| Today at a glance |
|
5
s
|
6
New papers
|
3
Categories
|
|
|
AI for Optimization
1 new papers | 1 | 66 total analyzed
|
|
2026-04-01 | Chinese Academy of Sciences, Nanjing University, Nanjing University of Science and Technology | 2604.00442
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.
|
Automated Algorithm Discovery
3 new papers | 3 | 106 total analyzed
|
|
CliffSearch is an LLM-based evolutionary framework that co-evolves algorithm theory and code, using specialized agents for crossover, two-path mutation (exploration vs. repair) and explicit reviewer gating. The results are backed by concrete empirical runs on nanoGPT optimizer discovery and transformer hyper-connection search, demonstrating the discovery of genuinely novel geometric routing and optimizer variants rather than trivial hyperparameter tweaks. The single most useful takeaway is the 'reviewer-gated selection' where an LLM explicitly scores candidates on originality and correctness as a hard survival gate before benchmark scores are considered.
|
|
COvolve uses LLMs to adversarially co-evolve Python code for both environments (tasks) and policies (agents), using Policy Space Response Oracles (PSRO) to compute a mixed-strategy Nash equilibrium (MSNE) that prevents catastrophic forgetting. The results are backed by solid empirical data across MiniGrid, PyGame and CARLA, demonstrating that the MSNE approach maintains robust performance across a growing historical archive of environments much better than greedy retention.
|
|
2026-03-30 | Machine Perception and Interaction Lab, Örebro University, Sweden | 2603.28416
This paper evolves executable reinforcement learning update rules using LLMs as macro-mutation and crossover operators, explicitly forbidding standard RL mechanisms to force the discovery of novel algorithms. The results are backed by solid empirical evaluations on Gymnasium benchmarks, showing the evolved algorithms match or beat standard baselines like PPO and SAC on several tasks, though they struggle on a few complex continuous control environments.
|
Optimizing AI Systems
2 new papers | 1 | 108 total analyzed
|
|
2026-03-27 | IMDEA Software Institute, Universidad Politécnica de Madrid | 2603.26498
RPS-Serve introduces a modality-aware scheduler for multimodal LLMs that classifies requests into 'rocks' (video), 'pebbles' (image) and 'sand' (text) based on predicted prefill latency and memory, using dynamic priorities and aging to prevent head-of-line blocking. The results are real and backed by solid systems experiments on vLLM, showing a 78.5% reduction in time-to-first-token for latency-critical text requests compared to FCFS and EDF baselines. The core takeaway is that multimodal workloads completely break standard text-only LLM serving assumptions because video/image prefill times and KV-cache footprints are orders of magnitude larger.
|
|
2026-03-26 | Microsoft Gray Systems Lab, SK Telecom, Yonsei University | 2603.25120
DFLOP optimizes distributed 3D parallelism for multimodal LLM training by combining offline profiling with an online ILP-based microbatch scheduler to minimize pipeline bubbles caused by heterogeneous data inputs. The results are real and backed by extensive hardware experiments, showing up to 3.6x throughput improvements over Megatron-LM.
|