To join the email distribution list of the cs colloquia, please visit the list subscription page.
Computer Science events calendar in HTTP ICS format for of Google calendars, and for Outlook.
Academic Calendar at Technion site.
Block Sparse Flash Attention
Modern large language models increasingly require long contexts for reasoning and multi-document tasks, but attention’s quadratic complexity creates a severe computational bottleneck. We present Block Sparse Flash Attention (BSFA), a drop-in replacement that accelerates long-context inference while preserving model quality. Unlike methods that predict importance before computing scores, BSFA computes exact query-key similarities to select the top-k most important value blocks for each query. By comparing per-block maximum scores against calibrated thresholds, we skip approximately 50% of the computation and memory transfers for pruned blocks. Our training-free approach needs only a single calibration step on a small dataset to set the thresholds for each layer and head. We provide a CUDA kernel implementation that can be used as a drop-in replacement for FlashAttention. On Llama-3.1-8B, BSFA achieves up to 1.13× end-to-end speedup on LongBench with only a 1.1% accuracy drop, and up to 1.24× on Needle-in-a-Haystack retrieval at a 1% accuracy drop. The attention kernel itself accelerates by up to 1.38×. We compare BSFA against five recent sparse attention baselines (SpargeAttention, MInference, FlexPrefill, XAttention, and BLASST), and verify the method on Qwen2.5-7B and on A6000 and H100 GPUs. The implementation is available at https://github.com/Danielohayon/Block-Sparse-Flash-Attention.
Large Language Models for Water Distribution Systems Modeling and Decision-MakingThe modeling of Water Distribution Networks is a subfield of Water Engineering, with impact of billions of lives worldwide, powering communities, cities, and countries.
To handle such massive loads of data intake, software is a necessary tool for engineers, used to describe, model, analyze and interact with this infrastructure.
The de-facto standard for Water Distribution Systems modeling software is EPANET, an ODE solver library used for its suite of computational tools. The library is written in C++, with many Python programming language wrapper libraries made over it, to enable access to high-level modeling.
Even with accessible libraries available to engineers, such tools remain underutilized due to technical or expertise barriers.
In recent years, Large Language Models changed the landscape of software engineering and accessible abilities. More code than ever is AI-generated, and coding agents are used not only by software engineers, but all vocations are using automated code generation.
In this work, we introduce LLM-EPANET, an agent-based framework that enables natural language interaction with EPANET.
The framework combines retrieval-augmented generation (RAG) and multi-agent orchestration to automatically translate user queries into executable code, run simulations, and return structured results, skipping the needed technical depth to write and debug executable code.
We propose WaterAgentBench, a curated set of 69 benchmark queries to evaluate generated code performance across multiple categories relevant to WDS modeling, in tasks ranging from basic question answering on networks to multiple-command hydraulic analysis queries.
Results show that LLMs can effectively support a wide range of modeling tasks, achieving 56–81% accuracy overall and over 90% for simpler queries, highlighting the potential of LLM-based modeling to support broader accessibility of data-driven tools and advance the democratization of water systems modeling through transparent, interactive AI interfaces.
