基础模型论文与资料索引¶
本页由统一论文规格生成的 docs/research-manifest.json 构建;元数据维护入口是
打包的 paper.yaml 和按领域保存的 catalog 规格,不直接编辑生成产物。
背景、架构、公式、原文效果和本地实验请进入独立详情页。
论文实现与研究资料¶
| 方向 | 方法 | 机构与日期 | 原作者代码 | 本地入口 |
|---|---|---|---|---|
| 推理与系统效率 | XLA from the Trenches: Correctness on TPU Workloads at Scale | Google,2026-10-31 | 原作者入口;原文明确macro-graph isolator已上游到OpenXLA,公开HLO isolation文档及evaluator;完整内部agent harness、TPU工具层、生产图集与微融合硬件调试路径未公开,不等于本文整套作者实现已开源 | 计划键 xla-trenches:成本过高,不复现(审查 DONE);仅研究资料,无可执行入口。 |
| 多模态基础模型 | GAVAGAI: Referential Alignment for Infant-Scale Vision-Language Models via Balanced Optimal Transport | St Thomas’ College of Engineering and Technology,2026-10-30 | 未发现官方代码 | gavagai:仅低成本组件,完整实验不复现 |
| 预训练与数据 | ProLand: A New Infeasible Manifold Optimization Method for LoRA Fine-Tuning | Shanghai Jiao Tong University,2026-10-30 | 未发现官方代码 | proland:仅低成本组件,完整实验不复现 |
| 推理与系统效率 | Folding Attention Biases into Keys Makes Compacted KV Caches Run on Standard Kernels | Independent Researcher,2026-10-27 | 未发现官方代码 | fold-bias:仅低成本组件,完整实验不复现 |
| JEPA与世界模型 | GUI-JEPA: Candidate-Conditioned JEPA Latents as a Fast Action Policy for Mobile GUI Agents | Shanghai Jiao Tong University / Nanyang Technological University,2026-10-27 | 原作者入口;原作者仓库可访问但固定commit仅README占位;方法源码尚未发布,不能称已开源可执行GUI-JEPA | gui-jepa:仅低成本组件,完整实验不复现 |
| 注意力与长上下文 | Multi-State Linear Attention with Generalized Second-Order Routing | Princeton University,2026-10-27 | 未发现官方代码 | merlin:仅低成本组件,完整实验不复现 |
| 注意力与长上下文 | Select-TTT: Query-Conditioned Selective Fast Weight Updates for In-Place Test-Time Training | University of Science and Technology of China,2026-10-27 | 未发现官方代码 | select-ttt:仅低成本组件,完整实验不复现 |
| JEPA与世界模型 | SWIM: Learning What Matters from Behaviors for JEPA World Models | Boston University / Unity Technologies,2026-10-27 | 未发现官方代码 | swim:仅低成本组件,完整实验不复现 |
| 推理与系统效率 | ResCom: Activation Residual Compensation for Large Language Model Quantization | Institute of Automation, Chinese Academy of Sciences,2026-10-25 | 未发现官方代码 | rescom:仅低成本组件,完整实验不复现 |
| JEPA与世界模型 | DT-JEPA: Learning to Predict Future Representations for Decision-Making | Southeast University,2026-10-23 | 未发现官方代码 | dt-jepa:仅低成本组件,完整实验不复现 |
| System One / JEV | EditJev: System One Routing for Image Editing Models | Peking University,2026-10-23 | 未发现官方代码 | editjev:仅低成本组件,完整实验不复现 |
| System One / JEV | PathJev: Decision-Native Vision–Language Modelling for Histopathology | University of Naples Federico II,2026-10-23 | 原作者入口;作者方法仓库公开,固定commit目录包含src/inference/analysis/configs/tests及结果;公开模型https://huggingface.co/Cristiandieti/PathJev-P;本轮只核资源,不下载模型/病理数据 | 计划键 pathjev:成本过高,不复现(审查 DONE);仅研究资料,无可执行入口。 |
| 世界模型 | Inside-JEPA: From Prediction to Relational Grounding in Latent World Models | Australian Institute for Machine Learning, Adelaide University,2026-10-22 | 未发现官方代码 | inside-jepa:仅低成本组件,完整实验不复现 |
| System One / JEV | Jev-DLC: Decision-Level Continual Learning from Typed Feedback | The Hong Kong University of Science and Technology (Guangzhou),2026-10-22 | 未发现官方代码 | jev-dlc:仅低成本组件,完整实验不复现 |
| 推理与系统效率 | LazyFlash: Forward-Dense, Backward-Sparse Attention for Accelerated Long-Context Fine-Tuning | Microsoft,2026-10-22 | 未发现官方代码 | 计划键 lazyflash:成本过高,不复现(审查 DONE);仅研究资料,无可执行入口。 |
| 高效注意力 | OpenAttention: One Percent Is Enough with Tail Proxies | Sogang University,2026-10-22 | 原作者入口;已公开Apache-2.0源码;核对固定commit 6e43d4fc8c0e95b26d17d198a4545c627b2d6931;本地独立实现单头张量参考,不移植Triton内核或宣称端到端加速 | openattention:仅低成本组件,完整实验不复现 |
| 序列压缩 | Rethinking Sequence Compression with Test-time Regression | The Chinese University of Hong Kong,2026-10-22 | 原作者入口;官方公开源码,固定commit 29f643f1348a4b3893f4740433b1fc8b8c39b2e7;未找到顶层或TTR核心文件的明确授权声明,仅第三方子项目带各自许可证;本地独立按论文公式重写,不复制上游核心代码 | ttr-compressor:仅低成本组件,完整实验不复现 |
| 策略蒸馏理论 | Reverse-KL Policy Distillation: Statistical Guarantees and Limits | University of Virginia,2026-10-22 | 未发现官方代码 | rkl-policy-distillation:仅低成本组件,完整实验不复现 |
| 视觉表征与世界模型 | ALPS: Visual State Encoders for World Models | University of California, Santa Cruz,2026-10-21 | 原作者入口;作者官方Apache-2.0代码及checkpoint已发布;本地固定30ab44b9fcdf3253fc67f5a53e31ea5141eb45c1,不自动下载权重或执行上游脚本 | alps:仅低成本组件,完整实验不复现 |
| 可解释性 | Building Auditable AI Text Detectors with Sparse Autoencoders | University of Pennsylvania,2026-10-21 | 原作者入口;作者官方Apache-2.0源码;建库时间2026-10-24T13:32:05Z,不能把Oct22历史Public Release提交时间当作公开首发时间;固定SHA的Oct24更改仅更新引用。独立实现小规模机制,不加载作者模型。 | detector-scope:仅低成本组件,完整实验不复现 |
