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Paper Citation Record · LEDGER

Deploying AI for Signal Processing education: Selected challenges and intriguing opportunities

As of 14 August 2026, this Paper Citation Record lists 98 of 98 outbound references and 0 inbound Pith citation observations for arXiv:2509.08950.

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

pith.paper-citation-record.v1
2509.08950 v1

Coverage vector

measured 98 of 98 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-04T19:59:17.520796Z

measured 98 of 98 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+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

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

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Reference resolution

98 of 98 outbound references displayed

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Outbound references

Observation 44dd6164-97e7-499a-801b-7b80549e898b · outbound

This paper cites The role of learning in returns to college major: evidence from 2.8 million reviews of 150,000 professors,.

Deploying AI for Signal Processing education: Selected challenges and intriguing opportunities The role of learning in returns to college major: evidence from 2.8 million reviews of 150,000 professors,

Reference 1

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Observation 9855927d-b86e-403c-9eaa-0e8ccc1b3f45 · outbound

This paper cites an unresolved cited work.

Deploying AI for Signal Processing education: Selected challenges and intriguing opportunities Unresolved cited work

Reference 2

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Observation 2d2cbcfe-cd25-45b4-aa1f-89abedbc7638 · outbound

This paper cites Cognitive load theory, educational research, and instructional design: Some food for thought,.

Deploying AI for Signal Processing education: Selected challenges and intriguing opportunities Cognitive load theory, educational research, and instructional design: Some food for thought,

Reference 3

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Observation 410581ce-4123-4f7c-be6c-12d9260ca3aa · outbound

This paper cites The flipped classroom: A review of its advantages and challenges,.

Deploying AI for Signal Processing education: Selected challenges and intriguing opportunities The flipped classroom: A review of its advantages and challenges,

Reference 4

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Observation c5cc9782-407c-4960-ab1c-7147636d47ff · outbound

This paper cites The flipped classroom: A survey of the research,.

Deploying AI for Signal Processing education: Selected challenges and intriguing opportunities The flipped classroom: A survey of the research,

Reference 5

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Observation 0e0691f4-0880-4baa-bd7a-285ba21ca6d2 · outbound

This paper cites The flipped classroom in engineering education: A survey of the research,.

Deploying AI for Signal Processing education: Selected challenges and intriguing opportunities The flipped classroom in engineering education: A survey of the research,

Reference 6

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Observation 692d4dd4-81bf-488c-9ae1-e604e5c146d8 · outbound

This paper cites Flipping signal-processing instruction [sp education],.

Deploying AI for Signal Processing education: Selected challenges and intriguing opportunities Flipping signal-processing instruction [sp education],

Reference 7

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Observation a70ffbf2-e31e-4ea6-b0be-91d338e2a62d · outbound

This paper cites GPT-4 — Wikipedia, the free encyclopedia,.

Deploying AI for Signal Processing education: Selected challenges and intriguing opportunities GPT-4 — Wikipedia, the free encyclopedia,

Reference 8

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Observation be773d9c-a6cd-4332-8f54-ad6ba8fe1d80 · outbound

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Deploying AI for Signal Processing education: Selected challenges and intriguing opportunities Unresolved cited work

Reference 9

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Observation 481ec1a3-2a60-4817-b1bb-609b9b6721b8 · outbound

This paper cites Scientific discovery in the age of artificial intelligence,.

Deploying AI for Signal Processing education: Selected challenges and intriguing opportunities Scientific discovery in the age of artificial intelligence,

Reference 10

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This paper cites Leandojo: Theorem proving with retrieval-augmented language models,.

Deploying AI for Signal Processing education: Selected challenges and intriguing opportunities Leandojo: Theorem proving with retrieval-augmented language models,

Reference 11

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Observation d9b7b70c-8fdd-44e0-a54d-f2991e17228a · outbound

This paper cites Highly accurate protein structure prediction with AlphaFold,.

Deploying AI for Signal Processing education: Selected challenges and intriguing opportunities Highly accurate protein structure prediction with AlphaFold,

Reference 12

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Observation eb0d85a5-e7fa-407a-af02-bd118df037af · outbound

This paper cites Artificial intelligence in drug discovery and development,.

Deploying AI for Signal Processing education: Selected challenges and intriguing opportunities Artificial intelligence in drug discovery and development,

Reference 13

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Observation 83630fc9-704f-4087-8abd-9ffaa9b3f238 · outbound

This paper cites 6g wireless communication systems: Applications, requirements, technologies, challenges, and research directions,.

