Bi-Daily Research Intelligence Briefing

Li et al. introduce MIPLIB-NL, a benchmark of 223 industrial-scale MILP instances (up to 10^7 variables) reverse-engineered from MIPLIB 2017, enforcing strict model-data separation. Results are sobering: SOTA models like GPT-4 and fine-tuned OR-LLMs drop from ~90% accuracy on existing toy benchmarks to ~18% here, failing primarily on structural consistency and index handling at scale.

Kravatskiy et al. introduce ImprovEvolve, a framework that restricts the LLM to evolving `improve()` (local search) and `perturb()` (mutation) operators, which are then executed by a fixed basin-hopping algorithm. They achieve new state-of-the-art results on Hexagon Packing and the Second Autocorrelation Inequality, demonstrating that this modular approach generalizes to unseen problem sizes where monolithic AlphaEvolve solutions fail.

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. 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.