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|---|---|---|---|---|---|---|---|---|---|
ARP-0000000 | Artificial Intelligence | robustness under distribution shift for artificial intelligence in real-world deployment | Can uncertainty-aware scoring improve robustness under distribution shift for artificial intelligence in real-world deployment without unacceptable trade-offs? | Existing evaluations may be narrow, benchmark-sensitive, or insufficiently representative of deployment conditions. | Construct a controlled benchmark and compare strong baselines against uncertainty-aware scoring; report both gains and failure cases. | accuracy; macro-F1; robustness | Beginner | synthetic_research_ideation_candidate | 90f51ca3edbc |
ARP-0000001 | Machine Learning | robustness under distribution shift for machine learning in real-world deployment | Can uncertainty-aware scoring improve robustness under distribution shift for machine learning in real-world deployment without unacceptable trade-offs? | Existing evaluations may be narrow, benchmark-sensitive, or insufficiently representative of deployment conditions. | Construct a controlled benchmark and compare strong baselines against uncertainty-aware scoring; report both gains and failure cases. | AUROC; detection delay; false alarm rate | Intermediate | synthetic_research_ideation_candidate | 91ca8f4666e7 |
ARP-0000002 | Deep Learning | robustness under distribution shift for deep learning in real-world deployment | Can uncertainty-aware scoring improve robustness under distribution shift for deep learning in real-world deployment without unacceptable trade-offs? | Existing evaluations may be narrow, benchmark-sensitive, or insufficiently representative of deployment conditions. | Construct a controlled benchmark and compare strong baselines against uncertainty-aware scoring; report both gains and failure cases. | calibration error; selective risk; coverage | Advanced | synthetic_research_ideation_candidate | 840de97c18e1 |
ARP-0000003 | Natural Language Processing | robustness under distribution shift for natural language processing in real-world deployment | Can uncertainty-aware scoring improve robustness under distribution shift for natural language processing in real-world deployment without unacceptable trade-offs? | Existing evaluations may be narrow, benchmark-sensitive, or insufficiently representative of deployment conditions. | Construct a controlled benchmark and compare strong baselines against uncertainty-aware scoring; report both gains and failure cases. | task success; latency; compute cost | Beginner | synthetic_research_ideation_candidate | 47db942d5102 |
ARP-0000004 | Large Language Models | robustness under distribution shift for large language models in real-world deployment | Can uncertainty-aware scoring improve robustness under distribution shift for large language models in real-world deployment without unacceptable trade-offs? | Existing evaluations may be narrow, benchmark-sensitive, or insufficiently representative of deployment conditions. | Construct a controlled benchmark and compare strong baselines against uncertainty-aware scoring; report both gains and failure cases. | privacy attack AUC; utility loss | Intermediate | synthetic_research_ideation_candidate | 78948fc9e2b9 |
ARP-0000005 | Retrieval Augmented Generation | robustness under distribution shift for retrieval augmented generation in real-world deployment | Can uncertainty-aware scoring improve robustness under distribution shift for retrieval augmented generation in real-world deployment without unacceptable trade-offs? | Existing evaluations may be narrow, benchmark-sensitive, or insufficiently representative of deployment conditions. | Construct a controlled benchmark and compare strong baselines against uncertainty-aware scoring; report both gains and failure cases. | worst-group performance; disparity; calibration | Advanced | synthetic_research_ideation_candidate | acaa95c37563 |
ARP-0000006 | Information Retrieval | robustness under distribution shift for information retrieval in real-world deployment | Can uncertainty-aware scoring improve robustness under distribution shift for information retrieval in real-world deployment without unacceptable trade-offs? | Existing evaluations may be narrow, benchmark-sensitive, or insufficiently representative of deployment conditions. | Construct a controlled benchmark and compare strong baselines against uncertainty-aware scoring; report both gains and failure cases. | human agreement; explanation fidelity | Beginner | synthetic_research_ideation_candidate | 337cd6433985 |
ARP-0000007 | AI Agents | robustness under distribution shift for ai agents in real-world deployment | Can uncertainty-aware scoring improve robustness under distribution shift for ai agents in real-world deployment without unacceptable trade-offs? | Existing evaluations may be narrow, benchmark-sensitive, or insufficiently representative of deployment conditions. | Construct a controlled benchmark and compare strong baselines against uncertainty-aware scoring; report both gains and failure cases. | recall@k; answer correctness; retrieval cost | Intermediate | synthetic_research_ideation_candidate | ac001b8ae1a2 |
ARP-0000008 | MCP | robustness under distribution shift for mcp in real-world deployment | Can uncertainty-aware scoring improve robustness under distribution shift for mcp in real-world deployment without unacceptable trade-offs? | Existing evaluations may be narrow, benchmark-sensitive, or insufficiently representative of deployment conditions. | Construct a controlled benchmark and compare strong baselines against uncertainty-aware scoring; report both gains and failure cases. | energy per request; peak memory; throughput | Advanced | synthetic_research_ideation_candidate | 541b4924b101 |
ARP-0000009 | Computer Vision | robustness under distribution shift for computer vision in real-world deployment | Can uncertainty-aware scoring improve robustness under distribution shift for computer vision in real-world deployment without unacceptable trade-offs? | Existing evaluations may be narrow, benchmark-sensitive, or insufficiently representative of deployment conditions. | Construct a controlled benchmark and compare strong baselines against uncertainty-aware scoring; report both gains and failure cases. | failure rate; recovery time; abstention quality | Beginner | synthetic_research_ideation_candidate | 83ff035fdbe4 |
ARP-0000010 | Speech AI | robustness under distribution shift for speech ai in real-world deployment | Can uncertainty-aware scoring improve robustness under distribution shift for speech ai in real-world deployment without unacceptable trade-offs? | Existing evaluations may be narrow, benchmark-sensitive, or insufficiently representative of deployment conditions. | Construct a controlled benchmark and compare strong baselines against uncertainty-aware scoring; report both gains and failure cases. | accuracy; macro-F1; robustness | Intermediate | synthetic_research_ideation_candidate | aa4d566b607b |
ARP-0000011 | Multimodal AI | robustness under distribution shift for multimodal ai in real-world deployment | Can uncertainty-aware scoring improve robustness under distribution shift for multimodal ai in real-world deployment without unacceptable trade-offs? | Existing evaluations may be narrow, benchmark-sensitive, or insufficiently representative of deployment conditions. | Construct a controlled benchmark and compare strong baselines against uncertainty-aware scoring; report both gains and failure cases. | AUROC; detection delay; false alarm rate | Advanced | synthetic_research_ideation_candidate | f8d81a0b2f30 |
