arXiv:2603.12286v2 Announce Type: replace Abstract: Modern neuroscience has accumulated extensive evidence on perception, memory, prediction, valuation, and consciousness, yet still lacks an explicit operational architecture capable of integrating these phenomena within a unified computational framework. Existing theories address specific aspects of neural function: predictive coding and active inference emphasize hierarchical inference and prediction error minimization; […]
Nemotron-CrossThink: Scaling Self-Learning beyond Math Reasoning
arXiv:2504.13941v3 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have shown strong reasoning capabilities, particularly when enhanced through Reinforcement Learning (RL). While prior work has successfully applied RL to mathematical reasoning — where rules and correctness are well-defined — generalizing these methods to broader reasoning domains remains challenging due to limited data, the lack of […]
Quantization Meets dLLMs: A Systematic Study of Post-training Quantization for Diffusion LLMs
arXiv:2508.14896v3 Announce Type: replace-cross Abstract: Recent advances in diffusion large language models (dLLMs) have introduced a promising alternative to autoregressive (AR) LLMs for natural language generation tasks, leveraging full attention and denoising-based decoding strategies. However, the deployment of these models on edge devices remains challenging due to their massive parameter scale and high resource demands. […]
Eyes on Target: Gaze-Aware Object Detection in Egocentric Video
arXiv:2511.01237v2 Announce Type: replace-cross Abstract: Human gaze offers rich supervisory signals for understanding visual attention in complex visual environments. In this paper, we propose Eyes on Target, a novel depth-aware and gaze-guided object detection framework designed for egocentric videos. Our approach injects gaze-derived features into the attention mechanism of a Vision Transformer (ViT), effectively biasing […]
Multi-Agent LLMs for Generating Research Limitations
arXiv:2601.11578v2 Announce Type: replace-cross Abstract: Identifying and articulating limitations is essential for transparent and rigorous scientific research. However, zero-shot large language models (LLMs) approach often produce superficial or general limitation statements (e.g., dataset bias or generalizability). They usually repeat limitations reported by authors without looking at deeper methodological issues and contextual gaps. This problem is […]
TimesNet-Gen: Deep Learning-based Site Specific Strong Motion Generation
arXiv:2512.04694v2 Announce Type: replace-cross Abstract: Effective earthquake risk reduction relies on accurate site-specific evaluations, which require models capable of representing the influence of local site conditions on ground motion characteristics. In this context, data-driven approaches that learn site-controlled signatures from recorded ground motions offer a promising direction. We address strong ground motion generation from time-domain […]
Central Dogma Transformer II: An AI Microscope for Understanding Cellular Regulatory Mechanisms
arXiv:2602.08751v3 Announce Type: replace-cross Abstract: Current biological AI models lack interpretability — their internal representations do not correspond to biological relationships that researchers can examine. Understanding gene regulation requires models whose learned structure can be directly interrogated to generate experimentally testable hypotheses. CDT-II mirrors the central dogma in its architecture — DNA self-attention, RNA self-attention, […]
Failure Detection in Chemical Processes Using Symbolic Machine Learning: A Case Study on Ethylene Oxidation
arXiv:2603.06767v2 Announce Type: replace-cross Abstract: Over the past decade, Artificial Intelligence has significantly advanced, mostly driven by large-scale neural approaches. However, in the chemical process industry, where safety is critical, these methods are often unsuitable due to their brittleness, and lack of explainability and interpretability. Furthermore, open-source real-world datasets containing historical failures are scarce in […]
EARCP: Self-Regulating Coherence-Aware Ensemble Architecture for Sequential Decision Making — Ensemble Auto-Regule par Coherence et Performance
arXiv:2603.14651v1 Announce Type: cross Abstract: We present EARCP (Ensemble Auto-R’egul’e par Coh’erence et Performance), a novel ensemble architecture that dynamically weights heterogeneous expert models based on both their individual performance and inter-model coherence. Unlike traditional ensemble methods that rely on static or offline-learned combinations, EARCP continuously adapts model weights through a principled online learning mechanism […]
POLCA: Stochastic Generative Optimization with LLM
arXiv:2603.14769v1 Announce Type: cross Abstract: Optimizing complex systems, ranging from LLM prompts to multi-turn agents, traditionally requires labor-intensive manual iteration. We formalize this challenge as a stochastic generative optimization problem where a generative language model acts as the optimizer, guided by numerical rewards and text feedback to discover the best system. We introduce Prioritized Optimization […]
Video Detector: A Dual-Phase Vision-Based System for Real-Time Traffic Intersection Control and Intelligent Transportation Analysis
arXiv:2603.14861v1 Announce Type: cross Abstract: Urban traffic management increasingly requires intelligent sensing systems capable of adapting to dynamic traffic conditions without costly infrastructure modifications. Vision-based vehicle detection has therefore become a key technology for modern intelligent transportation systems. This study presents Video Detector (VD), a dual-phase vision-based traffic intersection management system designed as a flexible […]
AnoleVLA: Lightweight Vision-Language-Action Model with Deep State Space Models for Mobile Manipulation
arXiv:2603.15046v1 Announce Type: cross Abstract: In this study, we address the problem of language-guided robotic manipulation, where a robot is required to manipulate a wide range of objects based on visual observations and natural language instructions. This task is essential for service robots that operate in human environments, and requires safety, efficiency, and task-level generality. […]