A Theoretical Framework for Adaptive Utility-Weighted Benchmarking
arXiv:2602.12356v1 Announce Type: new Abstract: Benchmarking has long served as a foundational practice in machine learning and, increasingly, in modern AI systems such as large language models, where shared tasks, metrics, and leaderboards offer a common basis for measuring progress...
Intent-Driven Smart Manufacturing Integrating Knowledge Graphs and Large Language Models
arXiv:2602.12419v1 Announce Type: new Abstract: The increasing complexity of smart manufacturing environments demands interfaces that can translate high-level human intents into machine-executable actions. This paper presents a unified framework that integrates instruction-tuned Large Language Models (LLMs) with ontology-aligned Knowledge Graphs...
AI Agents for Inventory Control: Human-LLM-OR Complementarity
arXiv:2602.12631v1 Announce Type: new Abstract: Inventory control is a fundamental operations problem in which ordering decisions are traditionally guided by theoretically grounded operations research (OR) algorithms. However, such algorithms often rely on rigid modeling assumptions and can perform poorly when...
Think Fast and Slow: Step-Level Cognitive Depth Adaptation for LLM Agents
arXiv:2602.12662v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly deployed as autonomous agents for multi-turn decision-making tasks. However, current agents typically rely on fixed cognitive patterns: non-thinking models generate immediate responses, while thinking models engage in deep reasoning...
From Biased Chatbots to Biased Agents: Examining Role Assignment Effects on LLM Agent Robustness
arXiv:2602.12285v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly deployed as autonomous agents capable of actions with real-world impacts beyond text generation. While persona-induced biases in text generation are well documented, their effects on agent task performance remain...
Retrieval-Augmented Self-Taught Reasoning Model with Adaptive Chain-of-Thought for ASR Named Entity Correction
arXiv:2602.12287v1 Announce Type: cross Abstract: End-to-end automatic speech recognition (ASR) systems frequently misrecognize domain-specific phrases like named entities, which can cause catastrophic failures in downstream tasks. A new family of named entity correction methods based on large language models (LLMs)...
Adaptive traffic signal control optimization using a novel road partition and multi-channel state representation method
arXiv:2602.12296v1 Announce Type: cross Abstract: This study proposes a novel adaptive traffic signal control method leveraging a Deep Q-Network (DQN) and Proximal Policy Optimization (PPO) to optimize signal timing by integrating variable cell length and multi-channel state representation. A road...
Quantum walk inspired JPEG compression of images
arXiv:2602.12306v1 Announce Type: cross Abstract: This work proposes a quantum inspired adaptive quantization framework that enhances the classical JPEG compression by introducing a learned, optimized Qtable derived using a Quantum Walk Inspired Optimization (QWIO) search strategy. The optimizer searches a...
Visible and Hyperspectral Imaging for Quality Assessment of Milk: Property Characterisation and Identification
arXiv:2602.12313v1 Announce Type: cross Abstract: Rapid and non-destructive assessment of milk quality is crucial to ensuring both nutritional value and food safety. In this study, we investigated the potential of visible and hyperspectral imaging as cost-effective and quick-response alternatives to...
AgenticShop: Benchmarking Agentic Product Curation for Personalized Web Shopping
arXiv:2602.12315v1 Announce Type: cross Abstract: The proliferation of e-commerce has made web shopping platforms key gateways for customers navigating the vast digital marketplace. Yet this rapid expansion has led to a noisy and fragmented information environment, increasing cognitive burden as...
ReFilter: Improving Robustness of Retrieval-Augmented Generation via Gated Filter
arXiv:2602.12709v1 Announce Type: new Abstract: Retrieval-augmented generation (RAG) has become a dominant paradigm for grounding large language models (LLMs) with external evidence in knowledge-intensive question answering. A core design choice is how to fuse retrieved samples into the LLMs, where...
Exploring a New Competency Modeling Process with Large Language Models
arXiv:2602.13084v1 Announce Type: new Abstract: Competency modeling is widely used in human resource management to select, develop, and evaluate talent. However, traditional expert-driven approaches rely heavily on manual analysis of large volumes of interview transcripts, making them costly and prone...
DiffuRank: Effective Document Reranking with Diffusion Language Models
arXiv:2602.12528v1 Announce Type: cross Abstract: Recent advances in large language models (LLMs) have inspired new paradigms for document reranking. While this paradigm better exploits the reasoning and contextual understanding capabilities of LLMs, most existing LLM-based rerankers rely on autoregressive generation,...
Decoder-only Conformer with Modality-aware Sparse Mixtures of Experts for ASR
arXiv:2602.12546v1 Announce Type: cross Abstract: We present a decoder-only Conformer for automatic speech recognition (ASR) that processes speech and text in a single stack without external speech encoders or pretrained large language models (LLM). The model uses a modality-aware sparse...
The Appeal and Reality of Recycling LoRAs with Adaptive Merging
arXiv:2602.12323v1 Announce Type: new Abstract: The widespread availability of fine-tuned LoRA modules for open pre-trained models has led to an interest in methods that can adaptively merge LoRAs to improve performance. These methods typically include some way of selecting LoRAs...
AMPS: Adaptive Modality Preference Steering via Functional Entropy
arXiv:2602.12533v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) often exhibit significant modality preference, which is a tendency to favor one modality over another. Depending on the input, they may over-rely on linguistic priors relative to visual evidence, or...
Exploring Accurate and Transparent Domain Adaptation in Predictive Healthcare via Concept-Grounded Orthogonal Inference
arXiv:2602.12542v1 Announce Type: new Abstract: Deep learning models for clinical event prediction on electronic health records (EHR) often suffer performance degradation when deployed under different data distributions. While domain adaptation (DA) methods can mitigate such shifts, its "black-box" nature prevents...
Fractional Order Federated Learning for Battery Electric Vehicle Energy Consumption Modeling
arXiv:2602.12567v1 Announce Type: new Abstract: Federated learning on connected electric vehicles (BEVs) faces severe instability due to intermittent connectivity, time-varying client participation, and pronounced client-to-client variation induced by diverse operating conditions. Conventional FedAvg and many advanced methods can suffer from...
Dual-Granularity Contrastive Reward via Generated Episodic Guidance for Efficient Embodied RL
arXiv:2602.12636v1 Announce Type: new Abstract: Designing suitable rewards poses a significant challenge in reinforcement learning (RL), especially for embodied manipulation. Trajectory success rewards are suitable for human judges or model fitting, but the sparsity severely limits RL sample efficiency. While...
Unifying Model-Free Efficiency and Model-Based Representations via Latent Dynamics
arXiv:2602.12643v1 Announce Type: new Abstract: We present Unified Latent Dynamics (ULD), a novel reinforcement learning algorithm that unifies the efficiency of model-free methods with the representational strengths of model-based approaches, without incurring planning overhead. By embedding state-action pairs into a...
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