ARP-0000012 | Reinforcement Learning | robustness under distribution shift for reinforcement learning in real-world deployment | Can uncertainty-aware scoring improve robustness under distribution shift for reinforcement learning in real-world deployment without unacceptable trade-offs? | Existing evaluations may be narrow, benchmark-sensitive, or insufficiently representative of deployment conditions. | Construct a controlled benchmark and compare strong baselines against uncertainty-aware scoring; report both gains and failure cases. | calibration error; selective risk; coverage | Beginner | synthetic_research_ideation_candidate | 539a509f5a89 |
ARP-0000013 | Federated Learning | robustness under distribution shift for federated learning in real-world deployment | Can uncertainty-aware scoring improve robustness under distribution shift for federated learning in real-world deployment without unacceptable trade-offs? | Existing evaluations may be narrow, benchmark-sensitive, or insufficiently representative of deployment conditions. | Construct a controlled benchmark and compare strong baselines against uncertainty-aware scoring; report both gains and failure cases. | task success; latency; compute cost | Intermediate | synthetic_research_ideation_candidate | d208a953aef3 |
ARP-0000014 | Privacy-Preserving ML | robustness under distribution shift for privacy-preserving ml in real-world deployment | Can uncertainty-aware scoring improve robustness under distribution shift for privacy-preserving ml in real-world deployment without unacceptable trade-offs? | Existing evaluations may be narrow, benchmark-sensitive, or insufficiently representative of deployment conditions. | Construct a controlled benchmark and compare strong baselines against uncertainty-aware scoring; report both gains and failure cases. | privacy attack AUC; utility loss | Advanced | synthetic_research_ideation_candidate | 40254b423d36 |
ARP-0000015 | AI Safety | robustness under distribution shift for ai safety in real-world deployment | Can uncertainty-aware scoring improve robustness under distribution shift for ai safety in real-world deployment without unacceptable trade-offs? | Existing evaluations may be narrow, benchmark-sensitive, or insufficiently representative of deployment conditions. | Construct a controlled benchmark and compare strong baselines against uncertainty-aware scoring; report both gains and failure cases. | worst-group performance; disparity; calibration | Beginner | synthetic_research_ideation_candidate | d5498bd1f4e7 |
ARP-0000016 | Responsible AI | robustness under distribution shift for responsible ai in real-world deployment | Can uncertainty-aware scoring improve robustness under distribution shift for responsible ai in real-world deployment without unacceptable trade-offs? | Existing evaluations may be narrow, benchmark-sensitive, or insufficiently representative of deployment conditions. | Construct a controlled benchmark and compare strong baselines against uncertainty-aware scoring; report both gains and failure cases. | human agreement; explanation fidelity | Intermediate | synthetic_research_ideation_candidate | e80a25514355 |
ARP-0000017 | MLOps | robustness under distribution shift for mlops in real-world deployment | Can uncertainty-aware scoring improve robustness under distribution shift for mlops in real-world deployment without unacceptable trade-offs? | Existing evaluations may be narrow, benchmark-sensitive, or insufficiently representative of deployment conditions. | Construct a controlled benchmark and compare strong baselines against uncertainty-aware scoring; report both gains and failure cases. | recall@k; answer correctness; retrieval cost | Advanced | synthetic_research_ideation_candidate | 4f775b06b711 |
ARP-0000018 | Edge AI | robustness under distribution shift for edge ai in real-world deployment | Can uncertainty-aware scoring improve robustness under distribution shift for edge ai in real-world deployment without unacceptable trade-offs? | Existing evaluations may be narrow, benchmark-sensitive, or insufficiently representative of deployment conditions. | Construct a controlled benchmark and compare strong baselines against uncertainty-aware scoring; report both gains and failure cases. | energy per request; peak memory; throughput | Beginner | synthetic_research_ideation_candidate | a9104de2e172 |
ARP-0000019 | Robotics | robustness under distribution shift for robotics in real-world deployment | Can uncertainty-aware scoring improve robustness under distribution shift for robotics in real-world deployment without unacceptable trade-offs? | Existing evaluations may be narrow, benchmark-sensitive, or insufficiently representative of deployment conditions. | Construct a controlled benchmark and compare strong baselines against uncertainty-aware scoring; report both gains and failure cases. | failure rate; recovery time; abstention quality | Intermediate | synthetic_research_ideation_candidate | 35bb83b8bc80 |
ARP-0000020 | Medical AI | robustness under distribution shift for medical ai in real-world deployment | Can uncertainty-aware scoring improve robustness under distribution shift for medical ai in real-world deployment without unacceptable trade-offs? | Existing evaluations may be narrow, benchmark-sensitive, or insufficiently representative of deployment conditions. | Construct a controlled benchmark and compare strong baselines against uncertainty-aware scoring; report both gains and failure cases. | accuracy; macro-F1; robustness | Advanced | synthetic_research_ideation_candidate | bb8666a226be |
ARP-0000021 | Education AI | robustness under distribution shift for education ai in real-world deployment | Can uncertainty-aware scoring improve robustness under distribution shift for education ai in real-world deployment without unacceptable trade-offs? | Existing evaluations may be narrow, benchmark-sensitive, or insufficiently representative of deployment conditions. | Construct a controlled benchmark and compare strong baselines against uncertainty-aware scoring; report both gains and failure cases. | AUROC; detection delay; false alarm rate | Beginner | synthetic_research_ideation_candidate | 08ea49ff01e8 |
ARP-0000022 | Cybersecurity AI | robustness under distribution shift for cybersecurity ai in real-world deployment | Can uncertainty-aware scoring improve robustness under distribution shift for cybersecurity ai in real-world deployment without unacceptable trade-offs? | Existing evaluations may be narrow, benchmark-sensitive, or insufficiently representative of deployment conditions. | Construct a controlled benchmark and compare strong baselines against uncertainty-aware scoring; report both gains and failure cases. | calibration error; selective risk; coverage | Intermediate | synthetic_research_ideation_candidate | 92360dade914 |
ARP-0000023 | Recommender Systems | robustness under distribution shift for recommender systems in real-world deployment | Can uncertainty-aware scoring improve robustness under distribution shift for recommender systems in real-world deployment without unacceptable trade-offs? | Existing evaluations may be narrow, benchmark-sensitive, or insufficiently representative of deployment conditions. | Construct a controlled benchmark and compare strong baselines against uncertainty-aware scoring; report both gains and failure cases. | task success; latency; compute cost | Advanced | synthetic_research_ideation_candidate | 8fe5feb00126 |
ARP-0000024 | Graph Machine Learning | robustness under distribution shift for graph machine learning in real-world deployment | Can uncertainty-aware scoring improve robustness under distribution shift for graph machine learning in real-world deployment without unacceptable trade-offs? | Existing evaluations may be narrow, benchmark-sensitive, or insufficiently representative of deployment conditions. | Construct a controlled benchmark and compare strong baselines against uncertainty-aware scoring; report both gains and failure cases. | privacy attack AUC; utility loss | Beginner | synthetic_research_ideation_candidate | d7a5a6cf551c |