Deploying AI for Signal Processing education: Selected challenges and intriguing opportunities 6g wireless communication systems: Applications, requirements, technologies, challenges, and research directions,

Reference 14

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Observation 62ae5502-e0c3-49d9-9f30-ab285709c049 · outbound

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Deploying AI for Signal Processing education: Selected challenges and intriguing opportunities Trust in artificial intelligence: A global study,

Reference 15

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Observation cb597832-6b40-450b-9f39-693e6d10701a · outbound

This paper cites Systematic review of research on artificial intelligence applications in higher education,.

Deploying AI for Signal Processing education: Selected challenges and intriguing opportunities Systematic review of research on artificial intelligence applications in higher education,

Reference 16

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Observation 315639ef-92bb-4599-bf0c-68df903c5663 · outbound

This paper cites Luckin,Machine learning and human intelligence: The future of education for the 21st century.

Deploying AI for Signal Processing education: Selected challenges and intriguing opportunities Luckin,Machine learning and human intelligence: The future of education for the 21st century

Reference 17

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Observation 031c2bf0-1609-411e-965e-2c504f088fa5 · outbound

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Deploying AI for Signal Processing education: Selected challenges and intriguing opportunities Holmes, M

Reference 18

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Deploying AI for Signal Processing education: Selected challenges and intriguing opportunities The impact of intelligent tutoring systems on student learning,

Reference 19

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Observation 51f14378-ea96-46cb-996c-302bd87bcb00 · outbound

This paper cites How effective is online learning? evidence from a large-scale study of k-12 students,.

Deploying AI for Signal Processing education: Selected challenges and intriguing opportunities How effective is online learning? evidence from a large-scale study of k-12 students,

Reference 20

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Deploying AI for Signal Processing education: Selected challenges and intriguing opportunities Unresolved cited work

Reference 21

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Observation f6a8574b-e076-4491-942e-aa268e555354 · outbound

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Deploying AI for Signal Processing education: Selected challenges and intriguing opportunities How artificial intelligence is transforming the future of education,

Reference 22

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Deploying AI for Signal Processing education: Selected challenges and intriguing opportunities Counterfactual fairness,

Reference 23

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Observation 4e3188b4-f87f-4842-89b5-8d14c4279d35 · outbound

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Deploying AI for Signal Processing education: Selected challenges and intriguing opportunities Fairness through awareness,

Reference 24

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Observation 94999fe7-04df-4fd3-95b6-53c2ed7aaa73 · outbound

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Deploying AI for Signal Processing education: Selected challenges and intriguing opportunities Equality of opportunity in supervised learning,

Reference 25

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Observation 86687239-0188-4963-966d-24fe68ddda09 · outbound

This paper cites Selwyn,Education and technology: Key issues and debates.

Deploying AI for Signal Processing education: Selected challenges and intriguing opportunities Selwyn,Education and technology: Key issues and debates

Reference 26

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Deploying AI for Signal Processing education: Selected challenges and intriguing opportunities Graph Machine Learning in the Era of Large Language Models (LLMs)

Reference 27

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Observation 433384d8-1280-474f-b8eb-6f22e45a88a1 · outbound

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Deploying AI for Signal Processing education: Selected challenges and intriguing opportunities Graph Learning in the Era of LLMs: A Survey from the Perspective of Data, Models, and Tasks

Reference 28

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Deploying AI for Signal Processing education: Selected challenges and intriguing opportunities A comprehensive survey on graph neural networks,

Reference 29

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Deploying AI for Signal Processing education: Selected challenges and intriguing opportunities Semi-supervised classification with graph convolutional networks,

Reference 30

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Deploying AI for Signal Processing education: Selected challenges and intriguing opportunities Graph neural networks: A review of methods and applications,

Reference 31

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Deploying AI for Signal Processing education: Selected challenges and intriguing opportunities Gnnexplainer: Generating explanations for graph neural networks,

Reference 32

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Deploying AI for Signal Processing education: Selected challenges and intriguing opportunities Demystifying and mitigating bias for node representation learning,

Reference 33

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Observation cb4d1afb-5d00-4afb-a671-130e73e6cc40 · outbound

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Deploying AI for Signal Processing education: Selected challenges and intriguing opportunities Fair contrastive learning on graphs,

Reference 34

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Deploying AI for Signal Processing education: Selected challenges and intriguing opportunities Fast&fair: Training acceleration and bias mitigation for GNNs,

Reference 35

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Observation 9f7668f0-1230-4a14-b7d8-777c59325a6a · outbound

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Deploying AI for Signal Processing education: Selected challenges and intriguing opportunities Fast&fair: Training acceleration and bias mitigation for gnns,

Reference 36

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This paper cites FairGAT: Fairness-aware graph attention networks,.

