Aug 2026· Proceedings of the 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2· 0 citations· 3 references
TL;DR
The second KDD Day on AI Reasoning brings together researchers and practitioners from academia and industry to examine how large language models and foundation models can be made more capable, reliable, interpretable, and efficient.
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 paper reviews several methods aimed at improving reasoning in large language models, including prompt-based approaches, including Chain-of-Thought, self-consistency, and Auto- CoT, which try to guide models to generate intermediate reasoning steps.
This work forms a unified approach to capability formation and process-centered evaluation, enabling discovery behavior to be trained, improved, and measured beyond final-answer performance.
Large language models have shifted AI toward statistical learning, but knowledge-based methods remain essential for tasks governed by combinatorial structure, declarative correctness, strong domain priors, and auditable reasoning. This paper treats the issue as one of task–architecture fit rather than paradigm competit...
Maikel Leon· WSEAS Transactions on System...· 0 citations
As machine intelligence becomes part of science's epistemic infrastructure, steps must be taken to ensure that researchers who did not build, own, or operate the system can still contest how ideas and evidence became a claim.
This work investigates LLM-based forecasting agents, meaning systems in which a language model contributes to a scored prediction about a future or currently unobserved target, and organizes architectures into three groups.
Xiao-Gang Xu, Jiaqi Tang, Jianmin Chen et al.· 0 citations
State of Thought is proposed, a new reasoning paradigm that enables endogenous reasoning in LLMs, with the model's internal reasoning state governing how reasoning unfolds, and provides a generalizable and efficient alternative to existing test-time reasoning paradigms.
Z. Gong, Yi-Kun Hou, Zi-Hao Zeng et al.· 0 citations
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