供应链可信论文趋势 2023–2026
模型供应链
统计范围：NeurIPS、ICLR、ICML、CVPR 四个会议，2023–2026 年，共 91 篇被归入该主题的论文。
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。
各会议论文数
按年份和会议统计
| 年份 | NeurIPS | ICLR | ICML | CVPR | 合计 |
|---|---|---|---|---|---|
| 2023 | 5 | 3 | 1 | 0 | 9 |
| 2024 | 3 | 5 | 8 | 5 | 21 |
| 2025 | 8 | 6 | 5 | 1 | 20 |
| 2026 | 19 | 8 | 7 | 7 | 41 |
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 35 篇，第 1 / 1 页
- NeurIPS 2026A Solvable Model of Chain-of-Thought in In-Context Learning
- NeurIPS 2026A Tight Hierarchy For Chain of Thought
- NeurIPS 2026Beyond Suspicious Steps: Ontological Trust in Long-Horizon Agents
- NeurIPS 2026Can We Trust Item Response Theory for AI Evaluation?
- NeurIPS 2026Chain of Dual Structures in Transformer Attention
- NeurIPS 2026Chain-of-Thought Is Not Explainability
- NeurIPS 2026ComPose: When to Trust Hands for Object Pose Tracking
- NeurIPS 2026Efficiently Representing Algorithms With Chain-of-Thought Transformers
- NeurIPS 2026From Infrastructure to Interface, the AI Value Chain Drives LLM Homogenization
- NeurIPS 2026Not All Routing Drift Is Harmful: Trust-Region Projection for Class-Incremental Learning
- NeurIPS 2026Reliable Chain-of-Thought via Prefix Consistency
- NeurIPS 2026SWE-Chain: Benchmarking Coding Agents on Chained Release-Level Package Upgrades
- NeurIPS 2026TERMINATOR: Learning Optimal Exit Points for Early Stopping in Chain-of-Thought Reasoning
- NeurIPS 2026Trust Guided Decision Transformer
- NeurIPS 2026Trust Region Continual Learning as an Implicit Meta-Learner
- NeurIPS 2026Trust Region Policy Distillation
- NeurIPS 2026Trust Region Q Adjoint Matching
- NeurIPS 2026When Alignment Fails: Stabilizing Cross-Dynamics RL with Prototype Trust Regions
- NeurIPS 2026When the Same Coefficients Reach Different Places: Asymmetric Realizability in Transplanting Tokenizers across Large Language Models
- NeurIPS 2025Analyzing the Power of Chain of Thought through Memorization Capabilities
- NeurIPS 2025Attention (as Discrete-Time Markov) Chains
- NeurIPS 2025Chain-of-Model Learning for Language Model
- NeurIPS 2025CoT Information: Improved Sample Complexity under Chain-of-Thought Supervision
- NeurIPS 2025Let Me Think! A Long Chain of Thought Can Be Worth Exponentially Many Short Ones
- NeurIPS 2025QUT-DV25: A Dataset for Dynamic Analysis of Next-Gen Software Supply Chain Attacks
- NeurIPS 2025Reasoning by Superposition: A Theoretical Perspective on Chain of Continuous Thought
- NeurIPS 2025You Can Trust Your Clustering Model: A Parameter-free Self-Boosting Plug-in for Deep Clustering
- NeurIPS 2024Accelerating Pre-training of Multimodal LLMs via Chain-of-Sight
- NeurIPS 2024Auditing Local Explanations is Hard
- NeurIPS 2024Chain-of-Thought Unfaithfulness as Disguised Accuracy
- NeurIPS 2023Deductive Verification of Chain-of-Thought Reasoning
- NeurIPS 2023DiffPack: A Torsional Diffusion Model for Autoregressive Protein Side-Chain Packing
- NeurIPS 2023Predicting mutational effects on protein-protein binding via a side-chain diffusion probabilistic model
- NeurIPS 2023Supply-Side Equilibria in Recommender Systems
- NeurIPS 2023Towards Revealing the Mystery behind Chain of Thought: A Theoretical Perspective
在 Claude、Cursor 里直接问这个趋势
接上 Paperlist MCP 后，写综述、挑方向或准备投稿时，不用离开对话框就能拿到这页的数字：每年各会议的论文数、增长倍数、涨得最快的子方向和代表论文，AI 会按你的问题整理。
可以直接复制这句话问 AI
用 paperlist 查供应链可信在 NeurIPS 2023–2026 的论文趋势，哪一年涨得最快？
AI 会调用的工具：get_topic_stats {"topic": "supply-chain-trust"}
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。