ARP-0000025 | Time Series | robustness under distribution shift for time series in real-world deployment | Can uncertainty-aware scoring improve robustness under distribution shift for time series in real-world deployment without unacceptable trade-offs? | Existing evaluations may be narrow, benchmark-sensitive, or insufficiently representative of deployment conditions. | Construct a controlled benchmark and compare strong baselines against uncertainty-aware scoring; report both gains and failure cases. | worst-group performance; disparity; calibration | Intermediate | synthetic_research_ideation_candidate | d42266bafb05 |
ARP-0000026 | Anomaly Detection | robustness under distribution shift for anomaly detection in real-world deployment | Can uncertainty-aware scoring improve robustness under distribution shift for anomaly detection in real-world deployment without unacceptable trade-offs? | Existing evaluations may be narrow, benchmark-sensitive, or insufficiently representative of deployment conditions. | Construct a controlled benchmark and compare strong baselines against uncertainty-aware scoring; report both gains and failure cases. | human agreement; explanation fidelity | Advanced | synthetic_research_ideation_candidate | 6fca02574d46 |
ARP-0000027 | Causal ML | robustness under distribution shift for causal ml in real-world deployment | Can uncertainty-aware scoring improve robustness under distribution shift for causal ml in real-world deployment without unacceptable trade-offs? | Existing evaluations may be narrow, benchmark-sensitive, or insufficiently representative of deployment conditions. | Construct a controlled benchmark and compare strong baselines against uncertainty-aware scoring; report both gains and failure cases. | recall@k; answer correctness; retrieval cost | Beginner | synthetic_research_ideation_candidate | 4fc622a798d9 |
ARP-0000028 | Explainable AI | robustness under distribution shift for explainable ai in real-world deployment | Can uncertainty-aware scoring improve robustness under distribution shift for explainable ai in real-world deployment without unacceptable trade-offs? | Existing evaluations may be narrow, benchmark-sensitive, or insufficiently representative of deployment conditions. | Construct a controlled benchmark and compare strong baselines against uncertainty-aware scoring; report both gains and failure cases. | energy per request; peak memory; throughput | Intermediate | synthetic_research_ideation_candidate | 0f44436a5df1 |
ARP-0000029 | Generative AI | robustness under distribution shift for generative ai in real-world deployment | Can uncertainty-aware scoring improve robustness under distribution shift for generative ai in real-world deployment without unacceptable trade-offs? | Existing evaluations may be narrow, benchmark-sensitive, or insufficiently representative of deployment conditions. | Construct a controlled benchmark and compare strong baselines against uncertainty-aware scoring; report both gains and failure cases. | failure rate; recovery time; abstention quality | Advanced | synthetic_research_ideation_candidate | 9e4de58865ee |
ARP-0000030 | Knowledge Graphs | robustness under distribution shift for knowledge graphs in real-world deployment | Can uncertainty-aware scoring improve robustness under distribution shift for knowledge graphs in real-world deployment without unacceptable trade-offs? | Existing evaluations may be narrow, benchmark-sensitive, or insufficiently representative of deployment conditions. | Construct a controlled benchmark and compare strong baselines against uncertainty-aware scoring; report both gains and failure cases. | accuracy; macro-F1; robustness | Beginner | synthetic_research_ideation_candidate | 2da32fd14ce5 |
ARP-0000031 | Database ML | robustness under distribution shift for database ml in real-world deployment | Can uncertainty-aware scoring improve robustness under distribution shift for database ml in real-world deployment without unacceptable trade-offs? | Existing evaluations may be narrow, benchmark-sensitive, or insufficiently representative of deployment conditions. | Construct a controlled benchmark and compare strong baselines against uncertainty-aware scoring; report both gains and failure cases. | AUROC; detection delay; false alarm rate | Intermediate | synthetic_research_ideation_candidate | be41e66e2073 |
ARP-0000032 | Distributed AI | robustness under distribution shift for distributed ai in real-world deployment | Can uncertainty-aware scoring improve robustness under distribution shift for distributed ai in real-world deployment without unacceptable trade-offs? | Existing evaluations may be narrow, benchmark-sensitive, or insufficiently representative of deployment conditions. | Construct a controlled benchmark and compare strong baselines against uncertainty-aware scoring; report both gains and failure cases. | calibration error; selective risk; coverage | Advanced | synthetic_research_ideation_candidate | 30fe414acf7f |
ARP-0000033 | Optimization | robustness under distribution shift for optimization in real-world deployment | Can uncertainty-aware scoring improve robustness under distribution shift for optimization in real-world deployment without unacceptable trade-offs? | Existing evaluations may be narrow, benchmark-sensitive, or insufficiently representative of deployment conditions. | Construct a controlled benchmark and compare strong baselines against uncertainty-aware scoring; report both gains and failure cases. | task success; latency; compute cost | Beginner | synthetic_research_ideation_candidate | 0c274cd10b35 |
ARP-0000034 | Artificial Intelligence | uncertainty calibration for artificial intelligence in real-world deployment | Can uncertainty-aware scoring improve uncertainty calibration for artificial intelligence in real-world deployment without unacceptable trade-offs? | Existing evaluations may be narrow, benchmark-sensitive, or insufficiently representative of deployment conditions. | Construct a controlled benchmark and compare strong baselines against uncertainty-aware scoring; report both gains and failure cases. | privacy attack AUC; utility loss | Intermediate | synthetic_research_ideation_candidate | 671cffdf5634 |
ARP-0000035 | Machine Learning | uncertainty calibration for machine learning in real-world deployment | Can uncertainty-aware scoring improve uncertainty calibration for machine learning in real-world deployment without unacceptable trade-offs? | Existing evaluations may be narrow, benchmark-sensitive, or insufficiently representative of deployment conditions. | Construct a controlled benchmark and compare strong baselines against uncertainty-aware scoring; report both gains and failure cases. | worst-group performance; disparity; calibration | Advanced | synthetic_research_ideation_candidate | 73230f7ec0e6 |
ARP-0000036 | Deep Learning | uncertainty calibration for deep learning in real-world deployment | Can uncertainty-aware scoring improve uncertainty calibration for deep learning in real-world deployment without unacceptable trade-offs? | Existing evaluations may be narrow, benchmark-sensitive, or insufficiently representative of deployment conditions. | Construct a controlled benchmark and compare strong baselines against uncertainty-aware scoring; report both gains and failure cases. | human agreement; explanation fidelity | Beginner | synthetic_research_ideation_candidate | ae4fc1155c11 |
ARP-0000037 | Natural Language Processing | uncertainty calibration for natural language processing in real-world deployment | Can uncertainty-aware scoring improve uncertainty calibration for natural language processing in real-world deployment without unacceptable trade-offs? | Existing evaluations may be narrow, benchmark-sensitive, or insufficiently representative of deployment conditions. | Construct a controlled benchmark and compare strong baselines against uncertainty-aware scoring; report both gains and failure cases. | recall@k; answer correctness; retrieval cost | Intermediate | synthetic_research_ideation_candidate | 6b8d7585ae75 |