| MoE路由 | MoEZip: Routing-Aware KV Cache Compression for Sparse Mixture-of-Experts LLMs | Korea University,2026-10-21 | 原作者入口;作者官方仓库存在且含Apache-2.0 LICENSE,但固定commit 197959dbae2adfab7ec233f584ce951b827e04cc仅README/LICENSE,未发布实现;本地按论文公式独立实现 | moezip:仅低成本组件,完整实验不复现 |
| 模型压缩与量化 | Right On Q: Recovering Reasoning under Quantization via Self-Generated Trajectories | University of Michigan, Ann Arbor,2026-10-21 | 未发现官方代码 | 计划键 right-on-q:成本过高,不复现(审查 DONE);仅研究资料,无可执行入口。 |
| System One / JEV | When Multiple Choices Are Correct: Asymmetric Probability Allocation in Jev and Other Decision Models | Independent Researcher,2026-10-21 | 原作者入口;MIT作者代码已发布;arxiv-v1固定7b161f96cd0ff1842845fc66f981d1471cc18151;未公开模型响应 | jev-multicorrect:仅低成本组件,完整实验不复现 |
| JEPA与世界模型 | LocaLeJEPA: Maximum Entropy Representations under Geometric Priors | University of Pennsylvania,2026-10-20 | 原作者入口;已公开MIT代码;本地独立实现核对固定commit 771361c18d37e65df3dc188761ffb26751ce4e37,论文结果以arXiv v1表格为准,不混用README旧数值 | locale-jepa:仅低成本组件,完整实验不复现 |
| 注意力与长上下文 | RACE: RoPE-Aligned Attention Compression Based on Query-Key Activation Energies | MIT CSAIL,2026-10-20 | 未发现官方代码 | race-rope-compression:仅低成本组件,完整实验不复现 |
| 注意力与长上下文 | Stratified Sparse Attention: Reducing the Sparse-to-Dense Gap at Extreme Sparsity | Rice University,2026-10-20 | 原作者入口;论文声明上述作者仓库开源;2026-10-21访问返回404,未取得可核验代码或revision。本地独立依据固定arXiv v1 Algorithm 1实现,不声称复现作者CUDA内核。 | stratified-sparse-attention:仅低成本组件,完整实验不复现 |
| 多语言与分词 | DeepSEA: A Multilingual Evaluation Benchmark for Southeast Asia | Google Research,2026-10-19 | 原作者入口;官方代码及数据已公开;固定revision 7c3abc06824c8cb634930ab6049176da30cfa0e7,评分代码Apache-2.0,数据保留上游复合许可。论文翻译MetricX-24未在公开evaluate.py实现;本地只提供明确命名的release chrF,绝不混用 | deepsea:仅低成本组件,完整实验不复现 |
| 推理与系统效率 | FLINT: Fixed-budget Late Interaction | University of Illinois Urbana-Champaign,2026-10-19 | 原作者入口;原作者已发布MIT源码与模型集合;本地核对固定commit 3b3f3e37d34e32ca5c92cc5b66e3f778ae4210ec,不下载模型权重 | flint:仅低成本组件,完整实验不复现 |
| 注意力与长上下文 | Kronecker Delta Rule for Long Context Linear Attention | Johannes Kepler University Linz,2026-10-19 | 未发现官方代码 | kronlstm:仅低成本组件,完整实验不复现 |
| 预训练与数据 | T-MUON: Tensorizing Muon via t-SVD for Large Language Model Pre-training | University of California, Santa Barbara,2026-10-18 | 未发现官方代码 | t-muon:仅低成本组件,完整实验不复现 |
| 网络架构 | Muse Glimmer 30B: An On-Device Agentic Model | Meta Superintelligence Labs,2026-10-17 | 原作者入口;官方已发布Apache-2.0权重、配置、tokenizer与chat template;发布物不等同完整预训练/OPD数据和训练源码,本轮未核到完整训练代码 | 计划键 muse-glimmer-30b:成本过高,不复现(审查 DONE);仅研究资料,无可执行入口。 |
| 预训练与数据 | BMuon: Budgeting Muon's Spectral Geometry for Structured Compressibility | Inria,2026-10-16 | 原作者入口;作者官方仓库存在,但本轮仅有README,明确Implementation and reproduction scripts will be released soon;尚未发布可运行实现,未核到代码许可证 | bmuon:仅低成本组件,完整实验不复现 |
| System One / JEV | Jev-as-a-Judge for Brazilian Portuguese Information Retrieval: Relevance Assessment and Reranking | Câmara dos Deputados,2026-10-16 | 未发现官方代码 | jev-portuguese-ir:CPU 评测接口,不执行模型 |
| 多模态基础模型 | Persistent Planar Memory for Video World Models | Tsinghua University,2026-10-16 | 未发现官方代码 | 计划键 ppmem:成本过高,不复现(审查 DONE);仅研究资料,无可执行入口。 |
| 推理与系统效率 | Sink-Sparse Hybrid Attention for Efficient Inference in T2I/T2V Diffusion Transformers | Xi'an Jiaotong University,2026-10-16 | 未发现官方代码 | sink-sparse:仅低成本组件,完整实验不复现 |
| 多模态基础模型 | AdaLoop: Thinking with the Vision Encoder | Harvard University,2026-10-15 | 未发现官方代码 | adaloop-vision:仅低成本组件,完整实验不复现 |
| 预训练与数据 | Architecting for Hardware Diversity: Meta's End-to-End Co-Design for High-Performance AI Training on AMD GPUs | Meta,2026-10-15 | 论文称改动已上游至 PyTorch、Triton、ROCm;未找到独立整套复现仓库 | 计划键 hardware-diversity:成本过高,不复现(审查 DONE);仅研究资料,无可执行入口。 |
| 预训练与数据 | BitSparse: Compact Activation Storage for ReLU Feed-Forward Networks | University of Cambridge,2026-10-15 | 原作者入口;公开实现,许可证未确认;本地独立实现,不复制上游代码 | bit-sparse:仅低成本组件,完整实验不复现 |
| KV缓存 | Compression Does Not End at Selection: Post-Selection Positional Geometry for Sparse KV Caches | National University of Defense Technology,2026-10-15 | 未找到已发布的方法仓库;原文附录A.1称将发布 | rope-relay:仅低成本组件,完整实验不复现 |
| 网络架构 | Designing Transformers for Continuous Diffusion Language Models | Dots Studio, Xiaohongshu,2026-10-15 | 未发现官方代码 | langdit:仅低成本组件,完整实验不复现 |
| 推理与系统效率 | DropKV: Decoupling Residual-Output Perturbation for Near-Optimal KV-Cache Eviction | University at Albany, SUNY,2026-10-15 | 原作者入口;Apache-2.0 | drop-kv:仅低成本组件,完整实验不复现 |
| 多模态基础模型 | EgoP²C: Internalizing Plan-Perceive-Compose for Spatial Reasoning in Egocentric Video | Arizona State University,2026-10-15 | 未发现官方代码 | ego-p2c:仅低成本组件,完整实验不复现 |
| 推理与系统效率 | Fractional Power-of-Two Quantization for Efficient and Effective Multiplier-Free LLM Inference | Seoul National University,2026-10-15 | 未发现官方代码 | fpot:仅低成本组件,完整实验不复现 |
