论文库

新论文 · v1arXiv:2611.12180

更新于 2026-11-09

TS-GEAR: Taobao Shangou GEnerative Ad Retrieval

Guofeng Tong, Xu Zhang, Guangliang Yu, Haiqiang Wang, Guannan Cong, Xing Guo, Yang Zhou, Jingjing Duan 等 11 位作者

原文摘要

On-demand delivery platforms such as DoorDash and Uber Eats impose a constraint absent in traditional e-commerce: merchants beyond the user's delivery range are invalid candidates rather than merely low-ranked. Generative retrieval has recently shown strong potential in large-scale retrieval systems. Adapting it to this setting, however, poses three challenges. First, existing semantic IDs encode geography only as a soft signal, so reachability can only rely on a non-differentiable post-generation hard truncation. Second, user preferences vary substantially across both regions and queries, so aggregating historical behaviors without conditioning on geography and the current query yields biased interest estimates. Third, the retrieved candidate set is evaluated by its collective value, yet autoregressive training scores each sequence independently, with no set-level signal to discourage redundant near-duplicates. To address the challenges, we propose TS-GEAR, a unified generative retrieval framework for on-demand delivery: (1) Geo-Semantic ID splits merchant identifiers into geographic prefixes and semantic suffixes, making reachability a prediction target rather than a post-hoc filter; (2) Query-Geo Target Attention aggregates historical behaviors using the real-time query and location as dual targets, while encoding each behavior's own location into its attention key; (3) GMPO (Group Marginal Policy Optimization) rewrites advantages using a deduplicated group reward and marginal advantage within GRPO, avoiding auxiliary diversity objectives. Deployed online with a lightweight 0.1B backbone, TS-GEAR serves over 150 million queries per day in the search advertising pipeline of Taobao Shangou, consistently outperforming state-of-the-art generative retrieval methods on both HitRate and geographic validity, and achieving 3.8% gains in ad impressions and 4.2% in ad revenue.

cs.IR · cs.AI · cs.CL

新论文 · v1arXiv:2611.11817

更新于 2026-11-09

VeilTune: Secure Fine-Tuning of Private LoRA Adapters on Public Pretrained Language Models

Huiyu Xie, Tanping Zhou, Tao Hu, Xinlei Liu, Hailong Ma

原文摘要

Fine-tuning public pretrained language models with lightweight adapters such as LoRA has become the mainstream way to adapt them to downstream tasks. A service provider often trains its adapter on a data owner's samples, so two assets need protection: the data owner's samples and the provider's adapter. Existing secure fine-tuning protocols protect the samples but reveal to the data owner intermediate results of every training step, such as the hidden representations or logits of its inputs. We show that a single step suffices to copy the adapter: from the hidden representations of one mini-batch of 32 samples, the data owner fits an adapter that agrees with the provider's on 99.2% of test inputs, and logits alone yield about 94%. Protecting both assets therefore requires that no value that depends on the adapter be revealed throughout training. We present VeilTune, to the best of our knowledge the first protocol that fine-tunes a pretrained language model with backpropagation entirely in secure multi-party computation (MPC). VeilTune computes only what the loss depends on, replaces generic MPC operators with training-aware nonlinear operators that stay accurate on the values that training produces, and obliviously skips the steps corrupted by truncation failures. On four few-shot tasks with RoBERTa-large, VeilTune nearly matches plaintext training and is 5.3 points more accurate on average than SecP-Tuning, the state-of-the-art secure fine-tuning protocol. On SST-2, it achieves a 3.5x end-to-end speedup and a 3.6x reduction in communication over SecP-Tuning, and 26x and 17x over full fine-tuning in MPC.

cs.CR · cs.AI

新论文 · v1arXiv:2611.11043

更新于 2026-11-08

GARDEN: Efficient GPU-Accelerated Billion-Scale GNN Training via Caching-Sampling-Batching Co-Design

