9 recent papers · updated 2026-04-26 · sources deduplicated from arXiv, OpenAlex and Semantic Scholar
2026-04-23 | Xi'an Jiaotong University, Lenovo Research Method: Dual-View Auditor Agent (DVA-Agent) with Semantic Triangulation | LLM role: adversarial_evaluator
This paper introduces OptiVerse, a 1,000-problem benchmark spanning six optimization domains (including stochastic and dynamic optimization) to evaluate LLM reasoning, alongside a Dual-View Auditor Agent that detects semantic modeling errors. Extensive evaluation of 22 LLMs shows severe performance degradation on hard problems (under 27% accuracy even for frontier models), while the proposed agent improves accuracy by 1.3-6.3% over baselines like OptiMUS. The key insight is the 'blind code abstraction' technique: forcing the LLM to reverse-engineer mathematical logic solely from its generated code without seeing the original prompt, which effectively mitigates the confirmation bias that plagues standard LLM self-correction.
2026-04-22 | Xi’an Jiaotong University, Ministry of Education Key Laboratory of Intelligent Networks and Network Security, Shaanxi Province Key Laboratory of Big Data Knowledge Engineering Method: Dual-Cluster Memory Agent (DCM-Agent) with Dual-Cluster Memory Construction and Memory-Augmented Inference | LLM role: knowledge_synthesizer_code_generator_verifier
This paper introduces a training-free Dual-Cluster Memory Agent that resolves multi-paradigm ambiguity in optimization modeling by decoupling abstract mathematical modeling from concrete coding implementation into separate memory clusters linked by a bipartite graph. The results are backed by strong empirical evidence, showing 11-21% average accuracy improvements across 7 OR benchmarks (including OptiBench and NLP4LP) over baselines like OptiMUS and OptiTree, while reducing inference time compared to heavy tree-search methods. The key insight is the structured extraction of 'Pitfalls' from persistent failures and the resulting 'knowledge inheritance'—using a large model to build a high-quality bipartite memory graph allows smaller, cheaper models to achieve SOTA performance during inference.
2026-04-20 | Harbin Institute of Technology, Nanjing University of Information Science and Technology Method: Co-evolutionary framework using Activity-on-Edge (AOE) networks for agent architecture and reasoning trajectory evolution | LLM role: problem_interpreter, heuristic_generator, code_writer, decomposition_guide, evolutionary_search_operator, evaluator
Huang et al. propose EvoOR-Agent, a co-evolutionary framework that represents LLM agent workflows as Activity-on-Edge (AOE) networks to simultaneously evolve the agent's architectural topology and its reasoning trajectories for operations research tasks. The results are backed by strong empirical evidence, showing up to 17% improvement over fixed-pipeline OR agents and 15% over general evolutionary agents on complex benchmarks like IndustryOR and BWOR. The key insight is that abstracting agent workflows into an explicit, evolvable AOE graph allows for path-conditioned recombination and structural pruning, enabling the evolutionary search to optimize the problem-solving process (e.g., formulation decomposition, solver routing, debugging loops) rather than just the prompt text or final code.
2026-04-20 | The Chinese University of Hong Kong, Hong Kong University of Science and Technology, City University of Hong Kong, Peking University, Tsinghua University, University of California, Los Angeles, Shanghai Jiao Tong University Method: Pipeline-Adapted Reward Model (PARM) training with Direct Preference Optimization (DPO) for stage-wise candidate selection | LLM role: generator, evaluator
Fan et al. introduce a Pipeline-Adapted Reward Model (PARM) that trains stage-specific reward models for LLM optimization pipelines using Direct Preference Optimization on automatically collected execution feedback. The results are backed by strong empirical numbers, demonstrating that a 7B model pipeline can outperform GPT-4o on operations research benchmarks like NL4Opt (0.52 vs 0.15 solving accuracy). The key insight is that intermediate pipeline stages, such as problem formulation, can be effectively scored by training a reward model via DPO where preference pairs are automatically labeled based on whether any downstream execution succeeds.
