This comprehensive survey from a 29-author team (January 2026) provides the most up-to-date unified framework for understanding agentic reasoning in large language models. The authors organize the landscape along three layered dimensions: foundational single-agent reasoning (planning, tool use, search), self-evolving reasoning (memory, feedback loops, adaptation), and collective multi-agent reasoning (coordination, knowledge sharing). Crucially, they draw a sharp distinction between in-context reasoning — which scales test-time compute through structured orchestration — and post-training reasoning, which bakes better behaviors in via RL and SFT. This framing is more principled than most prior surveys.
The field has been moving fast enough that no single prior survey covers the full arc from chain-of-thought prompting to autonomous multi-agent systems. This paper attempts to do exactly that, serving as a unified roadmap from "thought" to "action." The timing is significant: as of early 2026, agentic systems are no longer research prototypes — they are being deployed in healthcare, scientific research, and autonomous code generation. A rigorous taxonomy at this moment helps practitioners and researchers orient themselves in an increasingly fragmented landscape.
The survey's identification of open challenges is where it earns its keep. Long-horizon interaction remains deeply unsolved — current agents lose coherence over extended task horizons, and this is one of the primary limiters for real-world deployment. World modeling is another: agents that can only reason about what they've seen in training struggle when environments shift. The governance dimension is equally important; as collective multi-agent systems become more capable, questions of alignment, accountability, and emergent misalignment in agent networks become pressing. This paper provides researchers with the vocabulary and framework to address these problems systematically.
With 29 authors, surveys of this scope risk becoming reference lists rather than analytical works. The real test is whether the three-dimensional framework (foundational / self-evolving / collective) offers genuine predictive or organizational power, or whether it simply relabels existing categories. The in-context vs. post-training distinction is genuinely useful and underexplored in prior work. Overall this is a high-value orientation paper for anyone entering or repositioning within the agentic AI space in 2026.