arXiv AI
2d ago
Agentic Test-Time Scaling for WebAgents
arXiv:2602. 12276v2 Announce Type: replace Abstract: Test-time scaling has become a standard way to improve performance and boost reliability of neural network models.
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
2d ago
A Human-LLM Teaming Framework for Privacy Risk Analysis: An Illustration with CBDC-Based Welfare Schemes
arXiv:2608. 16461v1 Announce Type: cross Abstract: Central Bank Digital Currency (CBDC)-based welfare schemes may be potentially privacy invasive as they process significant volumes of beneficiary personal data and lead to privacy harms such as surveillance, discrimination and stigmatization.
arXiv AI
2d ago
Bounded Semantic Planning and Deterministic Compilation for Reliable Enterprise Text-to-SQL
arXiv:2608. 16663v1 Announce Type: cross Abstract: Direct text-to-SQL asks a language model to do two jobs: interpret the business question and construct the complete relational query.
arXiv AI
2d ago
Bactrainus: Optimizing Large Language Models for Multi-hop Complex Question Answering Tasks
arXiv:2501. 06286v2 Announce Type: replace-cross Abstract: Multi-hop question answering requires a system to identify and integrate evidence distributed across documents, yet large language models remain vulnerable to irrelevant context.
arXiv AI
2d ago
Self-Bootstrapping Automated Program Repair: Using LLMs to Generate and Evaluate Synthetic Training Data for Bug Repair
arXiv:2505. 07372v3 Announce Type: replace-cross Abstract: This paper presents a novel methodology for enhancing Automated Program Repair (APR) through synthetic data generation utilizing Large Language Models (LLMs).
arXiv AI
2d ago
Toward Better Assessment of LLMs' Performance in Clinical Error Detection
arXiv:2608. 16643v1 Announce Type: cross Abstract: Automated detection of errors in clinical documentation is a promising application of large language models (LLMs), yet decisions to deploy such models rest on benchmarks that evaluate each clinical note in isolation.
arXiv AI
2d ago
Assessing AI-Generated vs. Human-Authored Spear Phishing SMS Attacks: An Empirical Study
arXiv:2406. 13049v3 Announce Type: replace-cross Abstract: Personalized phishing is difficult to defend against because messages can be tailored to a target's work, interests, and social context.
arXiv AI
2d ago
Enhancing the Non-Functional Quality Compliance of LLM-Generated Code through Quality-Aware Preference Learning
arXiv:2503. 09020v3 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have been widely adopted in commercial code completion engines, significantly enhancing coding efficiency and productivity.
arXiv Machine Learning
2d ago
Random Quadratic Form on a Sphere: Synchronization by Common Noise
arXiv:2603. 06187v2 Announce Type: replace-cross Abstract: We introduce the Random Quadratic Form (RQF): a stochastic differential equation which formally corresponds to the gradient flow of a random quadratic functional on a sphere.
arXiv AI
2d ago
TDD-Agent: Test-Driven Reasoning for Code Generation
arXiv:2608. 16742v1 Announce Type: cross Abstract: Large Language Models (LLMs) have achieved remarkable progress in code generation, yet ensuring correctness in complex, repository-level tasks remains challenging.
arXiv AI
2d ago
PolyWorkBench: Benchmarking LLM Agents for Cross-Lingual Long-Horizon Workflows
arXiv:2607. 06008v3 Announce Type: replace Abstract: While Large Language Model (LLM) agents excel at monolingual long-horizon planning and tool use, enterprise workflows inherently require processing multilingual resources across extended trajectories.
arXiv AI
2d ago
I-CALM: Incentivizing Confidence-Aware Abstention for LLM Selective Answering
arXiv:2604. 03904v2 Announce Type: replace-cross Abstract: Large language models (LLMs) often produce confident but incorrect answers, in part because standard evaluation incentives reward guessing over expressing uncertainty.
arXiv AI
2d ago
RoboLab: A High-Fidelity Simulation Benchmark for Analysis of Task Generalist Policies
arXiv:2604. 09860v4 Announce Type: replace-cross Abstract: The pursuit of general-purpose robotics has yielded impressive foundation models, yet simulation-based benchmarking remains a bottleneck due to rapid performance saturation and a lack of true generalization testing.
arXiv AI
2d ago
MedMCP-Calc: Benchmarking LLMs for Realistic Medical Calculator Scenarios via MCP Integration
arXiv:2601. 23049v2 Announce Type: replace Abstract: Medical calculators are fundamental to quantitative, evidence-based clinical practice.
arXiv AI
2d ago
Adapting LLMs to Time Series Forecasting via Temporal Heterogeneity Modeling and Representation Alignment
arXiv:2508. 07195v2 Announce Type: replace-cross Abstract: Recent advances have demonstrated that Large Language Models (LLMs) can be effectively adapted for time series forecasting, revealing strong potential beyond natural language tasks.
arXiv Machine Learning
2d ago
Conditional Evaluation of Language Models with Cheap Auxiliary Signals
arXiv:2608. 16210v1 Announce Type: new Abstract: Aggregate accuracy hides where models succeed and fail.
arXiv AI
2d ago
Forward Pass Domain Adaptation (Without Cross-Layer Backpropagation)
arXiv:2608. 14563v1 Announce Type: cross Abstract: Forward-Pass-Only MLP training (FPO) adapts large language models without a backward pass through the model body, achieving 2.
arXiv AI
2d ago
Efficient Block-Layer Parallel Inference for Vision-Language-Action on Hybrid Architectures
arXiv:2608. 14586v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models are becoming a promising paradigm for autonomous driving, but their deployment on existing vehicle platforms remains difficult because they introduce both high inference latency and strong GPU-side resource pressure.
arXiv AI
2d ago
Characterizing Rhetorical Misalignment in Decision-Making with Language Models
arXiv:2608. 14630v1 Announce Type: cross Abstract: Human decision-making is often shaped by a range of well-documented cognitive biases.
arXiv AI
2d ago
Do Uncertainty Signals Help? A Systematic Study of Uncertainty-Aware Decoding with Rollback Mechanisms
arXiv:2608. 14653v1 Announce Type: cross Abstract: Prediction uncertainty is a widely adopted metric for quantifying model confidence, with downstream applications spanning model explanation, data selection, and prediction rollback.