供应链可信论文趋势 2023–2026

主要结论

• 供应链可信论文从 2023 年的 9 篇增长到 2026 年的 41 篇，三年间扩大到约 4.6 倍。
• NeurIPS 是发供应链可信论文最多的会议，2023–2026 年共 35 篇，占四个会议总量的 38%。
• 交叉方向里涨得最快的是驾驶仿真：同时属于两个主题的论文从 2023 年的 1 篇增至 2026 年的 5 篇。
• 在「安全与可信」的 5 个主题里，它的 2026 年论文数排第 5；同组增长最快的水印是 9.3 倍，供应链可信是 4.6 倍。

代表论文

按年份和会议均匀挑选，方便快速看到每一届的样貌

• NeurIPS 2026Can We Trust Item Response Theory for AI Evaluation?
• ICML 2026Who can we trust? LLM-as-a-jury for Comparative Assessment
• ICLR 2026Hey, That's My Model! Introducing Chain & Hash, An LLM Fingerprinting Technique
• CVPR 2026Trust-calibrated Collaborative Learning for Long-Tailed Visual Recognition
• NeurIPS 2025Chain-of-Model Learning for Language Model
• ICML 2025Navigating Conflicting Views: Harnessing Trust for Learning
• ICLR 2025Parameter Expanded Stochastic Gradient Markov Chain Monte Carlo
• CVPR 2025Interleaved-Modal Chain-of-Thought
• NeurIPS 2024Chain-of-Thought Unfaithfulness as Disguised Accuracy
• ICML 2024Implicit meta-learning may lead language models to trust more reliable sources
• ICLR 2024Chain of Log-Concave Markov Chains
• CVPR 2024CoG-DQA: Chain-of-Guiding Learning with Large Language Models for Diagram Question Answering
• NeurIPS 2023Supply-Side Equilibria in Recommender Systems
• ICML 2023Synthetic Prompting: Generating Chain-of-Thought Demonstrations for Large Language Models
• ICLR 2023Rethinking skip connection model as a learnable Markov chain
• NeurIPS 2026From Infrastructure to Interface, the AI Value Chain Drives LLM Homogenization
• ICML 2026Covariance estimation using Markov chain Monte Carlo
• ICLR 2026Output Supervision Can Obfuscate the Chain of Thought
• CVPR 2026Cut to the Chase Training-free Multimodal Summarization via Chain-of-Events
• NeurIPS 2025You Can Trust Your Clustering Model: A Parameter-free Self-Boosting Plug-in for Deep Clustering

全部供应链可信论文

共 91 篇，第 2 / 2 页

51. ICML 2025Navigating Conflicting Views: Harnessing Trust for Learning
52. ICML 2025Position: Machine Learning Models Have a Supply Chain Problem
53. ICML 2025Speeding up Policy Simulation in Supply Chain RL
54. NeurIPS 2025Analyzing the Power of Chain of Thought through Memorization Capabilities
55. NeurIPS 2025Attention (as Discrete-Time Markov) Chains
56. NeurIPS 2025Chain-of-Model Learning for Language Model
57. NeurIPS 2025CoT Information: Improved Sample Complexity under Chain-of-Thought Supervision
58. NeurIPS 2025Let Me Think! A Long Chain of Thought Can Be Worth Exponentially Many Short Ones
59. NeurIPS 2025QUT-DV25: A Dataset for Dynamic Analysis of Next-Gen Software Supply Chain Attacks
60. NeurIPS 2025Reasoning by Superposition: A Theoretical Perspective on Chain of Continuous Thought
61. NeurIPS 2025You Can Trust Your Clustering Model: A Parameter-free Self-Boosting Plug-in for Deep Clustering
62. CVPR 2024Can I Trust Your Answer? Visually Grounded Video Question Answering
63. CVPR 2024CHAIN: Enhancing Generalization in Data-Efficient GANs via lipsCHitz continuity constrAIned Normalization
64. CVPR 2024CoG-DQA: Chain-of-Guiding Learning with Large Language Models for Diagram Question Answering
65. CVPR 2024Compositional Chain-of-Thought Prompting for Large Multimodal Models
66. CVPR 2024PromptCoT: Align Prompt Distribution via Adapted Chain-of-Thought
67. ICLR 2024BadChain: Backdoor Chain-of-Thought Prompting for Large Language Models
68. ICLR 2024Chain of Log-Concave Markov Chains
69. ICLR 2024Chain of Thought Empowers Transformers to Solve Inherently Serial Problems
70. ICLR 2024Chain-of-Experts: When LLMs Meet Complex Operations Research Problems
71. ICLR 2024The Expressive Power of Transformers with Chain of Thought
72. ICML 2024Chain-of-Thought Predictive Control
73. ICML 2024Decoding Compressed Trust: Scrutinizing the Trustworthiness of Efficient LLMs Under Compression
74. ICML 2024Don't trust your eyes: on the (un)reliability of feature visualizations
75. ICML 2024Implicit meta-learning may lead language models to trust more reliable sources
76. ICML 2024Keypoint-based Progressive Chain-of-Thought Distillation for LLMs
77. ICML 2024Parallel Affine Transformation Tuning of Markov Chain Monte Carlo
78. ICML 2024Trust Regions for Explanations via Black-Box Probabilistic Certification
79. ICML 2024Trust the Model Where It Trusts Itself - Model-Based Actor-Critic with Uncertainty-Aware Rollout Adaption
80. NeurIPS 2024Accelerating Pre-training of Multimodal LLMs via Chain-of-Sight
81. NeurIPS 2024Auditing Local Explanations is Hard
82. NeurIPS 2024Chain-of-Thought Unfaithfulness as Disguised Accuracy
83. ICLR 2023Automatic Chain of Thought Prompting in Large Language Models
84. ICLR 2023Language models are multilingual chain-of-thought reasoners
85. ICLR 2023Rethinking skip connection model as a learnable Markov chain
86. ICML 2023Synthetic Prompting: Generating Chain-of-Thought Demonstrations for Large Language Models
87. NeurIPS 2023Deductive Verification of Chain-of-Thought Reasoning
88. NeurIPS 2023DiffPack: A Torsional Diffusion Model for Autoregressive Protein Side-Chain Packing
89. NeurIPS 2023Predicting mutational effects on protein-protein binding via a side-chain diffusion probabilistic model
90. NeurIPS 2023Supply-Side Equilibria in Recommender Systems
91. NeurIPS 2023Towards Revealing the Mystery behind Chain of Thought: A Theoretical Perspective

常见问题

2023–2026 年四大会议一共有多少篇供应链可信论文？

共 91 篇，按年份分别是 2023 年 9 篇、2024 年 21 篇、2025 年 20 篇、2026 年 41 篇。

NeurIPS 2026 有多少篇供应链可信论文？

NeurIPS 2026 有 19 篇供应链可信论文，上一届是 8 篇。

ICLR 2026 有多少篇供应链可信论文？

ICLR 2026 有 8 篇供应链可信论文，上一届是 6 篇。

ICML 2026 有多少篇供应链可信论文？

ICML 2026 有 7 篇供应链可信论文，上一届是 5 篇。

CVPR 2026 有多少篇供应链可信论文？

CVPR 2026 有 7 篇供应链可信论文，上一届是 1 篇。

哪个会议的供应链可信论文最多？

NeurIPS，2023–2026 年共 35 篇。