For the past year, AI devs have relied on the ReAct (Reasoning + Acting) pattern—a simple loop where an LLM thinks, picks a tool, and executes. But as any software engineer who has tried to move these agents into production…
For the past year, AI devs have relied on the ReAct (Reasoning + Acting) pattern—a simple loop where an LLM thinks, picks a tool, and executes. But as any software engineer who has tried to move these agents into production…
For the past year, AI devs have relied on the ReAct (Reasoning + Acting) pattern—a simple loop where an LLM thinks, picks a tool, and executes. But as any software engineer who has tried to move these agents into production…
For the past year, AI devs have relied on the ReAct (Reasoning + Acting) pattern—a simple loop where an LLM thinks, picks a tool, and executes. But as any software engineer who has tried to move these agents into production…
For the past year, AI devs have relied on the ReAct (Reasoning + Acting) pattern—a simple loop where an LLM thinks, picks a tool, and executes. But as any software engineer who has tried to move these agents into production…
For the past year, AI devs have relied on the ReAct (Reasoning + Acting) pattern—a simple loop where an LLM thinks, picks a tool, and executes. But as any software engineer who has tried to move these agents into production…
For the past year, AI devs have relied on the ReAct (Reasoning + Acting) pattern—a simple loop where an LLM thinks, picks a tool, and executes. But as any software engineer who has tried to move these agents into production…
arXiv:2510.06820v2 Announce Type: replace-cross Abstract: Multimodal retrieval still leans on embedding-based models like CLIP for fast vector search over pre-computed image embeddings. Yet, unlike text retrieval, where joint-encoder rerankers are standard, comparable vision-language rerankers are largely absent. We find that…
arXiv:2602.08104v2 Announce Type: replace-cross Abstract: Multi-Agent Reinforcement Learning (MARL) is increasingly deployed in safety-critical domains, yet methods for interpretable failure detection and attribution remain underdeveloped. We introduce a two-stage gradient-based framework that provides interpretable diagnostics for three critical failure analysis…
arXiv:2601.17074v2 Announce Type: replace Abstract: The accurate estimation of Arctic snow depth remains a critical time-varying inverse problem due to the extreme scarcity and noise inherent in associated sea ice parameters. Existing process-based and data-driven models are either highly sensitive…
