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新论文 · v1arXiv:2610.38231

3DCodeEdit: Benchmarking Instruction-Guided 3D Program Editing

Binjie Wang, Xinran Zhao, Shoubin Yu, Xiang Li, Silong Yong, Chenyang Yang, Tianyu Chen, Ruofei Du 等 11 位作者

原文摘要

Language models increasingly create 3D content through code, with practical workflows often relying on editing existing programs rather than regenerating them, for speed and precise control. Such edits require understanding what a program builds and foreseeing how a change reshapes it, yet prior benchmarks mostly evaluate generation or simple edits against a reference. In this paper, we introduce 3DCodeEdit, a benchmark of 2,592 instruction-guided editing tasks over 108 Blender programs that construct objects entirely in code. Its edits range from parameter to structural changes, style redesign, and scene composition, and are judged from renders against human-reviewed checklists. Across 17 models, the average success rate is only 37%, and even the strongest models complete fewer than 60% of structural and style edits. As models become more capable, their failures shift to geometric foresight, where the code runs but builds the wrong shape. We therefore study whether multi-turn feedback can compensate for this missing foresight. Feedback from a separate reviewer model that describes how the renders differ from the request helps most, more than maximum reasoning effort or an agent harness, and can let a 27B model surpass the single-shot rate of a 2.8T-parameter model.

cs.CL · cs.CV · cs.GR

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arXiv ↗PDF ↗
版本更新 · v4arXiv:2606.04874

Agent Planning Benchmark: A Diagnostic Framework for Planning Capabilities in LLM Agents

Haoyu Sun, Wenxuan Wang, Mingyang Song, Jujie He, Weinan Zhang, Yang Liu, Yang Yang, Yu Cheng

原文摘要

Planning is central to LLM agents: before acting, an agent must decompose goals, select tools, reason over constraints, and decide when a task is infeasible. Yet existing agent evaluations often report only end-to-end success, making it difficult to determine whether failures stem from planning or execution. We introduce Agent Planning Benchmark (APB), a planning-specific diagnostic benchmark with 4,209 multimodal cases across 22 domains and five settings, covering holistic planning, feedback-conditioned step-wise planning, and robustness under extraneous tools, broken tools, and unsolvable tasks. Across 12 MLLMs, APB reveals systematic weaknesses in long-horizon planning, tool-noise robustness, calibrated refusal, and inference-time refinement. We further validate APB on 200 ToolSandbox tasks and 200 $τ^2$-bench tasks, where APB-guided refinement consistently improves plan correctness, plan grade, and downstream execution metrics across three representative models. APB thus serves as an upstream diagnostic complement to execution benchmarks. The APB benchmark and code are available in \href{https://github.com/Mikivishy/AgentPlanningBenchmark}{this URL}.

cs.CL

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.35059

Reinforcement Learning Done Right for Explicit Confidence Targets

Zefeng He, Yichen He, Yuan Lin, Yang Yang, Yu Cheng

原文摘要

Large language models (LLMs) can produce plausible but incorrect answers when they are uncertain, which can mislead users and undermine model reliability. Training models to abstain with reinforcement learning is a natural approach, but most existing methods use a fixed error penalty and therefore cannot adapt their behavior to different confidence requirements at inference time. We study \emph{explicit confidence targets}, where a user specifies a target \(p\) in the prompt and the model directly chooses whether to answer or abstain, with the goal of achieving an accuracy of at least \(p\) among answered questions. However, learning to respond to different confidence targets with a single model has proven difficult, and prior attempts have failed to distinguish different targets. We show that this failure comes from two problems: the reward signal that distinguishes different values of $p$ is often lost during optimization, while the original error penalty grows without bound as $p$ approaches one. We propose RLEC (\textbf{R}einforcement \textbf{L}earning with \textbf{E}xplicit \textbf{C}onfidence Targets), which preserves the learning signal needed to distinguish confidence targets, introduces a stable reward family that keeps the desired answer condition unchanged, and uses reward scheduling to balance effective learning and training stability. Together, these designs enable the first successful training of a single LLM for explicit confidence target control. Extensive experiments across diverse benchmarks and model scales show that RLEC largely preserves general capabilities and achieves competitive calibration performance. RLEC also supports fine-grained target control without an external decision system or threshold calibration after training. By making explicit confidence targets part of the trained policy, RLEC opens a new and more direct path toward behavioral calibration.

cs.LG · cs.CL

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arXiv ↗PDF ↗
首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-industry.15ACL Anthology · ACL 2026

Detecting AI-Generated Content on Social Media with Multi-modal Language Models

Chenyang Yang, Shen Yan, Yibo Yang, Litao Hu, Yuchen Liu, Yuan Zeng, Hanchao Yu, Yinan Zhu 等 16 位作者

原文摘要

Generative AI has enabled the creation of photorealistic images and videos that are increasingly disseminated on social media, often used for spam, misinformation, manipulation, and fraud. Existing AI-generated content (AIGC) detection methods face challenges including poor generalization to new generation models, reliance on single modalities, and lack of interpretable explanations. We present our pipeline that mitigates these issues by continuously curating diverse multi-modal social media data and training a compact vision-language model for detection and explanation. Our model achieves state-of-the-art detection performance on public benchmarks and demonstrates robust detection and explanation capabilities on internal social media datasets across multiple platforms. We deployed our model for post recommendation on social media platforms and observed positive downstream impacts on user engagement, demonstrating that it is feasible to perform effective AIGC detection in dynamic, real-world social media environments.

