Speculate While You Reason: Teaching Agents to Predict Their Next Tool Call via Joint Agent-Speculator RL
Researchers propose a joint reinforcement learning method to teach AI agents to predict their own tool calls, reducing latency by pre-executing tasks.
- Joint RL unifies the agent and speculator into one model.
- Pre-executing tool calls hides wall-clock latency.
- The method eliminates the need for separate draft models.
- Agents can effectively predict their own next actions.
Large language model agents often face delays waiting for external tools to execute tasks. This paper introduces a technique called tool-call speculation, where the system predicts the next tool call and executes it in advance to hide this latency.
Previous methods relied on separate draft models or cached traces, which often misaligned with the actual agent's behavior. The researchers found that the target agent itself is highly capable of predicting its next move.
By unifying the agent and the speculator within the same model using joint reinforcement learning, the approach simplifies the architecture. This method bridges the gap between prediction and execution, leading to faster response times for complex agentic workflows.
Offers a new method to optimize agent speed and user experience.
Faster agents mean lower compute costs and better customer satisfaction.
Highlights efficiency gains in agentic AI infrastructure.
- Tool-call speculation
- Predicting and pre-executing a tool call before the agent officially requests it to reduce wait time.
AI ResearchAnthropic Mythos Preview Raises the Stakes for AI-Assisted Cryptography Research
New research framework aims to assess and track clinical AI models - Healthcare IT News
AI ResearchOpenAI’s GPT-5 Science Report Puts Human Stewardship at the Center of AI Research
Pusan National University Study Rethinks How Artificial Intelligence Supports Investment Decisions - PR Newswire
Tether, The Bio-Acoustic Sentinel - The New York Academy of Sciences
AI ToolsOpenWorker: Andrew Ng's Local-First AI Coworker, Explained for Developers
OpenWorker, a local AI coworker developed by Andrew Ng, has been shipped in late July 2026. It is an MIT-licensed tool that runs on users' own machines.
Why AI-driven enterprises are the future of entrepreneurship - MIT Sloan
MIT Sloan discusses the role of AI in shaping the future of entrepreneurship, highlighting its potential to drive innovation. AI-driven enterprises are expected to revolutionize the industry.
AI ToolsElevenLabs ElevenAgents Adds Per-Channel Controls and Channel-Scoped Testing
ElevenLabs has updated ElevenAgents with per-channel response controls and channel-scoped testing capabilities.
The challenge of artificial intelligence for democracy - Latinoamérica 21
The integration of artificial intelligence poses significant challenges to democratic systems, particularly in Latin America. Experts are exploring ways to address these challenges and ensure AI supports democratic values.
Moonshot AI: China’s Key Artificial Intelligence Project Exceeds Funding Target - The European Conservative
China's key artificial intelligence project, Moonshot AI, has exceeded its funding target, according to recent reports.
SecurityGoogle's SynthID watermark is hard to break, but it doesn't solve AI misinformation
Tests show Google's SynthID watermark is technically difficult to remove, yet it fails to fully address the broader challenge of AI misinformation.