| 多模态基础模型 | Gen4P: Video Generators are 4D Perceivers | Nanyang Technological University,2026-10-15 | 原作者入口;官方仓库已存在,但本轮检查仅有README与assets,Installation和Usage均为WIP;未发布可运行训练/推理源代码,不能把项目占位等同于实现已开源 | 计划键 gen4p:成本过高,不复现(审查 DONE);仅研究资料,无可执行入口。 |
| 多模态基础模型 | LAMP: Layerwise Adaptive Mixing of Predictors for Training-Free Few-Shot Vision-Language Adaptation | Taizhou Institute of Science and Technology, Nanjing University of Science and Technology,2026-10-15 | 未发现官方代码 | lamp:仅低成本组件,完整实验不复现 |
| 预训练与数据 | LCT: Losslessly Compressed Tensors with GPU friendly execution for Training | University of Cambridge,2026-10-15 | 原作者入口;MIT | lct:仅低成本组件,完整实验不复现 |
| JEV与单步决策 | MJev-Spatial: System One Spatial Intelligence via Native Attention Matching | 未注明(论文未列一作机构;公开发布主体为 MindLab),2026-10-15 | 原作者入口;原作者实现已发布;另有 mindlab-research/MJev-Spatial-4B 公开 checkpoint,本轮不下载 | mjev-spatial:仅低成本组件,完整实验不复现 |
| 字节级模型 | Patch where the text is new: a compression view of where byte-level models should spend compute | Independent Researcher, Chile,2026-10-15 | 原作者入口;MIT | novelty-patching:仅低成本组件,完整实验不复现 |
| 推理与系统效率 | Prism-1: Position- and Rival-Independent Scoring for System One Models | Nanyang Technological University,2026-10-15 | 未发现官方代码 | prism-1:仅低成本组件,完整实验不复现 |
| 网络架构 | Rethinking Routing Exploration in Modern Mixture-of-Experts | ETH Zurich,2026-10-15 | 原作者入口;MIT | mirror-moe:仅低成本组件,完整实验不复现 |
| 多模态基础模型 | Retrofitting Recurrent Vision-Language Models from Pretrained Backbones | University of Tübingen,2026-10-15 | 未发现官方代码 | rr-vlm:仅低成本组件,完整实验不复现 |
| 推理与系统效率 | RoleQ: Role-Aligned Scales for Converting Language Models to Ternary | Harvard University,2026-10-15 | 未找到作者发布的方法实现或可核验许可证;按原文公式独立实现 | roleq:仅低成本组件,完整实验不复现 |
| 推理与系统效率 | SoftCache: Adaptive Step Size Caching for Diffusion Transformers | Tel Aviv University,2026-10-15 | 未发现官方代码 | softcache:仅低成本组件,完整实验不复现 |
| 预训练与数据 | When Does Muon Actually Win? A Paired Data-, Time-, and Tuning-Budget Benchmark | 论文未列一作机构,2026-10-15 | 未发现官方代码 | muon-paired-budget:CPU 评测接口,不执行模型 |
| 推理与系统效率 | When the KV Cache Is Slower Than the GPU: Scaling Remote KV Tiers for Context-Parallel Prefill | Multiverse Computing,2026-10-15 | 原作者入口;相关实现以作者提交的LMCache上游PR公开;不将PR公开等同于全部已合入或完整集群复现实验可用 | 计划键 remote-kv-tiers:成本过高,不复现(审查 DONE);仅研究资料,无可执行入口。 |
| 预训练与数据 | Dataset-Level Regularization for Joint-Embedding Self-Supervised Learning | European Molecular Biology Laboratory (EMBL),2026-10-14 | 未发现官方代码 | oob-regularization:仅低成本组件,完整实验不复现 |
| JEPA与世界模型 | EgoJEPA: Beyond Retargeting for Dexterous Manipulation from Egocentric Videos | Mohamed bin Zayed University of Artificial Intelligence,2026-10-14 | 未发现官方代码 | 计划键 egojepa:成本过高,不复现(审查 DONE);仅研究资料,无可执行入口。 |
| 多模态基础模型 | ESC: Escaping 2D Diffusion Ambiguity with Geometric Constraints for Consistent Novel View Synthesis | FAIR at Meta / Mila - Québec AI Institute / Université de Montréal,2026-10-14 | 未发现官方代码 | 计划键 esc:成本过高,不复现(审查 DONE);仅研究资料,无可执行入口。 |
| 预训练与数据 | Memory-Efficient Subspace Optimization with Residual Correction: From Momentum SGD to Muon | Nanjing University / Alibaba Token Hub,2026-10-14 | 未发现官方代码 | smrc-pmrc:仅低成本组件,完整实验不复现 |
| 多模态基础模型 | One-Step Prediction Fidelity Is a Poor Proxy for Planning Performance in JEPA World Models | 论文未列机构,2026-10-14 | 未发现官方代码 | jepa-prediction-planning:CPU 评测接口,不执行模型 |
| 网络架构 | Open-world AI: a Tale of Three Requirements | FAIR, Meta,2026-10-14 | 原作者入口;官方仓库已发布 Memory Mosaics+ / two-stage 配置与代码;研究注明作者以个人身份完成 | memory-mosaics-plus:仅低成本组件,完整实验不复现 |
| 推理与系统效率 | PN-Mix: Pair-Norm Guided Mixed-Precision Key Cache Quantization for Efficient Long-Output LLM Inference | Seoul National University,2026-10-14 | 未发现官方代码 | pn-mix:仅低成本组件,完整实验不复现 |
| System One / JEV | Selective Reasoning for Typed Decision Models: When to Start, Read, and Stop | University of Electronic Science and Technology of China,2026-10-14 | 已开源 | selective-reasoning:CPU 评测接口,不执行模型 |
| JEPA与世界模型 | Time-JEPA: Grounding Video World Models with Physical Time Series | Harbin Institute of Technology,2026-10-14 | 原作者入口;作者官方仓库已公开,包含src/app/configs/tests和评测脚本;MIT及Apache-2.0许可证文件并存,本轮未下载或执行其完整视频训练 | time-jepa:仅低成本组件,完整实验不复现 |
| System One / JEV | The Company a Question Keeps: Auditing Open Jev-Style Models for Retrieval | Tsinghua University,2026-10-10 | 未找到本论文 artifact 的公开下载地址(2026-10-14 核验) | jev-request-audit |
| System One / JEV | Does System One Transfer? Calibrated Typed-Decision Models, Label Budgets, and Translation for Indonesian Regional Languages | Universitas Budi Luhur,2026-10-09 | 已开源 | jev-regional-transfer |