ARP-0000038 | Large Language Models | uncertainty calibration for large language models in real-world deployment | Can uncertainty-aware scoring improve uncertainty calibration for large language models in real-world deployment without unacceptable trade-offs? | Existing evaluations may be narrow, benchmark-sensitive, or insufficiently representative of deployment conditions. | Construct a controlled benchmark and compare strong baselines against uncertainty-aware scoring; report both gains and failure cases. | energy per request; peak memory; throughput | Advanced | synthetic_research_ideation_candidate | 2ec6b24c582a |
ARP-0000039 | Retrieval Augmented Generation | uncertainty calibration for retrieval augmented generation in real-world deployment | Can uncertainty-aware scoring improve uncertainty calibration for retrieval augmented generation in real-world deployment without unacceptable trade-offs? | Existing evaluations may be narrow, benchmark-sensitive, or insufficiently representative of deployment conditions. | Construct a controlled benchmark and compare strong baselines against uncertainty-aware scoring; report both gains and failure cases. | failure rate; recovery time; abstention quality | Beginner | synthetic_research_ideation_candidate | 30bcdf10018a |
ARP-0000040 | Information Retrieval | uncertainty calibration for information retrieval in real-world deployment | Can uncertainty-aware scoring improve uncertainty calibration for information retrieval in real-world deployment without unacceptable trade-offs? | Existing evaluations may be narrow, benchmark-sensitive, or insufficiently representative of deployment conditions. | Construct a controlled benchmark and compare strong baselines against uncertainty-aware scoring; report both gains and failure cases. | accuracy; macro-F1; robustness | Intermediate | synthetic_research_ideation_candidate | f0ed7a0116fe |
ARP-0000041 | AI Agents | uncertainty calibration for ai agents in real-world deployment | Can uncertainty-aware scoring improve uncertainty calibration for ai agents in real-world deployment without unacceptable trade-offs? | Existing evaluations may be narrow, benchmark-sensitive, or insufficiently representative of deployment conditions. | Construct a controlled benchmark and compare strong baselines against uncertainty-aware scoring; report both gains and failure cases. | AUROC; detection delay; false alarm rate | Advanced | synthetic_research_ideation_candidate | f004a32afae8 |
ARP-0000042 | MCP | uncertainty calibration for mcp in real-world deployment | Can uncertainty-aware scoring improve uncertainty calibration for mcp in real-world deployment without unacceptable trade-offs? | Existing evaluations may be narrow, benchmark-sensitive, or insufficiently representative of deployment conditions. | Construct a controlled benchmark and compare strong baselines against uncertainty-aware scoring; report both gains and failure cases. | calibration error; selective risk; coverage | Beginner | synthetic_research_ideation_candidate | dd2651e6530b |
ARP-0000043 | Computer Vision | uncertainty calibration for computer vision in real-world deployment | Can uncertainty-aware scoring improve uncertainty calibration for computer vision in real-world deployment without unacceptable trade-offs? | Existing evaluations may be narrow, benchmark-sensitive, or insufficiently representative of deployment conditions. | Construct a controlled benchmark and compare strong baselines against uncertainty-aware scoring; report both gains and failure cases. | task success; latency; compute cost | Intermediate | synthetic_research_ideation_candidate | cd9aa1f679e8 |
ARP-0000044 | Speech AI | uncertainty calibration for speech ai in real-world deployment | Can uncertainty-aware scoring improve uncertainty calibration for speech ai in real-world deployment without unacceptable trade-offs? | Existing evaluations may be narrow, benchmark-sensitive, or insufficiently representative of deployment conditions. | Construct a controlled benchmark and compare strong baselines against uncertainty-aware scoring; report both gains and failure cases. | privacy attack AUC; utility loss | Advanced | synthetic_research_ideation_candidate | 8157c42e55b6 |
ARP-0000045 | Multimodal AI | uncertainty calibration for multimodal ai in real-world deployment | Can uncertainty-aware scoring improve uncertainty calibration for multimodal ai in real-world deployment without unacceptable trade-offs? | Existing evaluations may be narrow, benchmark-sensitive, or insufficiently representative of deployment conditions. | Construct a controlled benchmark and compare strong baselines against uncertainty-aware scoring; report both gains and failure cases. | worst-group performance; disparity; calibration | Beginner | synthetic_research_ideation_candidate | 66408f9c7695 |
ARP-0000046 | Reinforcement Learning | uncertainty calibration for reinforcement learning in real-world deployment | Can uncertainty-aware scoring improve uncertainty calibration for reinforcement learning in real-world deployment without unacceptable trade-offs? | Existing evaluations may be narrow, benchmark-sensitive, or insufficiently representative of deployment conditions. | Construct a controlled benchmark and compare strong baselines against uncertainty-aware scoring; report both gains and failure cases. | human agreement; explanation fidelity | Intermediate | synthetic_research_ideation_candidate | 958063dccf2c |
ARP-0000047 | Federated Learning | uncertainty calibration for federated learning in real-world deployment | Can uncertainty-aware scoring improve uncertainty calibration for federated learning in real-world deployment without unacceptable trade-offs? | Existing evaluations may be narrow, benchmark-sensitive, or insufficiently representative of deployment conditions. | Construct a controlled benchmark and compare strong baselines against uncertainty-aware scoring; report both gains and failure cases. | recall@k; answer correctness; retrieval cost | Advanced | synthetic_research_ideation_candidate | a75378a8da84 |
ARP-0000048 | Privacy-Preserving ML | uncertainty calibration for privacy-preserving ml in real-world deployment | Can uncertainty-aware scoring improve uncertainty calibration for privacy-preserving ml in real-world deployment without unacceptable trade-offs? | Existing evaluations may be narrow, benchmark-sensitive, or insufficiently representative of deployment conditions. | Construct a controlled benchmark and compare strong baselines against uncertainty-aware scoring; report both gains and failure cases. | energy per request; peak memory; throughput | Beginner | synthetic_research_ideation_candidate | 031461638025 |
ARP-0000049 | AI Safety | uncertainty calibration for ai safety in real-world deployment | Can uncertainty-aware scoring improve uncertainty calibration for ai safety in real-world deployment without unacceptable trade-offs? | Existing evaluations may be narrow, benchmark-sensitive, or insufficiently representative of deployment conditions. | Construct a controlled benchmark and compare strong baselines against uncertainty-aware scoring; report both gains and failure cases. | failure rate; recovery time; abstention quality | Intermediate | synthetic_research_ideation_candidate | 265ce1c179f8 |
