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arXiv:2602.23974v1 Announce Type: new Abstract: Offline reinforcement learning aims to learn an agent from pre-collected datasets, avoiding unsafe and inefficient real-time interaction. However, inevitable access …
arXiv:2602.24037v1 Announce Type: new Abstract: Market regime shifts induce distribution shifts that can degrade the performance of portfolio rebalancing policies. We propose macro-conditioned scenario-context rollout …
arXiv:2602.24080v1 Announce Type: new Abstract: The pursuit of human-like conversational agents has long been guided by the Turing test. For modern speech-to-speech (S2S) systems, a …
arXiv:2602.24097v1 Announce Type: new Abstract: Winter road maintenance is critical for ensuring public safety and reducing environmental impacts, yet existing methods struggle to manage large-scale …
arXiv:2602.24100v1 Announce Type: new Abstract: This paper presents the Artificial Agency Program (AAP), a position and research agenda for building AI systems as reality embedded, …
arXiv:2602.24110v1 Announce Type: new Abstract: Reinforcement Learning from Verifiable Rewards (RLVR) has emerged as a powerful paradigm for enhancing the complex reasoning capabilities of Large …
arXiv:2602.24173v1 Announce Type: new Abstract: We present a new approach for benchmarking Large Language Model (LLM) capabilities on research-level mathematics. Existing benchmarks largely rely on …
arXiv:2602.24180v1 Announce Type: new Abstract: The Flexible Job Shop Scheduling Problem (FJSP) originates from real production lines, while some practical constraints are often ignored or …
arXiv:2602.24195v1 Announce Type: new Abstract: Despite their capabilities, Multimodal Large Language Models (MLLMs) may produce plausible but erroneous outputs, hindering reliable deployment. Accurate uncertainty metrics …
arXiv:2602.24273v1 Announce Type: new Abstract: We propose a minimal agentic baseline that enables systematic comparison across different AI-based theorem prover architectures. This design implements the …
arXiv:2602.24288v1 Announce Type: new Abstract: The fast-growing demands in using Large Language Models (LLMs) to tackle complex multi-step data science tasks create an emergent need …
arXiv:2409.06888v5 Announce Type: cross Abstract: We use the Quality Diversity (QD) algorithm with Neural Cellular Automata (NCA) to automatically evaluate Multi-Agent Path Finding (MAPF) algorithms …