Automatic multi-agent workflow generation now targets execution time: LAWA cuts total makespan through graph edits aimed at critical-path bottlenecks while keeping competitive task performance across seven benchmarks.
A new benchmark turns senior researchers' corrections of scientific AI agents into 248 two-way judgment questions: given only the project record from before the correction, four of five frontier models more often chose the original plan that a human had overruled, topping out at 63.5% against 73.4% for fresh researchers. The cases come from 26 real projects across six fields run over roughly half a year, and each asks a model to choose between the original plan and the human-corrected alternative.
The work introduces architectural sampling, a training-free method that generates candidates through distinct forward computations by reusing selected blocks of decoder layers in a frozen model; across five Qwen checkpoints and twelve multimodal benchmarks it improves pass@9 over standard-path temperature sampling by 6.58 percentage points on average at the same nine-candidate budget, with the strongest gains from reusing early layers, candidate-coverage improvement persisting under greedy decoding, lower lexical overlap among candidates, and higher accuracy when the candidates serve as rollouts for label-free test-time reinforcement learning.
The work introduces Symbolic Closure Analysis (SCA) to characterize exploration bias and compounding bias in long-horizon reasoning, and builds SAGE, a framework that injects structural priors during post-training via algebraic sparsification and hyperbolic structural guidance, outperforming SFT, GRPO, EMPO, and GRPO-PRM across 12 benchmarks and 7 model families and achieving up to an 8-fold improvement in Lean-verified proofs on the open Andrews-Curtis task.
You’re all caught up