| 推理与系统效率 | DualQuant: Dual-scale Calibration-free Weight-and-Activation Quantization for LLMs | d-Matrix,2026-10-09 | 已开源 | dualquant |
| 多模态基础模型 | EV-JEPA: Elastic V-JEPA for Any Device and Cross-Device Inference | Meta Reality Labs Research,2026-10-09 | 未发现官方代码 | 计划键 ev-jepa:成本过高,暂不复现;待验收,未注册可执行入口。 |
| 网络架构 | Multi-Stream Attention Residuals | Shanghai Jiao Tong University,2026-10-09 | 未发现官方代码 | msar |
| 注意力与长上下文 | Not All Dimensions Are Equal: Outlier-Aware Attention Linearization with Grouped Quadratic Kernels | The Chinese University of Hong Kong, Shenzhen,2026-10-09 | 已开源 | oal |
| 网络架构 | On the Design Space of Continuous Diffusion Language Models | University of Oxford / Apple(在 Apple 期间完成),2026-10-09 | 未发现官方代码 | doublecat-semicat |
| 注意力与长上下文 | Orthros: Phase-Aware Heterogeneous Attention for Efficient Transformers | The Hong Kong Polytechnic University,2026-10-09 | 未发现官方代码 | orthros |
| 推理与系统效率 | ReplaySSM: Cache SSM Inputs, Not State | Princeton University,2026-10-09 | 已开源 | replayssm |
| 多模态基础模型 | SmolWAM: A Latent World-Action Model for Robotic Manipulation on Edge Devices | ML Collective,2026-10-09 | 已开源 | smolwam |
| 连续推理 | CoTS: Competitive Token Selection for Latent Reasoning in Large Language Models | Xiaomi AI Lab,2026-10-08 | 原作者入口;未识别明确许可证;公式独立实现,未vendor | cots:仅低成本组件,完整实验不复现 |
| 生成模型 | Few-Step Generation via Data-Space Iteration | ByteDance Seed,2026-10-08 | 已开源 | data-space-iteration |
| 多模态基础模型 | LeWAM: End-to-End World and Action Modeling with JEPAs | UC San Diego,2026-10-08 | 已开源 | lewam |
| 多模态基础模型 | MetaEncoder: Exploring the Limit of Bi-Encoders for Multimodal System One Decision Making with Natural Language Interface | Meta AI,2026-10-08 | 原作者入口;官方Apache-2.0评测代码和facebook/meta-encoder权重已发布;不等于已公开完整训练数据与训练源码 | 计划键 metaencoder:成本过高,不复现(审查 DONE);仅研究资料,无可执行入口。 |
| 训练优化与稳定性 | Rounding in Preconditioner Space: Redesigning 4-bit AdamW Optimizer-State Quantization | University of California, Berkeley / Nubank,2026-10-08 | 已开源 | adamw4bit |
| 稀疏专家与路由 | Smoothing the Top-k Exposure Boundary for Sparse Mixture-of-Experts | Shanghai AI Laboratory / Shanghai Jiao Tong University,2026-10-08 | 未找到作者公开代码 | elastic-expert-routing |
| 推理与系统效率 | SparseDecoding: Decoding-Aware Pruning for Accurate and Efficient LLM Inference | Westlake University,2026-10-08 | 尚未发布;项目页 Code (soon): https://wang-qitong.github.io/SparseDecoding/ | sparse-decoding |
| System One / JEV | Specialized Decision Models vs. General-Purpose LLMs: Benchmarking Jev Across Knowledge, Reasoning, and Multilingual Tasks | Dalian Maritime University,2026-10-08 | 未找到原作者公开代码(2026-10-10 核验) | jev-capability |
| 推理与系统效率 | VFold: Symmetry-Aware Cross-Layer Value Cache Compression | Johns Hopkins University,2026-10-08 | 已开源 | vfold |
| 视频理解 | Video Encoders Built on Image Representations | Stanford University,2026-10-05 | 未发现官方代码 | video-encoder-selection:仅低成本组件,完整实验不复现 |
| 优化器 | AF-Muon: An AdamW-Free Muon Optimizer for Tied-Embedding Models | 原文首页未列第一作者机构,2026-10-01 | 未发现官方代码 | af-muon |
| 结构剪枝 | IrekoGPT: Turning Structured Pruning into Post-Hoc Slimmable LLMs | University of Modena and Reggio Emilia,2026-10-01 | 已开源 | irekogpt |
| Jev | LLM2Jev: LLMs Are Already Jev-Style Decision Models -- When and How to Fine-Tune Them | Microsoft(按作者邮箱),2026-10-01 | 未发现官方代码 | llm2jev |
| 多模态大模型 | MWOP: Modality-aware Width-wise Operation Pruning for Efficient MLLMs | 原文首页未列第一作者机构,2026-10-01 | 未发现官方代码 | mwop |
| 多模态嵌入 | Omni-Embed-Mini: Binding Modalities Without Forgetting via Dense Distillation | 原文首页未列第一作者机构,2026-10-01 | 未发现官方代码 | omni-embed-mini |
| 长上下文 | Role-aware Heuristic Episodic Attention for Conversational LLMs | 原文首页未列第一作者机构,2026-10-01 | 未发现官方代码 | rea |
| 优化器 | TACO: Ternary Absolute-max Column-wise One-sparse Optimizer for LLM Fine-Tuning | University of Central Florida,2026-10-01 | 已开源 | taco-optimizer |
| 预训练与数据 | TESS: Scalable, Transferable Meta-network for Data Selection Requires a Different Loss (and Why the Obvious Choice is Problematic) | Nanyang Technological University,2026-10-01 | 未发现官方代码 | tess |
| 视频 VLM | VETO: Video Efficient Token Optimization for Vision Language Models | National Taiwan University,2026-10-01 | 未发现官方代码 | veto |
| 多模态推理 | HAWK: Rethinking Multimodal Drafting for Speculative Decoding | 原文首页未列第一作者机构,2026-09-30 | 未发现官方代码 | hawk |
| System One / JEV | Chinese-Jev: Bringing System One Model to Chinese-Language Tasks | Fudan University,2026-09-29 | 已开源 | chinese-jev |
