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新论文 · v1arXiv:2611.15488

FloCOS: Predicting Compact Forward Occupancy Sets of Surrounding Vehicles via Conditional Flow Matching

Chuansheng Dong, Yinxiao Zhan, Zongdian Li, Peng Hou, Jin Wang, Jiayu Fan

原文摘要

Existing methods for predicting surrounding vehicles' forward occupancy may under-approximate the ground-truth occupancy, exposing the ego vehicle to collision risk, or produce overly conservative regions that unnecessarily restrict feasible space and cause planning failures. To address these issues, we propose FloCOS, a conditional flow-matching framework that predicts compact forward occupancy sets with high occupancy coverage. FloCOS represents the forward occupancy set as a time-varying union of half-space convex polygons parameterized by temporal basis coefficients. Conditional flow matching captures the conditional distribution of these coefficients to govern the temporal evolution of the convex components, trained under coverage and union-area losses that penalize missed occupancy and excess area. For downstream trajectory optimization, the predicted nonconvex union is directly incorporated into hyperplane-based collision-avoidance constraints. Evaluations on the Waymo Open Motion Dataset show that FloCOS generates compact forward occupancy sets with high coverage and achieves a higher interaction completion rate than evaluated baselines. Real-world vehicle experiments further confirm its ability to support collision-free planning in interactive scenarios.

cs.RO

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arXiv ↗PDF ↗
版本更新 · v4arXiv:2609.31494Google Research

面向可证明隐私的联邦数据学习

Toward provably private learning from federated data

Katharine Daly, Yu Xiao, Zachary Garrett, Brett McLarnon, Jianpeng Hou, Arun Ganesh, Yanxiang Zhang, Noriyuki Takahashi 等 12 位作者

AI 速读 基于摘要

提出基于可信执行环境（TEE）的下一代联邦学习系统，首次提供可外部验证的中心化差分隐私（DP）保证，并改善隐私-效用权衡。

cs.CR

arXiv ↗PDF ↗
查看原文摘要

Federated Learning (FL) allows devices with private data to collaborate in training a shared model. We present a next-generation FL system based on Trusted Execution Environments (TEEs) that addresses operational challenges associated with earlier systems and provides externally verifiable central Differential Privacy (DP) guarantees for the first time while offering a better privacy-utility tradeoff. In our system, devices upload data encrypted with keys managed by a TEE-hosted Key Management Service (KMS). The uploaded data is cryptographically tied to a policy limiting the set of Python programs that may later process the data in server-side TEEs. External parties may inspect public transparency logs to observe the set of workloads allowed by these policies. Our experimental results show that the new system improves device coverage and favorably shifts privacy-utility curves by enabling collected data to be integrated into the server-side workload at a schedule that optimizes DP guarantees and is unaffected by device availability. Our new system has been productionized, enabling models for the Android Keyboard (Gboard) to be trained faster and achieve better accuracy under smaller, now externally verifiable privacy budgets in comparison to models trained using the prior system.

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.1102ACL Anthology · ACL 2026

When TableQA Meets Noise: A Dual Denoising Framework for Complex Questions and Large-scale Tables

Shenghao Ye University of Science and Technology of China, Yu Guo University of Science and Technology of China, Dong Jin, Yuxiang Wang, Yikai Shen University of Science and Technology of China, Yunpeng Hou, Shuangwu Chen, Jianyang University of Science and Technology of China 等 9 位作者

