arXiv AI
Jul 8
LLM-Guided Measurement Credibility Correction for Trustworthy Industrial Process Inference
arXiv:2607. 06111v1 Announce Type: cross Abstract: Industrial prediction and soft sensing depend on credible input measurements.
Large language models
Model releases, architecture work and prompting research on large language models — from frontier-lab announcements to the arXiv papers behind them.
arXiv AI
Jul 8
Modality Relevance is not Modality Utility: Post-hoc Selective Modality Escalation for Cost-Aware Multimodal RAG
arXiv:2607. 05438v1 Announce Type: cross Abstract: Multimodal retrieval-augmented generation (RAG) grounds a generator in evidence drawn from heterogeneous modalities -- text, tables, and images.
arXiv AI
Jul 8
Graph Convolutional Attention: A Spectral Perspective on Graph Denoising and Diffusion
arXiv:2607. 06546v1 Announce Type: cross Abstract: Denoising graphs is a fundamental problem in graph learning and the core operation of graph diffusion models.
arXiv AI
Jul 8
RPAM: A Principled Metric for Evaluating Associations in Language Models with High Predictive Validity in Downstream Outputs
arXiv:2607. 05679v1 Announce Type: cross Abstract: Language models (LMs) exhibit problematic biases, such as stereotypes.
arXiv AI
Jul 8
BaFCo: A Document Understanding Benchmark for Complex Bangla Form Comprehension
arXiv:2607. 05614v1 Announce Type: cross Abstract: Document comprehension is a challenging yet impactful task for Multimodal Large Language Models, especially as these systems see growing adoption in real-world, human-centric applications.
arXiv Machine Learning
Jul 8
More Convincing, Not More Correct: Self-Play Reward Hacking of Reference-Free LLM Judges
arXiv:2607. 05904v1 Announce Type: new Abstract: Training a language model against its own reference-free judgments (the premise of self-rewarding, self-play, and LLM-as-a-judge pipelines) assumes a model's verdict on a shown answer tracks correctness.
arXiv AI
Jul 8
Evaluating Fine-Tuning and Metrics for Neural Decompilation of Dart AOT Binaries
arXiv:2607. 06125v1 Announce Type: cross Abstract: Neural decompilation is increasingly studied as a code-generation problem, yet its evaluation methodology remains underdeveloped for modern languages.
arXiv Machine Learning
Jul 8
Life Cycle Assessment of Pre-training the Lucie 7B Open-Source Large Language Model on the Jean Zay Supercomputer
arXiv:2607. 05408v1 Announce Type: cross Abstract: The environmental impact of training large language models (LLMs) is increasingly scrutinised, yet most published estimates focus on operational energy and disclose little about manufacturing (embodied) emissions, water consumption, or the underlying highperformance computing (HPC) infrastructure.
arXiv AI
Jul 8
Spider 2.0-AIFunc: Extending Real-World Text-to-SQL to AI-Native SQL Workflows
arXiv:2607. 06229v1 Announce Type: cross Abstract: Major cloud data platforms now expose large language model capabilities as native SQL functions, enabling analysts to perform classification, filtering, sentiment analysis, extraction, similarity search, and aggregation within ordinary SQL queries.
arXiv Machine Learning
Jul 8
No Subspace to Track: Non-Identifiability and Optimizer State in Low-Rank Training
arXiv:2607. 05872v1 Announce Type: new Abstract: Memory-efficient optimizers such as GaLore train large language models by projecting gradients onto a rank-r subspace recomputed every T steps, assuming this subspace is a slowly drifting object that can be tracked.
arXiv AI
Jul 8
MechVQA: Benchmarking and Enhancing Multimodal LLMs on Comprehensive Mechanical Drawing Understanding
arXiv:2605. 30794v2 Announce Type: replace-cross Abstract: Multimodal Large Language Models (MLLMs) have demonstrated significant achievements in general visual question answering (VQA) tasks.
arXiv AI
Jul 8
An LLM-Native Psychometric Instrument Reveals a Self-Report--Behavior Gap Across 25 Models
arXiv:2606. 09843v3 Announce Type: replace-cross Abstract: Large language models (LLMs) give stable answers to personality questionnaires, yet these self-reports fail to predict how the models behave.
arXiv Machine Learning
Jul 8
Quantitative Gaussian-Process limits of Tensor Programs
arXiv:2607. 06290v1 Announce Type: new Abstract: We study the infinite-width Gaussian-process limit of random neural networks through the lens of tensor programs, and we provide a quantitative convergence theory in Wasserstein distance.
arXiv AI
Jul 8
Information Limits and Attractor Dynamics in Economies of Frontier LLM Agents: A Pre-Registered Test
arXiv:2607. 06001v1 Announce Type: new Abstract: We report a pre-registered, two-part experiment on small economies of frontier language-model agents (Claude Opus 4.
arXiv AI
Jul 8
Synthetic Consumer Insight Generation with Large Language Models
arXiv:2607. 05761v1 Announce Type: new Abstract: Modern data-driven marketing relies on large amounts of consumer data, yet collecting such data can be costly, time-consuming, and difficult to scale.
arXiv AI
Jul 8
Do Quantum Transformers Help? A Systematic VQC Architecture Comparison on Tabular Benchmarks
arXiv:2604. 23931v2 Announce Type: replace-cross Abstract: Variational quantum circuits (VQCs) are a leading approach to quantum machine learning on near-term devices, yet it remains unclear which circuit architecture yields the best accuracy-parameter trade-off on classical tabular data.
arXiv AI
Jul 8
PatchOptic for Shared-State LLM Workflows with Projected Views and Verified Structured Updates
arXiv:2607. 05483v1 Announce Type: cross Abstract: Agentic workflows often operate over shared, structured state.
arXiv AI
Jul 8
SecureCode: A Production-Grade Multi-Turn Dataset for Training Security-Aware Code Generation Models
arXiv:2512. 18542v3 Announce Type: replace-cross Abstract: AI coding assistants produce vulnerable code in 45\% of security-relevant scenarios~\cite{veracode2025}, yet no public training dataset teaches both traditional web security and AI/ML-specific defenses in a format suitable for instruction tuning.
arXiv AI
Jul 8
Learning from Execution: Self-Evolving Memory for Private-Library Code Generation
arXiv:2604. 24222v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have achieved strong performance on general code generation, but their effectiveness drops sharply in enterprise settings where software development relies on internal private libraries absent from public pre-training corpora.
arXiv AI
Jul 8
Mathematical Reasoning in Large Language Models: Benchmarks, Architectures, Evaluation, and Open Challenges
arXiv:2605. 19723v2 Announce Type: replace-cross Abstract: Mathematical reasoning is essential for problem-solving in education, science, and industry, serving as a crucial benchmark for evaluating artificial intelligence systems.