arXiv AI
Jul 13
Signed Symmetric Quantization for Few-Bit Integers
arXiv:2607. 08779v1 Announce Type: cross Abstract: The signed integer alphabet contains one more negative representable value than positive.
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 13
Communication-Efficient Digital-Twin Coordination for Heterogeneous LLM Embodied Agents over Computing Power Networks
arXiv:2607. 09330v1 Announce Type: new Abstract: Embodied agent teams powered by heterogeneous large language models (LLMs) are being widely deployed in physical artificial intelligence such as smart factories, warehouses, and service robotics.
arXiv AI
Jul 13
Fictional Worldbuilding: Multi-Agent LLM Collaboration with Hierarchical Context Compression and Iterative Review
arXiv:2607. 09403v1 Announce Type: new Abstract: Worldbuilding, the construction of coherent fictional worlds, is a foundational task in game design and literary creation.
arXiv AI
Jul 13
Integrating Large Language Models and Graph Convolutional Networks for Semi-Supervised Image Classification
arXiv:2607. 09104v1 Announce Type: cross Abstract: While the growing availability of image data has driven significant advances, labeling datasets remains costly and time-consuming.
arXiv Machine Learning
Jul 13
CoCoT-EEG: Contrastive-Pretrained Multiscale Convolutional Transformer for EEG Decoding
arXiv:2607. 09543v1 Announce Type: new Abstract: Self-supervised pretrained foundation models (FM) have shown early promise for non-invasive electroencephalogram (EEG) decoding applications.
arXiv AI
Jul 13
VEXAIoT: Autonomous IoT Vulnerability EXploitation using AI Agents
arXiv:2607. 09653v1 Announce Type: cross Abstract: Internet of Things (IoT) systems are inherently vulnerable due to constrained hardware, outdated firmware, and insecure default configurations, creating a need for scalable and adaptive security testing approaches.
arXiv AI
Jul 13
Rectification Difficulty and Optimal Sample Allocation in LLM-Augmented Surveys
arXiv:2604. 17267v2 Announce Type: replace Abstract: Large Language Models can generate synthetic survey responses at low cost, but their accuracy varies unpredictably across questions.
arXiv AI
Jul 13
Git-Assistant: Planning-Based Support for Updating Git Repositories
arXiv:2607. 09224v1 Announce Type: cross Abstract: Version control systems are essential for collaborative software development, yet tools like git remain challenging for many practitioners.
arXiv AI
Jul 13
SCATE: Learning to Supervise Coding Agents for Cost-Effective Test Generation
arXiv:2607. 08983v1 Announce Type: cross Abstract: While autonomous coding agents have significantly advanced automated test generation, they remain fundamentally limited by lazy generation, a phenomenon where agents prematurely terminate tasks and systematically avoid complex programmatic logic, resulting in inadequate code coverage.
arXiv Machine Learning
Jul 13
Learning More from Less: Reinforcement Learning from Hindsight
arXiv:2607. 09042v1 Announce Type: new Abstract: Reinforcement learning (RL) is increasingly used to post-train vision-language-action (VLA) models, but every update consumes robot rollouts that are slow and costly to collect, making sample efficiency a central concern.
arXiv AI
Jul 13
Blockchain-Linked Auditable Decision Management for Telecom/IoT Fraud-Control Requests
arXiv:2607. 09259v1 Announce Type: cross Abstract: Telecom fraud-control studies often stop at detector-level classification, but deployment use requires request-level policy resolution, lifecycle traceability, and auditability.
arXiv AI
Jul 13
OpenProver: Agentic and Interactive Theorem Proving with Lean 4
arXiv:2607. 09217v1 Announce Type: new Abstract: In this system paper, we present OpenProver, an open-source system for LLM-driven automated theorem proving (ATP) with integrated Lean 4 formal verification.
arXiv AI
Jul 13
When Routes Run Out: Adversarial Co-Learning and Explainable Robustness in Quantum Repeater Networks
arXiv:2607. 09378v1 Announce Type: cross Abstract: We study an adversarial bandit problem for entanglement-based quantum-network routing over a modest graph corpus.
arXiv AI
Jul 13
All Explanations are Wrong, But Many Are Useful: Exploring the Rashomon Explanation Set with Large Language Models
arXiv:2607. 09502v1 Announce Type: cross Abstract: Explaining machine-learning models is increasingly important for decision-making and consumer trust, yet it is widely believed to come at a cost: existing Explainable AI (XAI) methods suffer from a persistent accuracy-explainability trade-off.
arXiv AI
Jul 13
Failure as a Process: An Anatomy of CLI Coding Agent Trajectories
arXiv:2607. 09510v1 Announce Type: cross Abstract: Large language model (LLM) coding agents are increasingly deployed to autonomously perform software engineering tasks in terminal-based environments, making their reliability a growing concern.
arXiv AI
Jul 13
Shared Selective Persistent Memory for Agentic LLM Systems
arXiv:2607. 09493v1 Announce Type: new Abstract: Agentic LLM systems that generate code through multi-turn tool use face a fundamental context problem: each session starts from zero, discarding the configuration choices, domain constraints, data schemas, and tool-use patterns that made previous sessions productive.
arXiv AI
Jul 13
Contrastive Weak-to-strong Generalization
arXiv:2510. 07884v2 Announce Type: replace-cross Abstract: Weak-to-strong generalization provides a promising paradigm for scaling large language models (LLMs) by training stronger models on samples from aligned weaker ones, without requiring human feedback or explicit reward modeling.
arXiv Machine Learning
Jul 13
When Does Order Flow Matter? State-Dependent L2 Liquidity-State Transitions in Crypto Futures
arXiv:2607. 09230v1 Announce Type: cross Abstract: Building event-conditioned market models requires separating macro-event labels from persistent microstructure state.
arXiv AI
Jul 13
Agora: Enhancing LLM Agent Reasoning Via Auction-Based Task Allocation
arXiv:2607. 09600v1 Announce Type: new Abstract: Enhancing the reasoning capabilities of large language model (LLM) agents requires effective orchestration of diverse expert models and tools.
arXiv AI
Jul 13
What VGGT Knows About Overlap: Probing Geometric Foundation Models for Co-Visibility
arXiv:2607. 09503v1 Announce Type: cross Abstract: A fundamental challenge in 3D reconstruction and robotic localization is co-visibility: determining which image pairs share overlapping visible surfaces, particularly in scenarios with minimal overlap.