LLM Routing as Reasoning: A MaxSAT View

• URL: http://arxiv.org/abs/2603.13612v1
• Date: Fri, 13 Mar 2026 21:39:22 GMT
• Title: LLM Routing as Reasoning: A MaxSAT View
• Abstract summary: We propose a constraint-based interpretation of language-conditioned LLM routing.<n>Under this view, routing corresponds to selecting models that approximately maximize satisfaction of feedback-conditioned clauses.
• Score: 9.154957475794612
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Routing a query through an appropriate LLM is challenging, particularly when user preferences are expressed in natural language and model attributes are only partially observable. We propose a constraint-based interpretation of language-conditioned LLM routing, formulating it as a weighted MaxSAT/MaxSMT problem in which natural language feedback induces hard and soft constraints over model attributes. Under this view, routing corresponds to selecting models that approximately maximize satisfaction of feedback-conditioned clauses. Empirical analysis on a 25-model benchmark shows that language feedback produces near-feasible recommendation sets, while no-feedback scenarios reveal systematic priors. Our results suggest that LLM routing can be understood as structured constraint optimization under language-conditioned preferences.

Related papers

• Evaluating LLM Personalization via Semantic Constraint Verification [25.539904066738288]
  We introduce Natural Language Inference Constraint Verification (NLICV)<n>NLICV maps sentence meanings to truth-condition sets to verify personalization constraints via a Natural Language Inference (NLI) model.<n>Experiments demonstrate that NLICV aligns closely with human annotations while drastically reducing the latency and token costs associated with LLM judges.
  arXiv  Detail & Related papers  (2026-06-15T08:04:56Z)
• Reliable Reasoning with Large Language Models via Preference-Based Maximum Satisfiability [19.16962344736341]
  Large Language Models (LLMs) excel at understanding natural language but struggle with optimisation tasks involving user-defined preferences.<n>We propose a hybrid reasoning approach in which LLMs externalise reasoning through code generation.<n>Our results demonstrate that LLM-driven code generation combined with preference-based MaxSAT enables solver-verifiable optimisation.
  arXiv  Detail & Related papers  (2026-05-28T09:51:33Z)
• Prompting Is All You Need: Multi-view Prompting Large Language Models for Aspect-Based Sentiment Analysis [5.3558051475405515]
  We introduce Multi-View Prompting (LLM-MvP), which adapts the multi-view principle of considering multiple element orderings to LLM prompting.<n>LLM-MvP achieves performance competitive or superior to finetuned approaches while substantially reducing computational overhead.
  arXiv  Detail & Related papers  (2026-05-27T07:04:39Z)
• Extending Czech Aspect-Based Sentiment Analysis with Opinion Terms: Dataset and LLM Benchmarks [1.9779500088459443]
  This paper introduces a novel Czech dataset in the restaurant domain for aspect-based sentiment analysis (ABSA)<n>We conduct extensive experiments using modern Transformer-based models, including large language models (LLMs) in monolingual, cross-lingual, and multilingual settings.<n>A detailed error analysis reveals key challenges, including the detection of subtle opinion terms and nuanced sentiment expressions.
  arXiv  Detail & Related papers  (2026-02-26T08:13:42Z)
• Prompt Optimization Via Diffusion Language Models [73.9599434962714]
  We propose a diffusion-based framework for prompt optimization.<n>Our method enables flexible, span-level prompt updates without requiring access or modifying the downstream language model.<n>We show that moderate diffusion step counts provide the best balance between refinement quality and stability.
  arXiv  Detail & Related papers  (2026-01-30T00:00:54Z)
• KBQA-R1: Reinforcing Large Language Models for Knowledge Base Question Answering [64.62317305868264]
  We present textbfKBQA-R1, a framework that shifts the paradigm from text imitation to interaction optimization via Reinforcement Learning.<n>Treating KBQA as a multi-turn decision process, our model learns to navigate the knowledge base using a list of actions.<n>Experiments on WebQSP, GrailQA, and GraphQuestions demonstrate that KBQA-R1 achieves state-of-the-art performance.
  arXiv  Detail & Related papers  (2025-12-10T17:45:42Z)
