LLM-based multi-agent trading systems are moving from research prototypes to live deployments that control real assets. They ingest news and social media from the open web and pass it between agents, so a single corrupted signal can reach the final decision and cause financial loss. We study two adversaries that can introduce such signals without access to model internals: a low-barrier data adversary that plants manipulated content in web sources, and a compromised-agent adversary that tampers with an agent's prompt. To our knowledge, this is the first systematic empirical study in finance of how an adversarial signal enters a multi-agent trading system and whether it survives to alter the final trading decision. Along the role axis, we pair each of four roles---Analyst, Researcher, Trader, and Risk Manager---with an attack matched to its interface. Along the structural axis, we compare four communication topologies, interpreted with a simple parameter-free heuristic, the Adversarial Signal Preservation Score. Experiments on post-cutoff data span five assets and two target directions. We find that no architecture is inherently robust: majority voting offers conditional protection depending on benign-agent agreement, while compromising a terminal decision node can bypass upstream safeguards. These findings provide empirical insights for developing more effective defense strategies for multi-agent systems.
Vision-language models (VLMs) can answer chart questions accurately, but output accuracy alone provides limited insight into the internal representations supporting exact value reading. We study value reading from vertical bar charts through controlled counterfactual activation patching in Qwen2.5-VL-7B-Instruct and InternVL3.5-8B. Our study connects three analyses. First, under one-tick bar-height counterfactuals, patching states selected by the changed-height region produces greater restoration of the original answer preference than patching states selected by the invariant bar body. Across language-model depth, restoration from legend- and series-related positions declines earlier than restoration from bar-geometry and axis-scale positions. Second, we identify middle-layer states at series-related prompt positions that can restore answer preference under legend swaps. Resetting these states selectively weakens restoration from an earlier visual-legend patch, demonstrating their causal role in the downstream effects of that intervention. Third, a factorial analysis shows that both models can combine geometry and scale states from separate donors to favor the combined target answer. Together, these results provide causal evidence about the internal states supporting exact bar-value reading.
Chart-to-code generation is commonly trained through supervised fine-tuning on reference plotting scripts, implicitly treating the gold code as a fully observable target. However, many chart programs contain latent variables that cannot be uniquely recovered from the rendered image. We identify this latent-observation mismatch in four forms across five chart types: aggregation-induced mismatch, where raw samples are reduced to box statistics or histogram bin masses; normalization-induced mismatch, where absolute scale is removed in pie charts; projection-induced mismatch, where 3D information is lost through 2D rendering; and level-set-induced mismatch, where a scalar field is observable only through selected contour lines. These mismatches introduce target ambiguity and require models to generate information unsupported by the image. We propose Observation-Aligned Supervision, which replaces latent variables with visually constrained quantities. And we study fixed-view 3D scatter and contour charts through controlled experiments. Across VLMs, observation-aligned supervision generally improves observable recovery end-to-end and under both-executable evaluation. Against compact analytic contour formulas, end-to-end performance declines despite better conditional boundary recovery in both-exec setting, suggesting a trade-off with representational compactness.
Xu Zheng, Wei Cheng, Zhuomin Chen, Dongkuan Xu, Mo Sha, Jingchao Ni, Dongsheng Luo
On-device fine-tuning lets a large language model (LLM) learn from its user's private data without uploading it. Low-rank adaptation (LoRA) reduces the trainable parameters, but each first-order training step still multiplies by the frozen base-model weights during the forward pass, activation recomputation, and input-gradient computation. A phone's neural processing unit (NPU) already holds these weights for inference; using it for training also requires computing input gradients, which its inference interface does not support. We present a system that runs all three frozen-weight products on the NPU while keeping the training graph, adapters, and optimizer on the CPU. Its input-gradient kernels reuse the resident weight tiles and the forward dequantizer, avoiding a transposed weight copy and ensuring forward and backward use the same effective weights. To preserve usable gradients, the system selects precision by gradient sensitivity and applies a per-row range guard for half-precision (FP16) outputs. Its runtime fuses the remaining CPU operators and overlaps NPU execution with adapter computation, while resumable execution and thermal pacing let training proceed as a background job. We evaluate it on two Qualcomm Snapdragon platforms using four LLMs from the Qwen and Llama families. Our workloads cover nightly personalization with labels supplied on the phone and the public LaMP-2 benchmark; comparisons include an optimized CPU trainer, a phone-GPU trainer, forward-only training, and GPU-trained references.
