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新论文 · v1arXiv:2611.06294

A.S.E 2.0: Understanding How Repository Context Impacts the Security of LLM-Generated Code

Jiongchi Yu, Keke Lian, Junqing Wang, Zhikang Fan, Zhaoxuan Li, Yongheng Huang, Ziming Zhao, Dong Zhang

原文摘要

Large language model (LLM) coding assistants and agents construct a repository context before generating code. For security-sensitive code, this context decides whether the model sees the project's safeguards (e.g., authorization checks) or insecure code to imitate. However, context construction is designed almost exclusively for functional correctness, and secure-code benchmarks treat the context as fixed, so how repository context affects the security of generated code remains unclear. We construct CASE-Bench, a benchmark of 145 repository-level tasks from real CVEs in 51 projects. Each task runs in Docker, has functional tests and a proof-of-concept exploit validated in both directions, and is annotated with the repository code that enforces or undermines its security. Using CASE-Bench, we compare 13 context construction methods and six controls with six LLMs, edit single context elements, and analyze coding-agent trajectories. We find that (1) functionality-oriented context raises the functional pass rate by up to 8.2 percentage points (pp) but the secure pass rate by at most 3.7 pp, (2) withholding a safeguard lowers the secure pass rate by up to 8.1 pp, while one inserted insecure exemplar raises correct-but-exploitable code by 6.3 pp, and (3) coding agents observe the relevant safeguard in only 27-36% of trajectories. These findings inform security-aware context construction and secure-code evaluation.

cs.CR · cs.AI · cs.SE

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.06132

Growing One's AI Tools: A Paradigm of Practice-Grown Tools

Zeming Zhao

原文摘要

Professionals have long used software built for them by others. This article reports the growth of a professional computer-assisted interpreting (CAI) tool built by its own user, a conference interpreter without programming training. Drawing on an eight-week version-controlled record with telemetry from 133 logged sessions and over 20,000 recognized utterances, the article traces how real conference work drove the tool through 62 commits and six versions. The record shows capabilities deployed on the day their need appeared and one strategy change that cut median end-to-end delay by over a third within one afternoon. The article then proposes the paradigm of practice-grown tools, defined by the shift from using to growing, the co-evolution of tool and practice through breakdown and repair, bounded local computation, and an assessment corollary, and it formulates three propositions toward tool-inclusive assessment of practitioners, from tool-making literacy to a compute-standardized format for certifying what they and their tools achieve together.

cs.HC · cs.CL · cs.SE

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arXiv ↗PDF ↗
版本更新 · v8arXiv:2508.01415

RoboMemory: A Brain-inspired Multi-memory Agentic Framework for Interactive Environmental Learning in Physical Embodied Systems

Mingcong Lei, Honghao Cai, Yuyuan Yang, Yimou Wu, Jinke Ren, Zezhou Cui, Liangchen Tan, Junkun Hong 等 19 位作者

原文摘要

Long-horizon embodied tasks require agents to learn, reason, and generalize robustly across complex real-world environments. However, existing approaches often struggle with partial observability, fragmented spatial reasoning, and inefficient integration of heterogeneous memories, limiting their capacity for long-horizon adaptation. To address this, we introduce RoboMemory, a brain-inspired framework that unifies Temporal, Spatial, Semantic, and Episodic memory for efficient long-horizon planning and interactive learning. Its core innovations are a comprehensive memory system designed for embodied tasks, and a dynamic spatial knowledge graph for scalable, consistent memory updates. Extensive experiments on EmbodiedBench show that RoboMemory improves the average success rate of Qwen2.5-VL-72B-Ins by 26.5 percentage points and achieves performance comparable to strong closed-source VLM agents. Real-world trials further suggest RoboMemory's potential for interactive environmental learning by reusing accumulated experience across repeated task attempts. These results demonstrate RoboMemory as a scalable memory-augmented framework for embodied agents, improving long-horizon planning and cumulative learning in complex interactive environments.

cs.RO · cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2606.22174

OpenHLM: An Empirical Recipe for Whole-Body Humanoid Loco-Manipulation

Yingdong Hu, Haodong Zhu, Boyuan Zheng, Yihang Hu, Tong Zhang, Zunhao Chen, Junming Zhao, Ruiqian Nai 等 9 位作者