• SUTA-LM: Bridging Test-Time Adaptation and Language Model Rescoring for Robust ASR [58.31068047426522]
  Test-Time Adaptation (TTA) aims to mitigate by adjusting models during inference.<n>Recent work explores combining TTA with external language models, using techniques like beam search rescoring or generative error correction.<n>We propose SUTA-LM, a simple yet effective extension of SUTA, with language model rescoring.<n> Experiments on 18 diverse ASR datasets show that SUTA-LM achieves robust results across a wide range of domains.
  arXiv  Detail & Related papers  (2025-06-10T02:50:20Z)
• Set-LLM: A Permutation-Invariant LLM [2.9665130256021]
  This paper is motivated by a specific vulnerability: the order sensitivity of large language models (LLMs)<n>We introduce Set-LLM, a novel architectural adaptation for pretrained LLMs that enables the processing of mixed set-text inputs with permutation invariance guarantees.
  arXiv  Detail & Related papers  (2025-05-21T12:14:26Z)
• From User Preferences to Optimization Constraints Using Large Language Models [37.90220668060883]
  This work explores using Large Language Models (LLMs) to translate user preferences into energy optimization constraints for home appliances.<n>We describe a task where natural language user utterances are converted into formal constraints for smart appliances, within the broader context of a renewable energy community.<n>We evaluate the effectiveness of various LLMs currently available for Italian in translating these preferences resorting to classical zero-shot, one-shot, and few-shot learning settings.
  arXiv  Detail & Related papers  (2025-03-27T10:52:10Z)
• LLM-Lasso: A Robust Framework for Domain-Informed Feature Selection and Regularization [59.75242204923353]
  We introduce LLM-Lasso, a framework that leverages large language models (LLMs) to guide feature selection in Lasso regression.<n>LLMs generate penalty factors for each feature, which are converted into weights for the Lasso penalty using a simple, tunable model.<n>Features identified as more relevant by the LLM receive lower penalties, increasing their likelihood of being retained in the final model.
  arXiv  Detail & Related papers  (2025-02-15T02:55:22Z)
• Language Representations Can be What Recommenders Need: Findings and Potentials [57.90679739598295]
  We show that item representations, when linearly mapped from advanced LM representations, yield superior recommendation performance.<n>This outcome suggests the possible homomorphism between the advanced language representation space and an effective item representation space for recommendation.<n>Our findings highlight the connection between language modeling and behavior modeling, which can inspire both natural language processing and recommender system communities.
  arXiv  Detail & Related papers  (2024-07-07T17:05:24Z)
• LaFFi: Leveraging Hybrid Natural Language Feedback for Fine-tuning Language Models [14.087415157225715]
  Fine-tuning Large Language Models (LLMs) adapts a trained model to specific downstream tasks. Supervised Fine-Tuning (SFT) is a common approach, where an LLM is trained to produce desired answers. This paper introduces an alternative to SFT called Natural Language Feedback for Finetuning LLMs (LaFFi)
  arXiv  Detail & Related papers  (2023-12-31T21:18:16Z)
• FollowBench: A Multi-level Fine-grained Constraints Following Benchmark for Large Language Models [79.62191017182518]
  FollowBench is a benchmark for Fine-grained Constraints Following Benchmark for Large Language Models. We introduce a Multi-level mechanism that incrementally adds a single constraint to the initial instruction at each increased level. By evaluating 13 popular LLMs on FollowBench, we highlight the weaknesses of LLMs in instruction following and point towards potential avenues for future work.
  arXiv  Detail & Related papers  (2023-10-31T12:32:38Z)
• ReLLa: Retrieval-enhanced Large Language Models for Lifelong Sequential Behavior Comprehension in Recommendation [43.270424225285105]
  We focus on adapting and empowering a pure large language model for zero-shot and few-shot recommendation tasks. We propose Retrieval-enhanced Large Language models (ReLLa) for recommendation tasks in both zero-shot and few-shot settings.
  arXiv  Detail & Related papers  (2023-08-22T02:25:04Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.