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.1034ACL Anthology · ACL 2026

Red Teaming Large Reasoning Models

Jiawei Chen, Yang Yang, Chao Yu Tsinghua University, Yu Tian, Zhi Cao, Xue Yang Shanghai Jiao Tong University, Linghao Li East China Normal University, Hang Su 等 9 位作者

原文摘要

Large Reasoning Models (LRMs) have emerged as a powerful advancement in multi-step reasoning tasks, offering enhanced transparency and logical consistency through explicit chains of thought (CoT). However, these models introduce novel safety and reliability risks, such as CoT-hijacking and prompt-induced inefficiencies, which are not fully captured by existing evaluation methods. To address this gap, we propose Rt-LRM, a unified benchmark designed to assess the trustworthiness of LRMs. Rt-LRM evaluates three core dimensions: truthfulness, safety and efficiency. Beyond metric-based evaluation, we further introduce the training paradigm as a key analytical perspective to investigate the systematic impact of different training strategies on model trustworthiness. We achieve this by designing a curated suite of 30 reasoning tasks from an observational standpoint. We conduct extensive experiments on 26 models and identify several valuable insights into the trustworthiness of LRMs. For example, LRMs generally face trustworthiness challenges and tend to be more fragile than Large Language Models (LLMs) when encountering reasoning-induced risks. These findings uncover previously underexplored vulnerabilities and highlight the need for more targeted evaluations. In addition, we release a scalable toolbox for standardized trustworthiness research to support future advancements in this important field.

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.1423ACL Anthology · ACL 2026

QuantumQA: Enhancing Scientific Reasoning via Physics-Consistent Dataset and Verification-Aware Reinforcement Learning

Songxin Qu, Tai-Ping Sun, Yun-Jie Wang University of Science and Technology of China, Huan-Yu Liu, Cheng Xue, Xiao-Fan Xu, Han Fang University of Science and Technology of China, Yang Yang Anhui University 等 11 位作者

原文摘要

Large language models (LLMs) show strong capabilities in general reasoning but typically lack reliability in scientific domains like quantum mechanics, which demand strict adherence to physical constraints. This limitation arises from the scarcity of verifiable training resources and the inadequacy of coarse feedback signals in standard alignment paradigms. To address the data challenge, we introduce QuantumQA, a large-scale dataset constructed via a task-adaptive strategy and a hybrid verification protocol that combines deterministic solvers with semantic auditing to guarantee scientific rigor. Building on this foundation, we propose the verification-aware reward model (VRM) tailored for Reinforcement Learning with Verifiable Rewards (RLVR), which employs an adaptive reward fusion (ARF) mechanism to dynamically integrate deterministic signals from a scientific execution suite (SES) with multidimensional semantic evaluations for precise supervision. Experimental results demonstrate that our method consistently outperforms baselines and general-purpose preference models. Notably, our optimized 8B model achieves performance competitive with proprietary models, validating that incorporating verifiable, rule-based feedback into the reinforcement learning loop offers a parameter-efficient alternative to pure scaling.

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.1431ACL Anthology · ACL 2026

MirrorCAPTCHA: Wild CAPTCHA, Wild Distribution, Wild Web-based Platform Meet Multimodal LLM Agents

Xiangyu Wu, Yuwei Hu Alibaba Group, Tianyu Cui Alibaba Group, Yueying Tian, Qing-Guo Chen Alibaba Group, Zhao Xu Alibaba Group, Weihua Luo Alibaba International Digital Commerce Group, Kaifu Zhang Alibaba Group 等 10 位作者

原文摘要

The path to fully autonomous web agents is currently hindered by a critical bottleneck: their limited ability to handle CAPTCHA. Existing agent benchmarks largely ignore this practical challenge, failing to evaluate an agent’s real-world capacity to solve CAPTCHA. To bridge this gap, we conduct a comprehensive analysis of real-world CAPTCHA distributions and introduce MirrorCAPTCHA, a benchmark annotated with Weighted Pass Rate and a newly proposed metric Completion Degree. MirrorCAPTCHA is designed to serve as a “mirror” that faithfully reflects the automation capabilities of agents in real scenarios. We filter 2095 websites from Common Crawl, identify the CAPTCHA deployed on these sites, and cluster them into 18 distinct categories using K-means algorithm. To ensure practicality, we extract a web subgraph from Common Crawl covering these websites and use random walks to simulate real-world CAPTCHA encounter frequencies, yielding a realistic measure of agents’ ability. Additionally, we develop a lightweight synthetic data pipeline to train Ovis2-Agent-CAPTCHA-8B, which significantly outperforms current state-of-the-art closed-source models on MirrorCAPTCHA, achieving a 9.4% higher average Weighted Pass Rate and a 2.13% higher average Completion Degree than the runner-up, Gemini-2.5-Pro.