2026-04-18 | X, University of Oxford Method: Reinforcement learning (GRPO) with scalable synthetic data generation and LoRA adapters | LLM role: code_writer
AutoOR trains an 8B LLM to autoformalize linear, mixed-integer and non-linear optimization problems using a scalable backtranslation data generation pipeline and GRPO with solver-execution rewards. The results are strongly backed by empirical evidence, showing the 8B model outperforming Gemini 2.5 Pro and matching Gemini 3 Pro across 9 benchmarks, including a leap from near 0% to 48.98% on a hard non-linear pump network task. The key insight is the backtranslation pipeline: by instantiating standard OR forms to guarantee ground-truth code and then generating natural language descriptions, the authors bypass the hard problem of verifying code generated from ambiguous text. Furthermore, their curriculum RL strategy—temporarily providing solver syntax as privileged information—successfully breaks the cold-start barrier in hard non-linear domains. This is a for work in OR benchmarking and LLM reasoning evaluation, as the data generation and solver-in-the-loop RL techniques directly address the scalability and verification bottlenecks in symbolic OR modeling.
2026-04-14 | Brown University, Fidelity Investments Method: LLM-based prompting strategies for MiniZinc model generation | LLM role: code_writer
This paper introduces Text2Zinc, a solver-agnostic benchmark of 1,775 natural language combinatorial problems and evaluates various LLM copilot strategies (CoT, knowledge graphs, grammar validation) for generating formal MiniZinc models. The results are backed by extensive empirical evaluation, demonstrating that even advanced models like GPT-4o struggle, achieving only ~40-50% solution accuracy despite much higher execution (compilation) accuracy. The key insight is that decoupling syntax enforcement from generation via post-hoc grammar validation significantly improves execution accuracy without requiring constrained decoding, though capturing the underlying optimization logic remains a major bottleneck.
2026-04-01 | Chinese Academy of Sciences, Nanjing University, Nanjing University of Science and Technology Method: Execution-Verified Reinforcement Learning (EVOM) with GRPO and DAPO for solver-conditioned code generation | LLM role: code_writer
EVOM trains LLMs for operations research modeling using execution-verified reinforcement learning (GRPO/DAPO) based solely on solver outcomes, bypassing expensive process-level supervision. The results are backed by solid empirical evaluations on OptiBench, NL4OPT and IndustryOR, demonstrating that it matches or beats process-supervised SFT (ORLM) and enables zero-shot transfer to new solvers (e.g., Gurobi to OR-Tools). The key takeaway is that outcome-only RL prevents the model from overfitting to solver-specific syntax (a major flaw in SFT), forcing it to learn invariant mathematical structures; additionally, their two-stage cold-start trick (LLM-translate 100 samples -> SFT -> RL) is a highly stealable technique for adapting to new environments.
2026-02-23 | Massachusetts Institute of Technology, Alibaba Group Method: Two-phase closed-loop LLM agent with IIS-guided diagnosis, domain-specific rationality oracle, iterative STaR and GRPO refinement | LLM role: diagnosis_and_repair
Ao et al. introduce OptiRepair, a closed-loop framework that repairs infeasible LPs using solver IIS feedback (Phase 1) and validates them with a 'Rationality Oracle' based on domain theory (Phase 2). Results are exceptionally strong: fine-tuned 8B models trained via iterative STaR and GRPO achieve 81.7% success, outperforming GPT-5.2 (42.2%) by a massive margin.
2026-02-17 | National University of Singapore, Northwestern University, City University of Hong Kong, Wenzhou University, Wenzhou Buyi Pharmacy Chain Co., Ltd. Method: Structured generation (underst and formalize, synthesize, verify) with two-layer behavioral verification (L1 execution recovery, L2 solver-based perturbation testing) and diagnosis-guided repair. | LLM role: code_writer
ReLoop proposes a verification pipeline for LLM-generated optimization models that detects 'silent failures' (code that runs but solves the wrong problem) by perturbing input parameters and checking for expected solver objective shifts. They demonstrate that standard execution feasibility is a poor proxy for correctness (90% gap) on their new RetailOpt-190 benchmark and that this perturbation testing significantly improves reliability.
Same pipeline, pointed at whatever you're working on — with relevance scored to how you read.
Request access →