Paper Citation Record · LEDGER

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

As of 13 August 2026, this Paper Citation Record lists 67 of 67 outbound references and 1 inbound Pith citation observation for arXiv:2411.15222.

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

pith.paper-citation-record.v1
2411.15222 v1

Coverage vector

measured 67 of 67 reference resolution

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Pith citing papers itemized under the disclosed page cap.

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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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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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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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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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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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This paper cites Deep imitation learning for complex manipulation tasks from virtual reality teleoperation,.

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

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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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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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:57:07.803757Z

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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

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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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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

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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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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

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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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Observation a9b7839f-ce39-47c2-b99b-1f2b574bb678 · outbound

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

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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

How Vulnerable Is My Learned Policy? Universal Adversarial Perturbation Attacks On Modern Behavior Cloning Policies cites this paper.

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

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