·
DataBubble
  • Home
  • Models
  • News
  • Compare
  • Boards
  • Pricing
  • About
  • Newsletter
  • Methodology
  • Contact
Latest
A satellite just learned to find things on its own — here’s what that means1h◆The AI layoff wave is becoming a powder keg6h◆ChronoID: Infusing Explicit Temporal Signals into Semantic IDs for Generative Recommendation9h◆Small LLMs: Pruning vs. Training from Scratch9h◆EqCollide: Equivariant and Collision-Aware Deformable Objects Neural Simulator9h◆Towards Efficient Large Language Reasoning Models via Extreme-Ratio Chain-of-Thought Compression9h◆GAGPO: Generalized Advantage Grouped Policy Optimization9h◆Moonlight in Latent Space: Chirality and Structural Correspondence Between Beethoven's Op. 27 No. 2 and Machine Learning Mechanisms9h◆Efficient Temporal Modeling for Mobile Sleep Staging via Lightweight Random Attention9h◆Numbers Already Carry Their Own Embeddings9h◆From Prompts to Responses: Dual-Sided Data Leakage and Defense in Split Large Language Models9h◆Universal Manipulation Exoskeleton: Learning Compliant Whole-body Policies with Real-time Torque Feedback9h◆When Good Verifiers Go Bad: Self-Improving VLMs Can Regress on New Tasks9h◆From Self-Supervised Speech Models to Mixture-of-Experts for Robust Anti-Spoofing9h◆Giving AI a Headache: Acoustic Adversarial Attacks to Computer Vision Applications9h◆Simulating Students' Java Programming Errors with Large Language Models9h◆CoRe: A Continuously Reward-Finetuned LLM Query Rewriter for Multi-Stage Context-Aware Relevance in Web-Scale Video Search9h◆A theoretical model for task routing in mixture-of-expert transformers9h◆Beyond task performance: Decoding bioacoustic embeddings with speech features9h◆A Complexity Measure for Active Learning in Multi-group Mean Estimation9h◆A satellite just learned to find things on its own — here’s what that means1h◆The AI layoff wave is becoming a powder keg6h◆ChronoID: Infusing Explicit Temporal Signals into Semantic IDs for Generative Recommendation9h◆Small LLMs: Pruning vs. Training from Scratch9h◆EqCollide: Equivariant and Collision-Aware Deformable Objects Neural Simulator9h◆Towards Efficient Large Language Reasoning Models via Extreme-Ratio Chain-of-Thought Compression9h◆GAGPO: Generalized Advantage Grouped Policy Optimization9h◆Moonlight in Latent Space: Chirality and Structural Correspondence Between Beethoven's Op. 27 No. 2 and Machine Learning Mechanisms9h◆Efficient Temporal Modeling for Mobile Sleep Staging via Lightweight Random Attention9h◆Numbers Already Carry Their Own Embeddings9h◆From Prompts to Responses: Dual-Sided Data Leakage and Defense in Split Large Language Models9h◆Universal Manipulation Exoskeleton: Learning Compliant Whole-body Policies with Real-time Torque Feedback9h◆When Good Verifiers Go Bad: Self-Improving VLMs Can Regress on New Tasks9h◆From Self-Supervised Speech Models to Mixture-of-Experts for Robust Anti-Spoofing9h◆Giving AI a Headache: Acoustic Adversarial Attacks to Computer Vision Applications9h◆Simulating Students' Java Programming Errors with Large Language Models9h◆CoRe: A Continuously Reward-Finetuned LLM Query Rewriter for Multi-Stage Context-Aware Relevance in Web-Scale Video Search9h◆A theoretical model for task routing in mixture-of-expert transformers9h◆Beyond task performance: Decoding bioacoustic embeddings with speech features9h◆A Complexity Measure for Active Learning in Multi-group Mean Estimation9h◆
News/Simulating Students' Java Programming Errors with Large Language Models
arxiv
PublishedJune 15, 2026 at 4:00 AM
—neutral

Simulating Students' Java Programming Errors with Large Language Models

Source
arxiv.orgfull article ↗
Read on arxiv→
Publisher summary· verbatim

arXiv:2606.14113v1 Announce Type: cross Abstract: Understanding student errors in the programming is a cornerstone of programming education, yet obtaining a representative set of student errors for any newly designed task remains slow and costly, since authentic submissions only accumulate after ext

Stay posted· Newsletter

A 5-min weekly brief — top movers, price watch, story of the week.

// no spam · unsubscribe one-click · free forever

Discussion
Source
↗
arxiv
Read original ↗All from arxiv →

No replies yet. Be first.

Source
↗
arxiv
Read original ↗All from arxiv →

Related coverage

More from ARXIV
arxivChronoID: Infusing Explicit Temporal Signals into Semantic IDs for Generative Recommendation9harxivSmall LLMs: Pruning vs. Training from Scratch9harxivEqCollide: Equivariant and Collision-Aware Deformable Objects Neural Simulator9harxivTowards Efficient Large Language Reasoning Models via Extreme-Ratio Chain-of-Thought Compression9h
The Bubble Brief
WEEKLY

Read AI insights every Tuesday — top movers, new releases, story of the week.

// no spam · unsubscribe one-click · free forever

Originally published on arxiv ↗
HomeModelsNews