RI

#1FLARE: Verifying MILP Reformulations with LLM-Based Theorem Proving

2026-08-25 | Stanford University |2608.25220

Robbins et al. introduce FLARE, an automated framework that uses an LLM agent and the Lean proof assistant to formally verify whether one MILP formulation is a valid reformulation of another. The authors demonstrate 100% accuracy on a new benchmark of 54 NP-hard formulation pairs, backed by machine-checkable proofs rather than heuristic execution checks. The key insight is that instance-level validation—commonly used to evaluate LLM-generated models—is fundamentally flawed and can accept invalid transformations; the authors prove this by identifying mathematically invalid cutting planes generated by recent LLM evolutionary search frameworks.

#1Recursive Experiential-Working Memory Evolution for Long-Horizon Agent Harnesses

2026-08-25 | Princeton University, Stanford University, University of Oxford, NUS |2608.24876

Recuris introduces a recursive memory evolution architecture that couples Working Memory (for task state tracking) with Experiential Memory (for skill storage), using structured execution traces to localize failures and apply validation-gated updates to specific memory components. The results are backed by rigorous empirical evidence, showing massive gains on long-horizon tasks (e.g., +17.8 points for GPT-5.6 Sol and +15.6 for Claude Opus 5 on the $\tau$2-Retail benchmark). The key insight is using structured execution traces to localize failures to specific harness components (skill content, state tracking, invocation trigger, or verification checker) rather than rewriting the entire memory or prompt, enabling highly targeted and stable evolutionary updates. This is highly relevant for LLM evolutionary search and multi-agent optimization, as it provides a concrete mechanism for persistent memory evolution, step-by-step credit assignment and safe recursive self-improvement.

#2FormuEvo: LLM-Guided Evolution for Discovering Solver-Efficient Mixed-Integer Programming Formulations

2026-08-24 | Tsinghua University, Nanyang Technological University, Singapore Management University |2608.23353

FormuEvo is an LLM-guided evolutionary framework that discovers solver-efficient mixed-integer programming (MIP) formulations by searching the symbolic space of executable modeling programs. Backed by strong empirical results, it accelerates solvers by up to 5.5x compared to expert-designed formulations and existing LLM-based cutting plane generation baselines across diverse OR benchmarks. THE KEY INSIGHT is the 'solver-informed diagnosis' mechanism, which translates fine-grained solver statistics (e.g., root gap, branch-and-bound node count, presolve reductions) into interpretable verbal gradients to guide LLM mutation, coupled with a structured memory of Condition-Strategy-Effect triplets.

#3Autonomous Mathematical Discovery in an Open-World Multi-Agent Environment

2026-08-24 | University of Cambridge, University of California San Diego, University of Hong Kong, DualverseAI |2608.23691

This paper introduces an open-world multi-agent environment where LLMs autonomously conduct mathematical research, write papers and build on shared literature to solve complex construction problems. The results are rigorously backed by numbers, with the system discovering novel infinite families and exact configurations that outperform AlphaEvolve on several benchmarks (e.g., a 604-point kissing configuration in d=11). The key insight is that replacing rigid evolutionary pipelines with a persistent 'Archive Room' (shared memory) and 'Stagnation/Holiday' protocols allows agents to escape local optima and accumulate knowledge across generations.

#4RecurSE: Bounded Recursive Self-Evaluation for LLM Rubric Judges

2026-08-25 | Zhejiang University, Meituan LongCat Team |2608.24231

RECURSE enables LLM judges to recursively self-improve via RL by using a synchronized copy of the policy as a process checker to provide scalar rewards, eliminating the need for external reward models. The method is backed by strong empirical results across Qwen and Gemma models, showing significant improvements on held-out transfer benchmarks like HealthBench and CheckEval. The key insight is 'interface decoupling'—structurally separating the judge's output format (YES/NO tokens) from the checker's reward format (0-4 scalar) to prevent the policy from exploiting token-copying shortcuts during self-play. Furthermore, tracking checker ranking fidelity on a small holdout set reliably prevents over-optimization.

#5AsmEvo: Agentic Assembly-Level Optimization of AMD GPU Kernels with Functional Equivalence Verification

2026-08-21 | Advanced Micro Devices, Inc. (AMD), Southern University of Science and Technology |2608.20711

AsmEvo uses a long-horizon LLM agent to optimize compiled AMD GPU kernels at the assembly level, relying on the original binary as a differential oracle to verify functional equivalence before measuring performance. The paper presents strong empirical results on real hardware (MI308X/MI300X), achieving up to 3.88x speedups on KernelBench and 1.18x geometric-mean speedups on production vLLM/SGLang Triton kernels. The key insight is the strict separation between the LLM proposer and a deterministic, correctness-gated verification harness (using real-dispatch capture for a non-hackable fitness signal), which prevents the search from exploiting fast but incorrect programs.

#1PowerSlider: Exploiting Phase Asymmetry for LLM Serving under Demand Response

2026-08-22 | Microsoft Azure Research, Cornell University, Cornell Tech |2608.21719

POWERSLIDER optimizes LLM serving under dynamic power caps by disaggregating the pipeline into Prefill, Think and Answer stages and using a KKT online solver to dynamically adjust GPU allocation and frequencies based on phase asymmetry. The results are rigorously backed by empirical data, demonstrating 1.64x higher goodput than the best baseline at a 30% power cap reduction while solving the allocation problem in just 7.7ms. The key insight is that reasoning workloads break standard prefill-decode disaggregation due to massive KV-cache accumulation during the 'thinking' phase; isolating this into a three-stage pipeline allows an online convex solver to aggressively scale down frequencies for memory-bound decode stages without starving compute-bound prefill.

#2TOPAS: Workflow-Aware Prefix-State Scheduling for Multi-Agent LLM Serving

2026-08-26 | University of Science and Technology of China, Hefei University of Technology |2608.25523

TOPAS is an online scheduler for multi-agent LLM serving that jointly optimizes KV cache prefix residency and request admission to minimize task-level job completion time. Backed by empirical numbers, it reduces mean and p99 JCT by up to 39.8% and 49.4% on synthetic DAGs and significantly improves performance on real MetaGPT workflows compared to baselines like Shortest-Path-First. The critical insight is treating prefix residency as an explicit, workflow-aware scheduling decision rather than a reactive byproduct of request ordering, using a utility function that balances the reduction of a task's longest remaining service path against near-term prefix reuse. This is highly relevant for research in OR formulations for LLM serving scheduling, offering a concrete heuristic search approach to manage the memory-compute tradeoff in complex multi-agent deployments.