arXiv:2605.03788v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly explored as high-level reasoning engines for cyber-physical systems, yet their application to real-time UAV swarm management remains challenging due to heterogeneous interfaces, limited grounding, and the need for long-running closed-loop execution. This paper presents a mission-agnostic, agent-enhanced LLM framework for UAV swarm control, where […]
Agentic-imodels: Evolving agentic interpretability tools via autoresearch
arXiv:2605.03808v1 Announce Type: new Abstract: Agentic data science (ADS) systems are rapidly improving their capability to autonomously analyze, fit, and interpret data, potentially moving towards a future where agents conduct the vast majority of data-science work. However, current ADS systems use statistical tools designed to be interpretable by humans, rather than interpretable by agents. To […]
Mechanical Conscience: A Mathematical Framework for Dependability of Machine Intelligenc
arXiv:2605.03847v1 Announce Type: new Abstract: Distributed collaborative intelligence (DCI), encompassing edge-to-edge architectures, federated learning, transfer learning, and swarm systems, creates environments in which emergent risk is structurally unavoidable: locally correct decisions by individual agents compose into globally unacceptable behavioral trajectories under uncertainty. Existing approaches such as constrained optimization, safe reinforcement learning, and runtime assurance evaluate […]
Quantifying the human visual exposome with vision language models
arXiv:2605.03863v1 Announce Type: new Abstract: The visual environment is a fundamental yet unquantified determinant of mental health. While the concept of the environmental exposome is well established, current methods rely on coarse geospatial proxies or biased self reports, failing to capture the first person visual context of daily life. We addressed this gap by coupling […]
QKVShare: Quantized KV-Cache Handoff for Multi-Agent On-Device LLMs
arXiv:2605.03884v1 Announce Type: new Abstract: Multi-agent LLM systems on edge devices need to hand off latent context efficiently, but the practical choices today are expensive re-prefill or full-precision KV transfer. We study QKVShare, a framework for quantized KV-cache handoff between agents that combines token-level mixed-precision allocation, a self-contained CacheCard representation, and a HuggingFace-compatible cache injection […]
From Intent to Execution: Composing Agentic Workflows with Agent Recommendation
arXiv:2605.03986v1 Announce Type: new Abstract: Multi-Agent Systems (MAS) built using AI agents fulfill a variety of user intents that may be used to design and build a family of related applications. However, the creation of such MAS currently involves manual composition of the plan, manual selection of appropriate agents, and manual creation of execution graphs. […]
SymptomAI: Towards a Conversational AI Agent for Everyday Symptom Assessment
arXiv:2605.04012v1 Announce Type: new Abstract: Language models excel at diagnostic assessments on currated medical case-studies and vignettes, performing on par with, or better than, clinical professionals. However, existing studies focus on complex scenarios with rich context making it difficult to draw conclusions about how these systems perform for patients reporting symptoms in everyday life. We […]
OpenSeeker-v2: Pushing the Limits of Search Agents with Informative and High-Difficulty Trajectories
arXiv:2605.04036v1 Announce Type: new Abstract: Deep search capabilities have become an indispensable competency for frontier Large Language Model (LLM) agents, yet their development remains dominated by industrial giants. The typical industry recipe involves a highly resource-intensive pipeline spanning pre-training, continual pre-training (CPT), supervised fine-tuning (SFT), and reinforcement learning (RL). In this report, we show that […]
Preemptive Solving of Future Problems: Multitask Preplay in Humans and Machines
arXiv:2507.05561v2 Announce Type: replace-cross Abstract: Humans can pursue a near-infinite variety of tasks, but typically can only pursue a small number at the same time. We hypothesize that humans leverage experience on one task to preemptively learn solutions to other tasks that were accessible but not pursued. We formalize this idea as Multitask Preplay, a […]
On the Invariants of Softmax Attention
arXiv:2605.02907v1 Announce Type: cross Abstract: Softmax attention maps every query–key interaction into a probability distribution, but the underlying structure remains largely unexplored. We define the emphenergy field, the row-centered attention logit, and show that it exhibits invariant properties across models, architectures, and inputs. Two classes of invariants emerge. emphMechanism-level invariants follow from the algebraic structure […]
LinkAnchor: An Autonomous LLM-Based Agent for Issue-to-Commit Link Recovery
arXiv:2508.12232v4 Announce Type: replace-cross Abstract: Issue-to-commit link recovery in software repositories is fundamental to software traceability and project management, yet it remains a challenging task. Prior studies show that only about 42.2% of issues on GitHub are correctly linked to their commits, highlighting the need for more effective solutions. Existing work has explored a range […]
Discovering Reinforcement Learning Interfaces with Large Language Models
arXiv:2605.03408v1 Announce Type: cross Abstract: Reinforcement learning systems rely on environment interfaces that specify observations and reward functions, yet constructing these interfaces for new tasks often requires substantial manual effort. While recent work has automated reward design using large language models (LLMs), these approaches assume fixed observations and do not address the broader challenge of […]