RI

#1Strategy-Aware Optimization Modeling with Reasoning LLMs

2026-05-04 | Beihang University, JIUTIAN Research |2605.02545

SAGE is a framework for automated optimization modeling that explicitly separates high-level modeling strategy from concrete formulation, training an LLM via supervised fine-tuning and Segment-Weighted GRPO with solver feedback. The results are backed by strong empirical evidence, improving average pass@1 from 72.7% to 80.3% over the strongest open-source baseline across eight OR benchmarks, while also producing more compact, solver-efficient constraint systems. The key insight is the use of Segment-Weighted GRPO, which assigns higher optimization weights to early, high-level strategic reasoning tokens than to later surface-level tokens, effectively mitigating the credit assignment problem in long-horizon reasoning.

#2EngiAgent: Fully Connected Coordination of LLM Agents for Solving Open-ended Engineering Problems with Feasible Solutions

2026-05-04 | Nanyang Technological University, The Chinese University of Hong Kong, Shenzhen, The University of Sydney, INSAIT Sofia University “St. Kliment Ohridski”, AIRS |2605.02289

EngiAgent is a multi-agent LLM framework that uses a fully connected coordinator to dynamically route debugging feedback across specialized agents (Analyzer, Modeler, Verifier, Solver) to generate feasible Pyomo models for open-ended engineering problems. The results are strongly backed by empirical data on a new 53-problem benchmark, achieving up to 75.4% feasibility with DeepSeek-V3—a massive improvement over fixed-pipeline baselines like DS-Agent. The key insight is that rigid multi-agent pipelines fail on complex OR tasks because errors can stem from semantic extraction, mathematical formulation, or solver execution; dynamically routing specific error traces to the responsible agent significantly improves the rate of physically and mathematically feasible solutions.

#1CoupleEvo: Evolving Heuristics for Coupled Optimization Problems Using Large Language Models

2026-05-07 | Karlsruhe Institute of Technology |2605.06341

CoupleEvo extends LLM-driven heuristic evolution to coupled optimization problems by generating interacting Large Neighborhood Search (LNS) destroy operators using sequential, iterative, or integrated prompting strategies. Results are backed by empirical evaluations on the Inventory Routing Problem and a multi-robot pre-marshalling problem, showing the approach outperforms classical matheuristics but slightly trails highly specialized state-of-the-art solvers. The key insight is that decomposing the evolution of interacting heuristics into sequential or iterative phases yields significantly better and more stable convergence than integrated generation, which overwhelms the LLM's search complexity.

#2SkillOS: Learning Skill Curation for Self-Evolving Agents

2026-05-07 | Google Cloud AI Research, Massachusetts Institute of Technology, University of Illinois Urbana-Champaign |2605.06614

SkillOS trains a dedicated skill curator model using Grouped Reward Policy Optimization (GRPO) to insert, update and delete reusable skills in a persistent repository for a frozen agent executor. The results are backed by strong empirical evidence, showing up to a 9.8% relative performance improvement and a 6.0% reduction in interaction steps on ALFWorld, WebShop and math reasoning benchmarks compared to strong memory baselines. The key insight is the training recipe for delayed memory feedback: grouping related tasks sequentially so that skills extracted from early tasks are explicitly rewarded based on their utility in solving later tasks, augmented by intermediate process rewards for skill conciseness and quality. This is highly relevant for LLM evolutionary search and multi-agent optimization, as it provides a scalable, RL-driven mechanism to maintain a persistent, cross-run memory of successful heuristics or strategies without relying on static rules.

#1Requests of a Feather Must Flock Together: Batch Size vs. Prefix Homogeneity in LLM Inference

2026-05-07 | Indian Institute of Technology Bombay |2605.06046

Feather introduces a prefix-aware LLM inference scheduler that uses a novel Chunked Hash Tree for fast prefix detection and a reinforcement learning policy to dynamically balance batch size against prefix homogeneity. The results are strongly backed by empirical evidence, demonstrating 2-10x higher end-to-end throughput compared to vLLM and SGLang baselines while reducing CPU scheduling overhead by up to 1000x. The key insight is a paradigm shift for inference batching: maximizing batch size is sub-optimal for prefix-shared workloads, as moderately small, prefix-homogeneous batches achieve higher throughput by maximizing spatial and temporal locality in KV cache accesses.

#2Nitsum: Serving Tiered LLM Requests with Adaptive Tensor Parallelism

2026-05-06 | University of California, San Diego, GenseeAI Inc. |2605.05467

Nitsum dynamically adjusts Tensor Parallelism (TP) levels and prefill/decode GPU allocations at runtime to maximize SLO-compliant goodput for multi-tenant LLM serving. The system achieves up to 5.3x higher goodput than state-of-the-art baselines like Llumnix, backed by rigorous experiments on real-world Azure and Alibaba traces. The key insight is that TP can be treated as a dynamic runtime control surface rather than a static deployment choice, enabled by keeping full weight copies on each GPU and using pipelined KV migration to reduce switching overhead to milliseconds.