论文库

新论文 · v1arXiv:2611.08790

更新于 2026-11-06

SteerGR: Steering Generative Recommendation via Sparse Autoencoders for Controllable Game Discovery

Sunkyung Lee, Xiaokai Wei, Da Long, Hyunsik Jeon, Jiajun Wu, Rithwik Seth, Jongwuk Lee, Michelle Gong 等 9 位作者

原文摘要

Generative recommendation aims to predict item identifiers from user interaction histories, but explicit control over the properties of recommended items remains underexplored. A key challenge is to connect human-readable concepts to internal features whose interventions effectively change the recommended items. To address this, we propose Steerable Generative Recommender (SteerGR), a framework that uses sparse autoencoders for concept-level control in game discovery. SteerGR proceeds through three stages: (i) Concept Definition and Grounding for defining play concepts with an LLM; (ii) Feature Discovery and Selection for discovering candidate features by their concept associations and selecting one by its steering effect; and (iii) Sparse Feature Intervention for amplifying or suppressing selected features to steer recommendations without retraining the recommender. Extensive experiments with two industrial generative recommendation models on a real-world game dataset show that the selected features successfully steer recommendations toward or away from target concepts. We believe this work takes a first step to extend SAE-based steering from LLMs to industrial generative recommendation.

cs.IR · cs.AI · cs.CL

新论文 · v1arXiv:2611.08237

更新于 2026-11-06

终端式数学建模基准：首个面向数学建模的智能体化可验证基准

Terminal-style Mathematical Modeling Bench: The First Agentic and Verifiable Benchmark for Mathematical Modeling

Xiaojun Wu, Cehao Yang, Xuhui Jiang, Chengjin Xu, Jia Li, Jian Guo

AI 速读基于摘要

提出 TB-MathModel，一个在容器化终端中运行、以多篇优秀论文为参照并对数值核心做方向感知评分与可行性硬校验的数学建模智能体基准。

cs.CL

查看原文摘要

Mathematical modeling turns an open-ended real-world problem into assumptions, a model, a solution, and a defended recommendation, and its answers matter only if they are both close to a credible reference and physically feasible; existing benchmarks either score solver fragments or grade reports with an LLM judge, leaving those numbers unchecked. We introduce TB-MathModel (the Terminal-style Mathematical Modeling Bench), the first benchmark to make complete contest-style modeling problems agentic, verifiable, and terminal at once, over 18 real CUMCM and MCM problems (2023--2025). Each task ships the verbatim statement, official attachments, and figures in a containerized terminal; the agent returns one JSON artifact, scored on the numeric core of the final question against a reproduction of the Outstanding-award paper. Two design choices distinguish it: direction-aware, one-sided scoring, which never penalises beating the anchor, and two-layer feasibility verification---a programmatic gate over six decidable invariant forms plus 624 Lean~4 obligations---with a hard gate that zeroes infeasible answers. Each task is scored against a panel of human outstanding papers rather than one, so a model must be close to many accepted answers; across seven multimodal entries (three seeds each) the strongest reach a multi-reference O-Eval of 64.58\%, while the \$2.54 open model holds 59.81\% at $1/100$ the cost. TB-MathModel turns the numeric core of modeling into a reproducible, machine-checkable signal for measuring long-horizon modeling competence and for collecting agent trajectories. The source code is available at https://github.com/wxj630/terminal-bench-math-modeling.

版本更新 · v2arXiv:2605.07139

更新于 2026-11-04

Structural Rationale Distillation via Reasoning Space Compression

Jialin Yang, Jiankun Wang, Jiajun Wu, Henry Leung, Jiayu Zhou, Steve Drew

原文摘要

When distilling reasoning from large language models (LLMs) into smaller ones, teacher rationales for similar problems often vary wildly in structure and strategy. Like a chef who makes the same dish differently each time, this inconsistency burdens the student with noisy supervision that is hard to internalize. We propose Distillation through Reasoning Path Compression (D-RPC), which constrains the teacher to follow a compact, dynamically maintained bank of reusable high-level reasoning paths. For each training question, D-RPC retrieves the most relevant path and conditions the teacher to follow it, producing rationales that are consistent across similar problems yet diverse enough to cover different problem types. A PAC-Bayes analysis formalizes a trade-off between bank size and coverage, with smaller banks limiting path diversity at the risk of coverage gaps. Our ablations favor an intermediate bank size, consistent with this trade-off. Across five math and commonsense reasoning benchmarks with two student models, D-RPC achieves the highest average accuracy among the evaluated distillation methods, while using fewer tokens than template-heavy alternatives.

