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arXiv:2603.20260v1 Announce Type: new Abstract: The integration of Large Language Models into Multi-Agent Systems (MAS) has enabled the so-lution of complex, long-horizon tasks through collaborative …
arXiv:2603.20815v1 Announce Type: new Abstract: The pharmaceutical industry is facing challenges with quality management such as high costs of compliance, slow responses and disjointed knowledge. …
arXiv:2603.20216v1 Announce Type: new Abstract: Diffusion language models (DLMs) have emerged as a promising alternative to autoregressive (AR) models, offering sub-linear generation latency and bidirectional …
arXiv:2603.21022v1 Announce Type: new Abstract: We propose Knowledge Boundary Discovery (KBD), a reinforcement learning based framework to explore the knowledge boundaries of the Large Language …
arXiv:2603.21340v1 Announce Type: new Abstract: This paper presents ARYA, a composable, physics-constrained, deterministic world model architecture built on five foundational principles: nano models, composability, causal …
arXiv:2603.20209v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) combine the linguistic strengths of LLMs with the ability to process multimodal data, enbaling them …
arXiv:2603.20293v1 Announce Type: new Abstract: Text-attributed graphs, where nodes are enriched with textual attributes, have become a powerful tool for modeling real-world networks such as …
arXiv:2603.20276v1 Announce Type: new Abstract: A hallmark of human intelligence is Introspection-the ability to assess and reason about one's own cognitive processes. Introspection has emerged …
arXiv:2603.20925v1 Announce Type: new Abstract: As agentic systems move into real-world deployments, their decisions increasingly depend on external inputs such as retrieved content, tool outputs, …
arXiv:2603.20595v1 Announce Type: new Abstract: Multi-agent systems (MAS) are increasingly used in healthcare to support complex decision-making through collaboration among specialized agents. Because these systems …
arXiv:2603.20212v1 Announce Type: new Abstract: Reward models (RMs) are critical for aligning Large Language Models via Reinforcement Learning from Human Feedback (RLHF). While Generative Reward …
arXiv:2603.20215v1 Announce Type: new Abstract: Large language models (LLMs) have recently demonstrated impressive capabilities in reasoning tasks. Currently, mainstream LLM reasoning frameworks predominantly focus on …