原文摘要

Whole-body humanoid loco-manipulation requires coordinating the robot's entire kinematic chain. However, most existing systems typically decouple the upper and lower bodies into separate controllers, limiting such coordination and yielding behaviors similar to those of a wheeled dual-arm platform. In this paper, we ask what it takes to build a whole-body native vision-language-action (VLA) model that maps language and pixels directly to all of the humanoid's degrees of freedom. We conduct a systematic empirical study organized as a roadmap of one-variable-at-a-time experiments across three phases: whole-body teleoperation, VLA model design, and heterogeneous co-training. Our study yields several intriguing findings: a joint-based whole-body teleoperation interface outperforms alternatives that only partially expose the humanoid's degrees of freedom; a VLA pretrained on static and wheeled dual-arm platforms transfers surprisingly well to a humanoid's full action space; and co-training with HuMI, the humanoid analog of UMI, extends the policy to new objects and instructions without additional whole-body teleoperation on those targets. Following this roadmap yields OpenHLM, an open-source recipe for whole-body humanoid loco-manipulation. In a challenging long-horizon task that spans a wide vertical range of the humanoid, OpenHLM outperforms two state-of-the-art humanoid VLA baselines (GR00T N1.6 and $Ψ_0$) using less than half the total demonstration time. Our code, training data, and model checkpoints are available at [https://openhlm-project.github.io/].

cs.RO

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.36454

SVIn+: Multi-Modal Framework for Robust Underwater State Estimation using 3D Sonar, Visual-Inertial, Water-Pressure, and DVL

Chinmay Burgul, Xiaoming Zhao, Yewei Huang, Michalis Chatzispyrou, Marios Xanthidis, Ioannis Rekleitis, Alberto Quattrini Li

原文摘要

As expansion and scalability in underwater oper- ations becomes more important and challenging, autonomous underwater vehicles (AUVs) become more relevant and the necessity for reliable autonomy more critical. A fundamental challenge in any autonomous underwater operation, from control to safe navigation and mapping, is state estimation; a notorious hard problem to address, especially in low visibility conditions. In this paper, we tackle this problem, by presenting SVIn+, a multi-sensor framework for robust state estimation of AUVs equipped with an IMU, cameras, a water-pressure sensor, a Doppler Velocity Log (DVL), and a compact 3D sonar. This framework extends an established open-source state-of-the-art visual-inertial SLAM, SVIn, by integrating water-pressure and DVL measurements into a tightly-coupled nonlinear optimiza- tion formulation resulting in improved estimates. To leverage the structural information of recently available 3D acoustic sonars while maintaining computational efficiency, we introduce a hierarchical architecture that incorporates estimates from a decoupled front-end via KISS-ICP, treated as relative pose factors within the factor graph. The proposed framework is validated through field deployments in a shipwreck and coral reef, demonstrating drift mitigation and global consistency. To support further research in underwater multi-modal fusion, the code and the datasets can be found on project website https://cmburgul.github.io/projects/svin+/ https://dartmouthrobotics.github.io/svin-plus

cs.RO

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2605.16893

NGM: A Plug-and-Play Training-Free Memory Module for LLMs

Yuwen Qu, Wenhui Dong, Camil Hamami, Ming Zhao, Chenyang Si, Caifeng Shan

原文摘要

Recent studies have introduced memory-augmented architectures that decouple knowledge storage from neural computation, enabling more direct access to stored representations. However, existing approaches often rely on learned memory embeddings or explicit memory and retrieval components, requiring additional training or increasing architectural complexity. To address these limitations, we propose N-gram Memory (NGM), a training-free, plug-and-play module that repurposes a backbone model's pretrained token embedding space as memory. NGM consists of a Causal N-Gram Encoder and a Cosine-Gated Memory Injector. The former constructs N-gram representations by directly aggregating pretrained token embeddings, while the latter uses cosine similarity with ReLU gating to adaptively control the amount of retrieved memory injected into contextual representations. Unlike conventional memory-augmented architectures, NGM requires neither additional embedding training nor a separately learned memory bank or external retrieval infrastructure. We evaluate NGM on ten benchmarks across six language-model backbones spanning two model families and multiple scales, including four Qwen3 models (0.6B, 1.7B, 4B, and 8B) and two Llama-3.2-Instruct models (1B and 3B). NGM consistently improves the ten-task average across all six backbones. Notably, it improves HumanEval by 4.26, HumanEval+ by 2.41, and GPQA-Diamond by 6.56 percentage points. Furthermore, NGM extends beyond text-only language models to vision-language models, with Qwen3-VL-2B achieving improvements across all five VLM benchmarks. Project page: https://pioneerqyw.github.io/ngm-project/

cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.35043

Steering for Safety, Hijacking for Profit: Trojan Threats in the LLM Activation Steering Supply Chain

