Evolution toward Scientific Reasoning via Enhanced Agency

Recent breakthroughs in model training emphasize that specialized tasks like coding act as a catalyst for broader reasoning capabilities. This shift marks an evolution where AI agents move beyond pattern recognition to active problem-solving and formal verification.

Key Arguments

• Enhanced Tooling/Coding Capability (GLM-5.3): Recent updates focus on upgrading agency and coding ability; internal tests show significant improvements such enoughto increase programming task success by 50% relative to previous versions.
• Cross-Domain Skill Transfer: Improvements in cybersecurity (finding vulnerabilities) emerged as a side effect of learning code, suggesting way certain technical skills create secondary cognitive benefits even if weights are not yet fully open due to security checks.
• Mathematical Problem Solving with Agents: Using multi-agent strategies involving heavy criticism cycles can lead to discovering key arguments or proofs previously stalled by human researchers, such as the Crouzeix hypothesis case using GPT models over extended periods without intervention.
• Increasing Success Rates in Formal Verification: Even when unable to solve 'millennium' level problems outright (like Riemann), advanced models have demonstrated measurable progress—such as increasing the verified portion of zeta function zeros from 41.6% up to 67.2%.

Potential Constraints & Counterpoints

• Formal Proof Limitations: While AI has successfully disproved some long-standing conjectures through agentic workflows, it hasn't able to crack all

! DYOR (Do Your Own Research)