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Neurosymbolic Routing for Reliable Reasoning on Resource-Constrained Edge Devices — arXiv:2609.35833

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.35833
License (as archived): CC0-1.0
Archived: 2026-10-07T16:22:03.578Z
SHA-256 of the archived text: fda0045b6a54f42572f6aa5cf4b103ecd783088688c966aad1cd4f02227b1501
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How to cite

Neurosymbolic Routing for Reliable Reasoning on Resource-Constrained Edge Devices — arXiv:2609.35833 — arXiv (en), https://arxiv.org/abs/2609.35833 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_1f28b00a7601 (Metraipolis archive dc_1f28b00a7601, sha256 fda0045b6a54f42572f6aa5cf4b103ecd783088688c966aad1cd4f02227b1501)

BibTeX

@misc{dc1f28b00a7601,
  title = {Neurosymbolic Routing for Reliable Reasoning on Resource-Constrained Edge Devices — arXiv:2609.35833},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.35833},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_1f28b00a7601; sha256 fda0045b6a54f42572f6aa5cf4b103ecd783088688c966aad1cd4f02227b1501},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.35833. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Running a language model on edge hardware provides private and low-latency reasoning without a network connection, and yet the small models that fit on such devices are unreliable on the tasks computers are expected to handle well, such as arithmetic, algebra, and formal logic problems. We argue that much of this unreliability is avoidable. Many queries appearing to demand reasoning are in fact structurally deterministic and permit fast and exact symbolic solutions. Therefore, forcing a probabilistic model to approximate them sacrifices accuracy and energy for little benefit. We present a neurosymbolic router that classifies each incoming query and dispatches it to the cheapest correct solver, sending structured tasks to deterministic engines and reserving the small language model (SLM) for open-ended word problems. Instead of hand-coding the routing logic, we learn a deterministic finite automaton (DFA) with the L* grammatical inference algorithm, using the SLM as a membership oracle and labeled data as an equivalence oracle. On a Raspberry Pi 4B (8 GB RAM, no GPU), evaluated on 100 untested prompts from DeepMind Mathematics, GSM8K, and RuleTaker, learned routing attains 100% routing accuracy and 98.3% overall accuracy with a 512-token reasoning budget (93.3% on word problems), compared with 72.0% for the strongest agent baseline, Program-of-Thought, and 58.7% for a tool-calling agent given the same solvers. Since formatted queries never reach the model, the router answers them in 1-11 ms and, in its 30-token configuration, runs 8.8x faster and 2.8x more energy-efficient than Program-of-Thought.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-09-30 in cs.AI).
Categorie arXiv: Artificial Intelligence (cs.AI) ; Computation and Language (cs.CL).
Pagina del lavoro: https://arxiv.org/abs/2609.35833 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-07T15:54:53.432Z dalla pagina https://arxiv.org/catchup/cs.AI/2026-09-30?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).