How researchers are working to build AI with common-sense reasoning, such as the COMET system, which combines symbolic reasoning and neural language modeling
The problem of common-sense reasoning has plagued the field of artificial intelligence for over 50 years. Tweets: @quantamagazine and @mslima Tweets: Quanta / @quantamagazine : COMET, a new AI system developed by @YejinChoinka, uses a neural network to answer common-sense questions. Its answers are surprisingly good. https://www.quantamagazine.org/ ... Manuel Lima / @mslima : “The problem of common-sense reasoning has plagued the field of artificial intelligence for over 50 years. Now a new approach, borrowing from two disparate lines of thinking, has made important progress.” https://www.quantamagazine.org/ ...
Context & Ripple Effects
COMET lands mid-arc in a debate that Wired framed in 2018: deep learning's pattern recognition stalls without everyday common sense, and devices built on it stay brittle. Yejin Choi's system answers that critique by fusing the field's two rival traditions — symbolic reasoning and neural language modeling — into one system for common-sense questions, with answers Quanta describes as surprisingly good.
The hybrid bet aged well on paper: months later, neurosymbolic AI drew its own dedicated coverage from Knowable, with proponents and critics weighing the same neural-plus-classical-AI combination COMET embodies. But the 2025 reporting on chain-of-thought inconsistencies at Anthropic, Google, OpenAI, and xAI shows the deeper problem COMET attacked — making a model's reasoning actually hold up — is still unresolved.
First-order effects
- Researchers working on common sense get a working template: COMET demonstrates that a neural language model can be paired with symbolic structure to answer common-sense questions, rather than treating the two approaches as competitors.
Second-order effects
- The result strengthens the case for neurosymbolic AI as a research program, forcing labs to argue from evidence — the later Knowable coverage of the approach's proponents and critics shows the field splitting into camps rather than converging quietly.
Third-order effects
- If hybrid reasoning architectures keep outperforming pure pattern recognition, AI systems could finally become trustworthy for everyday judgment — but the chain-of-thought inconsistency findings suggest the gap between a model's stated reasoning and its answers remains the structural obstacle, keeping verification of machine reasoning an open research and regulatory question.
The trend: AI research is converging on hybrid neural-symbolic methods to close the common-sense gap that pure deep learning left open, with reasoning reliability — not capability — emerging as the field's defining bottleneck.