ARP-0000050 | Responsible AI | uncertainty calibration for responsible ai in real-world deployment | Can uncertainty-aware scoring improve uncertainty calibration for responsible ai in real-world deployment without unacceptable trade-offs? | Existing evaluations may be narrow, benchmark-sensitive, or insufficiently representative of deployment conditions. | Construct a controlled benchmark and compare strong baselines against uncertainty-aware scoring; report both gains and failure cases. | accuracy; macro-F1; robustness | Advanced | synthetic_research_ideation_candidate | 536ef45f287f |
ARP-0000051 | MLOps | uncertainty calibration for mlops in real-world deployment | Can uncertainty-aware scoring improve uncertainty calibration for mlops in real-world deployment without unacceptable trade-offs? | Existing evaluations may be narrow, benchmark-sensitive, or insufficiently representative of deployment conditions. | Construct a controlled benchmark and compare strong baselines against uncertainty-aware scoring; report both gains and failure cases. | AUROC; detection delay; false alarm rate | Beginner | synthetic_research_ideation_candidate | db64cb54b2b1 |
ARP-0000052 | Edge AI | uncertainty calibration for edge ai in real-world deployment | Can uncertainty-aware scoring improve uncertainty calibration for edge ai in real-world deployment without unacceptable trade-offs? | Existing evaluations may be narrow, benchmark-sensitive, or insufficiently representative of deployment conditions. | Construct a controlled benchmark and compare strong baselines against uncertainty-aware scoring; report both gains and failure cases. | calibration error; selective risk; coverage | Intermediate | synthetic_research_ideation_candidate | a24a1e22dbc9 |
ARP-0000053 | Robotics | uncertainty calibration for robotics in real-world deployment | Can uncertainty-aware scoring improve uncertainty calibration for robotics in real-world deployment without unacceptable trade-offs? | Existing evaluations may be narrow, benchmark-sensitive, or insufficiently representative of deployment conditions. | Construct a controlled benchmark and compare strong baselines against uncertainty-aware scoring; report both gains and failure cases. | task success; latency; compute cost | Advanced | synthetic_research_ideation_candidate | cee369479869 |
ARP-0000054 | Medical AI | uncertainty calibration for medical ai in real-world deployment | Can uncertainty-aware scoring improve uncertainty calibration for medical ai in real-world deployment without unacceptable trade-offs? | Existing evaluations may be narrow, benchmark-sensitive, or insufficiently representative of deployment conditions. | Construct a controlled benchmark and compare strong baselines against uncertainty-aware scoring; report both gains and failure cases. | privacy attack AUC; utility loss | Beginner | synthetic_research_ideation_candidate | d8a962a9e46f |
ARP-0000055 | Education AI | uncertainty calibration for education ai in real-world deployment | Can uncertainty-aware scoring improve uncertainty calibration for education ai in real-world deployment without unacceptable trade-offs? | Existing evaluations may be narrow, benchmark-sensitive, or insufficiently representative of deployment conditions. | Construct a controlled benchmark and compare strong baselines against uncertainty-aware scoring; report both gains and failure cases. | worst-group performance; disparity; calibration | Intermediate | synthetic_research_ideation_candidate | 7a9eac559bb0 |
ARP-0000056 | Cybersecurity AI | uncertainty calibration for cybersecurity ai in real-world deployment | Can uncertainty-aware scoring improve uncertainty calibration for cybersecurity ai in real-world deployment without unacceptable trade-offs? | Existing evaluations may be narrow, benchmark-sensitive, or insufficiently representative of deployment conditions. | Construct a controlled benchmark and compare strong baselines against uncertainty-aware scoring; report both gains and failure cases. | human agreement; explanation fidelity | Advanced | synthetic_research_ideation_candidate | fcfe1d49a410 |
ARP-0000057 | Recommender Systems | uncertainty calibration for recommender systems in real-world deployment | Can uncertainty-aware scoring improve uncertainty calibration for recommender systems in real-world deployment without unacceptable trade-offs? | Existing evaluations may be narrow, benchmark-sensitive, or insufficiently representative of deployment conditions. | Construct a controlled benchmark and compare strong baselines against uncertainty-aware scoring; report both gains and failure cases. | recall@k; answer correctness; retrieval cost | Beginner | synthetic_research_ideation_candidate | 563f3e765c05 |
ARP-0000058 | Graph Machine Learning | uncertainty calibration for graph machine learning in real-world deployment | Can uncertainty-aware scoring improve uncertainty calibration for graph machine learning in real-world deployment without unacceptable trade-offs? | Existing evaluations may be narrow, benchmark-sensitive, or insufficiently representative of deployment conditions. | Construct a controlled benchmark and compare strong baselines against uncertainty-aware scoring; report both gains and failure cases. | energy per request; peak memory; throughput | Intermediate | synthetic_research_ideation_candidate | a9d19b0597bb |
ARP-0000059 | Time Series | uncertainty calibration for time series in real-world deployment | Can uncertainty-aware scoring improve uncertainty calibration for time series in real-world deployment without unacceptable trade-offs? | Existing evaluations may be narrow, benchmark-sensitive, or insufficiently representative of deployment conditions. | Construct a controlled benchmark and compare strong baselines against uncertainty-aware scoring; report both gains and failure cases. | failure rate; recovery time; abstention quality | Advanced | synthetic_research_ideation_candidate | c4262597598b |
ARP-0000060 | Anomaly Detection | uncertainty calibration for anomaly detection in real-world deployment | Can uncertainty-aware scoring improve uncertainty calibration for anomaly detection in real-world deployment without unacceptable trade-offs? | Existing evaluations may be narrow, benchmark-sensitive, or insufficiently representative of deployment conditions. | Construct a controlled benchmark and compare strong baselines against uncertainty-aware scoring; report both gains and failure cases. | accuracy; macro-F1; robustness | Beginner | synthetic_research_ideation_candidate | 8f5f4b23f7b1 |
ARP-0000061 | Causal ML | uncertainty calibration for causal ml in real-world deployment | Can uncertainty-aware scoring improve uncertainty calibration for causal ml in real-world deployment without unacceptable trade-offs? | Existing evaluations may be narrow, benchmark-sensitive, or insufficiently representative of deployment conditions. | Construct a controlled benchmark and compare strong baselines against uncertainty-aware scoring; report both gains and failure cases. | AUROC; detection delay; false alarm rate | Intermediate | synthetic_research_ideation_candidate | 63aa3c7dd9df |
ARP-0000062 | Explainable AI | uncertainty calibration for explainable ai in real-world deployment | Can uncertainty-aware scoring improve uncertainty calibration for explainable ai in real-world deployment without unacceptable trade-offs? | Existing evaluations may be narrow, benchmark-sensitive, or insufficiently representative of deployment conditions. | Construct a controlled benchmark and compare strong baselines against uncertainty-aware scoring; report both gains and failure cases. | calibration error; selective risk; coverage | Advanced | synthetic_research_ideation_candidate | d4c166039648 |
ARP-0000063 | Generative AI | uncertainty calibration for generative ai in real-world deployment | Can uncertainty-aware scoring improve uncertainty calibration for generative ai in real-world deployment without unacceptable trade-offs? | Existing evaluations may be narrow, benchmark-sensitive, or insufficiently representative of deployment conditions. | Construct a controlled benchmark and compare strong baselines against uncertainty-aware scoring; report both gains and failure cases. | task success; latency; compute cost | Beginner | synthetic_research_ideation_candidate | 50ffa4344cee |