Deploying AI for Signal Processing education: Selected challenges and intriguing opportunities FairGAT: Fairness-aware graph attention networks,

Reference 37

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Observation 62ecf8ca-bf34-4e3e-9c2f-042e5e49c983 · outbound

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Deploying AI for Signal Processing education: Selected challenges and intriguing opportunities Fairness-Aware Graph Filter Design

Reference 38

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Deploying AI for Signal Processing education: Selected challenges and intriguing opportunities Fairwire: Fair graph generation,

Reference 39

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Observation 526e3ca0-f70f-4f63-82c1-feb794ef5137 · outbound

This paper cites DP-FT: A differential privacy graph generation with field theory for social network data release,.

Deploying AI for Signal Processing education: Selected challenges and intriguing opportunities DP-FT: A differential privacy graph generation with field theory for social network data release,

Reference 40

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Observation 4687430f-f1b1-46ec-a8c7-25d346673cbf · outbound

This paper cites Generative pretrained autoregressive transformer graph neural network applied to the analysis and discovery of novel proteins,.

Deploying AI for Signal Processing education: Selected challenges and intriguing opportunities Generative pretrained autoregressive transformer graph neural network applied to the analysis and discovery of novel proteins,

Reference 41

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Observation e10f8900-73c3-43b8-9c35-15c251da2b6c · outbound

This paper cites Graph generation with prescribed feature constraints,.

Deploying AI for Signal Processing education: Selected challenges and intriguing opportunities Graph generation with prescribed feature constraints,

Reference 42

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Observation f7bd505e-23dd-47f7-904b-911df0154af9 · outbound

This paper cites GraphGPT: Generative Pre-trained Graph Eulerian Transformer.

Deploying AI for Signal Processing education: Selected challenges and intriguing opportunities GraphGPT: Generative Pre-trained Graph Eulerian Transformer

Reference 43

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Observation 1f1ca3f1-af6b-4aa5-a7dd-b223b63060a9 · outbound

This paper cites The challenge of using llms to simulate human behavior: A causal inference perspective,.

Deploying AI for Signal Processing education: Selected challenges and intriguing opportunities The challenge of using llms to simulate human behavior: A causal inference perspective,

Reference 44

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Observation 198ac7a1-efeb-41b0-921d-385edc2ff28c · outbound

This paper cites Causal Inference with Large Language Model: A Survey.

Deploying AI for Signal Processing education: Selected challenges and intriguing opportunities Causal Inference with Large Language Model: A Survey

Reference 45

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Observation 8415a655-3f8a-4e64-a407-b78ec110988b · outbound

This paper cites Large Language Models and Causal Inference in Collaboration: A Survey.

Deploying AI for Signal Processing education: Selected challenges and intriguing opportunities Large Language Models and Causal Inference in Collaboration: A Survey

Reference 46

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Observation e4319c4d-8555-4ee0-947a-bd696ac60169 · outbound

This paper cites Pearl,Causality: Models, reasoning, and inference.

Deploying AI for Signal Processing education: Selected challenges and intriguing opportunities Pearl,Causality: Models, reasoning, and inference

Reference 47

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Observation 14ed090c-1507-42f0-8e16-e9575579108b · outbound

This paper cites an unresolved cited work.

Deploying AI for Signal Processing education: Selected challenges and intriguing opportunities Unresolved cited work

Reference 48

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Observation fe6273a9-a64e-453a-af7c-1fc5891f2f6f · outbound

This paper cites Topology identification and learning over graphs: Accounting for nonlinearities and dynamics,.

Deploying AI for Signal Processing education: Selected challenges and intriguing opportunities Topology identification and learning over graphs: Accounting for nonlinearities and dynamics,

Reference 49

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Observation 5250f112-b81a-40db-a225-0fe2bb54e58d · outbound

This paper cites Kernel-based structural equation models for topology identification of directed networks,.

