arXiv:2511.19314v2 Announce Type: replace-cross Abstract: Information-seeking is a core capability for AI agents, requiring them to gather and reason over tool-generated information across long trajectories. However, such multi-step information-seeking tasks remain challenging for agents backed by language models. While process reward models (PRMs) can guide agents by ranking candidate steps at test-time, existing PRMs - designed for short reasoning with binary judgment - cannot capture richer dimensions of information-seeking steps, such as tool interactions and reasoning over tool outputs, n
The increasing sophistication and widespread deployment of AI agents necessitate more advanced reward modeling techniques to handle complex, multi-step information-seeking tasks, moving beyond simpler short-reasoning models.
This development improves autonomous AI agent capabilities, directly impacting their effectiveness in complex real-world problem-solving and white-collar automation, making them more reliable and broadly applicable.
AI agents will be able to manage longer, more intricate information-seeking trajectories and complex tool interactions, leading to more robust and higher-quality outputs than previously possible.
- · AI Agent Developers
- · Cloud Computing Providers
- · Enterprises Adopting AI
- · Tasks requiring manual information synthesis
- · Legacy AI agent architectures
Enhances the ability of AI agents to perform complex, multi-step tasks across various domains.
Accelerates the development and widespread adoption of highly autonomous AI systems in business and research.
Could lead to the creation of entirely new classes of AI-driven services and a redefinition of knowledge work processes.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.CL