Alexis Langlois-Rémillard, Mia N. Müßig, Helmut Podhaisky, Erika Roldan, Günter Rote, Maximilian Brömme, Matthes Norden, Maximilian Prietzel 等 10 位作者
原文摘要
The $n$-omino fence problem asks how many cells can be surrounded by placing every $n$-omino exactly once in the grid. We present a new dynamic programming approach for this problem, allowing us to prove optimality for the pentomino and hexomino fence and to classify all possible pentomino orders by optimal area. For the first time we also provide strong heuristic solutions and upper bounds for all fences up to $n = 15$, after which we even approximate the $500$-omino fence with computational experiments. Finally, we generalize the problem to 3D, where we prove optimality for pentacubes and derive heuristic solutions for hexacubes and heptacubes.
Soham Dan, Jeff Da, Sanyam Bhutani, Miguel Romero Calvo, Ivan Guevara, Fernando Carabedo, Jose Bernal, Julio Reyna 等 11 位作者
原文摘要
SWE-Bench Pro is a widely used benchmark of long-horizon software engineering tasks for coding agents. As agents improve, they reach task issues earlier agents never touched and interact with the evaluation environment in ways the original harness did not anticipate. We present SWE-Bench Pro V2, a revised public split built to keep the benchmark accurate as capabilities advance. Agentic audits, expert adjudication and blind re-implementation repaired or retired 480 of the 731 tasks: 23 contracted engineers spent 404 hours, each working in a language they write professionally, and every revised instruction was verified by a second engineer solving the task from the text alone. A hardened protocol restricts network access to the model endpoint and re-grades every patch in a clean container, so agents cannot retrieve upstream fixes or get credit for changes outside their patch. On the 642 V2 tasks frontier agents resolve up to 99.4% while other systems stay clearly separated, and a 51-task Hard split spreads systems from 52.9% to 98.0%. We release the benchmark and distill lessons for maintaining agent benchmarks over time.
Vincent-Daniel Yun, Junhyuk Jo, Sai Praneeth Karimireddy, Sunwoo Lee
原文摘要
Layer pruning removes entire Transformer decoder blocks from large language models, but introduces a mismatch between the hidden state received by the next surviving layer and the distribution it was trained to process, leading to significant performance degradation. We propose Ghosted Layers, a training-free recovery module that addresses this issue by solving a boundary activation alignment problem. Our method derives a closed-form optimal linear operator from a small calibration set to reconstruct the activation discrepancy introduced by the pruned layers. We show that this solution corresponds to the unconstrained optimum of the alignment objective, whereas LinearPatch is restricted to a fixed basis symmetric operator family. Across multiple LLM backbones, pruning strategies, and pruning depths, Ghosted Layers outperforms prior training-free recovery methods in accuracy and perplexity in most settings while preserving the efficiency benefits of layer pruning.