When Your Agent Stops Using Tools
Reinforcement learning was supposed to teach agents to use tools fluently. Instead, researchers are watching a consistent failure mode: models trained...
Clear, practical breakdowns of the AI papers and ideas that matter: agents, reasoning, safety, multi-agent systems. Written for practitioners, not academics.
Reinforcement learning was supposed to teach agents to use tools fluently. Instead, researchers are watching a consistent failure mode: models trained...
Anthropic's MCP and Google's A2A joined the Linux Foundation. IBM killed its own protocol to back A2A. 146 organizations signed on. The wars are ending.
SwarmBench tested 13 LLMs on swarm coordination tasks. The results show catastrophic overhead and communication that doesn't actually help.
Twenty-two researchers across four continents show how agent swarms fabricate consensus, infiltrate communities, and poison the training data of future AI models.
Models that can technically process 128K tokens routinely fail on tasks requiring reasoning across 32K. That gap isn't a context window problem. It's an...
Knowledge graphs have a well-documented lookup problem. When you ask an LLM to traverse a KG and reason over multi-hop paths, it doesn't search the graph...
Reasoning language models score in the top percentile on math olympiad benchmarks, yet a new study from Stanford found they fail to correctly recall their...
Reasoning tokens aren't free. Every chain-of-thought step an LLM generates costs inference budget, and most of the time that thinking is wasted on tasks...
The 2025 AI Agent Index just cataloged over 100 deployed agentic AI systems, and the finding that should alarm everyone isn't about capability. It's about...
SWE-bench has been the graveyard of small language models. While GPT-4 class systems resolve over 40% of real-world GitHub issues, models under 10 billion...
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