| MoE | Cross-Entropy Guided Routing in Mixture-of-Experts Large Language Models | Bar-Ilan University,2026-09-29 | 未发现官方代码 | ce-guided-moe |
| 推理与系统效率 | LeapQuant: Efficient Linear Attention with Accurate Recurrent State Quantization | UC Berkeley,2026-09-29 | 未发现官方代码 | leapquant |
| 扩散语言模型 | On Trajectory-Aware Training for Masked Diffusion Language Models | Apple,2026-09-29 | 未发现官方代码 | pumba |
| 网络架构 | Pretraining Latent Information Feedback Transformers with Teacher Supervision | Tel Aviv University,2026-09-29 | 已开源 | lift-feedback |
| 推理系统 | SPLASH: Switching Parallel Layouts of Attention with Seamless Handoff for LLM Serving | Institute of Computing Technology, Chinese Academy of Sciences,2026-09-29 | 已开源 | splash |
| 推理与系统效率 | STEPQuant: When and Where Errors Matter in Delta-Rule Recurrent State Quantization | Zhejiang University,2026-09-29 | 已开源 | stepquant |
| 扩散语言模型 | Time-Anchored Diffusion Language Models: Latent-Space Caching for Fast Generation | The University of Texas at Austin,2026-09-29 | 未发现官方代码 | tadm |
| 网络架构 | Triadic Linear Attention: Three-Dimensional Recurrent States for Long-Context Sequence Modeling | MIT / MIT-IBM Watson AI Lab,2026-09-29 | 已开源 | triadic-linear-attention |
| 世界动作模型 | V-JEPA Policy: Building Effective World-Action Models on Predictive Visual Latents | Tsinghua University,2026-09-29 | 已开源 | vjepa-policy |
| 状态空间模型 | Fractional State Space Transition for Long Sequence Modeling | Huawei Noah's Ark Lab, Montreal Research Center,2026-09-28 | 已开源 | frac-ssm |
| 网络架构 | MS-GLA: Multi-Scale Gated Linear Attention for Addressing Representational Bottlenecks via Multi-Temporal Resolution | International Institute of Information Technology Hyderabad,2026-09-28 | 已开源 | ms-gla |
| 网络架构 | Telescopic Language Models | University of Cambridge,2026-09-28 | 未发现官方代码 | telescopic-lm |
| 公开评测协议 | Words Speak Louder Than Order: A Behavioral Evaluation of Gemma 4 | Google,2026-09-25 | 原作者入口;作者官方代码与AD-series原始观测已公开;本地仅复算固定FINAL.json,不使用不可聚合pilot或执行J-Lens、patching、模型推理 | gemma-priority-protocol:仅低成本组件,完整实验不复现 |
| 扩散语言模型 | dQwen3.5: Adapting Hybrid Language Models into Bidirectional Diffusion Language Models | University of Texas at Austin,2026-09-17 | 未发现官方代码 | dqwen35 |
| 注意力与长上下文 | On-Demand Attention: Efficient Long-Context Decoding with Learned Recall | Southern University of Science and Technology,2026-09-17 | 未发现官方代码 | oda |
| 推测解码 | ASPIRE: Asynchronous Batch Self-Speculative Decoding | University of Southern California,2026-09-16 | 未发现官方代码 | aspire |
| 推测解码 | ECHO: Early-layer Collaborative Hierarchical Orchestration with Bonus Logits in Speculative Decoding | Wuhan University,2026-09-15 | 已开源 | echo |
| 基础模型检索与排序 | How Calibration Content Shapes Attention-Based Reranking | UC San Diego,2026-09-15 | 未发现官方代码 | interpolated-null-calibration:仅低成本组件,完整实验不复现 |
| 推测解码 | LoopSpec: Pipelined Self-Speculative Decoding for Looped Transformers | Seoul National University / KAIST,2026-09-15 | 未发现官方代码 | loopspec |
| 可复现训练 | OPEN-1B: A Fully Auditable Training Run | Gensyn,2026-09-15 | 已开源 | open-1b-audit |
| 网络架构 | Persistent Recurrent Memory Between Transformer Layers Improves Language Model Generalization | FITec Labs / Ericsson São Paulo,2026-09-15 | 未发现官方代码 | persistent-recurrent-memory |
| Agent 推理 | AgentKV: Phase-Aware KV Eviction for Agentic LLMs | University of Cambridge,2026-09-14 | 已开源 | agentkv |
| 扩散语言模型 | Register Tokens for Bounded-State Reasoning in Diffusion Language Models | University of Wisconsin–Madison,2026-09-14 | 未发现官方代码 | register-tokens-dllm |
| 注意力与长上下文 | SAS: Simple Attention Sparsification via End-to-End Optimization of Context Ranking | Tencent HY LLM Frontier / HKUST (Guangzhou) / HKUST,2026-09-11 | 已开源 | sas-attention |
| 长视频理解 | Caption-once, Frames-on-Demand: Visual-Need Routing for Budget-Aware Agentic Long Video Understanding | Queen Mary University of London,2026-09-10 | 未发现官方代码 | frames-on-demand |
| MoE | Data Scarcity and Model Sparsity: Mixtures-of-Experts Overfit More to Repeated Data | Stanford University,2026-09-10 | 未发现官方代码 | repeat-aware-moe |
| 优化器 | Musec: MomentUm SpEctral Clipping for Stable Muon-type Training | National University of Singapore,2026-09-10 | 未发现官方代码 | musec |
| 多模态推理 | OmniKVQuant: KV Cache Quantization for Omni-LLMs | KAIST,2026-09-10 | 已开源 | omnikvquant |
| 统一多模态 | SenseNova-U1.5: Towards Native Unified Visual Intelligence | SenseTime Research,2026-09-10 | 已开源 | sensenova-u1-5 |
