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 41 篇，第 1 / 1 页

1. CVPR 2026CaST-Bench Benchmarking Causal Chain-Grounded Spatio-Temporal Reasoning for Video Question Answering
2. CVPR 2026Chain of World World Model Thinking in Latent Motion
3. CVPR 2026Cut to the Chase Training-free Multimodal Summarization via Chain-of-Events
4. CVPR 2026Fuel Gauge Estimating Chain-of-Thought Length Ahead of Time in Large
5. CVPR 2026Grounded Chain-of-Thought for Multimodal Large Language Models
6. CVPR 2026Trust-calibrated Collaborative Learning for Long-Tailed Visual Recognition
7. CVPR 2026Unleashing the Power of Chain-of-Prediction for Monocular 3D Object Detection
8. ICLR 2026Emergence of Superposition: Unveiling the Training Dynamics of Chain of Continuous Thought
9. ICLR 2026Hey, That's My Model! Introducing Chain & Hash, An LLM Fingerprinting Technique
10. ICLR 2026Hidden Patterns in Chain-of-Thought Reasoning
11. ICLR 2026In Agents We Trust, but Who Do Agents Trust? Latent Preferences Steer LLM Generations
12. ICLR 2026Long Chain-of-Thought Reasoning Across Languages
13. ICLR 2026Output Supervision Can Obfuscate the Chain of Thought
14. ICLR 2026Pushing on Multilingual Reasoning Models with Language-Mixed Chain-of-Thought
15. ICLR 2026Scalable Chain of Thoughts via Elastic Reasoning
16. ICML 2026Chain-of-Thought Gradient Descent
17. ICML 2026Covariance estimation using Markov chain Monte Carlo
18. ICML 2026Reasoning about Reasoning: BAPO Bounds on Chain-of-Thought Token Complexity in LLMs
19. ICML 2026The Expressive Power of Low Precision Softmax Transformers with (Summarized) Chain-of-Thought
20. ICML 2026Thinking in Flow: A Dissipative Stabilization Operator for Robust Autoregressive Reasoning
21. ICML 2026When Can We Trust Survival Model Evaluation ?
22. ICML 2026Who can we trust? LLM-as-a-jury for Comparative Assessment
23. NeurIPS 2026A Solvable Model of Chain-of-Thought in In-Context Learning
24. NeurIPS 2026A Tight Hierarchy For Chain of Thought
25. NeurIPS 2026Beyond Suspicious Steps: Ontological Trust in Long-Horizon Agents
26. NeurIPS 2026Can We Trust Item Response Theory for AI Evaluation?
27. NeurIPS 2026Chain of Dual Structures in Transformer Attention
28. NeurIPS 2026Chain-of-Thought Is Not Explainability
29. NeurIPS 2026ComPose: When to Trust Hands for Object Pose Tracking
30. NeurIPS 2026Efficiently Representing Algorithms With Chain-of-Thought Transformers
31. NeurIPS 2026From Infrastructure to Interface, the AI Value Chain Drives LLM Homogenization
32. NeurIPS 2026Not All Routing Drift Is Harmful: Trust-Region Projection for Class-Incremental Learning
33. NeurIPS 2026Reliable Chain-of-Thought via Prefix Consistency
34. NeurIPS 2026SWE-Chain: Benchmarking Coding Agents on Chained Release-Level Package Upgrades
35. NeurIPS 2026TERMINATOR: Learning Optimal Exit Points for Early Stopping in Chain-of-Thought Reasoning
36. NeurIPS 2026Trust Guided Decision Transformer
37. NeurIPS 2026Trust Region Continual Learning as an Implicit Meta-Learner
38. NeurIPS 2026Trust Region Policy Distillation
39. NeurIPS 2026Trust Region Q Adjoint Matching
40. NeurIPS 2026When Alignment Fails: Stabilizing Cross-Dynamics RL with Prototype Trust Regions
41. NeurIPS 2026When the Same Coefficients Reach Different Places: Asymmetric Realizability in Transplanting Tokenizers across Large Language Models

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。