cs.CL · cs.AI · cs.LG

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-demo.31ACL Anthology · ACL 2026

来源日期 2026-07-01（仅到月份）

DataArc-SynData-Toolkit: A Unified Closed-Loop Framework for Multi-Path, Multimodal, and Multilingual Data Synthesis

Zhichao Shi UCAS, Cehao Yang The Hong Kong University of Science and Technology, Hao Zhou, Xiaojun Wu The Hong Kong University of Science and Technology and IDEA Institution, Huajie Li International Digital Economy Academy, International Digital Economy Academy, Xuhui Jiang International Digital Economy Academy, International Digital Economy Academy, Chengjin Xu International Digital Economy Academy, Yuanzhuo Wang Chinese Academy of Sciences 等 9 位作者

原文摘要

Synthetic data has emerged as a crucial solution to the data scarcity bottleneck in large language models (LLMs), particularly for specialized domains and low-resource languages. However, the broader adoption of existing synthetic data tools is severely hindered by convoluted workflows, fragmented data standards, and limited scalability across modalities.To address these limitations, we develop DataArc-SynData-Toolkit, an open-source framework featuring: (1) a configuration-driven, end-to-end pipeline equipped with an intuitive visual interface and simplified CLI for exceptional usability; (2) a unified, quality-controllable synthesis paradigm that standardizes multi-source data generation to ensure high reusability; and (3) a highly modular architecture designed for seamless multimodal, multilingual, and multi-task adaptation.We apply the toolkit in multiple application scenarios. Experimental results demonstrate that our toolkit achieves an optimal balance between generation efficiency and data quality. By offering an end-to-end and visually interactive pipeline, DataArc-SynData-Toolkit significantly lowers the technical barrier to synthetic data generation and subsequent model training, accelerating its practical deployment in real-world applications.

cs.CL · cs.CV

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-industry.3ACL Anthology · ACL 2026

来源日期 2026-07-01（仅到月份）

MinerU2.5: A Decoupled Vision-Language Model for Efficient High-Resolution Document Parsing

Junbo Niu, Zheng Liu, Zhuangcheng Gu, Bin Wang, Linke Ouyang, Zhiyuan Zhao, Tao Chu, Tianyao He 等 59 位作者

原文摘要

We introduce MinerU2.5, a 1.2B-parameter document parsing vision-language model that achieves state-of-the-art recognition accuracy while maintaining exceptional computational efficiency. Our approach employs a coarse-to-fine, two-stage parsing strategy that decouples global layout analysis from local content recognition. In the first stage, the model performs efficient layout analysis on downsampled images to identify structural elements, circumventing the computational overhead of processing high-resolution inputs. In the second stage, guided by the global layout, it performs targeted content recognition on native-resolution crops extracted from the original image, preserving fine-grained details in dense text, complex formulas, and tables. To support this strategy, we developed a comprehensive data engine that generates diverse, large-scale training corpora for both pretraining and fine-tuning. Ultimately, MinerU2.5 demonstrates strong document parsing ability, achieving state-of-the-art performance on multiple benchmarks, surpassing both general-purpose and domain-specific models across various recognition tasks, while maintaining significantly lower computational overhead.

cs.CL · cs.CV

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.1137ACL Anthology · ACL 2026

来源日期 2026-07-01（仅到月份）

GeoLaux: A Benchmark for Evaluating MLLMs’ Geometry Performance on Long-Step Problems Requiring Auxiliary Lines

Yumeng Fu Xi'an Jiaotong University, Jiayin Zhu Xi'an Jiaotong University, Lingling Zhang Xi'an Jiaotong University, Wenjun Wu Xi'an Jiaotong University, Bo Zhao Xi'an Jiaotong University, Shaoxuan Ma, Yushun Zhang, Jun Liu Xi'an Jiaotong University