| 模型路由 | SWRouter: Similarity-Contractive Window Routing for Multi-Turn Large Language Model Conversations | Shanghai Jiao Tong University,2026-09-10 | 未发现官方代码 | swrouter |
| 推理与系统效率 | BeaconKV: Key-Value Cache Compression Guided by Beacon Queries for Efficient Large Reasoning Model Inference | Hanyang University,2026-09-04 | 未发现官方代码 | beaconkv |
| 推理与系统效率 | KVMem: Virtualizing Million-Token Agent Workspaces on a Consumer GPU | Shanghai University of Finance and Economics,2026-09-04 | 已开源 | kvmem |
| 网络架构 | Lngram v2: Latent N-Gram Memory with Interpretable Discrete Representations | Beijing University of Posts and Telecommunications / Kuaishou Technology,2026-09-03 | 未发现官方代码 | lngram-v2 |
| 推理与系统效率 | Random Attention: Rethinking KV Cache Eviction for Efficient Reasoning | Salesforce AI Research / University of Illinois Urbana-Champaign,2026-09-03 | 已开源 | random-attention |
| 弱模型失败模式 ICL | CritICL: Inference-Time Weak-to-Strong Generalization from Small Language Model Failure Modes | The Ohio State University,2026-08-27 | 未发现官方代码 | criticl |
| 多模态基础模型 | PACE: A Unified Condense-and-Extract Paradigm for Fast VLM Inference | Sun Yat-sen University,2026-08-27 | 已开源 | pace-vlm |
| 注意力与长上下文 | TwinKV: A Composable Repair Pass for KV Cache Eviction via Pairwise Key Redundancy | The Hong Kong University of Science and Technology (Guangzhou),2026-08-27 | 未发现官方代码 | twinkv |
| 多模态基础模型 | MLLMCLIP: Feature-Level Distillation of MLLM for Robust Vision-Language Representations | KAIST / Sony AI,2026-08-26 | 未发现官方代码 | mllmclip |
| 多模态基础模型 | VBVR-Pro: A Scalable and Verifiable Suite for Native Visual Reasoning | Nanyang Technological University / VBVR Community,2026-08-26 | 已开源 | vbvr-pro |
| 多模态基础模型 | WeMM-Embedding: WeChat Multi-Modal Embedding Technical Report | WeChat Vision, Tencent,2026-08-25 | 已开源 | wemm-embedding |
| 网络架构 | RARE: Decoupling Representation Steering from Expert Routing in Mixture-of-Experts Language Models | Huazhong University of Science and Technology,2026-08-21 | 未发现官方代码 | rare |
| 预训练与数据 | Fast A/B/n Testing: Exact Multi-Policy Comparison via Tree-Coupled Feedback Sharing | New York University,2026-08-13 | 未发现官方代码 | tcab |
| 多模态基础模型 | Generation as Auxiliary Supervision: Enhancing Visual Understanding at Zero Inference Overhead via Decoupled Embedding Prediction | ByteDance,2026-08-12 | 未发现官方代码 | gas |
| 注意力与长上下文 | Cracks in the Foundation: Seemingly Minor Architectural Choices Impact Long Context Extension | Ai2,2026-08-10 | 已开源 | olmpool-long-context |
| 推理与系统效率 | DistillCache: KL-Guided Adaptive KV-Cache Eviction for Memory-Efficient LLM Inference | Oklahoma State University,2026-08-09 | 未发现官方代码 | distillcache |
| 注意力与长上下文 | Autonomy-of-Heads: Data-Free Sparse Attention from Frozen Query-Key Geometry | Institute of Computing Technology, Chinese Academy of Sciences,2026-08-07 | 未发现官方代码 | autonomy-heads |
| 推理与系统效率 | BaKron: Efficient Quantization with Kronecker-Factored Hessians | University of California, San Diego,2026-08-06 | 未发现官方代码 | bakron |
| 评测与可靠性 | Detecting Safety Training Modification in Language Models via Activation Analysis | Google Cloud,2026-08-06 | 原作者入口;原作者已开源;本轮固定7ae56d3c94d49ca023eb23ef79a241b72b89b35d | activation-model-scanner:仅低成本组件,完整实验不复现 |
| 预训练与数据 | Hierarchical Latent Prediction for Language Models | University of Texas at Austin,2026-08-06 | 未发现官方代码 | hilp |
| 网络架构 | MACRO: Markov Chain Routing of Transformer Layers | Heinrich Heine University Düsseldorf,2026-08-06 | 已开源 | macro |
| 推理与系统效率 | DBLast: Dependent Block Drafting for Stochastic Speculative Decoding | Huawei Technologies Canada,2026-08-05 | 未发现官方代码 | dblast |
| 推理与系统效率 | QEvict: Recoverable Quantized KV Eviction for Attention-Drift-Robust Long-Context Decoding | Indian Institute of Technology Roorkee,2026-08-05 | 未发现官方代码 | qevict |
| 多模态基础模型 | Towards Physics of Multimodal Pretraining: Knowledge Flow, Modality Synergy, Early Unification, and Recipes | Meta,2026-08-05 | 未发现官方代码 | physics-mm-pretraining |
| 推理与系统效率 | TaskPress: Query-Agnostic KV Cache Compression via Task-Guided Pruning | Google,2026-08-04 | 未发现官方代码 | taskpress:仅低成本组件,完整实验不复现 |
| 网络架构 | DART: Decoded Attention over Recurrent States for Efficient Long-Context Sequence Modeling | Zhejiang University,2026-08-03 | 未发现官方代码 | dart |
| 注意力与长上下文 | Learning What to Remember: Test-Time Training via Context Distillation | Princeton University,2026-08-03 | 已开源 | ttcd |
| 网络架构 | Role-Decoupled Attention Residuals | Kehan Wang(论文未列机构),2026-08-03 | 未发现官方代码 | rd-attnres |
| 网络架构 | TransMem: Transforming Hidden States into Memory for Large Language Models | Authors did not disclose affiliation,2026-07-31 | 未发现官方代码 | transmem |