Yu Yan, Jiahao Chen, Siqi Lu, Yongjuan Wang, Ziming Zhao, Zhaoxuan Li, Qingjun Yuan

原文摘要

As large language models (LLMs) are increasingly integrated into Web search, online recommendation, and intelligent decision-making, the third-party plugin ecosystem centered on model customization continues to grow. Among these, Steering Vectors guide model behavior by intervening in internal representations. However, the open plugin ecosystem also introduces new supply-chain security risks: malicious plugins can disguise themselves as having legitimate functionality and covertly manipulate outputs without significantly affecting model performance. Although this risk poses a real threat, the security issues of Steering plugins in the LLM supply-chain ecosystem still lack systematic research. This paper audits eight mainstream open-source Steering plugin libraries and reveals three structural vulnerabilities: insufficient distribution integrity verification, excessive plugin privileges, and missing runtime monitoring, which offers an attack surface for attackers. To investigate this threat, we propose a malicious plugin, IDH. Specifically, IDH designs a norm-gated mutually exclusive routing mechanism and proposes an information-decay inflection point localization method to automatically select the injection layer, thereby simultaneously achieving benign and malicious functions. To counter such threats, we propose a runtime defense framework, by suppressing anomalous activation interventions and orthogonalizing inter-layer trajectory. We evaluate IDH across five mainstream LLMs, using safety-refusal steering as the legitimate functionality and commercial recommendation manipulation as the hijack objective. Experiments covering six brands across three commercial domains demonstrate that IDH achieves an attack success rate (ASR) of up to 97.8% while preserving high refusal rates on harmful requests.

cs.CR

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arXiv ↗PDF ↗
首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.1324ACL Anthology · ACL 2026

GMSA: Enhancing Context Compression via Group Merging and Layer Semantic Alignment

Jiwei Tang, Zhicheng Zhang, Shunlong Wu, Jingheng Ye Northeastern University, Lichen Bai, Zitai Wang, Tingwei Lu, Lin Hai Tsinghua University, Tsinghua University 等 11 位作者

原文摘要

Large Language Models (LLMs) have achieved remarkable performance across a wide range of Natural Language Processing (NLP) tasks. However, in long-context scenarios, they face two challenges: high computational cost and information redundancy. To address these challenges, we propose GMSA, an encoder-decoder context compression framework that generates a compact sequence of soft tokens for downstream tasks. GMSA introduces Group Merging to achieve more uniform aggregation, mitigating semantic dominance during autoencoder pretraining, and Layer Semantic Alignment (LSA) to bridge the semantic gap between high-level abstract semantics and low-level input semantics. We first pretrain GMSA as an autoencoder and then fine-tune it for downstream tasks. Experiments demonstrate that GMSA improves context reconstruction compared to existing soft prompt compression paradigm and outperforms baselines on multiple long-context question answering and summarization benchmarks across two backbone models, while maintaining low end-to-end latency.

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.860ACL Anthology · ACL 2026

REG: Retrieval via Emotion Similarity for Guiding Empathetic Dialogue Generation

Xu Wang, Bo Wang, Yang Xiang Peng Cheng Laboratory, Yihong Tang, Dongming Zhao, Yuzifei, Yuexian Hou

原文摘要

Empathy relies on the cognitive capacity to relate to similar past experiences. Consequently, retrieval-based approaches utilize analogous exemplars to guide empathetic dialogue generation. However, existing methods prioritize semantic similarity over emotion characteristics, often leading to unempathetic responses. To address this, we propose REG, a framework that integrates four Emotion Attributes into the retrieval process to ensure explicit emotional alignment. Furthermore, to mitigate the noise and limited diversity caused by coarse-grained sentence-level attributes, we incorporate Token-level Retrieval for finer granularity and a Retrieval Candidate Augmentation strategy to enhance diversity. Empirical results on the EmpatheticDialogues dataset demonstrate that REG significantly outperforms baselines, offering a robust solution for empathetic generation.

cs.CL

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