arXiv:2601.15109v3 Announce Type: replace-cross Abstract: Interoperable data and intelligence flows among allied partners and operational end-users remain essential to NATO’s collective defense across both conventional and hybrid threat environments. Foreign Information Manipulation and Interference (FIMI) increasingly spans multiple societal domains and information ecosystems, complicating threat characterization, persistent situational awareness, and coordinated response. Concurrent advances in […]
Toward Reliable Evaluation of LLM-Based Financial Multi-Agent Systems: Taxonomy, Coordination Primacy, and Cost Awareness
arXiv:2603.27539v1 Announce Type: cross Abstract: Multi-agent systems based on large language models (LLMs) for financial trading have grown rapidly since 2023, yet the field lacks a shared framework for understanding what drives performance or for evaluating claims credibly. This survey makes three contributions. First, we introduce a four-dimensional taxonomy, covering architecture pattern, coordination mechanism, memory […]
daVinci-LLM:Towards the Science of Pretraining
arXiv:2603.27164v1 Announce Type: new Abstract: The foundational pretraining phase determines a model’s capability ceiling, as post-training struggles to overcome capability foundations established during pretraining, yet it remains critically under-explored. This stems from a structural paradox: organizations with computational resources operate under commercial pressures that inhibit transparent disclosure, while academic institutions possess research freedom but lack […]
ContraMap: Contrastive Uncertainty Mapping for Robot Environment Representation
arXiv:2603.27632v1 Announce Type: cross Abstract: Reliable robot perception requires not only predicting scene structure, but also identifying where predictions should be treated as unreliable due to sparse or missing observations. We present ContraMap, a contrastive continuous mapping method that augments kernel-based discriminative maps with an explicit uncertainty class trained using synthetic noise samples. This formulation […]
Feedback-Coupled Memory Systems: A Dynamical Model for Adaptive Coordination
arXiv:2603.11560v3 Announce Type: replace-cross Abstract: This paper develops a dynamical framework for adaptive coordination in systems of interacting agents referred to here as Feedback-Coupled Memory Systems (FCMS). Instead of framing coordination as equilibrium optimization or agent-centric learning, the model describes a closed-loop interaction between agents, incentives, and a persistent environment. The environment stores accumulated coordination […]
Needle in the Repo: A Benchmark for Maintainability in AI-Generated Repository Edits
arXiv:2603.27745v1 Announce Type: cross Abstract: AI coding agents can now complete complex programming tasks, but existing evaluations largely emphasize behavioral correctness and often overlook maintainability risks such as weak modularity or testability. We present Needle in the Repo (NITR), a diagnostic probe-and-oracle framework for evaluating whether behaviorally correct repository edits preserve maintainable structure. NITR distills […]
Aligning LLMs with Graph Neural Solvers for Combinatorial Optimization
arXiv:2603.27169v1 Announce Type: new Abstract: Recent research has demonstrated the effectiveness of large language models (LLMs) in solving combinatorial optimization problems (COPs) by representing tasks and instances in natural language. However, purely language-based approaches struggle to accurately capture complex relational structures inherent in many COPs, rendering them less effective at addressing medium-sized or larger instances. […]
A Revealed Preference Framework for AI Alignment
arXiv:2603.27868v1 Announce Type: cross Abstract: Human decision makers increasingly delegate choices to AI agents, raising a natural question: does the AI implement the human principal’s preferences or pursue its own? To study this question using revealed preference techniques, I introduce the Luce Alignment Model, where the AI’s choices are a mixture of two Luce rules, […]
The Multi-AMR Buffer Storage, Retrieval, and Reshuffling Problem: Exact and Heuristic Approaches
arXiv:2603.26542v2 Announce Type: replace-cross Abstract: Buffer zones are essential in production systems to decouple sequential processes. In dense floor storage environments, such as space-constrained brownfield facilities, manual operation is increasingly challenged by severe labor shortages and rising operational costs. Automating these zones requires solving the Buffer Storage, Retrieval, and Reshuffling Problem (BSRRP). While previous work […]
FedFG: Privacy-Preserving and Robust Federated Learning via Flow-Matching Generation
arXiv:2603.27986v1 Announce Type: cross Abstract: Federated learning (FL) enables distributed clients to collaboratively train a global model using local private data. Nevertheless, recent studies show that conventional FL algorithms still exhibit deficiencies in privacy protection, and the server lacks a reliable and stable aggregation rule for updating the global model. This situation creates opportunities for […]
AutoMS: Multi-Agent Evolutionary Search for Cross-Physics Inverse Microstructure Design
arXiv:2603.27195v1 Announce Type: new Abstract: Designing microstructures that satisfy coupled cross-physics objectives is a fundamental challenge in material science. This inverse design problem involves a vast, discontinuous search space where traditional topology optimization is computationally prohibitive, and deep generative models often suffer from “physical hallucinations,” lacking the capability to ensure rigorous validity. To address this […]
Implicit neural representations for larval zebrafish brain microscopy: a reproducible benchmark on the MapZebrain atlas
arXiv:2603.26811v1 Announce Type: cross Abstract: Implicit neural representations (INRs) offer continuous coordinate-based encodings for atlas registration, cross-modality resampling, sparse-view completion, and compact sharing of neuroanatomical data. Yet reproducible evaluation is lacking for high-resolution larval zebrafish microscopy, where preserving neuropil boundaries and fine neuronal processes is critical. We present a reproducible INR benchmark for the MapZebrain […]