arXiv subjects

Zhiheng Jin

Publications and source records attributed to Zhiheng Jin.

4 recordsLinked to original sources

arXiv2026

Signal-Independent and Signal-Dependent Neural Ambisonic Matrix Encoding for Arbitrary Arrays with Variable Microphone Counts

Recent neural Ambisonic encoders accommodate diverse array geometries, yet many existing neural encoders require a fixed microphone count because the number of microphone channels is embedded in the network architecture. This requirement limits deployment across devices with different microphone configurations and adaptation to changes in available channels. To address this limitation, we investigate Transformer-based matrix encoding for arbitrary microphone arrays with variable microphone counts. This is achieved through shared microphone-wise processing and masked self-attention that models inter-microphone relationships across variable-size arrays. Within this framework, we consider signal-independent (SI) encoding, which predicts encoding matrices from array transfer functions, and introduce a signal-dependent (SD) extension that additionally incorporates the observed microphone signals. Both models are trained on simulated scenes using LibriSpeech sources and extensively evaluated under changes in source type, unseen microphone counts, and increased source counts beyond those used during training. Both SI and SD outperform conventional least-squares (LS) encoding in aggregate reconstruction performance across the evaluated conditions. SD consistently achieves stronger overall performance than SI. These results demonstrate that the proposed framework enables array-agnostic Ambisonic encoding while retaining generalization across microphone counts and acoustic source conditions.

eess.AS↗

arXiv2026

GAMF: Learned and Analytical Array Transfer Function Matching for Array-Generic Direction-of-Arrival Estimation

Microphone positional encoding supports cross-array direction-of-arrival (DOA) estimation, but coordinates alone cannot fully describe device shadowing or microphone directivity. We propose a Generalizable ATF Matching Framework (GAMF) for DOA estimation across array geometries and microphone counts, using array transfer functions (ATFs) as acoustic descriptors. The learned branch incorporates ATF embeddings into geometry-conditioned neural estimation to match acoustic observations with candidate directions. The analytical branch performs normalized ATF matching adapted from generalized steered response power. A hybrid configuration combines their scores through adaptive gating. Simulations across array configurations show that both learned and hybrid configurations outperform a representative positional-encoding-based neural baseline, remain competitive with analytical ATF matching in clean, low-reverberation scenes, and substantially improve upon it under stronger noise or reverberation. On eight-microphone LOCATA Task 1 recordings, the hybrid configuration outperforms the evaluated state-of-the-art baselines for three-dimensional DOA estimation, achieving mean errors of $3.76^\circ$ for three-dimensional DOA and $2.87^\circ$ for azimuth.

eess.AS↗

arXiv2026

Spider-Sense: Intrinsic Risk Sensing for Efficient Agent Defense with Hierarchical Adaptive Screening

As large language models (LLMs) evolve into autonomous agents, their real-world applicability has expanded significantly, accompanied by new security challenges. Most existing agent defense mechanisms adopt a mandatory checking paradigm, in which security validation is forcibly triggered at predefined stages of the agent lifecycle. In this work, we argue that effective agent security should be intrinsic and selective rather than architecturally decoupled and mandatory. We propose Spider-Sense framework, an event-driven defense framework based on Intrinsic Risk Sensing (IRS), which allows agents to maintain latent vigilance and trigger defenses only upon risk perception. Once triggered, the Spider-Sense invokes a hierarchical defence mechanism that trades off efficiency and precision: it resolves known patterns via lightweight similarity matching while escalating ambiguous cases to deep internal reasoning, thereby eliminating reliance on external models. To facilitate rigorous evaluation, we introduce S$^2$Bench, a lifecycle-aware benchmark featuring realistic tool execution and multi-stage attacks. Extensive experiments demonstrate that Spider-Sense achieves competitive or superior defense performance, attaining the lowest Attack Success Rate (ASR) and False Positive Rate (FPR), with only a marginal latency overhead of 8.3\%.

cs.CR↗

arXiv2026

UniFinEval: Towards Unified Evaluation of Financial Multimodal Models across Text, Images and Videos

Multimodal large language models are playing an increasingly significant role in empowering the financial domain, however, the challenges they face, such as multimodal and high-density information and cross-modal multi-hop reasoning, go beyond the evaluation scope of existing multimodal benchmarks. To address this gap, we propose UniFinEval, the first unified multimodal benchmark designed for high-information-density financial environments, covering text, images, and videos. UniFinEval systematically constructs five core financial scenarios grounded in real-world financial systems: Financial Statement Auditing, Company Fundamental Reasoning, Industry Trend Insights, Financial Risk Sensing, and Asset Allocation Analysis. We manually construct a high-quality dataset consisting of 3,767 question-answer pairs in both chinese and english and systematically evaluate 10 mainstream MLLMs under Zero-Shot and CoT settings. Results show that Gemini-3-pro-preview achieves the best overall performance, yet still exhibits a substantial gap compared to financial experts. Further error analysis reveals systematic deficiencies in current models. UniFinEval aims to provide a systematic assessment of MLLMs' capabilities in fine-grained, high-information-density financial environments, thereby enhancing the robustness of MLLMs applications in real-world financial scenarios. Data and code are available at https://github.com/aifinlab/UniFinEval.

q-fin.GN↗