Verifier-Guided Model Discovery for Physical Dynamical Systems with Pretrained Symbolic Transformers
arXiv:2608.02662v1 Announce Type: new
Abstract: Reliable forecasting of nonlinear physical systems underpins scientific discovery and engineering decision-making. Yet high-fidelity simulations are prohibitively costly, and machine-learning surrogates can be opaque and encode assumptions about syste...
GeoID-PINN: Identifiability-Aware Regional Epidemic Inference with Geographic Coupling
arXiv:2608.02633v1 Announce Type: new
Abstract: Regional surveillance data reflect local transmission, reporting, seeding, and external infection pressure, which are difficult to identify separately. We introduce GeoID-PINN, a physics-informed neural network (PINN) for susceptible-infectious-recove...
arXiv:2608.02628v1 Announce Type: new
Abstract: Symbolic regression (SR) is the task of discovering underlying patterns from data and representing them using mathematical expressions. Current machine learning approaches to SR often lack a profound understanding of the intrinsic mathematical and phy...
Why Governing World Models Is AI's Next Big Policy Challenge
As artificial intelligence moves beyond language into the physical world through "world models," Stanford researchers warn that policymakers face an even steeper governance challenge than with large language models—and the window to get ahead of the technology is closing fast.
Can AI Evaluate AI Scientists? A Benchmarking Study of Autonomous Research Generation Systems Using Automated Multi-Model Review
arXiv:2607.28631v1 Announce Type: new
Abstract: AI Scientist systems capable of autonomous research have the potential to significantly accelerate scientific discovery. However, evaluating and comparing the quality of AI-generated papers remains an open challenge. We propose and implement a rigorou...
Understanding Alignment in Multimodal LLMs: A Comprehensive Study
Preference alignment has become a crucial component in enhancing the performance of Large Language Models (LLMs), yet its impact in Multimodal Large Language Models (MLLMs) remains comparatively underexplored. Similar to language models, MLLMs for image understanding tasks encounter challenges like ...
Trust: The next critical infrastructure layer for autonomous AI
We're at an inflection point. AI has moved well beyond generating recommendations for humans to review. It's taking actions. It's embedded in business workflows. It's making decisions autonomously, and in many cases, it's doing all of this faster than any human could intervene.
Regularizing modality contribution drift in multimodal continual learning
arXiv:2607.27260v1 Announce Type: new
Abstract: Multimodal continual learning (MMCL) aims to learn emerging knowledge from multimodal data while preserving knowledge. To mitigate forgetting, current MMCL methods usually focus on cross-modal representation alignment or semantic similarity, but they ...
GuideSkill: Evolving Executable LLM Agent Skills for Guideline-Grounded Clinical Reasoning
arXiv:2607.26160v1 Announce Type: new
Abstract: Clinical practice guidelines (CPGs) encode diagnostic criteria, but LLM systems typically retrieve guideline text or absorb it through training rather than execute its rules. We introduce GuideSkill, an external reasoning layer that compiles disease-s...
When benchmark inferences do not compose: Projectibility in AI evaluation
arXiv:2607.26159v1 Announce Type: new
Abstract: An AI benchmark result rarely reaches a consequential claim in one step. Evaluators generalize it to further cases, interpret it as evidence of capability, extrapolate it to new tasks, transport it to another system or site, and combine it with assump...
Probing the Origins of Reasoning Performance: Representational Quality for Mathematical Problem-Solving in RL vs. SFT Fine-Tuned Models
arXiv:2607.26119v1 Announce Type: new
Abstract: Large reasoning models trained via reinforcement learning (RL) have been increasingly shown to outperform their supervised fine-tuned (SFT) counterparts on mathematical reasoning tasks; Yet the mechanistic basis for this advantage remains unclear. We ...
Sim2Win: A Team-Agnostic, Event-Based Pre-Match Outcome Prediction and Tactical Profiling System for Football
arXiv:2607.26061v1 Announce Type: new
Abstract: Pre-match tactical decision-making in professional football relies heavily on subjective expert analysis and identity-based scouting systems that cannot generalize to unseen teams. This paper presents Sim2Win, a team-agnostic, event-based pre-match ta...
Dimensionality Reduction Meets Network Science: Sensemaking on UMAP’s kNN Graph
While UMAP is widely used for exploring high-dimensional data, typical workflows focus on its lower-dimensional embedding, largely overlooking the rich k-nearest-neighbor (kNN) graph that UMAP constructs internally. This graph encodes the data manifold in its original high-dimensional space, before ...
88% of enterprises report regular AI use. Only 7% have scaled it. Here are the five strategies separating the companies that made it work from everyone still demoing chatbots to their board.
From Frame-Level Recognition to Event-Level Confirmation: Repair Traces and Runtime Failure Analysis of Public-Space Gesture Interaction
arXiv:2607.21601v1 Announce Type: new
Abstract: Public-space gesture interaction is often evaluated as a frame-level recognition problem, but deployed systems expose a different failure boundary. In scenic kiosks, exhibition halls, and service terminals, users experience whether an intended action ...
MotifRole-Diff: Risk-Optimal Role-Aware Corruption for Masked Molecular Graph Diffusion
arXiv:2607.21634v1 Announce Type: new
Abstract: Masked discrete diffusion for molecular graph generation typically applies a uniform corruption schedule to all tokens in a lossless graph-to-sequence representation, implicitly treating structurally heterogeneous molecular components as equally diffi...
Cloud-Native Evaluation-as-a-Service: A Microservices Architecture for Scalable AI Monitoring with Conformal Guarantees
arXiv:2607.21623v1 Announce Type: new
Abstract: We present EaaS, a cloud-native reference architecture that operationalizes AI evaluation methods as six stateless Kubernetes microservices: conformal prediction with finite-sample-corrected Adaptive Prediction Sets, calibration assessment, drift dete...