arXiv:2607.02983v1 Announce Type: new Abstract: Recent reasoning-centric Large Language Models (LLMs) have made significant strides, yet they predominantly operate on a passive-inference pattern that assumes complete information. In contrast, real-world clinical intelligence is inherently an iterative investigative process requiring strategic evidence acquisition. To bridge this gap, we formalize medical diagnosis as an Iterative Evidence-Seeking Task. We leverage Reinforcement Learning with Verifiable Rewards (RLVR) to elicit intrinsic reasoning within a closed-loop environment, guided by a n
The increasing sophistication of LLMs and the recognition of their limitations in real-world diagnostic applications are driving research into more iterative and evidence-seeking approaches.
This research represents a significant step towards developing AI systems that can proactively gather information, mirroring human expert reasoning, crucial for high-stakes applications like medical diagnosis.
This shifts LLM reasoning from passive inference to active, iterative investigation, enabling more robust and reliable autonomous agentic systems in complex problem-solving domains.
- · AI agents developers
- · Healthcare technology providers
- · Patients
- · LLM researchers
- · AI systems relying solely on passive inference
- · Traditional diagnostic support software
More accurate and reliable AI-driven diagnostic tools will emerge, reducing medical errors.
The iterative evidence-seeking paradigm could extend beyond medicine to other complex decision-making fields, transforming white-collar professional services.
This could accelerate the adoption of fully autonomous AI agents in critical infrastructure, necessitating new regulatory frameworks and ethical considerations for accountability.
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Read at arXiv cs.AI