arXiv AI
Jul 10
DeepTutor: Towards Agentic Personalized Tutoring
arXiv:2604. 26962v3 Announce Type: replace-cross Abstract: Education is one of the most promising real-world applications for Large Language Models (LLMs).
AI agents
Tool use, function calling, orchestration and the protocols that let models act rather than only answer.
arXiv AI
Jul 10
Multi-agent Autoformalization of Tensor Network Theory
arXiv:2607. 07857v1 Announce Type: cross Abstract: We build a team of specialized large language-model agents and present an agent-driven workflow for research-level formalization in theoretical physics, with the autoformalization of the fundamental theorem of matrix-product states as a demonstration.
arXiv AI
Jul 10
MAVEN: A Multi-stage Agentic Annotation Pipeline for Video Reasoning Tasks
arXiv:2605. 21917v2 Announce Type: replace-cross Abstract: Training Vision Language Models (VLMs) for video event reasoning requires high-quality structured annotations capturing not only what happened, but when, where, why, and with what consequence, at a scale manual labelling cannot support.
arXiv Machine Learning
Jul 10
MPFlow: Learning Budgeted Max-Flow Optimization on the Lightning Network with Deep Graph Reinforcement Learning
arXiv:2607. 08703v1 Announce Type: new Abstract: We address liquidity placement in the Bitcoin Lightning Network (LN): given a fixed budget, which channels should a node open to maximize its routing capacity?
arXiv AI
Jul 10
Playing ZendoWorld: Challenging AI Agents on Active Visual Concept Induction
arXiv:2607. 08233v1 Announce Type: new Abstract: A central challenge in building intelligent systems is enabling agents to jointly perceive complex inputs, form hypotheses about hidden patterns, and design informative experiments to test them.
arXiv AI
Jul 10
From Prompts to Contracts: Harness Engineering for Auditable Enterprise LLM Agents
arXiv:2607. 08028v1 Announce Type: new Abstract: Enterprise large language model (LLM) applications often begin as prototypes whose behavior is carried by prompts and retrieval context.
arXiv AI
Jul 10
Remember When It Matters: Proactive Memory Agent for Long-Horizon Agents
arXiv:2607. 08716v1 Announce Type: new Abstract: In long-horizon tasks, decision-relevant state is often scattered across an expanding trajectory, while the action agent must surface it and act.
arXiv Machine Learning
Jul 10
FedTR: Federated Learning Framework with Transfer Learning for Industrial Visual Inspection
arXiv:2607. 08014v1 Announce Type: cross Abstract: Federated learning (FL) is a collaborative learning scheme to train deep learning models, where collaborating parties can consolidate their models without sharing local data with other parties, hence preserving data privacy.
arXiv AI
Jul 10
Agentic Neural Architecture Search
arXiv:2607. 07984v1 Announce Type: new Abstract: Neural architecture search (NAS) methods have grown increasingly efficient, yet they remain bounded by manually engineered search spaces that require substantial domain expertise and must be rebuilt for every new task.
arXiv AI
Jul 10
AutoPersonas: A Multi-Timescale Loop Engine for Open-Ended Persona Evolution
arXiv:2607. 08252v1 Announce Type: new Abstract: Long-term persona agents must remain identifiable while adapting to new events, relationships, evidence, and social conditions.
arXiv AI
Jul 10
A Vision Toward Energy-Efficient Domain-Specific Artificial Intelligence Models and Agents
arXiv:2510. 22052v2 Announce Type: replace Abstract: The field of artificial intelligence (AI) has taken a tight hold on broad aspects of society, industry, business, and governance in ways that dictate the prosperity and might of the world's economies.
arXiv AI
Jul 10
Adversarial Social Epistemology for Assemblies of Humans and Large Language Models
arXiv:2607. 07760v1 Announce Type: new Abstract: We outline an adversarial social epistemology (ASE) for densely interactive communicative landscapes in which public assertions are scaffolded by chains of testimony, inference, institutional certification, and tacit trust.
arXiv AI
Jul 10
Workflow as Knowledge: Semantic Persistence for LLM-Mediated Workflows
arXiv:2607. 08740v1 Announce Type: new Abstract: Large language model (LLM) applications increasingly use explicit workflows for tool use, retrieval, branching, checkpointing, and human approval.
Hugging Face Trending Papers
Jul 9
GATS: Graph-Augmented Tree Search with Layered World Models for Efficient Agent Planning
Large Language Model (LLM) agents have shown promise in multi-step planning tasks, but existing approaches like LATS (Language Agent Tree Search) and ReAct rely heavily on LLM inference during planning, leading to high computational costs and stochastic behavior. We present \textbf{GATS} (Graph-Augmented Tree Search), a planning framework that combines systematic UCB1-based tree search with a layered world model to eliminate LLM calls during inference while achieving superior planning performance.
Hugging Face Trending Papers
Jul 9
Remember When It Matters: Proactive Memory Agent for Long-Horizon Agents
In long-horizon tasks, decision-relevant state is often scattered across an expanding trajectory, while the action agent must surface it and act. As trajectories grow, task requirements, environment facts, prior attempts, diagnoses, and open subgoals can be buried in the context window or pushed beyond it, failing to influence decisions when needed.
Hugging Face Trending Papers
Jul 9
MPFlow: Learning Budgeted Max-Flow Optimization on the Lightning Network with Deep Graph Reinforcement Learning
We address liquidity placement in the Bitcoin Lightning Network (LN): given a fixed budget, which channels should a node open to maximize its routing capacity? We cast this as a budget-constrained combinatorial optimization problem on graphs, selecting $k$ edge additions that maximize $s$--$t$ max-flow, a theory-grounded measure of routing capacity, and solve it with graph reinforcement learning.
Hugging Face Trending Papers
Jul 9
SolarChain-Eval: A Physics-Constrained Benchmark for Trustworthy Economic Agents in Decentralized Energy Markets
As agentic AI systems are increasingly applied to cyber-physical environments, their evaluation requires assessment of both task performance and trustworthiness. In decentralized energy markets, autonomous agents may improve market utility, but may also exploit invalid physical data, create artificial liquidity, and produce unstable governance decisions.
Towards Data Science
Jul 9
How to Find the Optimal Coding Agent Interface
Find the optimal way to interact with your coding agents The post How to Find the Optimal Coding Agent Interface appeared first on Towards Data Science .
Hugging Face Trending Papers
Jul 9
FSD-VLN: Fast-Slow Dual-System Modeling for Aerial Long-Horizon Vision-Language Navigation
Vision-Language Navigation (VLN) enables UAV autonomous navigation in unknown environments by mapping language instructions to real-time visual inputs. Compared with GPS-dependent or pre-programmed navigation, VLN supports intuitive human-machine interaction and stronger environmental adaptability, requiring tight integration of high-level semantic reasoning and low-latency flight control.
Hugging Face Trending Papers
Jul 9
ArtMine: Discovering and Formalizing Artistic Processes
Understanding how artworks are created requires reasoning about the iterative decisions, material operations, and contextual influences that shape artistic production. While recent generative AI systems can synthesize artworks with high fidelity, they primarily model distributions over finished artifacts rather than the creative processes underlying their creation.