We study the problem of generating arbitrarily large environments to improve the throughput of multi-robot systems. Prior work proposes Quality Diversity (QD) algorithms as an effective method for optimizing the environments of automated warehouses. However, these approaches optimize only relatively small environments, falling short when it comes to replicating real-world warehouse sizes. The challenge arises from the exponential increase in the search space as the environment size increases.

Separating signal from noise is central to experimental science. Applying well-established statistical method effectively to LLM evals requires consideration of their unique noise characteristics. We clearly define and measure three types of noise: prediction noise from generating different answers on a given question, data noise from sampling questions, and their combined total noise following the law of total variance. To emphasize relative comparisons and gain statistical power, we propose the all-pairs paired method, which applies the paired analysis to all pairs of LLMs and measures all the noise components based on millions of question-level predictions across many evals and settings. These measurements revealed clear patterns. First, each eval exhibits a characteristic and highly predictable total noise level across all model pairs. Second, paired prediction noise typically exceeds paired data noise, which means reducing prediction noise by averaging can significantly increase statistical power. These findings enable practitioners to assess significance without custom testing and to detect much smaller effects in controlled experiments.

Abstract--Generative Artificial Intelligence Models like Large Language Models (LLMs) and Large Vision Models (VLMs) exhibit state-of-the-art performance across a wide range of tasks but remain vulnerable to hardware-based threats, specifically bit-flip attacks (BF As), posing a serious risk to their security in safety-critical applications. Existing BF A discovery methods--gradient-based, static analysis, and search-based--lack generalizability and struggle to scale, often failing to analyze the vast parameter space and complex interdependencies of modern foundation models in a reasonable time. This paper proposes FlipLLM, a reinforcement learning (RL) architecture-agnostic framework that formulates BF A discovery as a sequential decision-making problem. FlipLLM combines sensitivity-guided layer pruning with Q-learning to efficiently identify minimal, high-impact bit sets capable of inducing catastrophic failure. We demonstrate the effectiveness and generalizability of FlipLLM by applying it to a diverse set of models, including prominent text-only LLMs (GPT -2 Large, LLaMA 3.1 8B, and DeepSeek-V2 7B), VLMs such as LLaV A 1.6, and datasets, such as MMLU, MMLU-Pro, VQA v2, and T extVQA. Our results show that FlipLLM can identify critical bits that are vulnerable to BF As up to 2.5 faster than SOT A methods. We demonstrate that flipping the FlipLLM-identified bits plummets the accuracy of LLaMA 3.1 8B from 69.9% to 0.2%, and for LLaV A's VQA score from 78% to almost 0%, by flipping as few as 5 and 7 bits, respectively. Further analysis shows that applying standard hardware protection mechanisms, such as ECC SECDED, to the FlipLLM-identified bit locations completely mitigates the BF A impact, demonstrating the practical value of our framework for guiding hardware-level defenses. FlipLLM offers the first scalable and adaptive methodology for exploring the BF A vulnerability of both language and multimodal foundation models, paving the way for comprehensive hardware-security evaluation. Generative Artificial Intelligence models like Large Language Models (LLMs) [1] and Large Vision Models (VLMs) represent a transformative advancement in artificial intelligence, finding integration into mission-critical systems spanning healthcare, finance, and autonomous navigation [2], [3]. Their effective deployment mandates reliable and secure operation across diverse hardware infrastructures, from expansive cloud accelerators to resource-constrained edge devices.

Adapting CLIP to vertical domains is typically approached by novel fine-tuning strategies or by continual pre-training (CPT) on large domain-specific datasets. Y et, data itself remains an underexplored factor in this process. W e revisit this task from a data-centric perspective: Can effective data selection substitute for large-scale datasets in CPT? W e introduce CHIPS (Curvature-aware Hybrid Influence in Projection Subspace), which assigns each image-text pair a utility score that integrates three complementary factors aligned with three goals: faithfulness via a curvature-aware, Newton-style alignment computed in CLIP's endpoint subspace; scalability via an InfoNCE-aware curvature estimator with Johnson-Lindenstrauss (JL) sketching; and retention via a selection-aware relevance weight combined with learnability to balance target adaptation against general-domain preservation. W e justify this design theoretically by proving a lower-bound guarantee on the proxy's correlation with full-parameter alignment and by characterizing the bias-variance trade-offs introduced by curvature mixing and JL sketching. W e evaluate CHIPS empirically across various settings: 1) CHIPS attains state-of-the-art performance among selection baselines on 17 medical benchmarks, matches full-dataset CPT with 30% of the data, and outperforms half-dataset CPT using only 10%; 2) on 31 general-domain benchmarks, CHIPS yields the smallest performance drop under 10-30% data-retention budgets. Code, data, and checkpoints will be released.

In this work, we consider the problem of a two-player zero-sum game. In the literature, the successive over-relaxation Q-learning algorithm has been developed and implemented, and it is seen to result in a lower contraction factor for the associated Q-Bellman operator resulting in a faster value iteration-based procedure. However, this has been presented only for the tabular case and not for the setting with function approximation that typically caters to real-world high-dimensional state-action spaces. Furthermore, such settings in the case of two-player zero-sum games have not been considered. We thus propose a deep successive over-relaxation minimax Q-learning algorithm that incorporates deep neural networks as function approximators and is suitable for high-dimensional spaces. We prove the finite-time convergence of the proposed algorithm. Through numerical experiments, we show the effectiveness of the proposed method over the existing Q-learning algorithm. Our ablation studies demonstrate the effect of different values of the crucial successive over-relaxation parameter.

Feature attributions attempt to highlight what inputs drive predictive power. Good attributions or explanations are thus those that produce inputs that retain this predictive power; accordingly, evaluations of explanations score their quality of prediction. However, evaluations produce scores better than what appears possible from the values in the explanation for a class of explanations, called encoding explanations. Probing for encoding remains a challenge because there is no general characterization of what gives the extra predictive power. We develop a definition of encoding that identifies this extra predictive power via conditional dependence and show that the definition fits existing examples of encoding. This definition implies, in contrast to encoding explanations, that non-encoding explanations contain all the informative inputs used to produce the explanation, giving them a "what you see is what you get" property, which makes them transparent and simple to use.

A natural way to unlock particular capabilities is to improve this training data.

We hope that our comments in the first paragraph address the main concerns with our work.

Why compare to COBYLA instead of BOBYQA?

Table 10: Retrieval results on the KIL T test set(s). Reporting page-level R-precision (higher is better). Results are taken from the eval.ai Table 11: KIL T scores on the KIL T test set(s). Results are taken from the eval.ai