Deploying AI for Signal Processing education: Selected challenges and intriguing opportunities Kernel-based structural equation models for topology identification of directed networks,

Reference 50

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Observation badc6e92-5516-469a-a22d-5799449b21d0 · outbound

This paper cites Simulating Classroom Education with LLM-Empowered Agents.

Deploying AI for Signal Processing education: Selected challenges and intriguing opportunities Simulating Classroom Education with LLM-Empowered Agents

Reference 51

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Observation 7d2cda19-3588-4ea9-bd30-bac0bad81db3 · outbound

This paper cites Large Language Models for Education: A Survey.

Deploying AI for Signal Processing education: Selected challenges and intriguing opportunities Large Language Models for Education: A Survey

Reference 52

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Observation 19d00570-19a7-44fd-8c96-ce9c8df50067 · outbound

This paper cites On faithfulness and factuality in abstractive summarization,.

Deploying AI for Signal Processing education: Selected challenges and intriguing opportunities On faithfulness and factuality in abstractive summarization,

Reference 53

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Observation 753b6b80-3cfe-4415-b7f8-7925c1f678d0 · outbound

This paper cites Hallucinations in llms: Understanding and addressing challenges,.

Deploying AI for Signal Processing education: Selected challenges and intriguing opportunities Hallucinations in llms: Understanding and addressing challenges,

Reference 54

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Observation 03745c43-729a-477b-9e1e-68046bb7756b · outbound

This paper cites Risks of AI Scientists: Prioritizing Safeguarding Over Autonomy.

Deploying AI for Signal Processing education: Selected challenges and intriguing opportunities Risks of AI Scientists: Prioritizing Safeguarding Over Autonomy

Reference 55

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Observation d9b2f448-0186-4568-8dc6-11e87038b0ca · outbound

This paper cites The problems of llm-generated data in social science research,.

Deploying AI for Signal Processing education: Selected challenges and intriguing opportunities The problems of llm-generated data in social science research,

Reference 56

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Observation d2e4add2-c5be-4d1f-9451-aae1ca65922a · outbound

This paper cites What future do we want with artificial intelligence?.

Deploying AI for Signal Processing education: Selected challenges and intriguing opportunities What future do we want with artificial intelligence?

Reference 57

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Observation 02c1542f-9ecc-437e-964c-95d35a274c39 · outbound

This paper cites Unlocking the power of chatgpt: A framework for applying generative ai in education,.

Deploying AI for Signal Processing education: Selected challenges and intriguing opportunities Unlocking the power of chatgpt: A framework for applying generative ai in education,

Reference 58

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Observation b13b5690-b8f9-474c-8b57-04db6c6f412d · outbound

This paper cites Ai in education: The problem with hallucinations,.

Deploying AI for Signal Processing education: Selected challenges and intriguing opportunities Ai in education: The problem with hallucinations,

Reference 59

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Observation b2e3cdd1-2295-41d5-979f-dabd2be36155 · outbound

This paper cites Controlled hallucinations: Learning to generate faithfully from noisy data,.

Deploying AI for Signal Processing education: Selected challenges and intriguing opportunities Controlled hallucinations: Learning to generate faithfully from noisy data,

Reference 60

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Observation d0e8ca7b-987b-4ce4-9958-e91fbba3bfe8 · outbound

This paper cites Hallucinated but Factual! Inspecting the Factuality of Hallucinations in Abstractive Summarization.

Deploying AI for Signal Processing education: Selected challenges and intriguing opportunities Hallucinated but Factual! Inspecting the Factuality of Hallucinations in Abstractive Summarization

Reference 61

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Observation 411553c1-5093-4fe1-85b2-9af73faeaeb3 · outbound

This paper cites Entity-based knowledge conflicts in question answering,.

Deploying AI for Signal Processing education: Selected challenges and intriguing opportunities Entity-based knowledge conflicts in question answering,

Reference 62

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Observation 465271e5-3551-46ff-bb2b-6c4263514788 · outbound

This paper cites Survey of hallucination in natural language generation,.

Deploying AI for Signal Processing education: Selected challenges and intriguing opportunities Survey of hallucination in natural language generation,

Reference 63

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Observation 1c07c32e-ccba-4ad6-9926-5d0fe9a7b4be · outbound

This paper cites A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions,.

Deploying AI for Signal Processing education: Selected challenges and intriguing opportunities A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions,

Reference 64

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Observation 5de9d50c-0b56-4583-ae1a-bbe5bf2dc476 · outbound

This paper cites BatGPT: A Bidirectional Autoregessive Talker from Generative Pre-trained Transformer.

