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arXiv:2603.20396v1 Announce Type: new Abstract: Human mathematics (HM), the mathematics humans discover and value, is a vanishingly small subset of formal mathematics (FM), the totality …
arXiv:2603.20217v1 Announce Type: new Abstract: Reward models are a standard tool to score responses from LLMs. Reward models are built to rank responses to a …
arXiv:2603.20724v1 Announce Type: new Abstract: Multi-RF Fusion achieves a test ROC-AUC of 0.8476 +/- 0.0002 on ogbg-molhiv (10 seeds), placing #1 on the OGB leaderboard …
arXiv:2603.20639v1 Announce Type: new Abstract: The "AI singularity" is often miscast as a monolithic, godlike mind. Evolution suggests a different path: intelligence is fundamentally plural, …
arXiv:2603.20620v1 Announce Type: new Abstract: Can we trust the reasoning traces that large reasoning models (LRMs) produce? We investigate whether these traces faithfully reflect what …
arXiv:2603.20210v1 Announce Type: new Abstract: Masked Diffusion Models (MDMs) provide an efficient non-causal alternative to autoregressive generation but often struggle with token dependencies and semantic …
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:2603.21155v1 Announce Type: new Abstract: Text-attributed graphs (TAGs) enhance graph learning by integrating rich textual semantics and topological context for each node. While boosting expressiveness, …
arXiv:2603.21341v1 Announce Type: new Abstract: Improving embodied reasoning in multimodal-large-language models (MLLMs) is essential for building vision-language-action models (VLAs) on top of them to readily …
arXiv:2603.21237v1 Announce Type: new Abstract: Large language models (LLMs) deliver impressive capabilities but incur substantial inference latency and cost, which hinders their deployment in latency-sensitive …
arXiv:2603.20224v1 Announce Type: new Abstract: Large Language Models (LLMs) demonstrate exceptional performance across diverse tasks but come with substantial energy and computational costs, particularly in …
arXiv:2603.20206v1 Announce Type: new Abstract: Large language models (LLMs) have achieved impressive capabilities, yet ensuring their safety against harmful prompts remains a critical challenge. Recent …