| 多模态基础模型 | ReToken: One Token to Improve Vision–Language Models for Visual Retrieval | UIUC / Microsoft Research / Google DeepMind,2026-07-30 | 已开源 | retoken |
| 推理与系统效率 | WIDE: Boosting Adaptive LLM Inference via Token-level Dynamic Width Pruning | EIT-NLP / LMU Munich,2026-07-30 | 已开源 | wide |
| 网络架构 | Penelope: Localized Latent Recurrence for Efficient Structured Reasoning | Academic author team,2026-07-28 | 未发现官方代码 | penelope |
| 预训练与数据 | DataOrchestra: Learning to Orchestrate Per-Example Curation of Pretraining Data | Fudan University / Shanghai Jiao Tong University / SII-GAIR,2026-07-27 | 已开源 | data-orchestra |
| 推理与系统效率 | Adaptive Depth Sparse Framework: Similarity-Driven Resource Allocation for Pre-Trained LLMs | Huawei ACS Lab / Southern University of Science and Technology,2026-07-23 | 未发现官方代码 | adadsf |
| 注意力与长上下文 | Anti-Periodic Positional Encoding: Möbius Boundary Conditions Make In-Context Retrieval Reliable | Independent researcher,2026-07-23 | 未发现官方代码 | mobius-rope |
| 推理与系统效率 | Hilbert Operator for Progressive Encoding (HOPE): A Mathematical Framework for Deconstructing Learned Representations in Deep Networks | Google DeepMind,2026-07-23 | 原作者入口;Google DeepMind于2026-10-27新公开官方实现,固定f3c4b251d0c9dae05fb165b9bc645a5a52955e62;本地据论文独立实现子组件,不复制上游库 | hope-hilbert:仅低成本组件,完整实验不复现 |
| 网络架构 | Naju: A Native Discrete State-Space Model with Independent Retention and Writing for Long-Sequence Memory | Independent researchers,2026-07-23 | 未发现官方代码 | naju |
| 注意力与长上下文 | Parameter-free Adaptive Sparse Attention via Compression-Based Content Selection | Pennsylvania State University,2026-07-23 | 未发现官方代码 | gzip-sparse-attention |
| 推理与系统效率 | Windowed-MTP: Removing the Full-Context Draft-KV Tax at Million-Token Context | NVIDIA,2026-07-23 | 已开源 | windowed-mtp |
| 推理与系统效率 | GaugeQuant: Online Learning of Quantization-Optimal Bases from LLM Symmetries | University of Cambridge,2026-07-22 | 已开源 | gaugequant |
| 网络架构 | Convolution for Large Language Models | Huawei / Peking University / Tsinghua University,2026-07-20 | 未发现官方代码 | conv-llm |
| 推理与系统效率 | C²KV: Compressed and Composable KV Cache Reuse for Efficient LLM Inference | Shanghai Jiao Tong University,2026-07-20 | 已开源 | c2kv |
| 预训练与数据 | PPL-Factory: Task-Aware and Budget-Aware Data Selection from Language Modeling to Reasoning | McGill University,2026-07-20 | 未发现官方代码 | ppl-factory |
| 预训练与数据 | OpenLanguageModel: Readable and Composable Small-Language-Model Pretraining for Education and Research | Indian Institute of Technology Madras,2026-07-18 | 已开源 | open-language-model |
| 注意力与长上下文 | Looped Latent Attention: Cross-Loop KV Compression for Looped Transformers | University of Maryland / Meta AI,2026-07-16 | 未发现官方代码 | looped-latent-attention |
| 多语言推理与评估 | Cross-Lingual Exploration for Parametric Knowledge | The Hebrew University of Jerusalem,2026-06-23 | 未发现官方代码 | 计划键 cross-lingual-exploration:成本过高,不复现(审查 DONE);仅研究资料,无可执行入口。 |
| 注意力与长上下文 | MiniMax Sparse Attention | MiniMax,2026-06-11 | 已开源 | minimax-sparse-attention |
| 持续学习 | Language Models Need Sleep: Learning to Self-Modify and Consolidate Memories | Google Research,2026-06-02 | 未发现官方代码 | sleep-lti:仅低成本组件,完整实验不复现 |
| 跨语言评测 | XLGoBench: Detecting cross-lingual skill gaps with algorithmic tasks | Google DeepMind,2026-05-29 | 原作者入口;作者补充材料v1公开:Zenodo20423110,文件MD5=1fd5dd56c216a407dd5a13f08b0f1065;无Git SHA,不伪造。只核正文与公开元数据,独立实现结构化scorer,不复制官方多语模板 | xlgobench:仅低成本组件,完整实验不复现 |
| 网络架构 | Memory Grafting: Scaling Language Model Pre-training via Offline Conditional Memory | Tsinghua University / Microsoft Research Asia,2026-05-20 | 未发现官方代码 | memory-grafting |
| 企业 LLM | Customizing an LLM for Enterprise Software Engineering | Google,2026-05-15 | 未发现官方代码 | gemini-for-google |
| 模型路由 | Optimized Deferral for Imbalanced Settings | Google Research,2026-04-30 | 未发现官方代码 | mild:仅低成本组件,完整实验不复现 |
| 模型评测 | ProEval: Proactive Failure Discovery and Efficient Performance Estimation for Generative AI Evaluation | Google DeepMind,2026-04-25 | 原作者入口;作者原文明确链接公开仓库;本地未执行上游训练 | proeval:仅低成本组件,完整实验不复现 |
| 推理与系统效率 | FUSE: Ensembling Verifiers with Zero Labeled Data | Stanford University,2026-04-20 | 未发现官方代码 | 计划键 fuse-verifiers:成本过高,不复现(审查 DONE);仅研究资料,无可执行入口。 |
| 注意力与长上下文 | Switch Attention: Towards Dynamic and Fine-grained Hybrid Transformers | Peking University / Huawei Technologies,2026-03-27 | 未发现官方代码 | switch-attention |
