Aug 2026· Proceedings of the 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2· 0 citations· 1 references
Abstract
Large language models and foundation models are increasingly embedded in reasoning systems that plan, invoke tools, use memory, gather evidence, and iteratively refine their outputs. The second KDD Day on AI Reasoning brings together researchers and practitioners from academia and industry to examine how these systems can be made more capable, reliable, interpretable, and efficient. The program spans scientific discovery, human-centered interaction, software engineering, time-series analysis, deep research, computer use, and inference infrastructure. Across these domains, the day highlights shared challenges: grounding decisions in evidence, designing effective feedback and verification mechanisms, evaluating open-ended behavior, managing test-time computation, and preserving meaningful human control. Through keynote and invited presentations, the event provides a forum for connecting advances in models, agents, data, systems, and applications, and for identifying research directions toward trustworthy next-generation reasoning systems.
This article examines the emerging paradigm of agentic AI for scientific discovery, traces the conceptual shift from tools to agents, lays out a six-stage workflow spanning literature synthesis to manuscript generation, and reviews practical systems in chemistry, equation discovery, materials science, and general machine learning research.
Alexander Taktakidze· Longevity Horizon· 0 citations
ReasFlow is introduced, an end-to-end autonomous agent system for reasoning-centric scientific discovery that operationalizes a collaborative paradigm where the human expert acts as Principal Investigator while the agent executes rigorous derivations as a capable graduate student.
Yutong He, Daibo Li, Guohong Li et al.· 1 citation
Through applied case studies in pharmaceutical discovery and financial systems, common design patterns that make agentic systems successful are analyzed, and practical mitigation strategies for failure modes are discussed, such as verification pipelines, fallback mechanisms, and human-in-the-loop supervision.
Grace Hui Yang, P. Venkit, Hooman Sedghamiz et al.· Proceedings of the 32nd ACM...· 0 citations
This work introduces Mycelium, an active shared workspace that automatically connects researchers and AI agents, and describes networked intelligence as sparse conditional computation over distributed scientific contexts as sparse conditional computation over distributed scientific contexts.
Sutanay Choudhury, Jeffrey J. Czajka, L. Monteiro et al.· 0 citations
The approach, LeAct (Learning to reason from Actions), optimizes this latent variable: the student samples candidate CoTs for each expert action, and the student retains those that measurably improve its own probability of recovering the action.
Ziran Yang, Chengshuai Shi, Raj Ghugare et al.· 0 citations
Mechanist is an agentic system that uses AI as a scientific instrument for the autonomous discovery of mechanisms underlying AI intelligence, and develops a mechanism theory of belief, revealing how models represent world knowledge, form beliefs, infer the beliefs of others, and how these mechanisms emerge during pretraining.
Mengru Wang, Junfeng Fang, Shuofei Qiao et al.· 0 citations