948c · outbound

This paper cites Au- toprompt: Eliciting knowledge from language models with automatically generated prompts,.

Rethinking the Intermediate Features in Adversarial Attacks: Misleading Robotic Models via Adversarial Distillation Au- toprompt: Eliciting knowledge from language models with automatically generated prompts,

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:57:07.728255Z

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Observation 865786c5-00ef-4d30-bb8a-d74d2cc1a14d · outbound

This paper cites Hotflip: White-box adversarial examples for text classification,.

Rethinking the Intermediate Features in Adversarial Attacks: Misleading Robotic Models via Adversarial Distillation Hotflip: White-box adversarial examples for text classification,

Reference 53

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Observation d233d880-9dbf-4220-9a54-e89da518bb31 · outbound

This paper cites Calvin: A benchmark for language-conditioned policy learning for long-horizon robot manipulation tasks,.

Rethinking the Intermediate Features in Adversarial Attacks: Misleading Robotic Models via Adversarial Distillation Calvin: A benchmark for language-conditioned policy learning for long-horizon robot manipulation tasks,

Reference 54

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Observation 9a71ad0e-9624-4cfe-b7a7-9263f589e585 · outbound

This paper cites Modularity through attention: Efficient training and transfer of language- conditioned policies for robot manipulation,.

Rethinking the Intermediate Features in Adversarial Attacks: Misleading Robotic Models via Adversarial Distillation Modularity through attention: Efficient training and transfer of language- conditioned policies for robot manipulation,

Reference 55

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Observation fa7f95a2-bcf4-4621-a9bd-9f38e57cf472 · outbound

This paper cites Rearrangement: A Challenge for Embodied AI.

Rethinking the Intermediate Features in Adversarial Attacks: Misleading Robotic Models via Adversarial Distillation Rearrangement: A Challenge for Embodied AI

Reference 56

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Observation 7f6b13b0-5e1d-4179-b843-9092a14e9aad · outbound

This paper cites Ocrtoc: A cloud-based competition and benchmark for robotic grasping and manipulation,.

Rethinking the Intermediate Features in Adversarial Attacks: Misleading Robotic Models via Adversarial Distillation Ocrtoc: A cloud-based competition and benchmark for robotic grasping and manipulation,

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:57:07.603192Z

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Observation 1565fc1c-bd0a-42ed-85c6-866bbaa68bbb · outbound

This paper cites Transporter networks: Rearranging the visual world for robotic manipulation,.

Rethinking the Intermediate Features in Adversarial Attacks: Misleading Robotic Models via Adversarial Distillation Transporter networks: Rearranging the visual world for robotic manipulation,

Reference 58

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Observation 0ebcce89-b22c-4de4-8967-94f38414090a · outbound

This paper cites Cliport: What and where pathways for robotic manipulation,.

Rethinking the Intermediate Features in Adversarial Attacks: Misleading Robotic Models via Adversarial Distillation Cliport: What and where pathways for robotic manipulation,

Reference 59

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Observation c4d3cfad-a8ef-43d0-a892-83b8f28a8f35 · outbound

This paper cites Decision transformer: Reinforcement learn- ing via sequence modeling,.

Rethinking the Intermediate Features in Adversarial Attacks: Misleading Robotic Models via Adversarial Distillation Decision transformer: Reinforcement learn- ing via sequence modeling,

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:57:07.497215Z

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Observation dcf52139-1e11-418a-91a8-37dfdd41b590 · outbound

This paper cites Relay pol- icy learning: Solving long horizon tasks via imitation and reinforcement learning,.

Rethinking the Intermediate Features in Adversarial Attacks: Misleading Robotic Models via Adversarial Distillation Relay pol- icy learning: Solving long horizon tasks via imitation and reinforcement learning,

Reference 61

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This paper cites An image is worth 16x16 words: Transformers for image recognition at scale,.

Rethinking the Intermediate Features in Adversarial Attacks: Misleading Robotic Models via Adversarial Distillation An image is worth 16x16 words: Transformers for image recognition at scale,

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:57:07.434788Z

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Observation f1779177-a9df-4e5e-bdd9-78e9718357b5 · outbound

This paper cites Mask r-cnn,.

Rethinking the Intermediate Features in Adversarial Attacks: Misleading Robotic Models via Adversarial Distillation Mask r-cnn,

Reference 63

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This paper cites Exploring the limits of transfer learning with a unified text-to-text transformer,.

Rethinking the Intermediate Features in Adversarial Attacks: Misleading Robotic Models via Adversarial Distillation Exploring the limits of transfer learning with a unified text-to-text transformer,

Reference 64

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Observation 99273b85-92cf-42f8-9e8c-c58de0fc5c7a · outbound

This paper cites On the Multi-modal Vulnerability of Diffusion Models.

Rethinking the Intermediate Features in Adversarial Attacks: Misleading Robotic Models via Adversarial Distillation On the Multi-modal Vulnerability of Diffusion Models

Reference 65

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Observation aab7c635-b76f-4c7f-b9e0-c16a0745a34b · outbound

This paper cites Automatic and Universal Prompt Injection Attacks against Large Language Models.

Rethinking the Intermediate Features in Adversarial Attacks: Misleading Robotic Models via Adversarial Distillation Automatic and Universal Prompt Injection Attacks against Large Language Models

Reference 66

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Observation 079d90c1-9c6b-4048-8bc3-d48f9ca0e4fc · outbound

This paper cites Enhancing adversarial example transferability with an intermediate level attack,.

Rethinking the Intermediate Features in Adversarial Attacks: Misleading Robotic Models via Adversarial Distillation Enhancing adversarial example transferability with an intermediate level attack,

Reference 67

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Pith citing papers

Observation d852392c-1a71-4f30-a9ee-d6b6f11802a5 · inbound

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How Vulnerable Is My Learned Policy? Universal Adversarial Perturbation Attacks On Modern Behavior Cloning Policies Rethinking the Intermediate Features in Adversarial Attacks: Misleading Robotic Models via Adversarial Distillation

Reference 19

Resolution: verified exact; arxiv_id, observed 2026-05-23T03:32:28.016139Z

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Observation b031f665-e269-4f64-ac28-4449f049fb3e · inbound

A Comprehensive Survey on Physical Risk Control in the Era of Foundation Model-enabled Robotics cites this paper.

A Comprehensive Survey on Physical Risk Control in the Era of Foundation Model-enabled Robotics Rethinking the Intermediate Features in Adversarial Attacks: Misleading Robotic Models via Adversarial Distillation

Reference 104

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