Probing for Long-Horizon Deductive Reasoning Capabilities in Language Models with Prolog
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Computer Science > Computation and Language
arXiv:2610.11592 (cs)
[Submitted on 8 Oct 2026]
Title:Probing for Long-Horizon Deductive Reasoning Capabilities in Language Models with Prolog
View a PDF of the paper titled Probing for Long-Horizon Deductive Reasoning Capabilities in Language Models with Prolog, by Hadeel Al-Negheimish and 2 other authors
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Abstract:Current frontier LLMs can theoretically process long contexts with 1M tokens or more. But to what extent can they go beyond simple retrieval and perform deeper reasoning over such long contexts? We empirically investigate long-horizon reasoning capabilities of LLMs, focusing on deductive logic expressed in Prolog. We construct ProloNg, a synthetic testbed to probe Prolog Long Reasoning, which systematically varies the complexity (reasoning depth) of problems, where the hardest case has a reasoning depth of 22 and 62k context length. We study 8 reasoning models across 5 families of frontier LLMs, and find that performance degrades substantially as reasoning depth grows, with the majority of models approaching chance beyond depth 10.
| Comments: | Findings of EMNLP 2026 |
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2610.11592 [cs.CL] |
| (or arXiv:2610.11592v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2610.11592
arXiv-issued DOI via DataCite (pending registration)
|
Submission history
From: Hadeel Al-Negheimish [view email][v1] Thu, 8 Oct 2026 09:42:07 UTC (1,152 KB)
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View a PDF of the paper titled Probing for Long-Horizon Deductive Reasoning Capabilities in Language Models with Prolog, by Hadeel Al-Negheimish and 2 other authors
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