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.248ACL Anthology · ACL 2026

G-Cap: A Game Character Caption Generator

Yang Yang SUN YAT-SEN UNIVERSITY, Feng Hu IEG, Tencent, Haiming Zhang Tencent AI Research, XU Cheng , Tsinghua University, Gui Zheng, Liang Yao SUN YAT-SEN UNIVERSITY, Wenqi Ren SUN YAT-SEN UNIVERSITY

原文摘要

While Large Vision-Language Models (LVLMs) have demonstrated remarkable proficiency in image captioning, existing research primarily focuses on real-world scenarios, leaving surreal, highly stylized, and semantically hybrid virtual-world scenarios significantly underexplored. In this work, we introduce Game Character Captioning, a novel task designed to evaluate LVLMs’ capability to perceive and describe game character from the virtual-world. To facilitate evaluation, we establish GC-Bench, a manually annotated benchmark, and propose Graph-F1 to effectively assess performance on this task. Our evaluation reveals that: (1) current state-of-the-art LVLMs, including closed-source giants such as Gemini 3 Pro and GPT-5.1, struggle to maintain the high performance seen in real-world scenarios; and (2) a notable gap exists between open-source and closed-source models. To bridge this gap, we construct GC-148K, a large-scale dataset generated via a specialized data pipeline, and develop the G-Cap series. Experiments demonstrate that G-Cap series rivals the performance of advanced closed-source models at a lower cost, offering an efficient solution for industrial-grade production environment.

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.588ACL Anthology · ACL 2026

Lightweight LLM Agent Memory with Small Language Models

Jiaquan Zhang, Chaoning Zhang University of Electronic Science and Technology of China, Shuxu Chen, Zhenzhen Huang, Pengcheng Zheng, Zhicheng Wang, Ping Guo Hong Kong Yuwin Technology Co., Ltd., Fan Mo University of Electronic Science and Technology of China 等 12 位作者

原文摘要

Although LLM agents can leverage tools for complex tasks, they still need memory to maintain cross-turn consistency and accumulate reusable information in long-horizon interactions. However, retrieval-based external memory systems incur low online overhead but suffer from unstable accuracy due to limited query construction and candidate filtering. In contrast, many systems use repeated large-model calls for online memory operations, improving accuracy but accumulating latency over long interactions. We propose LightMem, a lightweight memory system for better agent memory driven by Small Language Models (SLMs). LightMem modularizes memory retrieval, writing, and long-term consolidation, and separates online processing from offline consolidation to enable efficient memory invocation under bounded compute. We organize memory into short-term memory (STM) for immediate conversational context, mid-term memory (MTM) for reusable interaction summaries, and long-term memory (LTM) for consolidated knowledge, and uses user identifiers to support independent retrieval and incremental maintenance in multi-user settings. Online, LightMem operates under a fixed retrieval budget and selects memories via a two-stage procedure: vector-based coarse retrieval followed by semantic consistency re-ranking. Offline, it abstracts reusable interaction evidence and incrementally integrates it into LTM. Experiments show consistent gains across model scales, with an average F1 improvement of about 2.5 over A-MEM on LoCoMo, while achieving higher efficiency and low median latency (83 ms for retrieval and 581 ms end-to-end).

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.827ACL Anthology · ACL 2026

Model-Based Imaginative Planning for Embodied Agents

Junru Song, Hengzhe Jin Shanghai Jiaotong University, Yucong Huang, Tingsong Jiang, Weien Zhou Defense Innovation Institute, Academy of Military Science, Feifei Wang Renmin University of China, Yang Yang Intelligence Game and Decision Laboratory, Ying Wen Shanghai Jiao Tong University 等 9 位作者

原文摘要

Reasoning and planning critically rely on a predictive dynamics model. In symbolic domains such as mathematics and code, large language models (LLMs) internalize transition rules during pretraining, allowing reinforcement learning or test-time scaling to effectively elicit and generalize their reasoning ability. Embodied decision making is fundamentally different: agents must reason from sparse visual evidence under partial observability, while coping with environment-specific dynamics and affordances not captured by language priors. Here we propose IMPLEMENT, a model-based reasoning framework that enables frozen LLMs to perform imaginative planning. A lightweight world model converts raw pixels into object-centric symbolic states amenable to language-based reasoning, and predicts their evolution under hypothetical actions. To address partial observability, we perform Monte Carlo state prediction via temperature sampling, enabling decision evaluation over multiple plausible futures. To support adaptation to unseen environments, we integrate Meta In-Context Learning, conditioning the world model on interaction history to continuously refine its predictions. At inference time, the LLM and world model form a tight co-reasoning loop: the LLM proposes candidate actions, the world model simulates future trajectories, and the LLM refines its decisions, effectively inducing an online policy iteration scheme. Extensive experiments in ALFWorld demonstrate consistent advantages over finetuning-based and strong test-time scaling approaches, validating IMPLEMENT as an effective framework for grounding language agents in visual embodied environments.

cs.CL

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