Qingrui Zhu, Lei Liu, Yushu Cai, Kai Sheng, Zhiwen Chen, Xin He

原文摘要

Graph Neural Networks (GNNs) have shown impressive performance in diverse tasks such as social network analysis and recommendation systems. However, scaling their training to billion-edge graphs is hindered by the memory wall: GPU memory capacity falls short of the massive storage requirements for node features and topology. Sampling-based GNN training frameworks mitigate this by streaming subgraphs from CPU to GPU, but they incur prohibitive PCIe data transfers and non-coalesced memory accesses, resulting in bandwidth underutilization and low compute efficiency. We propose GARDEN, a GPU-accelerated sampling-based system that co-designs caching, sampling, and batching to eliminate these bottlenecks. GARDEN introduces three key innovations: (1) an Adaptive High-Fidelity Cache Allocator (AHFCA) that combines offline reuse profiling and online hotness tracking to dynamically cache features and adjacency lists within a fixed GPU memory budget, avoiding static allocation waste; (2) a Grouping-based Parallel Sampler that reorders stochastic neighborhood accesses(GPS) to maximize warp occupancy and hide latency on power-law graphs; and (3) a Super-batch Degree Classifier(SDC) that fuses mini-batches into degree-homogeneous groups, enabling cache-friendly coalesced transfers and high GPU utilization. Evaluations on real-world billion-scale graphs demonstrate that GARDEN achieves 4.54x, 1.27x, and 2.97x end-to-end training speedups over DGL-UVA, DUCATI, and FastGL, respectively, while maintaining model convergence and accuracy.

cs.DC

新论文 · v1arXiv:2611.04493

更新于 2026-11-03

Physical Diffusion Policy via MPM-Guided 3D Gaussian Simulation

Can Wang, Qiyuan Qiao, Lei Liu, Ge Yuan, Xinhui Liu, Dong Xu

原文摘要

Learning robust manipulation policies for diverse everyday objects remains challenging because objects with different materials exhibit distinct deformation and contact dynamics. While diffusion policies provide a powerful framework for modeling multimodal manipulation behaviors, they typically lack explicit physical reasoning and may produce averaged actions when facing heterogeneous objects. We present a physics-aware diffusion policy that augments visual imitation learning with object physical understanding. Our framework recovers material properties from visual demonstrations by coupling 3D Gaussian Splatting with differentiable Material Point Method simulation, where video guidance and particle-level constraints enable accurate physical parameter estimation. The estimated physics conditions are further integrated into a material-aware Mixture-of-Experts diffusion policy, allowing adaptive behavior generation across different physical regimes. For practical deployment, we introduce a physics memory bank that provides efficient physical retrieval without requiring online simulation optimization. Experiments on a wide range of simulated and real-world objects demonstrate improved manipulation success and generalization over existing diffusion-based approaches. These results show that incorporating physical knowledge is critical for scalable robot manipulation in diverse environments.

cs.RO

新论文 · v1arXiv:2610.39558

更新于 2026-10-29

TabCUQ: Context-Uncertainty Quantification for Tabular Foundation Models

Yuebei Shi, Molei Liu, Zijian Guo

原文摘要

Tabular foundation models use in-context learning to estimate conditional expected outcomes and average treatment effects for specific groups of individuals. Using these estimates to guide scientific decisions requires reliable uncertainty quantification. The challenge is to account simultaneously for two sources of uncertainty: limited-context bias and context variation arising from the small labeled sample supplied at inference. Predictive spread does not directly quantify both sources of uncertainty, and we observe that the ordinary bootstrap may substantially overestimate context variation, whereas subsampling may underestimate it. We introduce TabCUQ, a perturbation-based framework that injects noise into observed outcomes and applies the foundation model to the perturbed contexts. TabCUQ estimates context variation using wild-bootstrap dispersion and accounts for limited-context bias using the maximum deviation between perturbed and original estimates. For a kernel-regression analogue whose estimation error admits an exact decomposition into these two components, we show that our framework provides asymptotically valid pointwise coverage. In simulations, TabCUQ substantially improves coverage, with average interval widths typically about twice those of an oracle bias-aware benchmark. Across seven real-world tasks, it also improves coverage of reference functions learned from much larger samples.

stat.ML · cs.LG

新论文 · v1arXiv:2610.38391

更新于 2026-10-28

SCOPE: Selective Critical-Step On-Policy Self-Distillation for Agentic Reinforcement Learning

Qirui Liu, Wei Cao, Yu Mi, Yichen Sun, Xuekun Zhang, Lei Liu, Yue Shen, Zhixuan Chu