Deploying AI for Signal Processing education: Selected challenges and intriguing opportunities BatGPT: A Bidirectional Autoregessive Talker from Generative Pre-trained Transformer

Reference 65

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Observation 9e4587fc-07d6-4977-9515-0c1e52f7c3f3 · outbound

This paper cites Lost in the middle: How language models use long contexts,.

Deploying AI for Signal Processing education: Selected challenges and intriguing opportunities Lost in the middle: How language models use long contexts,

Reference 66

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Observation c145c73e-073d-4355-9808-b910b3455b99 · outbound

This paper cites In-context pretraining: Language modeling beyond document boundaries,.

Deploying AI for Signal Processing education: Selected challenges and intriguing opportunities In-context pretraining: Language modeling beyond document boundaries,

Reference 67

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Observation 34df018a-e69b-4702-b86e-f687a3bdc460 · outbound

This paper cites Simple synthetic data reduces sycophancy in large language models,.

Deploying AI for Signal Processing education: Selected challenges and intriguing opportunities Simple synthetic data reduces sycophancy in large language models,

Reference 68

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Observation 020e2a94-690e-4ae2-82eb-aa189dbe0b4c · outbound

This paper cites In-context retrieval-augmented language models,.

Deploying AI for Signal Processing education: Selected challenges and intriguing opportunities In-context retrieval-augmented language models,

Reference 69

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Observation 34c443d4-c9f0-4ab8-94ce-ce667767cf52 · outbound

This paper cites Knowledge-augmented language model prompting for zero-shot knowledge graph question answering,.

Deploying AI for Signal Processing education: Selected challenges and intriguing opportunities Knowledge-augmented language model prompting for zero-shot knowledge graph question answering,

Reference 70

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Observation 4f443c68-4246-4db0-9b3d-19063fce68be · outbound

This paper cites MindMap: Knowledge graph prompting sparks graph of thoughts in large language models,.

Deploying AI for Signal Processing education: Selected challenges and intriguing opportunities MindMap: Knowledge graph prompting sparks graph of thoughts in large language models,

Reference 71

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Observation ec85c05e-d8b4-4342-a533-5c4df4dab4ef · outbound

This paper cites Rethinking with Retrieval: Faithful Large Language Model Inference.

Deploying AI for Signal Processing education: Selected challenges and intriguing opportunities Rethinking with Retrieval: Faithful Large Language Model Inference

Reference 72

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Observation 91b9d48e-07b6-45fa-8ae8-0e9395721550 · outbound

This paper cites Interleaving retrieval with chain-of-thought reasoning for knowledge-intensive multi-step questions,.

Deploying AI for Signal Processing education: Selected challenges and intriguing opportunities Interleaving retrieval with chain-of-thought reasoning for knowledge-intensive multi-step questions,

Reference 73

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Observation 2d17ddfc-f316-4124-870b-641e345f4577 · outbound

This paper cites ReAct: Synergizing Reasoning and Acting in Language Models.

Deploying AI for Signal Processing education: Selected challenges and intriguing opportunities ReAct: Synergizing Reasoning and Acting in Language Models

Reference 74

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source=pdf_text observed=2026-08-04T19:59:17.406067Z digest=sha256:c93d1b5749eb7dbb8026c7fc2aa989621e2c19c6dbd64fd98d86415eb3b05f91

Observation aab6e016-ce43-4694-ac93-f8af998a19aa · outbound

This paper cites Enhancing retrieval-augmented large language models with iterative retrieval-generation synergy,.

Deploying AI for Signal Processing education: Selected challenges and intriguing opportunities Enhancing retrieval-augmented large language models with iterative retrieval-generation synergy,

Reference 75

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Observation 83cf8fc8-0110-426a-9f1f-909c20297ae8 · outbound

This paper cites Active retrieval augmented generation,.

Deploying AI for Signal Processing education: Selected challenges and intriguing opportunities Active retrieval augmented generation,

Reference 76

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Observation d38975e6-fa73-4ca2-9a67-30236be56c10 · outbound

This paper cites LLMs Will Always Hallucinate, and We Need to Live With This.

