RI

#1From Soliloquy to Agora: Memory-Enhanced LLM Agents with Decentralized Debate for Optimization Modeling

2026-04-28 | Tsinghua University, University of Chicago Booth School of Business, Shanghai Jiao Tong University, The Chinese University of Hong Kong, Shenzhen (CUHK-Shenzhen) |2604.25847

This paper introduces Agora-Opt, a multi-agent framework for optimization modeling that combines decentralized debate across heterogeneous LLMs with a read-write memory bank. The results are backed by strong empirical evidence, achieving state-of-the-art Pass@1 accuracy (84.6%) across 7 OR benchmarks and outperforming both frontier zero-shot models and fine-tuned OR models. The key insight is that decentralized debate, where consensus is driven by solver-verified endpoints rather than a centralized LLM judge, can synthesize correct formulations even when all initial agent proposals are flawed. Furthermore, storing the trajectories of how these disagreements are resolved in a dedicated 'debate memory' allows the system to continuously improve its collaborative reasoning without parameter updates.

#2OptiVerse: A Comprehensive Benchmark towards Optimization Problem Solving

2026-04-23 | Xi'an Jiaotong University, Lenovo Research |2604.21510

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.

#1A2DEPT: Large Language Model-Driven Automated Algorithm Design via Evolutionary Program Trees

2026-04-27 | National University of Defense Technology, University of Electronic Science and Technology of China, Academy of Military Medical Sciences |2604.24043

A2DEPT advances LLM-driven algorithm design by evolving complete, open-ended solver programs via a tree-structured search with hierarchical operators and an automated dependency repair loop. The results are rigorously backed by empirical data, showing a 9.8% reduction in the mean normalized optimality gap relative to state-of-the-art baselines (FunSearch, EoH, ReEvo) across standard combinatorial optimization benchmarks like CVRP and FJSP. The key insight is the decoupling of mutations into hierarchical operators (macro-mutation for workflow restructuring vs. micro-tuning for local refinement) paired with a closed-loop dependency repair mechanism, which allows the LLM to safely rewrite system-level logic without permanently breaking executability. This is highly relevant for LLM evolutionary search research, as it provides a concrete, implementable mechanism to escape the performance ceiling of fixed algorithmic templates and synthesize entirely novel solver architectures.

#2SeaEvo: Advancing Algorithm Discovery with Strategy Space Evolution

2026-04-27 | The University of Hong Kong, City University of Hong Kong, JIUTIAN Research, China Mobile |2604.24372

SeaEvo augments LLM-driven evolutionary search by maintaining a persistent, population-level memory of natural-language strategy descriptions, clustering them to ensure diverse parent selection and tracking strategy saturation. Backed by strong empirical results, it achieves up to a 66% improvement on systems optimization tasks (like GPU model placement) over state-of-the-art backbones like ShinkaEvolve, while reducing cumulative API costs. The key insight is the Stratified Experience Retrieval (SER) module, which clusters candidate strategies using text embeddings and selects parents based on behavioral complementarity rather than just fitness, preventing premature convergence on syntactic variants of the same flawed idea.

#3GR-Evolve: Design-Adaptive Global Routing via LLM-Driven Algorithm Evolution

2026-04-24 | Arizona State University |2604.22234

GR-Evolve applies an LLM-driven evolutionary search framework to automatically specialize C++ global routing algorithms for specific chip designs. The results are empirically strong, demonstrating up to an 8.72% reduction in post-detailed-routing wirelength over established baselines like FastRoute and CUGR. The key insight is the use of 'warm-start evolution'—evolving heuristics on smaller, cheaper-to-evaluate instances and transferring the modified codebase as the initialization for larger instances—combined with a stateless, Git-backed persistent memory to avoid context degradation.

#1CacheFlow: Efficient LLM Serving with 3D-Parallel KV Cache Restoration

2026-04-28 | University of Illinois Urbana-Champaign, National University of Singapore |2604.25080

CacheFlow optimizes KV cache restoration in long-context LLM serving by formulating it as a 3D-parallel scheduling problem (token, layer, GPU) that overlaps recomputation and I/O transfer. The results are backed by strong empirical numbers, showing a 10%-62% reduction in Time-To-First-Token (TTFT) compared to state-of-the-art frameworks like vLLM and SGLang across diverse hardware and bandwidth conditions. The key insight is that the optimal balance between recomputation (which scales quadratically) and I/O loading (which scales linearly but is bandwidth-bound) can be achieved using a batch-aware two-pointer scheduler that prioritizes I/O for requests with the longest remaining lengths to restore.