原文摘要

Geometry problem solving (GPS) poses significant challenges for Multimodal Large Language Models (MLLMs) in diagram comprehension, knowledge application, long-step reasoning, and auxiliary line construction. However, current benchmarks lack fine-grained evaluation for long-step problems necessitating auxiliary construction. To address these limitations, we present GeoLaux, a fine-grained annotated dataset comprising 2186 calculation and proof problems. It features long-step reasoning (with an average solution length of 6.51 steps, maximum of 24 steps) and auxiliary line construction (required in 41.8% of problems). Building on the dataset, we conduct a comprehensive five-dimensional evaluation of 23 leading MLLMs. The evaluation yields three pivotal findings: First, models perform significantly worse on long-step problems compared to short-step ones, with 18 models exhibiting a performance drop of over 50%. Second, it is crucial to enhance models’ understanding, awareness, and proficiency in auxiliary line construction, which is vital for overall geometric reasoning. Third, limited answer hints effectively improve process correctness, whereas explicit answers lead models to neglect intermediate reasoning steps. These findings position GeoLaux both to benchmark MLLMs geometry reasoning abilities and to guide their improvement. Data and code are available at https://github.com/Candice-yu/GeoLaux

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.1229ACL Anthology · ACL 2026

来源日期 2026-07-01（仅到月份）

Data Pollination: An Emergent Ecological Process Driving AI Population Evolution

Shufang Xie Renmin University of China, Qizhi Pei, Ang Lv, Jingyang Hu, Lijun Wu Shanghai Artificial Intelligence Laboratory, Rui Yan Wuhan University

原文摘要

AI development is often framed as the outcome of isolated research and engineering efforts, yet evidence from deployed systems suggests that language models interact through a shared data ecosystem. While the optimization of individual models is extensively studied, the emergent properties of this interconnected population remain largely unexplored, limiting our ability to predict long-term ecosystem trajectories We term this process data pollination, the unintentional circulation of synthetic model outputs through shared online platforms and web-scale training corpora, and formalize it as a population-based evolutionary framework to investigate stability dynamics under synthetic data training. Our theoretical analysis and controlled experiments involving 320 language models demonstrate that population dynamics can mitigate the model collapse observed in single-lineage recursive training, yielding stable or improving performance across diverse benchmarks. Crucially, we find that ecological diversity functions as a fundamental resilience mechanism that safeguards the ecosystem against collapse, highlighting the critical importance of maintaining model diversity for sustainable AI development.

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.1296ACL Anthology · ACL 2026

来源日期 2026-07-01（仅到月份）

REST: Stress Testing Large Reasoning Models by Asking Multiple Problems at Once

Zhuoshi Pan, Qizhi Pei, Yu Li, Zinan Tang Alibaba Group, QiYao Sun, H. Vicky Zhao Tsinghua University, Tsinghua University, Conghui He Shanghai AI Lab, Lijun Wu Shanghai Artificial Intelligence Laboratory

原文摘要

Recent Large Reasoning Models (LRMs) have achieved remarkable progress, yet their evaluation still relies on a narrow paradigm: evaluating one question at a time. This single-question setup suffers from two major limitations: (1) vulnerability to data contamination and diminishing difficulty, forcing costly creation of new questions with significant human effort, (2) failure to evaluate models under multi-context pressure, a key requirement for real-world deployment. To bridge this gap, we present REST (Reasoning Evaluation through Simultaneous Testing), a stress-testing framework that exposes LRMs to multiple problems simultaneously. Beyond basic reasoning, REST evaluates two under-tested capabilities: contextual priority allocation and robustness against contextual interference. Our evaluation of more than 30 advanced reasoning models on 9 reasoning benchmarks reveals several striking findings: Even state-of-the-art (SOTA) models such as DeepSeek-R1 exhibit substantial performance degradation under stress testing, challenging the prevailing assumption that “LLMs are multi-problem solvers”. Crucially, REST demonstrates stronger discriminative power than existing benchmarks, revealing performance gaps among models that exhibit similar, near-ceiling performance under traditional evaluation. Some key insights emerge from our analysis: (1) the “overthinking trap” is a critical factor contributing to the performance degradation; (2) models trained with the “Long2Short” technique preserve more of their single-problem accuracy under REST, outperforming their standard-trained counterparts. These results establish REST as a cost-efficient, future-proof evaluation paradigm while reducing reliance on continuous human annotation. Code is available at https://github.com/opendatalab/REST.

