arXiv AI
Neurosymbolic Routing for Reliable Reasoning on Resource-Constrained Edge Devices
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When2Think: Learning Difficulty-Aware Length Control for Efficient Hybrid Reasoning Models
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arXiv AI
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Forethought: Verifiable Reasoning from Neurosymbolic Primitive Programming
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Agentic Routing: The Harness-Native Data Flywheel
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arXiv AI
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Agent-as-a-Router: Agentic Model Routing for Coding Tasks
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arXiv AI
Sep 2
Learning What to Retain: Gated-Memory Routing for Efficient Collaboration in Multi-Agent LLM Systems
The paper introduces Gated-Memory Routing, a method for efficient collaboration in multi‑agent large language model systems. It uses a learned execution memory with write and retrieval gates to keep only non‑redundant reasoning steps, and an adaptive halting controller to stop execution when enough evidence is gathered. Experiments on five reasoning and code‑generation benchmarks show the approach achieves higher accuracy and reduces inference cost by 31.9% compared to the strongest baseline.
arXiv AI
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When LLMs Develop Languages: Symbolic Communication for Efficient Multi-Agent Reasoning
arXiv:2606. 29354v1 Announce Type: new Abstract: Chain-of-Thought (CoT) improves large language models (LLMs) on difficult reasoning tasks, but it often incurs long natural-language rationales that are poorly aligned with efficient machine reasoning.