Deploying AI for Signal Processing education: Selected challenges and intriguing opportunities LLMs Will Always Hallucinate, and We Need to Live With This

Reference 77

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Observation 24817516-c4d9-4e8c-b1de-4dedf550e8ac · outbound

This paper cites Trusting your evidence: Hallucinate less with context-aware decoding,.

Deploying AI for Signal Processing education: Selected challenges and intriguing opportunities Trusting your evidence: Hallucinate less with context-aware decoding,

Reference 78

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Observation 9622ab6d-a5af-41ea-a760-bf81bfac22eb · outbound

This paper cites RARR: Researching and revising what language models say, using language models,.

Deploying AI for Signal Processing education: Selected challenges and intriguing opportunities RARR: Researching and revising what language models say, using language models,

Reference 79

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Observation 3a06ed0e-2ee0-412e-ade3-a1e0af3d1ee8 · outbound

This paper cites Chain of Natural Language Inference for Reducing Large Language Model Ungrounded Hallucinations.

Deploying AI for Signal Processing education: Selected challenges and intriguing opportunities Chain of Natural Language Inference for Reducing Large Language Model Ungrounded Hallucinations

Reference 80

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Observation 93c66ef7-b345-48d8-bda1-a489dc411533 · outbound

This paper cites an unresolved cited work.

Deploying AI for Signal Processing education: Selected challenges and intriguing opportunities Unresolved cited work

Reference 81

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Observation b886f166-0c36-4383-a0b1-ef242f053d18 · outbound

This paper cites Shortcutted commonsense: Data spuriousness in deep learning of commonsense reasoning,.

Deploying AI for Signal Processing education: Selected challenges and intriguing opportunities Shortcutted commonsense: Data spuriousness in deep learning of commonsense reasoning,

Reference 82

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Observation 90451158-3f6e-49c3-bf19-555c0ec825de · outbound

This paper cites SCOTT: Self-consistent chain-of-thought distillation,.

Deploying AI for Signal Processing education: Selected challenges and intriguing opportunities SCOTT: Self-consistent chain-of-thought distillation,

Reference 83

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Observation 78245064-7b0d-43cb-8b11-76680c1b5195 · outbound

This paper cites Making reasoning matter: Measuring and improving faithfulness of chain- of-thought reasoning,.

Deploying AI for Signal Processing education: Selected challenges and intriguing opportunities Making reasoning matter: Measuring and improving faithfulness of chain- of-thought reasoning,

Reference 84

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Observation 2ae65ba5-d4b4-494b-bf7d-19e8867c1b75 · outbound

This paper cites Faithful logical reasoning via symbolic chain-of-thought,.

Deploying AI for Signal Processing education: Selected challenges and intriguing opportunities Faithful logical reasoning via symbolic chain-of-thought,

Reference 85

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Observation bc2f0e4a-9629-41bb-964a-d6a28e6f4356 · outbound

This paper cites Efficient hyperparameter tuning for predicting student performance with Bayesian optimization,.

Deploying AI for Signal Processing education: Selected challenges and intriguing opportunities Efficient hyperparameter tuning for predicting student performance with Bayesian optimization,

Reference 86

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Observation 9ebcb68e-a040-4366-8277-dbc740a6b010 · outbound

This paper cites Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing,.

Deploying AI for Signal Processing education: Selected challenges and intriguing opportunities Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing,

Reference 87

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source=pdf_text observed=2026-08-04T19:59:17.467256Z digest=sha256:160e7f7b82de5af838ded695dcf84a64b27efd5e56d023bb9ff5e0d21c6e7cd7

Observation 2f67e690-0103-461f-bcf1-eca8462e1c6d · outbound

This paper cites Garnett,Bayesian Optimization.

Deploying AI for Signal Processing education: Selected challenges and intriguing opportunities Garnett,Bayesian Optimization

Reference 88

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source=pdf_text observed=2026-08-04T19:59:17.472036Z digest=sha256:4ff065c2ba07dde52e8dae4f6cc8add3f8ba6906e9cde081c732bc209eda3b2d

Observation 2af7b094-ce78-434c-be16-64f6bd589bc9 · outbound

This paper cites an unresolved cited work.

Deploying AI for Signal Processing education: Selected challenges and intriguing opportunities Unresolved cited work

Reference 89

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source=pdf_text observed=2026-08-04T19:59:17.476440Z digest=sha256:469306617374090833dc51241000aee483d7afc938468e68f67f88ef28453258

Observation 3f3dab64-f38e-4880-ac6c-fd1184db8acb · outbound

This paper cites InstructZero: Efficient Instruction Optimization for Black-Box Large Language Models.

