ImplicitHop: Evaluating knowledge integration across context and memory

Authors: Alexander Detkov , Dashiel Karish , Matthew Oleynikov , Matt Thomson

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Abstract: The ability to integrate new information and deduce its consequences is central to learning, understanding, and reasoning in large language models (LLMs). Whether information arrives during training or in-context, models must not only store local facts, but propagate their long-range implications globally. To explore information integration in LLMs, we introduce two complementary natural language benchmarks for large-scale evaluation of deductive reasoning as a function of inference depth. The first is a formal graph benchmark enabling controlled evaluation over a wide range of graph topologies and integration tasks; the second is a knowledge composition benchmark grounded in real-world knowledge graphs. We use these benchmarks to compare knowledge integration from information encoded in context versus information encoded in model weights during pretraining, evaluating a set of open models implicitly, without chain of thought. We find that accuracy decays sharply with increased composition length for both pretrained and in-context knowledge. Information presented in-context, however, substantially outperforms deduction over information encoded in weights. These results quantify a fundamental limitation of current models with direct implications for prompt-based control of agent behavior and for continual learning strategies, and they provide a foundation for developing training, prompting, and integration methods that support deeper reasoning.

Comments:

6 pages, 2 figures

Subjects:

Artificial Intelligence (cs.AI)

Cite as:

arXiv:2610.41169 [cs.AI]

(or

arXiv:2610.41169v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2610.41169

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arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Alexander Detkov [ view email ]

[v1]

Fri, 30 Oct 2026 02:44:51 UTC (75 KB)

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