ARP-0000064 | Knowledge Graphs | uncertainty calibration for knowledge graphs in real-world deployment | Can uncertainty-aware scoring improve uncertainty calibration for knowledge graphs in real-world deployment without unacceptable trade-offs? | Existing evaluations may be narrow, benchmark-sensitive, or insufficiently representative of deployment conditions. | Construct a controlled benchmark and compare strong baselines against uncertainty-aware scoring; report both gains and failure cases. | privacy attack AUC; utility loss | Intermediate | synthetic_research_ideation_candidate | e9bd11e1a486 |
ARP-0000065 | Database ML | uncertainty calibration for database ml in real-world deployment | Can uncertainty-aware scoring improve uncertainty calibration for database ml in real-world deployment without unacceptable trade-offs? | Existing evaluations may be narrow, benchmark-sensitive, or insufficiently representative of deployment conditions. | Construct a controlled benchmark and compare strong baselines against uncertainty-aware scoring; report both gains and failure cases. | worst-group performance; disparity; calibration | Advanced | synthetic_research_ideation_candidate | 685ba109bb48 |
ARP-0000066 | Distributed AI | uncertainty calibration for distributed ai in real-world deployment | Can uncertainty-aware scoring improve uncertainty calibration for distributed ai in real-world deployment without unacceptable trade-offs? | Existing evaluations may be narrow, benchmark-sensitive, or insufficiently representative of deployment conditions. | Construct a controlled benchmark and compare strong baselines against uncertainty-aware scoring; report both gains and failure cases. | human agreement; explanation fidelity | Beginner | synthetic_research_ideation_candidate | 1ca77ef59d99 |
ARP-0000067 | Optimization | uncertainty calibration for optimization in real-world deployment | Can uncertainty-aware scoring improve uncertainty calibration for optimization in real-world deployment without unacceptable trade-offs? | Existing evaluations may be narrow, benchmark-sensitive, or insufficiently representative of deployment conditions. | Construct a controlled benchmark and compare strong baselines against uncertainty-aware scoring; report both gains and failure cases. | recall@k; answer correctness; retrieval cost | Intermediate | synthetic_research_ideation_candidate | 7c2bca5632cb |
ARP-0000068 | Artificial Intelligence | data quality assessment for artificial intelligence in real-world deployment | Can uncertainty-aware scoring improve data quality assessment for artificial intelligence in real-world deployment without unacceptable trade-offs? | Existing evaluations may be narrow, benchmark-sensitive, or insufficiently representative of deployment conditions. | Construct a controlled benchmark and compare strong baselines against uncertainty-aware scoring; report both gains and failure cases. | energy per request; peak memory; throughput | Advanced | synthetic_research_ideation_candidate | d52693a1fd8d |
ARP-0000069 | Machine Learning | data quality assessment for machine learning in real-world deployment | Can uncertainty-aware scoring improve data quality assessment for machine learning in real-world deployment without unacceptable trade-offs? | Existing evaluations may be narrow, benchmark-sensitive, or insufficiently representative of deployment conditions. | Construct a controlled benchmark and compare strong baselines against uncertainty-aware scoring; report both gains and failure cases. | failure rate; recovery time; abstention quality | Beginner | synthetic_research_ideation_candidate | 774bb6758816 |
ARP-0000070 | Deep Learning | data quality assessment for deep learning in real-world deployment | Can uncertainty-aware scoring improve data quality assessment for deep learning in real-world deployment without unacceptable trade-offs? | Existing evaluations may be narrow, benchmark-sensitive, or insufficiently representative of deployment conditions. | Construct a controlled benchmark and compare strong baselines against uncertainty-aware scoring; report both gains and failure cases. | accuracy; macro-F1; robustness | Intermediate | synthetic_research_ideation_candidate | ccb891d70fce |
ARP-0000071 | Natural Language Processing | data quality assessment for natural language processing in real-world deployment | Can uncertainty-aware scoring improve data quality assessment for natural language processing in real-world deployment without unacceptable trade-offs? | Existing evaluations may be narrow, benchmark-sensitive, or insufficiently representative of deployment conditions. | Construct a controlled benchmark and compare strong baselines against uncertainty-aware scoring; report both gains and failure cases. | AUROC; detection delay; false alarm rate | Advanced | synthetic_research_ideation_candidate | f4432d505583 |
ARP-0000072 | Large Language Models | data quality assessment for large language models in real-world deployment | Can uncertainty-aware scoring improve data quality assessment for large language models in real-world deployment without unacceptable trade-offs? | Existing evaluations may be narrow, benchmark-sensitive, or insufficiently representative of deployment conditions. | Construct a controlled benchmark and compare strong baselines against uncertainty-aware scoring; report both gains and failure cases. | calibration error; selective risk; coverage | Beginner | synthetic_research_ideation_candidate | c44d4ad1a734 |
ARP-0000073 | Retrieval Augmented Generation | data quality assessment for retrieval augmented generation in real-world deployment | Can uncertainty-aware scoring improve data quality assessment for retrieval augmented generation in real-world deployment without unacceptable trade-offs? | Existing evaluations may be narrow, benchmark-sensitive, or insufficiently representative of deployment conditions. | Construct a controlled benchmark and compare strong baselines against uncertainty-aware scoring; report both gains and failure cases. | task success; latency; compute cost | Intermediate | synthetic_research_ideation_candidate | 17aa73dedbcb |
ARP-0000074 | Information Retrieval | data quality assessment for information retrieval in real-world deployment | Can uncertainty-aware scoring improve data quality assessment for information retrieval in real-world deployment without unacceptable trade-offs? | Existing evaluations may be narrow, benchmark-sensitive, or insufficiently representative of deployment conditions. | Construct a controlled benchmark and compare strong baselines against uncertainty-aware scoring; report both gains and failure cases. | privacy attack AUC; utility loss | Advanced | synthetic_research_ideation_candidate | 7528603b3bda |
ARP-0000075 | AI Agents | data quality assessment for ai agents in real-world deployment | Can uncertainty-aware scoring improve data quality assessment for ai agents in real-world deployment without unacceptable trade-offs? | Existing evaluations may be narrow, benchmark-sensitive, or insufficiently representative of deployment conditions. | Construct a controlled benchmark and compare strong baselines against uncertainty-aware scoring; report both gains and failure cases. | worst-group performance; disparity; calibration | Beginner | synthetic_research_ideation_candidate | 5a2c8d77f436 |
