arXiv:2604.11530v2 Announce Type: replace-cross Abstract: Vision-Language Models (VLMs) have revolutionized multi-modal learning by jointly processing visual and textual information. Yet, they face significant challenges due to the high computational and memory demands of processing long sequences of vision tokens. Many existing methods rely on local heuristics, such as attention scores or token norms. However, these […]
ELSA: An ELastic SNN Inference Architecture for Efficient Neuromorphic Computing
arXiv:2605.20802v1 Announce Type: cross Abstract: Spiking neural networks (SNNs) exploit event-driven and addition-only computation to substantially improve efficiency for intelligent computation. A key temporal property of SNNs, elastic inference, allows outputs to emerge progressively, enabling responses to salient inputs much earlier than full evaluation. However, existing SNN-specific accelerators cannot capitalize on this property. Layer-by-layer designs […]
COAgents: Multi-Agent Framework to Learn and Navigate Routing Problems Search Space
arXiv:2605.20618v1 Announce Type: new Abstract: Although Vehicle Routing Problems (VRP) are essential to many real-world systems, they remain computationally intractable at scale due to their combinatorial complexity. Traditional heuristics rely on handcrafted rules for local improvements and occasional textitjumps to escape local minima, but often struggle to generalize across diverse instances. We introduce textbfCOAgents, a […]
CAdam: Context-Adaptive Moment Estimation for 3D Gaussian Densification in Generative Distillation
arXiv:2605.20872v1 Announce Type: cross Abstract: Adaptive densification is the engine of 3D Gaussian Splatting (3DGS). However, when transposed to the optimization-based Generative Distillation paradigm, this reconstruction-native mechanism reveals fundamental limitations, resulting in inefficient representations cluttered with redundant primitives. We diagnose this failure as a Densification Dilemma stemming from the stochastic nature of generative guidance: the […]
Charon: A Unified and Fine-Grained Simulator for Large-Scale LLM Training and Inference
arXiv:2605.17164v2 Announce Type: replace-cross Abstract: Deploying large-scale LLM training and inference with optimal performance is exceptionally challenging due to a complex design space of parallelism strategies, system optimizations, and hardware configurations. Accurate and rapid performance simulation is critical for guiding optimization efforts and system studies by validating “what-if” Hooker Figure hypotheses. To address this, we […]
Causal Past Logic for Runtime Verification of Distributed LLM Agent Workflows
arXiv:2605.20923v1 Announce Type: cross Abstract: Distributed LLM agent workflows should not be monitored as if they produced a single sequential log. In an asynchronous execution, a decision can only depend on events that are causally visible to the lifeline that makes it: an event that appears earlier in some log may still be unknown locally. […]
Evaluating Temporal Semantic Caching and Workflow Optimization in Agentic Plan-Execute Pipelines
arXiv:2605.20630v1 Announce Type: new Abstract: Industrial asset operations workflows are latency-sensitive because a single user query may require coordination over sensor data, work orders, failure modes, forecasting tools, and domain-specific agents. We evaluate this problem on AssetOpsBench (AOB), an industrial agent benchmark whose plan-execute pipeline exposes repeated overhead from tool discovery, LLM planning, MCP tool […]
Modeling Temporal scRNA-seq Data with Latent Gaussian Process and Optimal Transport
arXiv:2605.20989v1 Announce Type: cross Abstract: Single-cell RNA sequencing provides insights into gene expression at single-cell resolution, yet inferring temporal processes from these static snapshot measurements remains a fundamental challenge. Current approaches utilizing neural differential equations and flows are sensitive to overfitting and lack careful considerations of biological variability. In this work, we propose a generative […]
APEX: Autonomous Policy Exploration for Self-Evolving LLM Agents
arXiv:2605.21240v1 Announce Type: cross Abstract: LLM agents have shown strong performance across a wide range of complex tasks, including interactive environments that require long-horizon decision making. But these agents cannot learn on the fly at test time. Self-evolving agents address this by accumulating memory and reflection across episodes rather than requiring model-weight updates. However, these […]
Fine-grained Claim-level RAG Benchmark for Law
arXiv:2605.21071v1 Announce Type: cross Abstract: The rapid progress of large language models (LLMs) is shifting semantic search toward a question-answering paradigm, where users ask questions and LLMs generate responses. In high-stake domains such as law, retrieval-augmented generation (RAG) is commonly used to mitigate hallucinations in generated responses. Nonetheless, prior work shows that RAG systems, whether […]
Declarative Data Services: Structured Agentic Discovery for Composing Data Systems
arXiv:2605.20690v1 Announce Type: new Abstract: Agentic discovery has shown that LLM-driven search can find novel algorithms, designs, and code under benchmark conditions. Translating the paradigm to multi-system data backends surfaces a harder problem: the search space is heterogeneous, the verifier is whether a deployed stack actually runs, and composition knowledge is unevenly captured in pretraining. […]
Comparative Analysis of Military Detection Using Drone Imagery Across Multiple Visual Spectrums
arXiv:2605.21157v1 Announce Type: cross Abstract: In modern warfare, drones are becoming an essential part of intelligence gathering and carrying out precise attacks in different kinds of hostile environments. Their ability to operate in real-time and hostile environments from a safe distance makes them invaluable for surveillance and military operations. The KIIT-MiTA dataset is comprised of […]