Shuai Zhang Zhejiang University of Finance and Economics, Weibo Xu, Jiahao Nie Zhejiang University of Finance and Economics, Kecheng Huang
Existing hierarchical text classification (HTC) methods typically use prompt tuning or contrastive learning to inject the label hierarchy into a model as prior knowledge to implicitly learn label embeddings for classification. However, such implicit learning fails to accurately reflect label geometry (i.e., feature spatial distribution of label embeddings), as it does not model hierarchy-aware geometric relations among labels. To address this issue, we propose a novel two-stage label geometry structuring and aligning framework, termed LGSA, which transforms the label hierarchy from an implicit prior into an explicit embedding. First, we propose a hierarchical geometric structuring (HGS) module that leverages a general orthogonal frame (GOF) to reconstruct an explicit label geometry conforming to the label hierarchy. The label geometry is then treated as a label prototype to guide model training. To facilitate the guidance, we thereby propose a hierarchical geometric aligning (HGA) module as a regularization term to align label geometry learned by the model with the explicit label prototype. Experiments on three real-world HTC datasets confirm that LGSA consistently outperforms existing state-of-the-art methods. The code and models are available at https://anonymous.4open.science/r/LGSA-1E0C.
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cs.CL
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表
Tianhao Niu, Ziyu Han, Qiguang Chen, Shiqi Zhou, Baocai Shan Harbin Institute of Technology, Hengjie Fang, Qingfu Zhu Harbin Institute of Technology, Wanxiang Che Harbin Institute of Technology
Automatic data visualization generation have advanced rapidly with multi-modal large language models, yet existing efforts largely focus on static charts and overlook the interactive dashboards commonly used for real-world data exploration. We introduce Dashboard2Code, a novel task that requires a model to proactively explore an interactive dashboard, acquire and integrate feedback from its own interactions (e.g., clicking and filtering), and generate code that reproduces the target dashboard. To support comprehensive evaluation, we present DashboardMimic, the first Plotly+Dash benchmark for Dashboard2Code, comprising 180 carefully designed and manually verified dashboard–code pairs spanning three difficulty levels and covering eight common real-world interaction patterns. We further propose an automated evaluation framework tailored to dashboards that combines code semantic analysis with dynamic interaction-based testing to assess visual and interaction consistency, showing strong agreement with human judgments. Experiments across a range of open- and closed-source multi-modal models reveal that even the strongest systems struggle on high-complexity dashboards and that a substantial performance gap remains between open-source and closed-source models on the Dashboard2Code task.
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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
Xuan Dong, Zhe Han, Tianhao Niu, Qingfu Zhu Harbin Institute of Technology, Wanxiang Che Harbin Institute of Technology
Vision-Language-Action (VLA) models have shown strong performance in language-conditioned robotic manipulation, yet their robustness to linguistic variation remains poorly understood. In this work, We present the first systematic multilingual evaluation of VLA models by translating the LIBERO benchmark into ten languages, revealing severe performance degradation under non-English instructions, with success rates dropping by 30–50%. Through fine-grained analysis of task executions, we find that language influence is highly non-uniform across steps: certain steps exhibit strong language dependence and dominate overall task failure, while others are largely language-agnostic. Based on this insight, we propose a step-wise inference-time intervention that aligns representations according to step language sensitivity, substantially improving performance under linguistic variation. Our results indicate that language robustness in VLA models is fundamentally a step-wise control problem, highlighting the importance of temporally structured analysis for reliable embodied agents.
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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