Temporal QA

QA: Question Answering

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6 papers in the last four weeks, against 1 the four weeks before. 0.1% of all new papers.

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Temporal Reasoning Is Not the Bottleneck: A Probabilistic Inconsistency Framework for Neuro-Symbolic QA
May 5, 2026Tran Quang LiemTemporal Reasoning Question Answering
Mathematics · VNUHCM - HIGH SCHOOL FOR THE GIFTED · Ho Chi Minh City, Vietnam
AdapTime: Enabling Adaptive Temporal Reasoning in Large Language Models
Apr 27, 2026Yimin Deng, Yejing Wang, Zhenxi Lin +8Temporal Reasoning Temporal QA
Xi’an Jiaotong University · City University of Hong Kong · Tencent Jarvis Lab +1
ARFBench: Benchmarking Time Series Question Answering Ability for Software Incident Response
Apr 23, 2026Stephan Xie, Ben Cohen, Mononito Goswami +6Time Series Reasoning Time Series QA
Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA, USA · Datadog AI Research, New York, NY, USA · Amazon Web Services, Seattle, WA, USA
ForeSea: AI Forensic Search with Multi-modal Queries for Video Surveillance
Mar 24, 2026Hyojin Park, Yi Li, Janghoon Cho +8Multimodal RAG Multimodal IR
Qualcomm AI Research, San Diego, CA, USA · Kyunghee University, Gyeonggi, South Korea
HiMu: Hierarchical Multimodal Frame Selection for Long Video Question Answering
Mar 19, 2026Dan Ben-Ami, Gabriele Serussi, Kobi Cohen +1Multimodal Large Language Models Multimodal QA
INSIGHT Lab, Ben-Gurion University of the Negev, Israel · Ben-Gurion University of the Negev, Israel
Event-Grounded Question Answering over Long Audio via Structured Retrieval
Feb 16, 2026Kartik Hegde, Arvind Krishna Sridhar, Naveen Vakada +2Multimodal RAG Audio QA
Qualcomm Technologies, Inc., India · Qualcomm Technologies, Inc., USA
TS-Haystack: A Multi-Task Retrieval Benchmark for Long-Context Time-Series Reasoning
Feb 15, 2026Nicolas Zumarraga, Thomas Kaar, Ning Wang +12Time Series Reasoning Time Series QA
1Agentic Systems Lab, ETH Zurich · 2Stanford University · 3Traffic Engineering Group, Institute for Transport Planning and Systems, ETH Zurich +4
Evidence-Guided Schema Normalization for Temporal Tabular Reasoning
Nov 29, 2025Ashish Thanga, Vibhu Dixit, Abhilash Shankarampeta +1KG Construction Table QA
Arizona State University · UC San Diego

May 5, 2026, cs.AIJ/K move · Enter open · S save

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

Tran Quang Liem

Mathematics · VNUHCM - HIGH SCHOOL FOR THE GIFTED · Ho Chi Minh City, Vietnam

Temporal Reasoning Question Answering Neuro-Symbolic Reasoning Temporal QA

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Despite significant advances, large language models (LLMs) continue to exhibit brittle performance on complex temporal reasoning tasks. This failure mode is widely attributed to inherent deficits in autoregressive logical deduction. In this paper, we challenge this prevailing narrative, demonstrating that temporal reasoning is not the fundamental bottleneck; rather, the locus of failure lies in unstructured text-to-event representation. We introduce a novel neuro-symbolic question-answering framework governed by a Probabilistic Inconsistency Signal (PIS) that explicitly isolates perceptual errors from reasoning failures. By lifting unstructured text into explicit event graphs and interval constraints, our architecture strictly decouples semantic extraction from a symbolic reasoning engine. To robustly detect structural breaks, the PIS elegantly unifies symbolic credal intervals with epistemic neural uncertainty extracted via Evidential Deep Learning on LLM hidden states. Empirical evaluations reveal a striking paradigm shift: when provided with correct structural representations, our system's explicit proof traces achieve perfect 1.0 accuracy (4000/4000) and strictly zero false positives/negatives on temporal arithmetic benchmarks. On broader, noise-injected QA settings, the framework maintains a competitive 75.1% accuracy while enabling deterministic, step-level failure localization. Ultimately, by isolating the representation bottleneck from the reasoning substrate, this work reframes temporal QA from an algorithmic reasoning challenge to a structural alignment problem, charting a verifiable path forward for reliable neuro-symbolic AI.

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