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.1745ACL Anthology · ACL 2026

来源日期 2026-07-01（仅到月份）

R^3: End-to-End Reasoning-based Planning for Multi-step Retrosynthesis via Reinforcement Learning

YiFei Wang, Qizhi Pei, Jiangtao Feng, Yuntian Shi, Yi Duan, Lihao Wang, Lei Bai Shanghai AI Laboratory, Lijun Wu Shanghai Artificial Intelligence Laboratory 等 10 位作者

原文摘要

Multi-step retrosynthetic planning is a fundamental challenge in organic chemistry, traditionally modeled as a combinatorial search problem guided by single-step prediction models. However, this search-centric paradigm often disconnects from the explicit chemical reasoning processes employed by human experts. In this paper, we propose R^3 (Reinforced Reasoning Retrosynthesis), a novel framework that reformulates this task as end-to-end generative reasoning. Instead of traversing a search tree, R^3 simulates the problem-solving logic of chemists to directly generate complete synthetic pathways. To achieve this, we initialize the model with domain knowledge and employ end-to-end Reinforcement Learning (RL) to optimize the entire planning policy. Experimental results on Retrobench show that R^3 achieves a state-of-the-art Top-1 accuracy of 43.7%, demonstrating that generative reasoning offers a superior alternative to traditional search algorithms in solving complex retrosynthetic problems.

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.2090ACL Anthology · ACL 2026

来源日期 2026-07-01（仅到月份）

OMIBench: Benchmarking Olympiad-Level Multi-Image Reasoning in Large Vision-Language Models

Qiguang Chen, Chengyu Luan, Jiajun Wu, Qiming Yu, Yi Yang, Yizhuo Li, Jingqi Tong, Xiachong Feng University of Hong Kong 等 10 位作者

原文摘要

Large vision-language models (LVLMs) have made substantial advances in reasoning tasks at the Olympiad level. Nevertheless, current Olympiad-level multimodal reasoning benchmarks for these models often emphasize single-image analysis and fail to exploit contextual information across multiple images. We present OMIBench, a benchmark designed to evaluate Olympiad-level reasoning when the required evidence is distributed over multiple images. It contains problems from biology, chemistry, mathematics, and physics Olympiads, together with manually annotated rationales and evaluation protocols for both exact and semantic answer matching. Across extensive experiments on OMIBench, we observe meaningful performance gaps in existing models. Even the strongest LVLMs, such as Gemini-3-Pro, attain only about 50% on the benchmark. These results position OMIBench as a focused resources for studying and improving multi-image reasoning in LVLMs.

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.344ACL Anthology · ACL 2026

来源日期 2026-07-01（仅到月份）

ChartVerse: Scaling Chart Reasoning via Reliable Programmatic Synthesis from Scratch

Zheng Liu, Honglin Lin, Xiaoyang Wang Shanghai Artificial Intelligence Laboratory, Xin Gao Shanghai Artificial Intelligence Laboratory, Yu Li, Mengzhang Cai, Yun Zhu Shanghai Artificial Intelligence Laboratory, Zhanping Zhong 等 15 位作者

