arXiv:2512.15737v2 Announce Type: replace Abstract: We investigate how enzymatic binding kinetics regulate diffusion-driven instabilities in a two-step metabolic pathway. Starting from a mechanistic description in which the substrate reversibly binds to the first enzyme before catalytic conversion, we formulate two reaction-diffusion models: a simplified system with effective kinetics and an extended model that explicitly includes […]
Mechanism-Based Intelligence (MBI): Differentiable Incentives for Rational Coordination and Guaranteed Alignment in Multi-Agent Systems
arXiv:2512.20688v1 Announce Type: cross Abstract: Autonomous multi-agent systems are fundamentally fragile: they struggle to solve the Hayekian Information problem (eliciting dispersed private knowledge) and the Hurwiczian Incentive problem (aligning local actions with global objectives), making coordination computationally intractable. I introduce Mechanism-Based Intelligence (MBI), a paradigm that reconceptualizes intelligence as emergent from the coordination of multiple […]
Memory Bear AI A Breakthrough from Memory to Cognition Toward Artificial General Intelligence
arXiv:2512.20651v1 Announce Type: new Abstract: Large language models (LLMs) face inherent limitations in memory, including restricted context windows, long-term knowledge forgetting, redundant information accumulation, and hallucination generation. These issues severely constrain sustained dialogue and personalized services. This paper proposes the Memory Bear system, which constructs a human-like memory architecture grounded in cognitive science principles. By […]
A Physics Informed Neural Network For Deriving MHD State Vectors From Global Active Regions Observations
arXiv:2512.20747v1 Announce Type: cross Abstract: Solar active regions (ARs) do not appear randomly but cluster along longitudinally warped toroidal bands (‘toroids’) that encode information about magnetic structures in the tachocline, where global-scale organization likely originates. Global MagnetoHydroDynamic Shallow-Water Tachocline (MHD-SWT) models have shown potential to simulate such toroids, matching observations qualitatively. For week-scale early prediction […]
Interaction, Process, Infrastructure: A Unified Framework for Human-Agent Collaboration
arXiv:2506.11718v2 Announce Type: replace-cross Abstract: While AI tools are increasingly prevalent in knowledge work, they remain fragmented, lacking the architectural foundation for sustained, adaptive collaboration. We argue this limitation stems from their inability to represent and manage the structure of collaborative work. To bridge this gap, we propose a layered conceptual framework for human-agent systems […]
AI-Driven Decision-Making System for Hiring Process
arXiv:2512.20652v1 Announce Type: new Abstract: Early-stage candidate validation is a major bottleneck in hiring, because recruiters must reconcile heterogeneous inputs (resumes, screening answers, code assignments, and limited public evidence). This paper presents an AI-driven, modular multi-agent hiring assistant that integrates (i) document and video preprocessing, (ii) structured candidate profile construction, (iii) public-data verification, (iv) technical/culture-fit […]
MediEval: A Unified Medical Benchmark for Patient-Contextual and Knowledge-Grounded Reasoning in LLMs
arXiv:2512.20822v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly applied to medicine, yet their adoption is limited by concerns over reliability and safety. Existing evaluations either test factual medical knowledge in isolation or assess patient-level reasoning without verifying correctness, leaving a critical gap. We introduce MediEval, a benchmark that links MIMIC-IV electronic health […]
Learning to Compress: Unlocking the Potential of Large Language Models for Text Representation
arXiv:2511.17129v2 Announce Type: replace-cross Abstract: Text representation plays a critical role in tasks like clustering, retrieval, and other downstream applications. With the emergence of large language models (LLMs), there is increasing interest in harnessing their capabilities for this purpose. However, most of the LLMs are inherently causal and optimized for next-token prediction, making them suboptimal […]
From Fake Focus to Real Precision: Confusion-Driven Adversarial Attention Learning in Transformers
arXiv:2512.20661v1 Announce Type: new Abstract: Transformer-based models have been widely adopted for sentiment analysis tasks due to their exceptional ability to capture contextual information. However, these methods often exhibit suboptimal accuracy in certain scenarios. By analyzing their attention distributions, we observe that existing models tend to allocate attention primarily to common words, overlooking less popular […]
Quantifying Laziness, Decoding Suboptimality, and Context Degradation in Large Language Models
arXiv:2512.20662v1 Announce Type: new Abstract: Large Language Models (LLMs) often exhibit behavioral artifacts such as laziness (premature truncation of responses or partial compliance with multi-part requests), decoding suboptimality (failure to select higher-quality sequences due to myopic decoding), and context degradation (forgetting or ignoring core instructions over long conversations). We conducted three controlled experiments (A, B, […]