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arXiv:2603.22459v1 Announce Type: new Abstract: Enhancing reader engagement while preserving informational fidelity is a central challenge in controllable text generation for news media. Optimizing news …
arXiv:2603.23234v1 Announce Type: new Abstract: Large language model (LLM)-based agents rely on memory mechanisms to reuse knowledge from past problem-solving experiences. Existing approaches typically construct …
arXiv:2603.22404v1 Announce Type: new Abstract: Consider a market of competing model providers selling query access to models with varying costs and capabilities. Customers submit problem …
arXiv:2603.22288v1 Announce Type: new Abstract: Prompting strategies affect LLM reasoning performance, but their role in chart-based QA remains underexplored. We present a systematic evaluation of …
arXiv:2603.22582v1 Announce Type: new Abstract: Chain-of-thought (CoT) reasoning has been proposed as a transparency mechanism for large language models in safety-critical deployments, yet its effectiveness …
arXiv:2603.22473v1 Announce Type: new Abstract: Hybrid language models combining attention with state space models (SSMs) or linear attention offer improved efficiency, but whether both components …
arXiv:2603.22846v1 Announce Type: new Abstract: Embodied Visual Tracking (EVT), a core dynamic task in embodied intelligence, requires an agent to precisely follow a language-specified target. …
arXiv:2603.23059v1 Announce Type: new Abstract: Recent advances in game AI, such as AlphaZero and Ath\'enan, have achieved superhuman performance across a wide range of board …
arXiv:2603.22633v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) systems for biomedical literature are typically evaluated using ranking metrics like Mean Reciprocal Rank (MRR), which measure …
arXiv:2603.22293v1 Announce Type: new Abstract: Search-augmented large language models (LLMs) trained with reinforcement learning (RL) have achieved strong results on open-domain question answering (QA), but …
arXiv:2603.22576v1 Announce Type: new Abstract: We introduce CAPITU, a benchmark for evaluating instruction-following capabilities of Large Language Models (LLMs) in Brazilian Portuguese. Unlike existing benchmarks …
arXiv:2603.22345v1 Announce Type: new Abstract: Multimodal emotion recognition in conversations (MERC) aims to identify and understand the emotions expressed by speakers during utterance interaction from …