原文摘要

Table question answering (TableQA) is a fundamental task in natural language processing (NLP). The strong reasoning capabilities of large language models (LLMs) have brought significant advances in this field. However, as real-world applications involve increasingly complex questions and larger tables, substantial noisy data is introduced, which severely degrades reasoning performance. To address this challenge, we focus on improving two core capabilities: Relevance Filtering, which identifies and retains information truly relevant to reasoning, and Table Pruning, which reduces table size while preserving essential content. Based on these principles, we propose EnoTab, a dual denoising framework for complex questions and large-scale tables. Specifically, we first perform Evidence-based Question Denoising by decomposing the question into minimal semantic units and filtering out those irrelevant to answer reasoning based on consistency and usability criteria. Then, we propose Evidence Tree-guided Table Denoising, which constructs an explicit and transparent table pruning path to remove irrelevant data step by step. At each pruning step, we observe the intermediate state of the table and apply a post-order node rollback mechanism to handle abnormal table states, ultimately producing a highly reliable sub-table for final answer reasoning. Finally, extensive experiments show that EnoTab achieves outstanding performance on TableQA tasks with complex questions and large-scale tables, confirming its effectiveness.

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.1145ACL Anthology · ACL 2026

CharTide: Data-Centric Chart-to-Code Generation via Tri-Perspective Tuning and Inquiry-Driven Evolution

Xiangxi Zheng Nanjing Nniversity, Kuang He Shopee, Jiayi Hu East China Normal University, Ping Yu Nanjing University, Rui Yan Nanjing University of Science and Technology, Yuan Yao, Peng Hou Shopee, Anxiang Zeng Shopee 等 9 位作者

原文摘要

Chart-to-code generation demands strict visual precision and syntactic correctness from Vision-Language Models (VLMs). However, existing approaches are fundamentally constrained by data-centric limitations: despite the availability of growing chart-to-code datasets, simply scaling homogeneous chart-code pairs conflates visual perception with program logic, preventing models from fully leveraging the richness of multimodal supervision. We present CharTide, a novel data-centric framework that systematically redesigns both training and alignment data for chart-to-code generation. First, we construct a 2M-sample dataset via a Tri-Perspective Tuning strategy, explicitly decoupling training into visual perception, pure-text code logic, and modality fusion streams, enabling a 7B model to surpass specialized baselines using only supervised data. Second, we reformulate alignment as a data verification problem rather than a heuristic scoring task. To this end, we introduce an Inquiry-Driven RL framework grounded in the principle of information invariance: a downstream model should yield consistent answers to identical visual queries across both original and generated charts. Moving beyond rigid rule matching or VLM scoring, we employ a frozen Inspector to objectively verify generated charts through atomic QA tasks, providing verifiable reward signals based on answer accuracy. Experiments on ChartMimic, Plot2Code, and ChartX show that CharTide-7B/8B significantly outperforms open-source baselines, surpasses GPT-4o, and is competitive with GPT-5.

cs.CL · cs.CV

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.147ACL Anthology · ACL 2026

Bridging Language and Items for Retrieval and Recommendation: Benchmarking LLMs as Semantic Encoders

Yupeng Hou University of California, San Diego, Jiacheng Li Facebook, Xiangjun Fu University of Pennsylvania, University of Pennsylvania, Zhankui He Google DeepMind, An Yan SalesForce, Xiusi Chen University of Illinois at Urbana-Champaign, Julian McAuley University of California, San Diego, University of California, San Diego

原文摘要

Feature engineering has long been central to recommender systems, yet effectively leveraging textual item features remains challenging. Recent advances in large language models (LLMs) have enabled their use as semantic encoders for recommendation, but their roles and behaviors in this setting are still not well understood. Prior studies often rely on general-purpose embedding benchmarks (e.g., MTEB) when selecting LLMs, overlooking the unique characteristics of recommendation tasks. To address this gap, we introduce BLaIR, a comprehensive benchmark for evaluating LLMs as semantic encoders in recommendation scenarios. We contribute (1) a new large-scale Amazon Reviews 2023 dataset with over 570 million reviews and 48 million items, (2) a unified benchmark covering sequential recommendation, collaborative filtering, and product search, and (3) a new complex-query product search task featuring both semi-synthetic and real-world evaluation datasets. Experiments with 11 leading LLMs show that their rankings on BLaIR show little correlation with MTEB, highlighting the unique challenges of semantic encoding in recommendation.

