temporal reasoning

2 items

RESEARCHarXiv CS.AI·5/7/2026

Temporal Reasoning Is Not the Bottleneck: A Probabilistic Inconsistency Framework for Neuro-Symbolic QA

This research paper argues that the bottleneck in large language models' temporal reasoning is not logical deduction but rather unstructured text-to-event representation. It introduces a neuro-symbolic question-answering framework utilizing a Probabilistic Inconsistency Signal (PIS) to decouple semantic extraction from symbolic reasoning, improving performance.

LLMs temporal reasoning Question Answering Neuro-symbolic AI

RESEARCHarXiv CS.CL·4/21/2026

LiFT: Does Instruction Fine-Tuning Improve In-Context Learning for Longitudinal Modelling by Large Language Models?

LiFT is a new instruction fine-tuning framework designed to improve in-context learning for large language models on longitudinal NLP tasks, which require reasoning over temporally ordered text. It uses a curriculum that progressively increases temporal difficulty, incorporating few-shot structure and temporal conditioning, consistently outperforming base models across various datasets and parameter sizes.

LLMs temporal reasoning Natural Language Processing in-context learning