Let the Agent Search: Autonomous Exploration Beats Rigid Workflows in Temporal Question Answering

• URL: http://arxiv.org/abs/2603.01853v1
• Date: Mon, 02 Mar 2026 13:33:39 GMT
• Title: Let the Agent Search: Autonomous Exploration Beats Rigid Workflows in Temporal Question Answering
• Authors: Xufei Lv, Jiahui Yang, Yifu Gao, Linbo Qiao, Houde Liu,
• Abstract summary: Temporal Knowledge Graph Question Answering (TKGQA) demands multi-hop reasoning under temporal constraints.<n>We show that granting an off-the-shelf autonomy, that is, letting it decide what to do next, already yields substantial gains.<n>We propose AT2QA, an autonomous, training-free agent for temporal question answering.
• Score: 12.204337131764852
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Temporal Knowledge Graph Question Answering (TKGQA) demands multi-hop reasoning under temporal constraints. Prior approaches based on large language models (LLMs) typically rely on rigid, hand-crafted retrieval workflows or costly supervised fine-tuning. We show that simply granting an off-the-shelf LLM autonomy, that is, letting it decide what to do next, already yields substantial gains even in a strict zero-shot setting. Building on this insight, we propose AT2QA, an autonomous, training-free agent for temporal question answering that iteratively interacts with the temporal knowledge graph via a general search tool for dynamic retrieval. Experiments on MultiTQ demonstrate large improvements: AT2QA achieves 88.7% Hits@1 (+10.7% over prior SOTA), including a +20.1% gain on challenging multi-target queries, showing that agentic autonomy can decisively outperform fine-tuning for temporal question answering. Code and the full set of sampled trajectories are available on https://github.com/AT2QA-Official-Code/AT2QA-Official-Code

Related papers

• Temp-R1: A Unified Autonomous Agent for Complex Temporal KGQA via Reverse Curriculum Reinforcement Learning [51.79753403262177]
  Temporal Knowledge Graph Question Answering (TKGQA) is inherently challenging, as it requires sophisticated reasoning over dynamic facts with multi-hop dependencies and complex temporal constraints.<n>We propose Temp-R1, the first autonomous end-to-end agent for TKGQA trained through reinforcement learning.<n>Our 8B- parameter Temp-R1 achieves state-of-the-art performance on MultiTQ and TimelineKGQA, improving 19.8% over strong baselines on complex questions.
  arXiv  Detail & Related papers  (2026-01-26T09:23:53Z)
• The benefits of query-based KGQA systems for complex and temporal questions in LLM era [55.20230501807337]
  Large language models excel in question-answering (QA) yet still struggle with multi-hop reasoning and temporal questions.<n> Query-based knowledge graph QA (KGQA) offers a modular alternative by generating executable queries instead of direct answers.<n>We explore multi-stage query-based framework for WikiData QA, proposing multi-stage approach that enhances performance on challenging multi-hop and temporal benchmarks.
  arXiv  Detail & Related papers  (2025-07-16T06:41:03Z)
• Question-Aware Gaussian Experts for Audio-Visual Question Answering [8.377705744753047]
  Audio-Visual Question Answering (AVQA) requires question-based multimodal reasoning and precise temporal grounding.<n>This paper proposes QA-TIGER, a novel framework that explicitly incorporates question information and models continuous temporal dynamics.
  arXiv  Detail & Related papers  (2025-03-06T14:11:46Z)
• Time-MQA: Time Series Multi-Task Question Answering with Context Enhancement [55.2439260314328]
  Time Series Multi-Task Question Answering (Time-MQA) is a unified framework that enables natural language queries across multiple time series tasks.<n>Central to Time-MQA is the TSQA dataset, a large-scale dataset containing $sim $200k question-answer pairs.
  arXiv  Detail & Related papers  (2025-02-26T13:47:13Z)
• TimeLogic: A Temporal Logic Benchmark for Video QA [64.32208175236323]
  We introduce the TimeLogic QA (TLQA) framework to automatically generate temporal logical questions.<n>We leverage 4 datasets, STAR, Breakfast, AGQA, and CrossTask, and generate 2k and 10k QA pairs for each category.<n>We assess the VideoQA model's temporal reasoning performance on 16 categories of temporal logic with varying temporal complexity.
  arXiv  Detail & Related papers  (2025-01-13T11:12:59Z)
• AutoReason: Automatic Few-Shot Reasoning Decomposition [0.0]
  Chain of Thought (CoT) was introduced in recent research as a method for improving step-by-step reasoning in Large Language Models.<n>We propose a system to automatically generate rationales using CoT.<n>Our method improves multi-step implicit reasoning capabilities by decomposing the implicit query into several explicit questions.
  arXiv  Detail & Related papers  (2024-12-09T20:35:39Z)
• Self-Improvement Programming for Temporal Knowledge Graph Question Answering [31.33908040172437]
  Temporal Knowledge Graph Question Answering (TKGQA) aims to answer questions with temporal intent over Temporal Knowledge Graphs (TKGs) Existing end-to-end methods implicitly model the time constraints by learning time-aware embeddings of questions and candidate answers. We introduce a novel self-improvement Programming method for TKGQA (Prog-TQA)
  arXiv  Detail & Related papers  (2024-04-02T08:14:27Z)
• NExT-QA:Next Phase of Question-Answering to Explaining Temporal Actions [80.60423934589515]
  We introduce NExT-QA, a rigorously designed video question answering (VideoQA) benchmark. We set up multi-choice and open-ended QA tasks targeting causal action reasoning, temporal action reasoning, and common scene comprehension. We find that top-performing methods excel at shallow scene descriptions but are weak in causal and temporal action reasoning.
  arXiv  Detail & Related papers  (2021-05-18T04:56:46Z)
• Harvesting and Refining Question-Answer Pairs for Unsupervised QA [95.9105154311491]
  We introduce two approaches to improve unsupervised Question Answering (QA) First, we harvest lexically and syntactically divergent questions from Wikipedia to automatically construct a corpus of question-answer pairs (named as RefQA) Second, we take advantage of the QA model to extract more appropriate answers, which iteratively refines data over RefQA.
  arXiv  Detail & Related papers  (2020-05-06T15:56:06Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.