原文摘要

Chart reasoning is a critical capability for Vision Language Models (VLMs). However, the development of open-source models is severely hindered by the lack of high-quality training data. Existing datasets suffer from a dual challenge: synthetic charts are often simplistic and repetitive, while the associated QA pairs are prone to hallucinations and lack the reasoning depth required for complex tasks. To bridge this gap, we propose ChartVerse, a scalable framework designed to synthesize complex charts and reliable reasoning data from scratch. (1) To address the bottleneck of simple patterns, we first introduce Rollout Posterior Entropy (RPE), a novel metric that quantifies chart complexity. Guided by RPE, we develop complexity-aware chart coder to autonomously synthesize diverse, high-complexity charts via executable programs. (2) To guarantee reasoning rigor, we develop truth-anchored inverse QA synthesis. Diverging from standard generation, we adopt an answer-first paradigm: we extract deterministic answers directly from the source code, generate questions conditional on these anchors, and enforce strict consistency verification. To further elevate difficulty and reasoning depth, we filter samples based on model fail-rate and distill high-quality Chain-of-Thought (CoT) reasoning. We curate ChartVerse-SFT-600K and ChartVerse-RL-40K using Qwen3-VL-30B-A3B-Thinking as the teacher. Experimental results demonstrate that ChartVerse-8B achieves state-of-the-art performance, notably surpassing its teacher and rivaling the stronger Qwen3-32B-Thinking.

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.435ACL Anthology · ACL 2026

来源日期 2026-07-01（仅到月份）

Tracing the Roots: A Multi-Agent Framework for Uncovering Data Lineage in Post-Training LLMs

Yu Li, Xiaoran Shang, Qizhi Pei, Yun Zhu Shanghai Artificial Intelligence Laboratory, Xin Gao Shanghai Artificial Intelligence Laboratory, Honglin Lin, Zhanping Zhong, Zhuoshi Pan 等 14 位作者

原文摘要

Post-training data plays a pivotal role in shaping the capabilities of Large Language Models (LLMs), yet datasets are often treated as isolated artifacts, overlooking the systemic connections that underlie their evolution. To disentangle these complex relationships, we introduce the concept of data lineage to the LLM ecosystem and propose an automated multi-agent framework to reconstruct the evolutionary graph of dataset development. Through large-scale lineage analysis, we characterize domain-specific structural patterns, such as vertical refinement in Math-oriented datasets and horizontal aggregation in General-domain corpora. Moreover, we uncover pervasive systemic issues, including structural redundancy induced by implicit dataset intersections and the propagation of benchmark contamination along lineage paths. To demonstrate the practical value of lineage analysis for data construction, we leverage the reconstructed lineage graph to create a lineage-aware diversity-oriented dataset. By anchoring instruction sampling at upstream leaf sources, this approach mitigates downstream homogenization and hidden redundancy, yielding a more diverse post-training corpus. We further highlight lineage-centric analysis as an efficient and robust topological alternative to sample-level dataset comparison for large-scale data ecosystems. By grounding data construction in explicit lineage structures, our work advances post-training data curation toward a more systematic and controllable paradigm.

cs.CL · cs.MA

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-tutorials.2ACL Anthology · ACL 2026

来源日期 2026-07-01（仅到月份）

The Data Frontier for Large Language Models: Selection, Synthesis, and Tools

Lijun Wu Shanghai AI Laboratory, Wentao Zhang Peking University, Conghui He Shanghai AI Lab

原文摘要

As the development of Large Language Models (LLMs) matures, the focus of the research community is undergoing a critical shift from a purely model-centric to a data-centric paradigm. It is now evident that the quality, diversity, and composition of training data—not merely its scale—are the primary drivers of a model’s advanced capabilities, from complex reasoning to reliable instruction following. However, acquiring and curating such high-quality data remains a significant bottleneck. This tutorial provides a comprehensive and practical guide to the state-of-the-art in data research directions for LLMs. We structure the tutorial around the two core pillars of modern data strategy: intelligent data selection and advanced data synthesis. In the first part, we delve into methods for curating the most valuable information from vast, noisy datasets, covering techniques like LLM-as-a-judge for automated quality filtering and active learning for maximizing annotation efficiency. The second part explores the synthetic data revolution, detailing paradigms that range from generating complex reasoning traces (e.g., Chain-of-Thought) to deploying sophisticated multi-agent workflows that can autonomously create high-quality, diverse instruction data from raw seeds. Finally, we will conclude with a practical overview of open-source tools and platforms that facilitate these data-centric workflows, empowering researchers and practitioners to build better models through better data. Attendees will leave with a principled framework and actionable insights for designing and implementing the advanced data strategies required to build the next generation of powerful, specialized, and aligned LLMs.

cs.CL · cs.MA

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 5: Tutorial Abstracts) · 2026 · 正式发表