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.568ACL Anthology · ACL 2026

Deriving Character Logic from Storyline as Codified Decision Trees

Letian Peng, Kun Zhou University of California, San Diego, Longfei Yun Facebook, Yupeng Hou University of California, San Diego, Jingbo Shang University of California, San Diego

原文摘要

Role-playing (RP) agents rely on behavioral profiles to act consistently across diverse narrative contexts, yet existing profiles are largely unstructured, non-executable, and weakly validated, leading to brittle agent behavior. We propose Codified Decision Trees (CDT), a data-driven framework that induces an executable and interpretable decision structure from large-scale narrative data. CDT represents behavioral profiles as a tree of conditional rules, where internal nodes correspond to validated scene conditions and leaves encode grounded behavioral statements, enabling deterministic retrieval of context-appropriate rules at execution time. The tree is learned by iteratively inducing candidate scene–action rules, validating them against data, and refining them through hierarchical specialization, yielding profiles that support transparent inspection and principled updates. Across multiple benchmarks, CDT substantially outperforms human-written profiles and prior profile induction methods, indicating that codified and validated behavioral representations lead to more reliable agent grounding.

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.591ACL Anthology · ACL 2026

Rethinking Table Pruning in TableQA: From Sequential Revisions to Gold Trajectory-Supervised Parallel Search

Yu Guo University of Science and Technology of China, Shenghao Ye University of Science and Technology of China, Shuangwu Chen, Zijian Wen, Tao Zhang University of Science and Technology of China, Bai Qirui, Dong Jin, Yunpeng Hou 等 11 位作者

原文摘要

Table Question Answering (TableQA) benefits significantly from table pruning, which extracts compact sub-tables by eliminating redundant cells to streamline downstream reasoning. However, existing pruning methods typically rely on sequential revisions driven by unreliable critique signals, often failing to detect the loss of answer-critical data. To address this limitation, we propose TabTrim, a novel table pruning framework which transforms table pruning from sequential revisions to gold trajectory-supervised parallel search. TabTrim derives a gold pruning trajectory using the intermediate sub-tables in the execution process of gold SQL queries, and trains a pruner and a verifier to make the step-wise pruning result align with the gold pruning trajectory. During inference, TabTrim performs parallel search to explore multiple candidate pruning trajectories and identify the optimal sub-table. Extensive experiments demonstrate that TabTrim achieves state-of-the-art performance across diverse tabular reasoning tasks: TabTrim-8B reaches 73.5% average accuracy, outperforming the strongest baseline by 3.2%, including 79.4% on WikiTQ and 61.2% on TableBench.

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.859ACL Anthology · ACL 2026

SpecCache: Speculative KV Cache Reuse for Efficient RAG Serving

Zijian Wen, Tao Zhang University of Science and Technology of China, Shuangwu Chen, Shenghao Ye University of Science and Technology of China, Yu Guo University of Science and Technology of China, Qirui Chen University of Science and Technology of China, Jingxian Shuai, Yunpeng Hou 等 10 位作者

原文摘要

Retrieval-Augmented Generation (RAG) significantly enhances LLMs but faces high prefill latency during long-context processing. While KV cache reuse can mitigate this, current methods relying on shallow features or static heuristics often fail to identify critical tokens for recomputation, resulting in generation quality degradation.We have an insight that KV deviations are more pronounced in deep layers.However, directly extracting deep-layer features from the target model is computationally prohibitive. Crucially, we find that the deep-layer features of a lightweight speculative model exhibit strong consistency with the target model in the selection of critical tokens for recomputation.In light of these insights, we propose SpecCache, which employs deep-layer hidden-state norms from a speculative model as a proxy to guide the critical token selection for target large model.Experiments demonstrate that SpecCache outperforms state-of-the-art (SOTA) baselines. Compared to full KV recomputation, it reduces time-to-first-token (TTFT) by 2.17-3.95\times and increases inference throughput by 2.7-5.2\times, with negligible degradation in generation quality relative to full recomputation.

cs.CL

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