Related papers: Deterministic Legal Agents: A Canonical Primitive API for Auditable Reasoning over Temporal Knowledge Graphs

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arXiv Detail & Related papers (2026-08-04T13:01:30Z)
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We introduce ScrambleToolBench, an interactive terminal benchmark designed to isolate behavioral reasoning.<n>The benchmark requires agents to uncover hidden tool behaviors entirely through trial-and-error interaction.<n>Our evaluation of state-of-the-art language models reveals that successful initial discovery does not translate into robust adaptation.
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arXiv Detail & Related papers (2026-07-23T16:41:56Z)
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arXiv Detail & Related papers (2026-05-28T02:12:41Z)
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arXiv Detail & Related papers (2026-05-03T19:13:11Z)
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arXiv Detail & Related papers (2026-03-11T09:23:20Z)
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arXiv Detail & Related papers (2026-01-21T15:22:21Z)
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arXiv Detail & Related papers (2026-01-19T20:33:53Z)
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Laser: Governing Long-Horizon Agentic Search via Structured Protocol and Context Register [38.329346729947304]
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arXiv Detail & Related papers (2025-12-23T15:53:33Z)
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Existing benchmarks fail to rigorously evaluate the long-horizon capabilities required to build complete software systems.<n>We present NL2Repo Bench, a benchmark explicitly designed to evaluate the long-horizon repository generation ability of coding agents.
arXiv Detail & Related papers (2025-12-14T15:12:13Z)
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arXiv Detail & Related papers (2025-10-27T16:22:02Z)
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arXiv Detail & Related papers (2025-10-11T03:29:18Z)
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Large Language Models (LLMs) struggle with hallucinations and factual errors in knowledge-intensive scenarios like knowledge graph question answering (KGQA)<n>We attribute this to the semantic gap between structured knowledge graphs (KGs) and unstructured queries, caused by inherent differences in their focuses and structures.<n>Existing methods usually employ resource-intensive, non-scalable reasoning on vanilla KGs, but overlook this gap.<n>We propose a flexible framework, Enrich-on-Graph (EoG), which leverages LLMs' prior knowledge to enrich KGs, bridge the semantic gap between graphs and queries.
arXiv Detail & Related papers (2025-09-25T06:48:52Z)
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arXiv Detail & Related papers (2025-08-08T08:07:40Z)
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arXiv Detail & Related papers (2025-06-16T15:34:29Z)
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Respecting Temporal-Causal Consistency: Entity-Event Knowledge Graphs for Retrieval-Augmented Generation [69.45495166424642]
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Verify-in-the-Graph: Entity Disambiguation Enhancement for Complex Claim Verification with Interactive Graph Representation [3.864321514889099]
VeGraph operates in three phases: (1) Graph Representation - an input claim is decomposed into structured triplets, forming a graph-based representation that integrates both structured and unstructured information; (2) Entity Disambiguation -VeGraph iteratively interacts with the knowledge base to resolve ambiguous entities within the graph for deeper sub-claim verification; and (3) Verification - remaining triplets are verified to complete the fact-checking process.
arXiv Detail & Related papers (2025-05-29T02:02:55Z)
An Ontology-Driven Graph RAG for Legal Norms: A Structural, Temporal, and Deterministic Approach [0.0]
RAG systems in the legal domain face a critical challenge: standard, flat-text retrieval is blind to the hierarchical, diachronic, and causal structure of law, leading to anachronistic and unreliable answers.<n>This paper introduces the Structure-Aware Temporal Graph RAG (SAT-Graph RAG), an ontology-driven framework designed to overcome these limitations by explicitly modeling the formal structure and diachronic nature of legal norms.
arXiv Detail & Related papers (2025-04-29T18:36:57Z)
Spatial-Temporal Graph Enhanced DETR Towards Multi-Frame 3D Object Detection [54.041049052843604]
We present STEMD, a novel end-to-end framework that enhances the DETR-like paradigm for multi-frame 3D object detection. First, to model the inter-object spatial interaction and complex temporal dependencies, we introduce the spatial-temporal graph attention network. Finally, it poses a challenge for the network to distinguish between the positive query and other highly similar queries that are not the best match.
arXiv Detail & Related papers (2023-07-01T13:53:14Z)
Reinforcement Learning Based Query Vertex Ordering Model for Subgraph Matching [58.39970828272366]
Subgraph matching algorithms enumerate all is embeddings of a query graph in a data graph G. matching order plays a critical role in time efficiency of these backtracking based subgraph matching algorithms. In this paper, for the first time we apply the Reinforcement Learning (RL) and Graph Neural Networks (GNNs) techniques to generate the high-quality matching order for subgraph matching algorithms.
arXiv Detail & Related papers (2022-01-25T00:10:03Z)
Complex Event Forecasting with Prediction Suffix Trees: Extended Technical Report [70.7321040534471]
Complex Event Recognition (CER) systems have become popular in the past two decades due to their ability to "instantly" detect patterns on real-time streams of events. There is a lack of methods for forecasting when a pattern might occur before such an occurrence is actually detected by a CER engine. We present a formal framework that attempts to address the issue of Complex Event Forecasting.
arXiv Detail & Related papers (2021-09-01T09:52:31Z)

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