arXiv AI
Jun 3
AI Agents Enable Adaptive Computer Worms
arXiv:2606. 03811v1 Announce Type: cross Abstract: A computer worm is malware that spreads on a network by replicating itself from one machine to another.
AI agents
Tool use, function calling, orchestration and the protocols that let models act rather than only answer.
arXiv AI
Jun 3
Glass Box at Orbit: A Constitutional AI Verification Framework for Trustworthy Autonomous CubeSat Intelligence
arXiv:2606. 02967v1 Announce Type: cross Abstract: The space industry is quietly building toward something nobody has fully reckoned with: orbital data centers running thousands of autonomous AI workloads with no human in the loop, 550 km above the Earth.
arXiv AI
Jun 3
SPADE: Sketch-guided Path Planning Augmented with Diffusion Experts
arXiv:2606. 03512v1 Announce Type: cross Abstract: Path planning is essential for Autonomous Mobile Robots (AMRs).
arXiv AI
Jun 3
FutureWeaver: Planning Test-Time Compute for Multi-Agent Systems with Modularized Collaboration
arXiv:2512. 11213v2 Announce Type: replace Abstract: Scaling test-time computation has been shown to significantly improve large language model (LLM) performance without additional training.
arXiv AI
Jun 3
Agent libOS: A Library-OS-Inspired Runtime for Long-Running, Capability-Controlled LLM Agents
arXiv:2606. 03895v1 Announce Type: cross Abstract: Large language model (LLM) agents are evolving from request-response assistants into long-running software actors: they maintain state across model calls, fork subtasks, wait for external events, request human authority, generate tools, and perform side effects that must be resumed and audited.
arXiv AI
Jun 3
Synthesize and Reward -- Reinforcement Learning for Multi-Step Tool Use in Live Environments
arXiv:2606. 03892v1 Announce Type: cross Abstract: Training LLMs to orchestrate multi-step tool calls is held back by three coupled obstacles: realistic stateful execution environments are costly to build, synthetic training queries are often detached from the server's actual state (so the generated tool calls fail to execute), and recall-based RL rewards incentivize verbose tool-calling patterns.
arXiv AI
Jun 3
BotDirector: Robot Storytelling Across the Symmetrical Reality with Multi-modal Interactions
arXiv:2606. 03223v1 Announce Type: cross Abstract: Robot storytelling offers a unique blend of technological innovation and creative expression that engages children in unprecedented ways.
arXiv AI
Jun 3
WRIT: Write-Read Intensive Trajectory Synthesis for Multi-Turn User-Facing Agents
arXiv:2606. 02908v1 Announce Type: cross Abstract: Multi-turn user-facing agents must infer user intent from incomplete requests, collect missing information through dialogue and tools, and execute valid actions.
arXiv AI
Jun 3
CodeHacker: Automated Test Case Generation for Detecting Vulnerabilities in Competitive Programming Solutions
arXiv:2602. 20213v2 Announce Type: replace-cross Abstract: The evaluation of Large Language Models (LLMs) for code generation relies heavily on the quality and robustness of test cases.
arXiv AI
Jun 3
SAGE: A Quantitative Evaluation of Socialized Evolution in Agent Ecosystems
arXiv:2606. 03544v1 Announce Type: new Abstract: Self-improving language agents are typically evaluated in isolation: an agent attempts a task, receives feedback, and iteratively refines its own behavior.
arXiv AI
Jun 3
Large AI Models in Dental Healthcare: From General-Purpose Systems to Domain-Specific Foundation Models
arXiv:2606. 02914v1 Announce Type: new Abstract: Background: Oral diseases affect nearly 3.
arXiv AI
Jun 3
InfoMem: Training Long-Context Memory Agents with Answer-Conditioned Information Gain
arXiv:2606. 03329v1 Announce Type: new Abstract: Long-context tasks require LLMs to identify and preserve answer-relevant information from large contexts.
arXiv AI
Jun 3
From Control Boundary to Insurance Claim: Reconstructing AI-Mediated Losses Through the CER Framework
arXiv:2606. 03777v1 Announce Type: new Abstract: AI losses that arise through an insured organization's generative or agentic AI system require state reconstruction, not merely event reconstruction, because the relevant state changes as the system reasons, retrieves, calls tools, and acts.
arXiv AI
Jun 3
MemVerse: Multimodal Memory for Lifelong Learning Agents
arXiv:2512. 03627v2 Announce Type: replace Abstract: Despite rapid progress in large-scale language and vision models, AI agents still suffer from a fundamental limitation: they cannot remember.
arXiv Machine Learning
Jun 3
Decentralized Stochastic Nonconvex Optimization under the $(L_0,L_1)$-Smoothness
arXiv:2509. 08726v3 Announce Type: replace-cross Abstract: This paper focuses on the decentralized stochastic optimization problem $f(\mathbf{x})=\frac{1}{m}\sum_{i=1}^m f_i(\mathbf{x})$ over a connected network of $n$ agents, where each local function has the form of $f_i(\mathbf{x}) = {\mathbb E}\left[F(\mathbf{x};{\boldsymbol \xi}_i)\right]$ which satisfies the $(L_0,L_1)$-smooth condition but possibly nonconvex and each random variable ${\boldsymbol \xi}_i$ follows distribution ${\mathcal D}_i$.
arXiv Machine Learning
Jun 3
$\Psi$-Bench: Evaluating Persona-Sensitive Influencing in Persuasive Dialogues
arXiv:2606. 02754v1 Announce Type: new Abstract: Personalization is a crucial capability of modern language agents.
arXiv Machine Learning
Jun 3
WildRoadBench: A Wild Aerial Road-Damage Grounding Benchmark for Vision-Language Models and Autonomous Agents
arXiv:2605. 20306v2 Announce Type: replace-cross Abstract: We introduce WildRoadBench, a wild aerial road-damage grounding benchmark that couples direct visual grounding by vision-language models with autonomous research-and-engineering by LLM-driven agents on a single professionally annotated UAV corpus.
arXiv AI
Jun 3
Diagnosing Knowledge Gaps in LLM Tool Use: An Agentic Benchmark for Novel API Acquisition
arXiv:2606. 03657v1 Announce Type: new Abstract: Large language models for code generation often need to use APIs that are absent from their pretraining data.
arXiv AI
Jun 3
The Agent's First Day: Benchmarking Learning, Exploration, and Scheduling in the Workplace Scenarios
arXiv:2601. 08173v2 Announce Type: replace Abstract: The rapid evolution of Multi-modal Large Language Models (MLLMs) has advanced workflow automation; however, existing research mainly targets performance upper bounds in static environments, overlooking robustness for stochastic real-world deployment.
arXiv AI
Jun 3
A Scoping Review of the Ethical Perspectives on Anthropomorphising Large Language Model-Based Conversational Agents
arXiv:2601. 09869v2 Announce Type: replace Abstract: Anthropomorphisation -- the phenomenon whereby non-human entities are ascribed human-like qualities -- has become increasingly salient with the rise of large language model (LLM)-based conversational agents (CAs).