RI

#1ReLoop: Structured Modeling and Behavioral Verification for Reliable LLM-Based Optimization

MUST-READ

2026-02-17 | National University of Singapore, Northwestern University, City University of Hong Kong, Wenzhou University, Wenzhou Buyi Pharmacy Chain Co., Ltd. | 2602.15983

M=8 P=9 I=8

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. The critical takeaway is the use of sensitivity analysis as a ground-truth-free process reward signal: we can validate evolved algorithms in AlgoEvo by asserting that specific input perturbations must trigger output changes, filtering out semantically invalid candidates before expensive evaluation.

#1Heuristic Search as Language-Guided Program Optimization

MUST-READ

2026-02-17 | Massachusetts Institute of Technology | 2602.16038

M=9 P=10 I=9

LaGO decomposes automated heuristic design into three explicit modules: evaluation, a code-writing 'Analyst' (backward pass), and a diversity-aware 'Generator' (update), while co-evolving constructive and refinement heuristics. The authors demonstrate significant gains (+0.17 QYI) on PDPTW and Crew Pairing against ReEvo and EoH, showing that joint optimization of initialization and improvement prevents local optima. The critical takeaway is the 'Analyst' module: instead of asking the LLM for text critiques, they ask it to write Python feature extraction functions to statistically characterize solution quality—a technique we should immediately adopt to upgrade our fitness signals in AlgoEvo.

#2MadEvolve: Evolutionary Optimization of Cosmological Algorithms with Large Language Models

MUST-READ

2026-02-17 | University of Wisconsin-Madison | 2602.15951

M=9 P=6 I=9

MadEvolve extends AlphaEvolve by embedding a gradient-based optimization loop (via JAX) inside the fitness evaluation, allowing the LLM to focus purely on code structure while an optimizer (Adam) handles continuous parameters. They demonstrate 20-30% performance gains on complex cosmological reconstruction tasks, validated on held-out simulations. The critical takeaway is the architectural pattern: prompt the LLM to write differentiable code rather than tuning constants, and use a UCB1 bandit to dynamically select between cheap and expensive models. We should immediately adopt the differentiable inner-loop strategy for our continuous heuristic search projects.

#1Efficient Multi-round LLM Inference over Disaggregated Serving

MUST-READ

2026-02-16 | University of Cambridge, Peking University, Shanghai Jiao Tong University, Ant Group, Southeast University | 2602.14516

M=7 P=9 I=7

AMPD introduces a disaggregated serving framework tailored for multi-round LLM agents, utilizing an offline ILP solver to optimize resource allocation (TP/DP configurations) and an online adaptive routing mechanism to handle incremental prefill tasks. The results are strong, showing 67-340% improvements in SLO attainment over vLLM and NVIDIA Dynamo by dynamically routing incremental prefill to decode workers when slack exists. For our 'GPUSched' project, the key takeaway is the specific ILP formulation (Eq. 5) for partitioning prefill/decode resources under global GPU constraints, and the insight that multi-agent workflows create a unique 'incremental prefill' bottleneck that standard disaggregation handles poorly.