2026· Annual Meeting of the Association for Computational Linguistics· pp. 19856-19874· 0 citations· 44 references
Computer Science
TL;DR
X-R OUTER is presented, a dual-axis routing framework that separates retrieval necessity from reasoning necessity under a user-defined cost–quality trade-off and reduces QA benchmarks across six QA benchmarks.
Abstract
Large Language Models (LLMs) are often augmented with Retrieval-Augmented Generation (RAG) and Chain-of-Thought (CoT) prompting, yet static “always-on” use is computationally wasteful. Existing adaptive meth-ods typically optimize a single axis, overlooking that evidence need and reasoning depth are only partially correlated. We present X-R OUTER , a dual-axis routing framework that separates retrieval necessity from reasoning necessity under a user-defined cost–quality trade-off. Offline, X-R OUTER profiles four pipelines (D IRECT , RAG, C O T, RAG+C O T) and derives supervision by selecting the utility-maximizing strategy that trades answer quality against token usage and latency. On-line, a compact dual-head router, conditioned on cost weights, uses lightweight probes— retrieval-score dispersion (NQC) and single-pass draft negative log-likelihood (NLL)—to decide whether to invoke RAG and/or CoT without sampling or model internals. Across six QA benchmarks, X-R OUTER reduces
Results show that learned token-level handoffs can reduce LLM use while preserving strong reasoning performance, and show that learned token-level handoffs can reduce LLM use while preserving strong reasoning performance.
The Difficulty-Aware Hybrid Router is proposed, a lightweight framework that jointly leverages query-level, context-level, and draft-level features to classify each request into one of three inference tiers: direct answering by a small model, small-model answering with lightweight verification, or full processing by a...
Experiments on LoCoMo and LongMemEval show that MemoryCPT improves the cost-performance trade-off over the evaluated baselines, while ablation and sensitivity analyses characterize the contributions of its components and the effects of key design choices.
Songxin Lei, Kun Ouyang, Wei-Lin Ruan et al.· 1 citation· ⚡1
This work proposes MARS, a scalable knowledge graph question answering (KGQA) approach that requires no model fine-tuning, and performs a structured retrieval procedure that links question entities to the KG and iteratively retrieves relevant next-hop information.
Nikit Srivastava, Daniel Vollmers, René Speck et al.· arXiv.org· 0 citations
This work proposes Knowledge Access Planning (KAP), a paradigm-shifting execution abstraction that elevates structured knowledge priors from passive prompt-construction hints into first-class physical execution artifacts, and derives a phase-boundary model for the positive-speedup regime of plan-guided execution.
Large Language Models (LLMs) trained using Chain-of-Thought (CoT) supervision have achieved state-of-the-art performance on complex reasoning tasks. However, the generation of long reasoning chains introduces substantial computational overhead during inference, limiting their deployment in low-latency and resource-cons...
V. A, Mithaguru, Amrita Kundu et al.· 2026 4th International Confe...· 0 citations
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