| 生成式检索 | Efficient, Property-Aligned Fan-Out Retrieval via RL-Compiled Diffusion | University of Illinois Urbana-Champaign / Google Research,2026-03-06 | 未发现官方代码 | r4t |
| 预训练与数据 | Theoretical Perspectives on Data Quality and Synergistic Effects in Pre- and Post-Training Reasoning Models | University of Southern California / Google Research,2026-03-01 | 未发现官方代码 | data-quality-lsa:仅低成本组件,完整实验不复现 |
| 网络架构 | A Theoretical Framework for Modular Learning of Robust Generative Models | Google Research,2026-02-19 | 未发现官方代码 | modular-robust-generative:仅低成本组件,完整实验不复现 |
| 预训练与数据 | ECO: Quantized Training without Full-Precision Master Weights | Google Research / ISTA,2026-01-29 | 未发现官方代码 | 计划键 eco-optimizer:成本过高,不复现(审查 DONE);仅研究资料,无可执行入口。 |
| 预训练与数据 | Mobility-Embedded POIs: Learning What A Place Is and How It Is Used from Human Movement | University of Southern California,2026-01-29 | 未发现官方代码 | me-pois:仅低成本组件,完整实验不复现 |
| 网络架构 | Conditional Memory via Scalable Lookup: A New Axis of Sparsity for Large Language Models | DeepSeek,2026-01-12 | 已开源 | engram |
| 网络架构 | mHC: Manifold-Constrained Hyper-Connections | DeepSeek-AI,2025-12-31 | 未发现官方代码 | mhc |
| 安全与可验证性 | Detecting Post-generation Edits to Watermarked LLM Outputs via Combinatorial Watermarking | University of Minnesota,2025-10-02 | 未发现官方代码 | combinatorial-watermark:仅低成本组件,完整实验不复现 |
| 动态候选分类 | GLiClass: Generalist Lightweight Model for Sequence Classification Tasks | Knowledgator,2025-08-11 | 已开源 | gliclass |
| 可解释性 | Model Directions, Not Words: Mechanistic Topic Models Using Sparse Autoencoders | Columbia University,2025-07-31 | 原作者入口;作者方法代码公开,固定commit核验MIT;本地独立按固定v3公式实现mETM/steering子组件,不复制上游代码 | mechanistic-etm:仅低成本组件,完整实验不复现 |
| 注意力与长上下文 | Gated Attention for Large Language Models: Non-linearity, Sparsity, and Attention-Sink-Free | Qwen / Alibaba,2025-05-10 | 已开源 | gated-attention |
| 多模态基础模型 | SmolVLM: Redefining small and efficient multimodal models | Hugging Face,2025-04-07 | 已开源 | smolvlm |
| 预训练与数据 | Muon is Scalable for LLM Training | Moonshot AI / UCLA,2025-02-24 | 已开源 | muon |
| 多模态基础模型 | SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features | Google DeepMind,2025-02-20 | 已开源 | siglip2 |
| 注意力与长上下文 | MoBA: Mixture of Block Attention for Long-Context LLMs | Moonshot AI,2025-02-18 | 已开源 | moba |
| 注意力与长上下文 | Native Sparse Attention: Hardware-Aligned and Natively Trainable Sparse Attention | DeepSeek,2025-02-16 | 未发现官方代码 | native-sparse-attention |
| 网络架构 | Byte Latent Transformer: Patches Scale Better Than Tokens | Meta FAIR,2024-12-13 | 已开源 | blt |
| 网络架构 | Hymba: A Hybrid-head Architecture for Small Language Models | NVIDIA,2024-11-20 | 已开源 | hymba |
| 预训练与数据 | Data Mixing Laws: Optimizing Data Mixtures by Predicting Language Modeling Performance | University of Cambridge / Shanghai AI Laboratory,2024-03-25 | 已开源 | data-mixing-laws |
| 推理与系统效率 | Medusa: Simple LLM Inference Acceleration Framework with Multiple Decoding Heads | Together AI / Princeton University / University of Illinois Urbana-Champaign,2024-01-19 | 已开源 | medusa |
| 网络架构 | Mamba: Linear-Time Sequence Modeling with Selective State Spaces | Carnegie Mellon University / Princeton University,2023-12-01 | 已开源 | mamba |
| 推理与系统效率 | AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration | MIT / NVIDIA / Harvard / SJTU,2023-06-01 | 已开源 | awq |
| 注意力与长上下文 | GQA: Training Generalized Multi-Query Transformer Models from Multi-Head Checkpoints | Google Research,2023-05-22 | 未发现官方代码 | gqa |
| 预训练与数据 | DoReMi: Optimizing Data Mixtures Speeds Up Language Model Pretraining | Stanford University / Google Research,2023-05-17 | 已开源 | doremi |
| 多模态基础模型 | Visual Instruction Tuning | University of Wisconsin-Madison / Microsoft Research / Columbia University,2023-04-17 | 已开源 | llava |
| 多模态基础模型 | BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models | Salesforce Research,2023-01-30 | 已开源 | blip2 |
| 推理与系统效率 | Fast Inference from Transformers via Speculative Decoding | Google Research,2022-11-30 | 已开源 | speculative-decoding |
| 注意力与长上下文 | Train Short, Test Long: Attention with Linear Biases Enables Input Length Extrapolation | University of Washington / Meta AI,2021-08-27 | 已开源 | alibi |
| 注意力与长上下文 | RoFormer: Enhanced Transformer with Rotary Position Embedding | Zhuiyi Technology,2021-04-20 | 已开源 | rope |
| 多模态基础模型 | Learning Transferable Visual Models From Natural Language Supervision | OpenAI,2021-02-26 | 已开源 | clip |
| 网络架构 | Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity | Google Brain,2021-01-11 | 已开源 | switch-transformer |
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