arXiv:2506.06216v3 Announce Type: replace Abstract: The Maximum Satisfiability problem (MaxSAT) is a major optimization challenge with numerous practical applications. In recent MaxSAT evaluations, most MaxSAT solvers have incorporated an Integer Linear Programming (ILP) solver into their portfolios. However, a good portfolio strategy requires a lot of tuning work and is limited to the profiling benchmark. […]
Intelligence Requires Grounding But Not Embodiment
arXiv:2601.17588v1 Announce Type: new Abstract: Recent advances in LLMs have reignited scientific debate over whether embodiment is necessary for intelligence. We present the argument that intelligence requires grounding, a phenomenon entailed by embodiment, but not embodiment itself. We define intelligence as the possession of four properties — motivation, predictive ability, understanding of causality, and learning […]
Networks of Causal Abstractions: A Sheaf-theoretic Framework
arXiv:2509.25236v2 Announce Type: replace Abstract: Causal artificial intelligence aims to improve explainability, robustness, and trustworthiness by leveraging causal models. Recent work has shown that sheaf-theoretic approaches offer a principled framework for representing and aligning causal knowledge across collections of subjective and imperfect causal models connected by relational structures. In this work, we introduce the causal […]
Travelling Waves in Wolbachia Spread Dynamics
arXiv:2601.17590v1 Announce Type: new Abstract: Wolbachia, a maternally transmitted endosymbiont, offers a powerful biological control strategy for mosquito-borne diseases such as dengue, Zika, and malaria. We develop an integro-difference equation (IDE) model that integrates Wolbachia’s nonlinear growth with spatially explicit mosquito dispersal kernels to study invasion dynamics in heterogeneous landscapes. Analytical results establish the existence […]
Towards Privacy-Preserving Mental Health Support with Large Language Models
arXiv:2601.01993v2 Announce Type: replace Abstract: Large language models (LLMs) have shown promise for mental health support, yet training such models is constrained by the scarcity and sensitivity of real counseling dialogues. In this article, we present MindChat, a privacy-preserving LLM for mental health support, together with MindCorpus, a synthetic multi-turn counseling dataset constructed via a […]
Health-ORSC-Bench: A Benchmark for Measuring Over-Refusal and Safety Completion in Health Context
arXiv:2601.17642v1 Announce Type: new Abstract: Safety alignment in Large Language Models is critical for healthcare; however, reliance on binary refusal boundaries often results in emphover-refusal of benign queries or emphunsafe compliance with harmful ones. While existing benchmarks measure these extremes, they fail to evaluate Safe Completion: the model’s ability to maximise helpfulness on dual-use or […]
Near-Optimal Partially Observable Reinforcement Learning with Partial Online State Information
arXiv:2306.08762v4 Announce Type: replace-cross Abstract: Partially observable Markov decision processes (POMDPs) are a general framework for sequential decision-making under latent state uncertainty, yet learning in POMDPs is intractable in the worst case. Motivated by sensing and probing constraints in practice, we study how much online state information (OSI) is sufficient to enable efficient learning guarantees. […]
Quantitative cancer-immunity cycle modeling to optimize bevacizumab and atezolizumab combination therapy for advanced renal cell carcinoma
arXiv:2601.17669v1 Announce Type: new Abstract: The incidence of advanced renal cell carcinoma(RCC) has been rising, presenting significant challenges due to the limited efficacy and severe side effects of traditional radiotherapy and chemotherapy. While combination immunotherapies show promise, optimizing treatment strategies remains difficult due to individual heterogeneity. To address this, we developed a Quantitative Cancer-Immunity Cycle […]
CondensNet: Enabling stable long-term climate simulations via hybrid deep learning models with adaptive physical constraints
arXiv:2502.13185v2 Announce Type: replace-cross Abstract: Accurate and efficient climate simulations are crucial for understanding Earth’s evolving climate. However, current general circulation models (GCMs) face challenges in capturing unresolved physical processes, such as cloud and convection. A common solution is to adopt cloud resolving models, that provide more accurate results than the standard subgrid parametrisation schemes […]
DIML: Differentiable Inverse Mechanism Learning from Behaviors of Multi-Agent Learning Trajectories
arXiv:2601.17678v1 Announce Type: new Abstract: We study inverse mechanism learning: recovering an unknown incentive-generating mechanism from observed strategic interaction traces of self-interested learning agents. Unlike inverse game theory and multi-agent inverse reinforcement learning, which typically infer utility/reward parameters inside a structured mechanism, our target includes unstructured mechanism — a (possibly neural) mapping from joint actions […]
Your Classifier Can Do More: Towards Bridging the Gaps in Classification, Robustness, and Generation
arXiv:2505.19459v2 Announce Type: replace-cross Abstract: Joint Energy-based Models (JEMs) are well known for their ability to unify classification and generation within a single framework. Despite their promising generative and discriminative performance, their robustness remains far inferior to adversarial training (AT), which, conversely, achieves strong robustness but sacrifices clean accuracy and lacks generative ability. This inherent […]
Knowing the Facts but Choosing the Shortcut: Understanding How Large Language Models Compare Entities
arXiv:2510.16815v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) are increasingly used for knowledge-based reasoning tasks, yet understanding when they rely on genuine knowledge versus superficial heuristics remains challenging. We investigate this question through entity comparison tasks by asking models to compare entities along numerical attributes (e.g., “Which river is longer, the Danube or the […]