RI

#1ModelEquivBench: Certifying Multi-Relational Evaluation of LLM-Generated Optimization Models

2026-07-31 | University of the Chinese Academy of Sciences |2607.29431

This paper introduces ModelEquivBench, a certifying evaluation system that assesses LLM-generated optimization models across a seven-dimensional semantic profile (E0-E6) using exact-rational mathematical certificates rather than simple execution or structural matching. The results are rigorously backed by numbers on 173 Bench4Opt problems, demonstrating that standard execution-success metrics overestimate correctness by up to 49 cases per model, while structural baselines falsely reject mathematically equivalent models. The key insight is that the evaluation of generated OR models cannot be reduced to a single scalar or structural graph match; instead, using exact-rational certificates (e.g., Farkas lemma for feasible set containment) provides a mathematically sound, multi-relational profile of model correctness.

#2IR2Solve: Structured Intermediate Representations for Cost-Efficient Optimization Autoformulation

2026-07-31 | University of the Chinese Academy of Sciences |2608.02641

IR2Solve translates natural language optimization problems into solver-ready code using a single LLM call to generate a structured JSON intermediate representation (ModelIR), followed by deterministic verification and compilation. The results are rigorously backed by numbers across six cleaned benchmarks, demonstrating that the system matches the accuracy of complex multi-agent and iterative systems while using up to 22x fewer tokens and only one API call. The key insight is that constraining the LLM to output a strict mathematical IR—thereby separating semantic modeling from solver API syntax—eliminates the need for expensive LLM-based iterative repair.

#1DGA$_2$D: Directed Graph-Guided Automated Algorithm Design with Large Language Models

2026-08-01 | Beihang University |2608.00700

DGA2D is an LLM-driven automated algorithm design framework that represents the search space as a directed graph, allowing simultaneous evolution of both the algorithmic pipeline structure and the underlying operator implementations. The results are rigorously backed by numbers across 12 combinatorial optimization problems (including routing and scheduling), showing a reduction in the optimality gap by up to 10.96 percentage points compared to state-of-the-art baselines like EoH and ReEvo. The KEY INSIGHT is the first-order path-dependent credit assignment mechanism, which evaluates the fitness of a specific code implementation based on its immediate topological context (the preceding operator) rather than just the end-to-end score.

#2MuEvo: LLM-Driven Evolution of Multi-Heuristic Ensemble

2026-08-04 | Southern University of Science and Technology, The Hong Kong University of Science and Technology |2608.03636

MuEvo introduces an LLM-driven co-evolutionary framework that simultaneously optimizes multiple interacting heuristics within an algorithmic solver (like ACO or Selection Hyper-Heuristics) rather than isolating a single component. The results are backed by extensive numerical evidence across TSP, CVRP, BPP and Flowshop, showing consistent improvements over human-designed defaults and single-component LLM-AHD baselines (e.g., reducing the TSP-L optimality gap from 6.26% to 5.24%). KEY INSIGHT: To prevent a component from overfitting to a specific set of collaborators, candidate heuristics should be evaluated across multiple contexts (e.g., the current best ensemble, the initial ensemble and a diverse secondary ensemble), while an LLM summarizes cross-component interaction history to guide joint pair evolution.

#3MOT-SR: Multi-Objective Tool-Augmented Scientific Equation Discovery with Large Language Models

2026-07-31 | Institute of Automation, Chinese Academy of Sciences, University of the Chinese Academy of Sciences, Goethe University Frankfurt |2607.29561

Wang et al. propose MOT-SR, an LLM-based evolutionary search framework for symbolic regression that uses a meta-strategy LLM to analyze data residuals with external tools and maintains a multi-objective Pareto front to guide equation generation. The results are rigorously backed by numbers, achieving state-of-the-art NMSE on 40 benchmark tasks and a 26.8x error reduction on a real-world astrophysics dataset compared to prior LLM-SR methods. The key insight is the 'Meta Strategy Generator' which autonomously invokes external analysis tools on the residuals of the current Pareto-optimal candidates to synthesize a natural language search strategy for the generator LLM, effectively acting as an automated planner for the evolutionary process.

#1When Does Disaggregation Pay? Simulating Prefill--Decode--Attention--FFN Specialization for Agentic LLM Inference

2026-08-04 | Imperial College London, University of Cambridge |2608.03741

Forys et al. introduce HeteroPanacea, a simulation framework to evaluate 4-way (Prefill-Decode-Attention-FFN) disaggregated LLM serving across heterogeneous hardware and parallelization strategies. Backed by extensive simulation data, they demonstrate that this PDAF disaggregation yields up to a 2.06x throughput gain over non-disaggregated serving for prefill-heavy agentic workloads on custom NPUs. The key insight is that the hardware demands of attention (KV-bandwidth bound) and FFN (compute/weight bound) diverge so sharply in agentic workloads that they require entirely different hardware profiles to avoid resource stranding.

#2Energy-Efficient LLM Serving via Disaggregated Attention--FFN and Flexible Frequency Scaling

2026-08-03 | University of Science and Technology of China, China Telecom Cloud Computing Research Institute, Xidian University, SKLP, ICT, CAS |2608.01891

AFlex optimizes energy consumption in LLM serving by disaggregating Attention and FFN operators and applying an Integer Linear Program (ILP) alongside a local controller to dynamically scale GPU frequencies. The results are empirically validated on NVIDIA A800 GPUs, demonstrating up to a 49% reduction in energy per token compared to state-of-the-art disaggregated serving systems while maintaining strict latency SLOs. The key insight is that Attention and FFN operators exhibit distinct frequency sensitivities; formulating an ILP to independently provision and scale frequencies for these operators yields massive energy savings over coarse-grained phase-level controls.

#3HorizonServe: Coordinating Request Scheduling with GPU Sharing for Omni-Model Serving

2026-08-03 | The University of Sydney |2608.01785

HorizonServe introduces a joint temporal-spatial scheduler for single-GPU omni-model serving that coordinates request admission and streaming multiprocessor (SM) allocation to meet heterogeneous service-level objectives (SLOs). Backed by strong empirical results, it improves SLO attainment by up to 7.0x and reduces p95 first-response latency by up to 63.7% compared to vLLM-Omni and EDF baselines on RTX 6000 GPUs. The key insight is that the critical bottleneck in omni-model serving is cross-stage memory bandwidth contention between the shared multimodal backbone and downstream generators; bounding the shared-stage SM allocation during co-running prevents goodput collapse for tight-SLO text requests.

#4BANDMAS: Causality-Inspired Semantic Packet Scheduling for Bandwidth-Efficient Multi-Agent Collaboration

2026-08-01 | The Hong Kong Polytechnic University |2608.00458

BANDMAS optimizes multi-agent LLM communication by breaking messages into semantic packets and scheduling their transmission based on a causality-inspired value predictor and resource constraints. Backed by strong empirical results, it reduces transmitted bytes by 53-77% on QA benchmarks while maintaining or improving task accuracy compared to pruning baselines. The key insight is the 'causality-inspired replay valuation'—using offline counterfactual removal (testing sufficiency and necessity of individual message chunks) to train a lightweight predictor of a message's contribution to the final outcome.