arXiv:2603.29997v1 Announce Type: cross Abstract: Analogical reasoning is a key driver of human generalization in problem-solving and argumentation. Yet, analogies between narrative structures remain challenging for machines. Cognitive engines for structural mapping are not directly applicable, as they assume pre-extracted entities, whereas LLMs’ performance is sensitive to prompt format and the degree of surface similarity […]
Wildfire Suppression: Complexity, Models, and Instances
arXiv:2603.29865v1 Announce Type: cross Abstract: Wildfires cause major losses worldwide, and the frequency of fire-weather conditions is likely to increase in many regions. We study the allocation of suppression resources over time on a graph-based representation of a landscape to slow down fire propagation. Our contributions are theoretical and methodological. First, we prove that this […]
A Convex Route to Thermomechanics: Learning Internal Energy and Dissipation
arXiv:2603.28707v2 Announce Type: replace-cross Abstract: We present a physics-based neural network framework for the discovery of constitutive models in fully coupled thermomechanics. In contrast to classical formulations based on the Helmholtz energy, we adopt the internal energy and a dissipation potential as primary constitutive functions, expressed in terms of deformation and entropy. This choice avoids […]
Multi-Agent Memory from a Computer Architecture Perspective: Visions and Challenges Ahead
arXiv:2603.10062v2 Announce Type: replace-cross Abstract: As LLM agents evolve into collaborative multi-agent systems, their memory requirements grow rapidly in complexity. This position paper frames multi-agent memory as a computer architecture problem. We distinguish shared and distributed memory paradigms, propose a three-layer memory hierarchy (I/O, cache, and memory), and identify two critical protocol gaps: cache sharing […]
ZeroFlood: Flood Hazard Mapping from Single-Modality SAR Using Geo-Foundation Models
arXiv:2510.23364v2 Announce Type: replace-cross Abstract: Flood hazard mapping is essential for disaster prevention but remains challenging in data-scarce regions, where traditional hydrodynamic models require extensive geophysical inputs. This paper introduces textitZeroFlood, a framework that leverages Geo-Foundation Models (GeoFMs) to predict flood hazard maps using single-modality Earth Observation (EO) data, specifically SAR imagery. We construct a […]
LLM-Meta-SR: In-Context Learning for Evolving Selection Operators in Symbolic Regression
arXiv:2505.18602v3 Announce Type: replace-cross Abstract: Large language models (LLMs) have revolutionized algorithm development, yet their application in symbolic regression, where algorithms automatically discover symbolic expressions from data, remains limited. In this paper, we propose a meta-learning framework that enables LLMs to automatically design selection operators for evolutionary symbolic regression algorithms. We first identify two key […]
SecureVibeBench: Evaluating Secure Coding Capabilities of Code Agents with Realistic Vulnerability Scenarios
arXiv:2509.22097v2 Announce Type: replace-cross Abstract: Large language model-powered code agents are rapidly transforming software engineering, yet the security risks of their generated code have become a critical concern. Existing benchmarks have provided valuable insights, but they fail to capture scenarios in which vulnerabilities are actually introduced by human developers, making fair comparisons between humans and […]
The Mouth is Not the Brain: Bridging Energy-Based World Models and Language Generation
arXiv:2601.17094v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) generate fluent text, yet whether they truly understand the world or merely produce plausible texts about it remains contested. We propose an architectural principle, the mouth is not the brain, that explicitly separates world models from language models. Our architecture comprises three components: a DBM that […]
Robust Safety Monitoring of Language Models via Activation Watermarking
arXiv:2603.23171v2 Announce Type: replace-cross Abstract: Large language models (LLMs) can be misused to reveal sensitive information, such as weapon-making instructions or writing malware. LLM providers rely on $emphmonitoring$ to detect and flag unsafe behavior during inference. An open security challenge is $emphadaptive$ adversaries who craft attacks that simultaneously (i) evade detection while (ii) eliciting unsafe […]
TSHA: A Benchmark for Visual Language Models in Trustworthy Safety Hazard Assessment Scenarios
arXiv:2603.29759v1 Announce Type: cross Abstract: Recent advances in vision-language models (VLMs) have accelerated their application to indoor safety hazards assessment. However, existing benchmarks suffer from three fundamental limitations: (1) heavy reliance on synthetic datasets constructed via simulation software, creating a significant domain gap with real-world environments; (2) oversimplified safety tasks with artificial constraints on hazard […]
Growth-rate distributions at stationarity
arXiv:2603.29916v1 Announce Type: cross Abstract: We propose new analytical tools for describing growth-rate distributions generated by stationary time-series. Our analysis shows how deviations from normality are not pathological behaviour, as suggested by some traditional views, but instead can be accounted for by clean and general statistical considerations. In contrast, strict normality is the effect of […]
Improving Plan Execution Flexibility using Block-Substitution
arXiv:2406.03091v2 Announce Type: replace Abstract: Partial-order plans in AI planning facilitate execution flexibility due to their less-constrained nature. Maximizing plan flexibility has been studied through the notions of plan deordering, and plan reordering. Plan deordering removes unnecessary action orderings within a plan, while plan reordering modifies them arbitrarily to minimize action orderings. This study, in […]