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Learning from flowsheets: A generative transformer model for autocompletion of flowsheets
arXiv:2208.00859v2 Announce Type: replace Abstract: We propose a novel method enabling autocompletion of chemical flowsheets. This idea is inspired by the autocompletion of text. We represent flowsheets as strings using the text-based SFILES 2.0 notation and learn the grammatical structure of the SFILES 2.0 language and common patterns in flowsheets using a transformer-based language model.
RelGT-AC: A Relational Graph Transformer for Autocomplete Tasks in Relational Databases
arXiv:2606.03040v1 Announce Type: new Abstract: Relational databases underpin modern enterprise, scientific, and healthcare systems, yet predictive machine learning on such data remains challenging due to their multi-table, heterogeneous, and temporal structure. Relational Deep Learning (RDL) addresses this by representing databases as heterogeneous graphs and applying graph neural networks (GNNs) directly. RelBench v2 recently introduced autocomplete tasks -- a practically motivated task...
From Prompt to Process: a Process Taxonomy and Comparative Assessment of Frameworks Supporting AI Software Development Agents
arXiv:2606.04967v1 Announce Type: new Abstract: AI tools for programming are no longer just autocomplete or chat assistants: they organize themselves as development frameworks, with process, roles, artifacts and verification. Recent surveys map agents and LLMs for software engineering, but a study centered on the operational frameworks that turn these capabilities into process is missing. We ran a directed search of primary sources, with a functional inclusion criterion and traction...
Synthetic Hallucinations, Real Gains: Hard Negatives from Frontier Models for FIM Hallucination Mitigation
arXiv:2606.03130v1 Announce Type: new Abstract: Small open-source code models that power IDE autocomplete still emit hallucinated Fill-in-the-Middle (FIM) completions: syntactically natural calls to methods, parameters, variables, and imports that do not exist in the surrounding project. Existing mitigations either require per-language execution sandboxes that do not apply at mid-keystroke or preference-optimisation pipelines that need large human-labelled corpora.
Go-UT-Bench: A Fine-Tuning Dataset for LLM-Based Unit Test Generation in Go
arXiv:2511.10868v2 Announce Type: replace Abstract: Training data imbalance poses a major challenge for code LLMs. Most available data heavily over represents raw opensource code while underrepresenting broader software engineering tasks, especially in low resource languages like Golang. As a result, models excel at code autocompletion but struggle with real world developer workflows such as unit test generation.
CS336: Language Modeling from Scratch
Course Staff Logistics - Lectures: Monday/Wednesday 3:00-4:20pm in Skilling Auditorium - Recordings: YouTube playlist - Office hours: - Percy Liang: Fridays 11am-12pm in Gates 366 - Tatsu Hashimoto: Tuesdays 11-12am in Gates 364 - Marcel Rød: Tuesdays 4:30-5:30pm in Gates 498, Wednesdays 4:30-5:30pm in Gates 415 - Herman Brunborg: Wednesdays 1:30-2:30pm, Fridays 1:30-2:30pm, location Gates 392 - Steven Cao: Mondays 4:30-5:30pm, Thursdays 9:30-10:30am, Gates 200 - Contact: Students should ask...
Pluto.jl 1.0 release – reactive notebook for Julia
After six years, it’s time to release Pluto version 1.0! With this release, I want to celebrate all the progress over the past years, and to symbolise that Pluto is ready. I am proud of what we have achieved, and I hope you enjoy it!
Backpressure is all you need
Backpressure is all you need There are two obvious ways to use coding agents. The first is to let the LLM run unattended and hope the repository survives. This is fast, exciting, and stupid.
Ask HN: What are tools you have made for yourself since the advent of AI?
I've made a number of ceramic molds for slumping fused glass into bowls. As well as wooden templates for ceramic mugs. I've devised a few carrying tools to move glass frit paintings from my studio down to my barn where the kilns sit without spilling the glass.
Microsoft tells engineers to stop using Anthropic's Claude
Microsoft is canceling most internal Claude Code licenses by June 30, pushing engineers to its own GitHub Copilot CLI. Anthropic's tool got too popular, undercutting Microsoft's homegrown product. The official reason is toolchain unification, but The Verge reports the fiscal-year-end timing points to cost-cutting.