Deploying AI for Signal Processing education: Selected challenges and intriguing opportunities InstructZero: Efficient Instruction Optimization for Black-Box Large Language Models

Reference 90

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source=pdf_text observed=2026-08-04T19:59:17.481108Z digest=sha256:9baa56a2f51d51682d672bfa863c9b64901bf284d85bf345b727654db298036f

Observation 105ce81b-3aa7-49d1-acf9-6d0c0e6c5e23 · outbound

This paper cites Use your INSTINCT: INSTruction optimization for LLMs usIng Neural bandits coupled with Transformers,.

Deploying AI for Signal Processing education: Selected challenges and intriguing opportunities Use your INSTINCT: INSTruction optimization for LLMs usIng Neural bandits coupled with Transformers,

Reference 91

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Observation ca43169d-8051-409d-a5af-19ee06280357 · outbound

This paper cites Bayesian optimization in a billion dimensions via random embeddings,.

Deploying AI for Signal Processing education: Selected challenges and intriguing opportunities Bayesian optimization in a billion dimensions via random embeddings,

Reference 92

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source=pdf_text observed=2026-08-04T19:59:17.491049Z digest=sha256:139e086ec41e7eef96e8f0435a634982efcd2b3c280d92c0f8301f8b3c348dfb

Observation 45bd652f-c2b3-4e1e-b4e3-2aa4278e5e83 · outbound

This paper cites Surrogate modeling for Bayesian optimization beyond a single Gaussian process,.

Deploying AI for Signal Processing education: Selected challenges and intriguing opportunities Surrogate modeling for Bayesian optimization beyond a single Gaussian process,

Reference 93

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source=pdf_text observed=2026-08-04T19:59:17.496569Z digest=sha256:81231f9800a08fcdaf50b493930b1187dcedac47085458ef6186ca0eb5808798

Observation 6363357b-d806-44f7-aeb5-c69ff2b79d80 · outbound

This paper cites Bayesian optimization with ensemble learning models and adaptive expected improvement,.

Deploying AI for Signal Processing education: Selected challenges and intriguing opportunities Bayesian optimization with ensemble learning models and adaptive expected improvement,

Reference 94

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source=pdf_text observed=2026-08-04T19:59:17.501344Z digest=sha256:a6595bce940636582bc9f411d0a03b7f7934e5aca5fa164f01b3c8851060a6b0

Observation 817e95e2-066b-46db-9f69-96c35017a432 · outbound

This paper cites Prompt Optimization with Human Feedback.

Deploying AI for Signal Processing education: Selected challenges and intriguing opportunities Prompt Optimization with Human Feedback

Reference 95

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source=pdf_text observed=2026-08-04T19:59:17.505827Z digest=sha256:ce94515f42023f6cdb5850b509d1bc14d02f228d1f281e506fbc061056082942

Observation e093d5fc-f875-4750-9f39-3f65e06dfdfe · outbound

This paper cites Signal processing with AI,.

Deploying AI for Signal Processing education: Selected challenges and intriguing opportunities Signal processing with AI,

Reference 96

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source=pdf_text observed=2026-08-04T19:59:17.511550Z digest=sha256:3e941e33f5e3cbd9e38dbd5fd72b64440393409bc8dcb2fe69a686d9b43a7150

Observation 430df0bd-6cbb-464a-90cb-a0db78ca7e47 · outbound

This paper cites Five levels of intelligent textbooks,.

Deploying AI for Signal Processing education: Selected challenges and intriguing opportunities Five levels of intelligent textbooks,

Reference 97

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source=pdf_text observed=2026-08-04T19:59:17.516012Z digest=sha256:3e523e3dca9e5184b9e7da3d0887d052497f20bd6052a8829f5680b20e954d07

Observation b6c1bd83-5fb6-485b-801e-8053c56a7a70 · outbound

This paper cites The pagerank citation ranking: Bringing order to the web.

Deploying AI for Signal Processing education: Selected challenges and intriguing opportunities The pagerank citation ranking: Bringing order to the web

Reference 98

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source=pdf_text observed=2026-08-04T19:59:17.520796Z digest=sha256:6ee8b2c46f0d4dff37eed20ba9bddfd8b4d58fb5f0c1035529437e2dab09a5f7

Pith citing papers

No inbound Pith citation observations are available.