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...
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...
Static vs. Dynamic vs. Continuous Batching in LLM Inference
In this article, you will learn how static, dynamic, and continuous batching work in LLM inference, and why the differences between them matter at production...
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...
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...
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 ...
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...
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...
Ollama vs. LM Studio vs. llama.cpp: Which Local AI Runtime Should You Use in 2026?
In this article, you will learn how Ollama, LM Studio, and llama.cpp differ across the dimensions that matter most to practitioners, and how to choose...
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 ...
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...
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...
Stateful vs. Stateless Agent Design: Tradeoffs for Scalable Agentic Systems
In this article, you will learn how an agent's approach to managing state — stateless or stateful — shapes both its implementation and the deployment...
arXiv:2607.20512v1 Announce Type: new
Abstract: The Muon optimizer reaches the grokking threshold on modular arithmetic faster than AdamW. Prior work attributes this to "spectral-norm constraints plus orthogonalized momentum" but does not isolate which mechanism matters. To better understand Moun's...
Multimodal CoLRAG-TF: Triple-Filtered Retrieval for Complex PDFs
arXiv:2607.20517v1 Announce Type: new
Abstract: Retrieval-augmented generation (RAG) over heterogeneous PDF collections remains challenging due to multimodal content, domain-specific terminology, and the need for multi-hop reasoning across dispersed evidence. We present Multimodal CoLRAG-TF, a four...
It's tempting to treat loop engineering as something invented in a single week in June, but the mechanics behind it are closer to five years old, and knowing the lineage is what separates a real understanding of the idea from just repeating the trend piece.
FALCON-Discover: Discovering Concentrated False-Confidence Regions for Calibration
arXiv:2607.18278v1 Announce Type: new
Abstract: Calibration is usually evaluated in aggregate, but the most dangerous failures are often local: predictions that remain highly confident despite being wrong. We study this failure mode as false-confidence concentration, the extent to which confident e...
Beyond Output-Space Calibration: Spectral Evidence Bundling for Selective Reliability Estimation in Time-Series Classification
arXiv:2607.18279v1 Announce Type: new
Abstract: Post-hoc calibration for time-series classification usually remaps output scores, but deployment decisions such as trust, abstention, and review depend on whether a confident prediction is supported by the current temporal signal. We address three tim...
A Survey on GNN-based Link Prediction: Techniques, Applications, and Challenges
arXiv:2607.16198v1 Announce Type: new
Abstract: Graph Neural Networks (GNNs) have emerged as the leading paradigm for link prediction, enabling the inference of missing connections and the anticipation of potential future links. However, existing reviews lack systematic exploration specifically tar...
Operator-Aware Mixed-Precision Tolerance Calibration for Tensor Kernels
arXiv:2607.16228v1 Announce Type: new
Abstract: Most tensor-kernel correctness tests go through a fixed-shape all close-style check with hand-picked absolute and relative tolerances. The thresholds are copied across the corpus and rarely revisited. We mine the element-wise error distribution of eve...
Reinforcement Learning-Guided NSGA-II Enhanced with Gray Relational Coefficient for Multi-Objective Optimization: Application to NASDAQ Portfolio Optimization
arXiv:2607.16194v1 Announce Type: new
Abstract: In modern financial markets, decision-makers increasingly rely on quantitative methods to navigate complex trade-offs among multiple, often conflicting objectives. This paper addresses constrained multi-objective optimization (MOO) with an application...