Knowledge graphs and RL
The latest being “prompting is dead” or better known as agent loops. In essense, agents that plan, act, observe, and try again. You define the goal but not the path. The implicit promise is that these loops get smarter over time through reinforcement learning (RL).
Thing is that RL needs a reward signal and for most agent tasks (“research this,” “fix this bug,” “summarize these documents”), that signal is mushy. You end up scoring free-text outputs with another LLM as judge, which is shaky ground to build a learning loop on.
Once again, I think that graphs are inevitable.
A graph gives you something checkable: did the agent retrieve the right subgraph, does the extracted relation actually exist, does the path it followed lead somewhere correct. Recent work (like Graph-R1, AgentGL) builds exactly this kind of RL loop: “think → query → retrieve subgraph → rethink” with an explicit, structural reward at each step.
My personal conviction has actually more to do with how nature exhibits intelligence than some marketing for my own graph expertise, but that’s an altogether different topic.
There is a real opportunity here beyond “KGs ground your agent’s answers”: graphs give RL something solid to learn against. For document-to-knowledge-graph extraction specifically, this matters directly. Extraction is a task with checkable correctness, entities and relations either match a schema/gold graph or they don’t. That makes it a much better candidate for an RL-trained agentic loop than a single-shot LLM extraction pass.
The agentic loop hype glosses over this. Loops don’t automatically get better, they need something to push against. Structure is what makes that possible.