原文摘要

Outcome-based reinforcement learning provides coarse supervision for long-horizon LLM agents: failed trajectories may contain correct steps, while successful trajectories can contain errors. Trajectory-level skills can also shift teacher preferences at unrelated steps, introducing off-target distillation signals. We introduce SCOPE, a framework for Selective Critical-Step On-Policy Self-Distillation. After a supervised warm-up, the policy analyzes its own trajectories, generates step-specific hindsight skills, and distills their guidance only at selected error steps. A bounded signed teacher-student coefficient stabilizes auxiliary updates while preserving positive reinforcement and negative correction. A success-relative pairwise length reward further encourages efficient task completion. Experiments on ALFWorld and WebShop demonstrate consistent improvements across multiple model backbones. With Qwen2.5-3B, SCOPE achieves 97.7% success on ALFWorld and 85.2% on WebShop, outperforming SEED by 5.9 and 6.3 percentage points, respectively. On unseen ALFWorld tasks, SCOPE reaches 89.6% success, improving over GRPO by 23.2 percentage points with 46.7% fewer interactions. Scaling to Qwen3-32B, SCOPE achieves 99.2% success on seen ALFWorld tasks and 94.0% on unseen tasks.

cs.AI

版本更新 · v4arXiv:2505.00940

更新于 2026-10-28

StablePCA: Distributionally Robust Learning of Shared Representations from Multi-Source Data

Zhenyu Wang, Molei Liu, Jing Lei, Francis Bach, Zijian Guo

原文摘要

When synthesizing multi-source high-dimensional data, a key objective is to extract low-dimensional representations that effectively approximate the original features across different sources. Such representations facilitate the discovery of transferable structures and help mitigate systematic biases such as batch effects. We introduce Stable Principal Component Analysis (StablePCA), a distributionally robust framework for constructing stable latent representations by maximizing the worst-case explained variance over multiple sources. A primary challenge in extending classical PCA to the multi-source setting lies in the nonconvex rank constraint, which renders the StablePCA formulation a nonconvex optimization problem. To overcome this challenge, we conduct a convex relaxation of StablePCA and develop an efficient Mirror-Prox algorithm to solve the relaxed problem, with global convergence guarantees. Since the relaxed problem generally differs from the original formulation, we further introduce a data-dependent certificate to assess how well the algorithm solves the original nonconvex problem and establish the condition under which the relaxation is tight. Finally, we explore alternative distributionally robust formulations of multi-source PCA based on different loss functions.

cs.LG · math.OC · stat.CO · stat.ME

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.1309ACL Anthology · ACL 2026

来源日期 2026-07-01（仅到月份）

Read As Human: Compressing Context via Parallelizable Close Reading and Skimming

Jiwei Tang, Shilei Liu, Zhicheng Zhang, Qingsong Lv, Runsong Zhao, Tingwei Lu, Langming Liu Alibaba Group, Haibin Chen 等 12 位作者

原文摘要

Large Language Models (LLMs) demonstrate exceptional capability across diverse tasks. However, their deployment in long-context scenarios is hindered by two challenges: computational inefficiency and redundant information. We propose RAM (Read As HuMan), a context compression framework that adopts an adaptive hybrid reading strategy, to address these challenges. Inspired by human reading behavior (i.e., close reading important content while skimming less relevant content), RAM partitions the context into segments and encodes them with the input query in parallel. High-relevance segments are fully retained (close reading), while low-relevance ones are query-guided compressed into compact summary vectors (skimming). Both explicit textual segments and implicit summary vectors are concatenated and fed into decoder to achieve both superior performance and natural language format interpretability. To refine the decision boundary between close reading and skimming, we further introduce a contrastive learning objective based on positive and negative query–segment pairs. Experiments demonstrate that RAM outperforms existing baselines on multiple question answering and summarization benchmarks across two backbones, while delivering up to a 12x end-to-end speedup on long inputs (average length 16K; maximum length 32K).