ARP-0000076 | MCP | data quality assessment for mcp in real-world deployment | Can uncertainty-aware scoring improve data quality assessment for mcp in real-world deployment without unacceptable trade-offs? | Existing evaluations may be narrow, benchmark-sensitive, or insufficiently representative of deployment conditions. | Construct a controlled benchmark and compare strong baselines against uncertainty-aware scoring; report both gains and failure cases. | human agreement; explanation fidelity | Intermediate | synthetic_research_ideation_candidate | 5d4f38da7008 |
ARP-0000077 | Computer Vision | data quality assessment for computer vision in real-world deployment | Can uncertainty-aware scoring improve data quality assessment for computer vision in real-world deployment without unacceptable trade-offs? | Existing evaluations may be narrow, benchmark-sensitive, or insufficiently representative of deployment conditions. | Construct a controlled benchmark and compare strong baselines against uncertainty-aware scoring; report both gains and failure cases. | recall@k; answer correctness; retrieval cost | Advanced | synthetic_research_ideation_candidate | 49e6353298ec |
ARP-0000078 | Speech AI | data quality assessment for speech ai in real-world deployment | Can uncertainty-aware scoring improve data quality assessment for speech ai in real-world deployment without unacceptable trade-offs? | Existing evaluations may be narrow, benchmark-sensitive, or insufficiently representative of deployment conditions. | Construct a controlled benchmark and compare strong baselines against uncertainty-aware scoring; report both gains and failure cases. | energy per request; peak memory; throughput | Beginner | synthetic_research_ideation_candidate | d93b54d80ad1 |
ARP-0000079 | Multimodal AI | data quality assessment for multimodal ai in real-world deployment | Can uncertainty-aware scoring improve data quality assessment for multimodal ai in real-world deployment without unacceptable trade-offs? | Existing evaluations may be narrow, benchmark-sensitive, or insufficiently representative of deployment conditions. | Construct a controlled benchmark and compare strong baselines against uncertainty-aware scoring; report both gains and failure cases. | failure rate; recovery time; abstention quality | Intermediate | synthetic_research_ideation_candidate | 0a0822303e8e |
ARP-0000080 | Reinforcement Learning | data quality assessment for reinforcement learning in real-world deployment | Can uncertainty-aware scoring improve data quality assessment for reinforcement learning in real-world deployment without unacceptable trade-offs? | Existing evaluations may be narrow, benchmark-sensitive, or insufficiently representative of deployment conditions. | Construct a controlled benchmark and compare strong baselines against uncertainty-aware scoring; report both gains and failure cases. | accuracy; macro-F1; robustness | Advanced | synthetic_research_ideation_candidate | 91ad0d4c5e65 |
ARP-0000081 | Federated Learning | data quality assessment for federated learning in real-world deployment | Can uncertainty-aware scoring improve data quality assessment for federated learning in real-world deployment without unacceptable trade-offs? | Existing evaluations may be narrow, benchmark-sensitive, or insufficiently representative of deployment conditions. | Construct a controlled benchmark and compare strong baselines against uncertainty-aware scoring; report both gains and failure cases. | AUROC; detection delay; false alarm rate | Beginner | synthetic_research_ideation_candidate | a2573d20d56f |
ARP-0000082 | Privacy-Preserving ML | data quality assessment for privacy-preserving ml in real-world deployment | Can uncertainty-aware scoring improve data quality assessment for privacy-preserving ml in real-world deployment without unacceptable trade-offs? | Existing evaluations may be narrow, benchmark-sensitive, or insufficiently representative of deployment conditions. | Construct a controlled benchmark and compare strong baselines against uncertainty-aware scoring; report both gains and failure cases. | calibration error; selective risk; coverage | Intermediate | synthetic_research_ideation_candidate | b9ea05475120 |
ARP-0000083 | AI Safety | data quality assessment for ai safety in real-world deployment | Can uncertainty-aware scoring improve data quality assessment for ai safety in real-world deployment without unacceptable trade-offs? | Existing evaluations may be narrow, benchmark-sensitive, or insufficiently representative of deployment conditions. | Construct a controlled benchmark and compare strong baselines against uncertainty-aware scoring; report both gains and failure cases. | task success; latency; compute cost | Advanced | synthetic_research_ideation_candidate | 7885e7f34633 |
ARP-0000084 | Responsible AI | data quality assessment for responsible ai in real-world deployment | Can uncertainty-aware scoring improve data quality assessment for responsible ai in real-world deployment without unacceptable trade-offs? | Existing evaluations may be narrow, benchmark-sensitive, or insufficiently representative of deployment conditions. | Construct a controlled benchmark and compare strong baselines against uncertainty-aware scoring; report both gains and failure cases. | privacy attack AUC; utility loss | Beginner | synthetic_research_ideation_candidate | 3a9f587361e2 |
ARP-0000085 | MLOps | data quality assessment for mlops in real-world deployment | Can uncertainty-aware scoring improve data quality assessment for mlops in real-world deployment without unacceptable trade-offs? | Existing evaluations may be narrow, benchmark-sensitive, or insufficiently representative of deployment conditions. | Construct a controlled benchmark and compare strong baselines against uncertainty-aware scoring; report both gains and failure cases. | worst-group performance; disparity; calibration | Intermediate | synthetic_research_ideation_candidate | ec7ecc24a1d7 |
ARP-0000086 | Edge AI | data quality assessment for edge ai in real-world deployment | Can uncertainty-aware scoring improve data quality assessment for edge ai in real-world deployment without unacceptable trade-offs? | Existing evaluations may be narrow, benchmark-sensitive, or insufficiently representative of deployment conditions. | Construct a controlled benchmark and compare strong baselines against uncertainty-aware scoring; report both gains and failure cases. | human agreement; explanation fidelity | Advanced | synthetic_research_ideation_candidate | 2d4fffdf138d |
ARP-0000087 | Robotics | data quality assessment for robotics in real-world deployment | Can uncertainty-aware scoring improve data quality assessment for robotics in real-world deployment without unacceptable trade-offs? | Existing evaluations may be narrow, benchmark-sensitive, or insufficiently representative of deployment conditions. | Construct a controlled benchmark and compare strong baselines against uncertainty-aware scoring; report both gains and failure cases. | recall@k; answer correctness; retrieval cost | Beginner | synthetic_research_ideation_candidate | 0262a8384422 |
ARP-0000088 | Medical AI | data quality assessment for medical ai in real-world deployment | Can uncertainty-aware scoring improve data quality assessment for medical ai in real-world deployment without unacceptable trade-offs? | Existing evaluations may be narrow, benchmark-sensitive, or insufficiently representative of deployment conditions. | Construct a controlled benchmark and compare strong baselines against uncertainty-aware scoring; report both gains and failure cases. | energy per request; peak memory; throughput | Intermediate | synthetic_research_ideation_candidate | b533b203ec11 |
ARP-0000089 | Education AI | data quality assessment for education ai in real-world deployment | Can uncertainty-aware scoring improve data quality assessment for education ai in real-world deployment without unacceptable trade-offs? | Existing evaluations may be narrow, benchmark-sensitive, or insufficiently representative of deployment conditions. | Construct a controlled benchmark and compare strong baselines against uncertainty-aware scoring; report both gains and failure cases. | failure rate; recovery time; abstention quality | Advanced | synthetic_research_ideation_candidate | 4b200c52000d |
