· ai
[Paper] Meta-Learning for Quantum Optimization via Quantum Sequence Model
The Quantum Approximate Optimization Algorithm (QAOA) is a leading approach for solving combinatorial optimization problems on near-term quantum processors. How...
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- · ai
[Paper] Arbitrage: Efficient Reasoning via Advantage-Aware Speculation
Modern Large Language Models achieve impressive reasoning capabilities with long Chain of Thoughts, but they incur substantial computational cost during inferen...
- · ai
[Paper] Plug-and-Play Homeostatic Spark: Zero-Cost Acceleration for SNN Training Across Paradigms
Spiking neural networks offer event driven computation, sparse activation, and hardware efficiency, yet training often converges slowly and lacks stability. We ...
- · ai
[Paper] Factuality and Transparency Are All RAG Needs! Self-Explaining Contrastive Evidence Re-ranking
This extended abstract introduces Self-Explaining Contrastive Evidence Re-Ranking (CER), a novel method that restructures retrieval around factual evidence by f...
- · ai
[Paper] Evolutionary Architecture Search through Grammar-Based Sequence Alignment
Neural architecture search (NAS) in expressive search spaces is a computationally hard problem, but it also holds the potential to automatically discover comple...
- · ai
[Paper] Nex-N1: Agentic Models Trained via a Unified Ecosystem for Large-Scale Environment Construction
The evolution of Large Language Models (LLMs) from passive responders to autonomous agents necessitates a fundamental shift in learning paradigms -- from static...
- · devops
[Paper] Federated Learning for Terahertz Wireless Communication
The convergence of Terahertz (THz) communications and Federated Learning (FL) promises ultra-fast distributed learning, yet the impact of realistic wideband imp...
- · ai
[Paper] LLMs Know More Than Words: A Genre Study with Syntax, Metaphor & Phonetics
Large language models (LLMs) demonstrate remarkable potential across diverse language related tasks, yet whether they capture deeper linguistic properties, such...
- · ai
[Paper] CARL: Critical Action Focused Reinforcement Learning for Multi-Step Agent
Agents capable of accomplishing complex tasks through multiple interactions with the environment have emerged as a popular research direction. However, in such ...
- · ai
[Paper] Algorithmic Thinking Theory
Large language models (LLMs) have proven to be highly effective for solving complex reasoning tasks. Surprisingly, their capabilities can often be improved by i...
- · ai
[Paper] Functional Stability of Software-Hardware Neural Network Implementation The NeuroComp Project
This paper presents an innovative approach to ensuring functional stability of neural networks through hardware redundancy at the individual neuron level. Unlik...
- · software
[Paper] POLARIS: Is Multi-Agentic Reasoning the Next Wave in Engineering Self-Adaptive Systems?
The growing scale, complexity, interconnectivity, and autonomy of modern software ecosystems introduce unprecedented uncertainty, challenging the foundations of...