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.1779ACL Anthology · ACL 2026

来源日期 2026-07-01（仅到月份）

CoMeT: Collaborative Memory Transformer for Efficient Long Context Modeling

Runsong Zhao, Shilei Liu, Jiwei Tang, Langming Liu Alibaba Group, Haibin Chen, Weidong Zhang Alibaba Group, Yujin Yuan, Tong Xiao Northeastern University 等 11 位作者

原文摘要

The quadratic complexity and indefinitely growing key-value (KV) cache of standard Transformers pose a major barrier to long-context processing. To overcome this, we introduce the Collaborative Memory Transformer (CoMeT), a novel architecture that enables LLMs to handle arbitrarily long sequences with constant memory usage and linear time complexity. Designed as an efficient, plug-in module, CoMeT can be integrated into pre-trained models with only minimal fine-tuning. It operates on sequential data chunks, using a dual-memory system to manage context: a temporary memory on a FIFO queue for recent events, and a global memory with a gated update rule for long-range dependencies. These memories then act as a dynamic soft prompt for the next chunk. The effectiveness of our approach is remarkable: a model equipped with CoMeT and fine-tuned on 32k contexts can accurately retrieve a passkey from any position within a 1M token sequence. On the SCROLLS benchmark, CoMeT surpasses other efficient methods and achieves performance comparable to a full-attention baseline on summarization tasks. Its practical effectiveness is further validated on real-world agent and user behavior QA tasks, supported by a novel layer-level pipeline parallel training strategy that enables fine-tuning on extremely long contexts. The code is available at: https://github.com/LivingFutureLab/Comet

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.2219ACL Anthology · ACL 2026

来源日期 2026-07-01（仅到月份）

SELECting over Tokens: Curating Pre-training Data at Scale via Token Classification

Xin Tong Alibaba Group, Weidong Zhang Alibaba Group, Jiaang Li Alibaba Group, Haibin Chen, Shilei Liu, Langming Liu Alibaba Group, Kangtao Lv, Yujin Yuan 等 10 位作者

原文摘要

The quality of pre-training data critically impacts the capabilities of large language models. Existing pipelines rely on expert-crafted heuristic rules, which primarily operate at the sample level and are based on coarse statistical indicators, thus lacking content-aware, fine-grained noise detection. While recent generative approaches, e.g., ProX-C, enable token-level refinement, their reliance on synthesizing Python code incurs prohibitive computational cost at scale and can introduce hallucinations into the refined data. To overcome these limitations, we propose Selecting over Tokens (SelecT), a novel framework that reframes data refinement as a highly efficient token classification task. SelecT classifies each token as either informative or noisy and subsequently removes the latter. This design achieves fine-grained data optimization while avoiding the inefficiency of generation, ensuring scalability. When evaluated on diverse downstream benchmarks, the model trained on SelecT-refined corpora, on average, outperforms the one trained on raw data by over 2% and exceeds the best heuristic baselines by more than 1% while preserving 17% more tokens than the latter. Furthermore, SelecT achieves higher average performance than the generative ProX-C across all experimental settings, and is 2.5x faster at inference, even with twice the parameters. Our results establish SelecT as an effective, efficient, and scalable solution for pre-training data optimization.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.999ACL Anthology · ACL 2026

来源日期 2026-07-01（仅到月份）

Perplexity-Aware Data Scaling Law: Perplexity Landscapes Predict Performance for Continual Pre-training

Lei Liu Alibaba Group, Hao Zhu Ant Group, Xiaoyan Yang, Yue Shen antgroup, Zhixuan Chu Zhejiang University, Jian Wang, Jinjie Gu, Kui Ren Shanghai Institute for Advanced Study, Zhejiang University, The State Key Laboratory of Blockchain and Data Security, Zhejiang University and Zhejiang University

原文摘要

Continual Pre-training (CPT) serves as a fundamental approach for adapting foundation models to domain-specific applications. Scaling laws for pre-training define a power-law relationship between dataset size and the test loss of an LLM. However, the marginal gains from simply increasing data for CPT diminish rapidly, yielding suboptimal data utilization and inefficient training. To address this challenge, we propose a novel perplexity-aware data scaling law to establish a predictive relationship between the perplexity landscape of domain-specific data and the test loss. Our approach leverages the pre-trained model’s own perplexity on domain data as a proxy for estimating the knowledge gap, effectively quantifying the informational perplexity landscape of candidate training samples. By fitting this scaling law across diverse perplexity regimes, we enable adaptive selection of high-utility data subsets, prioritizing content that maximizes knowledge absorption while minimizing redundancy and noise. Extensive experiments on both medical and general-domain benchmarks demonstrate that our method consistently identifies near-optimal training subsets, achieving superior performance with significantly reduced data consumption.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表