ARP-0000090 | Cybersecurity AI | data quality assessment for cybersecurity ai in real-world deployment | Can uncertainty-aware scoring improve data quality assessment for cybersecurity ai in real-world deployment without unacceptable trade-offs? | Existing evaluations may be narrow, benchmark-sensitive, or insufficiently representative of deployment conditions. | Construct a controlled benchmark and compare strong baselines against uncertainty-aware scoring; report both gains and failure cases. | accuracy; macro-F1; robustness | Beginner | synthetic_research_ideation_candidate | 1f6d472ef37f |
ARP-0000091 | Recommender Systems | data quality assessment for recommender systems in real-world deployment | Can uncertainty-aware scoring improve data quality assessment for recommender systems in real-world deployment without unacceptable trade-offs? | Existing evaluations may be narrow, benchmark-sensitive, or insufficiently representative of deployment conditions. | Construct a controlled benchmark and compare strong baselines against uncertainty-aware scoring; report both gains and failure cases. | AUROC; detection delay; false alarm rate | Intermediate | synthetic_research_ideation_candidate | d99c1482df2a |
ARP-0000092 | Graph Machine Learning | data quality assessment for graph machine learning in real-world deployment | Can uncertainty-aware scoring improve data quality assessment for graph machine learning in real-world deployment without unacceptable trade-offs? | Existing evaluations may be narrow, benchmark-sensitive, or insufficiently representative of deployment conditions. | Construct a controlled benchmark and compare strong baselines against uncertainty-aware scoring; report both gains and failure cases. | calibration error; selective risk; coverage | Advanced | synthetic_research_ideation_candidate | 168ead30e791 |
ARP-0000093 | Time Series | data quality assessment for time series in real-world deployment | Can uncertainty-aware scoring improve data quality assessment for time series in real-world deployment without unacceptable trade-offs? | Existing evaluations may be narrow, benchmark-sensitive, or insufficiently representative of deployment conditions. | Construct a controlled benchmark and compare strong baselines against uncertainty-aware scoring; report both gains and failure cases. | task success; latency; compute cost | Beginner | synthetic_research_ideation_candidate | 427640b72b5b |
ARP-0000094 | Anomaly Detection | data quality assessment for anomaly detection in real-world deployment | Can uncertainty-aware scoring improve data quality assessment for anomaly detection in real-world deployment without unacceptable trade-offs? | Existing evaluations may be narrow, benchmark-sensitive, or insufficiently representative of deployment conditions. | Construct a controlled benchmark and compare strong baselines against uncertainty-aware scoring; report both gains and failure cases. | privacy attack AUC; utility loss | Intermediate | synthetic_research_ideation_candidate | 62f3aec8dff8 |
ARP-0000095 | Causal ML | data quality assessment for causal ml in real-world deployment | Can uncertainty-aware scoring improve data quality assessment for causal ml in real-world deployment without unacceptable trade-offs? | Existing evaluations may be narrow, benchmark-sensitive, or insufficiently representative of deployment conditions. | Construct a controlled benchmark and compare strong baselines against uncertainty-aware scoring; report both gains and failure cases. | worst-group performance; disparity; calibration | Advanced | synthetic_research_ideation_candidate | d45d6e8b4ca5 |
ARP-0000096 | Explainable AI | data quality assessment for explainable ai in real-world deployment | Can uncertainty-aware scoring improve data quality assessment for explainable ai in real-world deployment without unacceptable trade-offs? | Existing evaluations may be narrow, benchmark-sensitive, or insufficiently representative of deployment conditions. | Construct a controlled benchmark and compare strong baselines against uncertainty-aware scoring; report both gains and failure cases. | human agreement; explanation fidelity | Beginner | synthetic_research_ideation_candidate | 95a830d580cb |
ARP-0000097 | Generative AI | data quality assessment for generative ai in real-world deployment | Can uncertainty-aware scoring improve data quality assessment for generative ai in real-world deployment without unacceptable trade-offs? | Existing evaluations may be narrow, benchmark-sensitive, or insufficiently representative of deployment conditions. | Construct a controlled benchmark and compare strong baselines against uncertainty-aware scoring; report both gains and failure cases. | recall@k; answer correctness; retrieval cost | Intermediate | synthetic_research_ideation_candidate | 73ace104ff50 |
ARP-0000098 | Knowledge Graphs | data quality assessment for knowledge graphs in real-world deployment | Can uncertainty-aware scoring improve data quality assessment for knowledge graphs in real-world deployment without unacceptable trade-offs? | Existing evaluations may be narrow, benchmark-sensitive, or insufficiently representative of deployment conditions. | Construct a controlled benchmark and compare strong baselines against uncertainty-aware scoring; report both gains and failure cases. | energy per request; peak memory; throughput | Advanced | synthetic_research_ideation_candidate | c9609bb2bea1 |
ARP-0000099 | Database ML | data quality assessment for database ml in real-world deployment | Can uncertainty-aware scoring improve data quality assessment for database ml in real-world deployment without unacceptable trade-offs? | Existing evaluations may be narrow, benchmark-sensitive, or insufficiently representative of deployment conditions. | Construct a controlled benchmark and compare strong baselines against uncertainty-aware scoring; report both gains and failure cases. | failure rate; recovery time; abstention quality | Beginner | synthetic_research_ideation_candidate | 7d1c6c0df8d6 |
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AI Research Problems 1M
Summary
This dataset contains 1,000,000 synthetic research-ideation candidates across AI, machine learning, LLMs, RAG, AI agents, MCP, computer vision, robotics, safety, MLOps, and related fields.
Important warning
These records are synthetic combinations for research ideation. They are not claims that the problems are novel, unsolved, or absent from the literature. A researcher must verify novelty using papers, benchmarks, patents where relevant, and GitHub issues before using any item as a research claim.
The dataset is intended for:
- research-topic retrieval experiments;
- dataset-scale testing;
- research assistant prototypes;
- semantic search and clustering;
- generation and ranking experiments;
- testing RAG and agent pipelines.
Fields
id: Stable record identifier.domain: Research area.research_problem: Problem statement.research_question: Testable question.motivation: General motivation.proposed_direction: Candidate method direction.evaluation_metrics: Suggested metrics.difficulty: Beginner, Intermediate, or Advanced.record_type: Data provenance label.template_signature: Deterministic signature for traceability.
Data creation
Generated programmatically from transparent templates. No private data or scraped copyrighted text is included.
Recommended next version
For a research-grade release, add verified literature references, benchmark links, evidence snippets, and human-review labels. The current release should be treated as a scale and ideation dataset.
License
CC BY 4.0.
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