Papers with broken temporal reasoning
Regret-Now: A Physics-Inspired Regret Framework for Temporal Knowledge Graph Question Answering with LLMs (2026.findings-acl)
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| Challenge: | Large Language Models have impressive results in general reasoning tasks, but they still exhibit a lack of dynamic error-correction. |
| Approach: | They propose a temporal reasoning framework that uses the principle of minimum potential energy to model the reasoning process as a dynamic trajectory moving toward a more stable state. |
| Outcome: | The proposed framework shows consistent gains over strong baselines on two standard TKGQA benchmarks. |