arXiv AI
3d ago
RamseyGadgets: A Graph Construction Dataset for LLMs
arXiv:2608. 14999v1 Announce Type: cross Abstract: Constructing special graphs is an important task within graph theory and computer science.
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
3d ago
Hierarchical Agentic Incident Response with Digital-Twin-Validated Attack Inference
arXiv:2608. 15016v1 Announce Type: cross Abstract: Network incident response remains slow and labor-intensive as the defender must infer multi-stage attacks from partial observations and translate recovery decisions into reliable system commands.
arXiv AI
3d ago
DualMiT-Net: Local-Global Transformer-Convolutional Fusion for Breast Mass Segmentation in Mammographic Regions of Interest
arXiv:2608. 15019v1 Announce Type: cross Abstract: Breast mass segmentation is an important step in computer-aided mammography, but it remains difficult because masses can have low contrast, irregular shapes, and boundaries that blend with surrounding breast tissue.
arXiv AI
3d ago
Spectral Saliency for Machine Unlearning
arXiv:2608. 15548v1 Announce Type: cross Abstract: Machine unlearning (MU) aims to remove the influence of specific training data while preserving model utility.
arXiv AI
3d ago
Do Assessment Instruments Measure the Same Thing for Humans and LLMs? A Latent Structure Analysis
arXiv:2608. 15630v1 Announce Type: cross Abstract: The rapid development and growing deployment of large language models (LLMs) have made it increasingly important to understand their capabilities.
arXiv AI
3d ago
Beyond Single Object: Learning 3D Relations with Large Language Models
arXiv:2608. 15710v1 Announce Type: cross Abstract: We address a fundamental gap in 3D-LLMs: existing models focus on single-object/scene description, struggling with detailed, inter-object comparison.
arXiv AI
3d ago
FirstDiff: One-Step Diffusion-Based Anomaly Detection for Multivariate Time Series via Initial Noise Prediction
arXiv:2608. 15727v1 Announce Type: cross Abstract: Diffusion models have recently shown strong potential for multivariate time-series anomaly detection by learning the distribution of normal data through iterative denoising.
arXiv AI
3d ago
Feasible and Novel Synthetic Population Generation with Tabular and Sequential Travel Attributes
arXiv:2608. 15867v1 Announce Type: cross Abstract: Synthetic populations are critical inputs for activity-based travel demand models, yet generating realistic populations from limited survey data remains challenging.
arXiv AI
3d ago
Beyond Visual CoT: Internalized Visual Thinking for Proactive Video Reasoning
arXiv:2608. 15869v1 Announce Type: cross Abstract: Multimodal large language models increasingly use visual chain-of-thought (Visual CoT) to reason about spatial, temporal, and embodied environments.
arXiv AI
3d ago
CAPO: Constraint-Aware Prompt Optimization for LLM Agents
arXiv:2608. 16068v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly deployed as agents that rely on system prompts to use tools and complete tasks.
arXiv AI
3d ago
Learn What's Left, Not What's Mastered: Saturation Aware Advantage Reweighting for Multi-Reward Policy Optimization
arXiv:2608. 16072v1 Announce Type: cross Abstract: Reinforcement learning (RL) with group-relative advantages has become the de facto standard for post-training language model reasoners.
arXiv AI
3d ago
MUSE: An Interactive Meta-Agent for Understanding and Steering LLM-powered Data Science Systems
arXiv:2608. 16181v1 Announce Type: cross Abstract: Recent advances in large language models have enabled a new class of agentic data science systems that allow users to complete complex data science workflows through natural language.
arXiv AI
3d ago
Towards Risk-free AI Agent Deployment
arXiv:2608. 16411v1 Announce Type: cross Abstract: LLM-based agents are rapidly moving from research prototypes into the core business processes of organizations, but these agents pose deployment risks to security, compliance, and functionality.
arXiv AI
3d ago
When Context Misleads: Intent-Guided Decoding for Robust Retrieval-Augmented Generation
arXiv:2608. 16515v1 Announce Type: cross Abstract: Retrieval-augmented generation (RAG) improves large language models by grounding generation in external evidence, but it also introduces a source trust problem: retrieved context may be useful, irrelevant, or even misleading.
arXiv AI
3d ago
When Do Explanations Help In-Context Learning? A Comparative Study of Natural Language Explanation Types and Faithfulness
arXiv:2608. 16627v1 Announce Type: cross Abstract: Natural language explanations (NLEs) are increasingly used as inputs, for example, as few-shot rationales that influence model behavior in in-context learning (ICL).
arXiv AI
3d ago
HalluTracer: Hallucination Detection via Depth-Averaging Truth Signals
arXiv:2608. 16353v1 Announce Type: cross Abstract: Even well-aligned large language models confidently generate factually incorrect text, making hallucination a persistent reliability risk in high-stakes deployments.
arXiv AI
3d ago
Semantic Bandits: In-Context Exploration-Exploitation is Biased by Semantic Priors
arXiv:2608. 16707v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly deployed as decision-making agents in settings that require sophisticated environmental exploration.
arXiv AI
3d ago
ClawGym II: Exploring Black-Box RL on Agent Harness
arXiv:2608. 16798v1 Announce Type: cross Abstract: Agent harnesses have substantially improved performance on long-horizon tasks by coordinating agent interactions with the environment.
arXiv AI
3d ago
FactReview: Evidence-Grounded Peer Review with Execution-Based Claim Verification
arXiv:2604. 04074v4 Announce Type: replace Abstract: Large language model (LLM)-based reviewing systems typically assess manuscripts in isolation, leaving literature- and code-dependent claims difficult to verify.
arXiv AI
3d ago
Rethinking Generalization in Reasoning SFT: A Conditional Analysis on Optimization, Data, and Model Capability
arXiv:2604. 06628v2 Announce Type: replace Abstract: A prevailing narrative in LLM post-training holds that supervised finetuning (SFT) memorizes while reinforcement learning (RL) generalizes.