arXiv:2504.04640v3 Announce Type: replace-cross Abstract: Variation in language use, shaped by speakers’ sociocultural background and specific context of use, offers a rich lens into cultural perspectives, values, and opinions. For example, Chinese students discuss “healthy eating” with words like “timing,” “regularity,” and “digestion,” whereas Americans use vocabulary like “balancing food groups” and “avoiding fat and […]
TurboAgent: An LLM-Driven Autonomous Multi-Agent Framework for Turbomachinery Aerodynamic Design
arXiv:2604.06747v2 Announce Type: replace Abstract: The aerodynamic design of turbomachinery is a complex and tightly coupled multi-stage process involving geometry generation, performance prediction, optimization, and high-fidelity physical validation. Existing intelligent design approaches typically focus on individual stages or rely on loosely coupled pipelines, making fully autonomous end-to-end design challenging. To address this issue, this study […]
TTVS: Boosting Self-Exploring Reinforcement Learning via Test-time Variational Synthesis
arXiv:2604.08468v1 Announce Type: cross Abstract: Despite significant advances in Large Reasoning Models (LRMs) driven by reinforcement learning with verifiable rewards (RLVR), this paradigm is fundamentally limited in specialized or novel domains where such supervision is prohibitively expensive or unavailable, posing a key challenge for test-time adaptation. While existing test-time methods offer a potential solution, they […]
MemPO: Self-Memory Policy Optimization for Long-Horizon Agents
arXiv:2603.00680v3 Announce Type: replace Abstract: Long-horizon agents face the challenge of growing context size during interaction with environment, which degrades the performance and stability. Existing methods typically introduce the external memory module and look up the relevant information from the stored memory, which prevents the model itself from proactively managing its memory content and aligning […]
Behavior-Aware Item Modeling via Dynamic Procedural Solution Representations for Knowledge Tracing
arXiv:2604.08260v1 Announce Type: cross Abstract: Knowledge Tracing (KT) aims to predict learners’ future performance from past interactions. While recent KT approaches have improved via learning item representations aligned with Knowledge Components, they overlook the procedural dynamics of problem solving. We propose Behavior-Aware Item Modeling (BAIM), a framework that enriches item representations by integrating dynamic procedural […]
Scalable Neural Decoders for Practical Fault-Tolerant Quantum Computation
arXiv:2604.08358v1 Announce Type: cross Abstract: Quantum error correction (QEC) is essential for scalable quantum computing. However, it requires classical decoders that are fast and accurate enough to keep pace with quantum hardware. While quantum low-density parity-check codes have recently emerged as a promising route to efficient fault tolerance, current decoding algorithms do not allow one […]
A Unified Framework for Evaluating and Enhancing the Transparency of Explainable AI Methods via Perturbation-Gradient Consensus Attribution
arXiv:2412.03884v3 Announce Type: replace Abstract: Explainable Artificial Intelligence (XAI) methods are increasingly used in safety-critical domains, yet there is no unified framework to jointly evaluate fidelity, interpretability, robustness, fairness, and completeness. We address this gap through two contributions. First, we propose a multi-criteria evaluation framework that formalizes these five criteria using principled metrics: fidelity via […]
MALLM-GAN: Multi-Agent Large Language Model as Generative Adversarial Network for Synthesizing Tabular Data
arXiv:2406.10521v5 Announce Type: replace-cross Abstract: In the era of big data, access to abundant data is crucial for driving research forward. However, such data is often inaccessible due to privacy concerns or high costs, particularly in healthcare domain. Generating synthetic (tabular) data can address this, but existing models typically require substantial amounts of data to […]
$textttSEM-CTRL$: Semantically Controlled Decoding
arXiv:2503.01804v4 Announce Type: replace-cross Abstract: Ensuring both syntactic and semantic correctness in Large Language Model (LLM) outputs remains a significant challenge, despite being critical for real-world deployment. In this paper, we introduce $textttSEM-CTRL$, a unified approach that allows for enforcing rich context-sensitive constraints, and task and instance specific semantics directly on the LLM decoder. Our […]
CompoDistill: Attention Distillation for Compositional Reasoning in Multimodal LLMs
arXiv:2510.12184v2 Announce Type: replace-cross Abstract: Recently, efficient Multimodal Large Language Models (MLLMs) have gained significant attention as a solution to their high computational complexity, making them more practical for real-world applications. In this regard, the knowledge distillation (KD) approach has emerged as a promising alternative, which transfers the rich visual and linguistic knowledge from a […]
Hardware Efficient Approximate Convolution with Tunable Error Tolerance for CNNs
arXiv:2603.10100v2 Announce Type: replace-cross Abstract: Modern CNNs’ high computational demands hinder edge deployment, as traditional “hard” sparsity (skipping mathematical zeros) loses effectiveness in deep layers or with smooth activations like Tanh. We propose a “soft sparsity” paradigm using a hardware efficient Most Significant Bit (MSB) proxy to skip negligible non-zero multiplications. Integrated as a custom […]
Multimodal Reasoning with LLM for Encrypted Traffic Interpretation: A Benchmark
arXiv:2604.08140v1 Announce Type: cross Abstract: Network traffic, as a key media format, is crucial for ensuring security and communications in modern internet infrastructure. While existing methods offer excellent performance, they face two key bottlenecks: (1) They fail to capture multidimensional semantics beyond unimodal sequence patterns. (2) Their black box property, i.e., providing only category labels, […]