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ARXIV:2605.07357 · GRAPH REASONING · SUBMITTED 11 MAY · 20:41 UTC · FRESHNESS STALE
ARXIV:2605.07357GRAPH REASONINGSUBMITTED 11 MAY · 20:41 UTCFRESHNESS STALEXingtong Yu · Zhongwei Kuai · Chang Zhou · Xuanting Xie · Renhe Jiang · Xikun Zhang · +3 at arXiv
GraphReAct is a framework for multi-step graph inference that combines reasoning with specialized retrieval and context refinement actions.
Opportunity summary
Pain GraphReAct is a framework for multi-step graph inference that combines reasoning with specialized retrieval and context refinement actions.
Evidence 0 refs | 3 sources | 50% coverage
Blocker Evidence unverified
GraphReAct is a framework for multi-step graph inference that combines reasoning with specialized retrieval and context refinement actions. However, extending this paradigm to graph learning remains underexplored.
Reasoning-acting frameworks enhance large language models (LLMs) by interleaving reasoning with actions for dynamic information acquisition. However, extending this paradigm to graph learning remains underexplored.
ScienceToStartup currently rates this 7.0/10 on the public viability pass. As a result, effective reasoning over graphs requires not only retrieving informative evidence from the graph, but also progressively refining the accumulated context during…
Graph Reasoning moved forward this cycle; last verified May 2026. Public score 7.0/10. Production flags indicate code availability.
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GraphReAct is a framework for multi-step graph inference that combines reasoning with specialized retrieval and context refinement actions.
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10.48550/arXiv.2605.07357GraphReAct is a framework for multi-step graph inference that combines reasoning with specialized retrieval and context refinement actions.
Abstract
Reasoning-acting frameworks enhance large language models (LLMs) by interleaving reasoning with actions for dynamic information acquisition. However, extending this paradigm to graph learning remains underexplored. Graph data is inherently structured, with information distributed across nodes and edges and encoded through both topology and latent representations. As a result, effective reasoning over graphs requires not only retrieving informative evidence from the graph, but also progressively refining the accumulated context during multi-step inference. In this work, we propose GraphReAct, a graph reasoning-acting framework that enables step-by-step inference over graph-structured data. Specifically, we design a graph-based action space with two complementary retrieval actions: topological retrieval, which captures local structural dependencies, and semantic retrieval, which accesses non-local but relevant evidence in the representation space. These actions dynamically expand the reasoning context. To further support multi-step reasoning, we introduce another type of action, context refinement, which distills and reorganizes accumulated information into a compact representation. By interleaving reasoning with both retrieval and refinement actions, our framework enables a progressive transition from context expansion to compression. Extensive experiments on six benchmark datasets demonstrate that GraphReAct consistently outperforms state-of-the-art methods, validating the effectiveness of reasoning-acting for graph learning.
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PROBLEM
GraphReAct is a framework for multi-step graph inference that combines reasoning with specialized retrieval and context refinement actions. However, extending this paradigm to graph learning remains underexplored.
METHOD
Reasoning-acting frameworks enhance large language models (LLMs) by interleaving reasoning with actions for dynamic information acquisition. However, extending this paradigm to graph learning remains underexplored.
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. As a result, effective reasoning over graphs requires not only retrieving informative evidence from the graph, but also progressively refining the accumulated context during multi-step inference. Code ava...
WHY NOW
Graph Reasoning moved forward this cycle; last verified May 2026. Public score 7.0/10. Production flags indicate code availability.
Abstract-backed public claims while anchored extraction refreshes.
GraphReAct is a framework for multi-step graph inference that combines reasoning with specialized retrieval and context refinement actions. However, extending this paradigm to graph learning remains underexplored.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Reasoning-acting frameworks enhance large language models (LLMs) by interleaving reasoning with actions for dynamic information acquisition. However, extending this paradigm to graph learning remains underexplored.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 7.0/10 on the public viability pass. As a result, effective reasoning over graphs requires not only retrieving informative evidence from the graph, but also progressively refining the accumulated context during multi-step inference. Code availability is flagged in the production record; the public repository link still needs proof alignment.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Graph Reasoning moved forward this cycle; last verified May 2026. Public score 7.0/10. Production flags indicate code availability.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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GraphReAct is a framework for multi-step graph inference that combines reasoning with specialized retrieval and context refinement actions.
Segment
Graph Reasoning
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Commercial read
7.0/10 public viability
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