Bodun Hu, Shuozhe Li, Saurabh Agarwal, Myungjin Lee, Akshay Jajoo, Jiamin Li, Le Xu, Geon-Woo Kim, Donghyun Kim, Hong Xu, Amy Zhang, Aditya Akella. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2025.

Chart understanding presents a unique challenge for large vision-language models (LVLMs), as it requires the integration of sophisticated textual and visual reasoning capabilities. However, current LVLMs exhibit a notable imbalance between these skills, falling short on visual reasoning that is difficult to perform in text. We conduct a case study using a synthetic dataset solvable only through visual reasoning and show that model performance degrades significantly with increasing visual complexity, while human performance remains robust. We then introduce ChartMuseum, a new Chart Question Answering (QA) benchmark containing 1,162 expert-annotated questions spanning multiple reasoning types, curated from real-world charts across 184 sources, specifically built to evaluate complex visual and textual reasoning. Unlike prior chart understanding benchmarks---where frontier models perform similarly and near saturation---our benchmark exposes a substantial gap between model and human performance, while effectively differentiating model capabilities: although humans achieve 93% accuracy, the best-performing model Gemini-2.5-Pro attains only 63.0%, and the leading open-source LVLM Qwen2.5-VL-72B-Instruct achieves only 38.5%. Moreover, on questions requiring primarily visual reasoning, all models experience a 35%-55% performance drop from text-reasoning-heavy question performance. Lastly, our qualitative error analysis reveals specific categories of visual reasoning that are challenging for current LVLMs. Both ChartMuseum and the evaluation code are available at https://github.com/Liyan06/ChartMuseum.

Rapid advancements over the years have helped machine learning models reach previously hardto-achieve goals, sometimes even exceeding human capabilities.However, achieving desired accuracy comes at the cost of larger model sizes and increased computational demands.Thus, serving predictions from these models to meet any latency and cost requirements of applications remains a key challenge, despite recent work in building inference serving systems as well as algorithmic approaches that dynamically adapt models based on inputs.Our paper introduces a new form of dynamism, modality selection, where we adaptively choose modalities from inference inputs while maintaining the model quality.We introduce MOSEL, an automated inference serving system for multi-modal ML models that carefully picks input modalities per request based on resource availability, as we as user-defined service level objectives (SLOs).MOSEL extensively leverages modality configurations, improving system throughput by 3.6× with an accuracy guarantee.It also reduces job completion times by 11× compared to modalityagnostic approaches.

Autoregressive Large Language Models (e.g., LLaMa, GPTs) are omnipresent achieving remarkable success in language understanding and generation.However, such impressive capability typically comes with a substantial model size, which presents significant challenges for autoregressive token-by-token generation.To mitigate computation overload incurred during generation, several early-exit and layer-dropping strategies have been proposed.Despite some promising success due to the redundancy across LLMs layers on metrics like Rough-L/BLUE, our careful knowledgeintensive evaluation unveils issues such as generation collapse, hallucination, and noticeable performance drop even at the trivial exit ratio of ∼ 10-15% of layers.We attribute these errors primarily to ineffective handling of the KV cache through state copying during early exit.In this work, we observe the saturation of computationally expensive feed-forward blocks of LLM layers and propose FFN-SkipLLM, which is a novel fine-grained skip strategy for autoregressive LLMs.FFN-SkipLLM leverages an input-adaptive feed-forward skipping approach that can skip ∼ 25-30% of FFN blocks of LLMs with marginal change in performance on knowledge-intensive generation tasks without any requirement to handle the KV cache.Our extensive experiments and ablation studies across benchmarks like MT-Bench, Factoid-QA, and variable-length text summarization illustrate how our simple and easy-touse method can facilitate faster autoregressive decoding.

The complexity of modern operating systems (OSes), rapid diversification of hardware, and steady evolution of machine learning (ML) motivate us to explore the potential of ML to improve decision-making in OS kernels. We conjecture that ML can better manage tradeoff spaces for subsystems such as memory management and process and I/O scheduling that currently rely on hand-tuned heuristics to provide reasonable average-case performance. We explore the replacement of heuristics with ML-driven decision-making in five kernel subsystems, consider the implications for kernel design, shared OS-level components, and access to hardware acceleration. We identify obstacles, address challenges and characterize tradeoffs for the benefits ML can provide that arise in kernel-space. We find that use of specialized hardware such as GPUs is critical to absorbing the additional computational load required by ML decisioning, but that poor accessibility of accelerators in kernel space is a barrier to adoption. We also find that the benefits of ML and acceleration for OSes is subsystem-, workload- and hardware-dependent, suggesting that using ML in kernels will require frameworks to help kernel developers navigate new tradeoff spaces. We address these challenge by building a system called LAKE for supporting ML and exposing accelerators in kernel space. LAKE includes APIs for feature collection and management across abstraction layers and module boundaries. LAKE provides mechanisms for managing the variable profitability of acceleration, and interfaces for mitigating contention for resources between user and kernel space. We show that an ML-backed I/O latency predictor can have its inference time reduced by up to 96% with acceleration.

This paper presents ALTIS, a benchmark suite for modern GPGPU computing. Previous benchmark suites such as Rodinia and SHOC have served the research community well, but were developed years ago when hardware was more limited, software supported fewer features, and production hardware-accelerated workloads were scarce. Since that time, GPU compute density and memory capacity has grown exponentially, programmability features such as unified memory, demand paging, and HyperQ have matured, and new workloads such as deep neural networks (DNNs), graph analytics, and crypto-currencies have emerged in production environments, stressing the hardware and software in ways that previous benchmarks did not anticipate. Drawing inspiration from Rodinia and SHOC, ALTIS is a benchmark suite designed for modern GPU architectures and modern GPU runtimes, representing a diverse set of application domains. By adopting and extending applications from Rodinia and SHOC, adding new applications, and focusing on CUDA platforms, ALTIS better represents modern GPGPU workloads to enable support GPGPU research in both architecture and system software.