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...
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...
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...
MIT’s new lidar chip could give self-driving cars a wider view
MIT engineers have found a way to give chip-based lidar a wider, clearer view without relying on moving parts. Their design uses differently shaped antennas that can sit close together without scrambling one another’s signals. In tests, the system sharply reduced interference while steering a single...
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...
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...
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...
Precise but Uncoupled: Reviewer Precision Does Not Guarantee Critique Uptake in Multi-Agent Math Reasoning
arXiv:2607.15388v1 Announce Type: new
Abstract: Many math- and science-oriented agent systems use hierarchical designs with specialized reviewer roles, assuming that a dedicated review stage should help turn wrong candidates into correct ones. We test this assumption on 4,181 verifier-grounded Omni...
Regularity-Aware Stochastic MGDA with Adaptive Conflict-Avoidant Update Direction Control
arXiv:2607.15412v1 Announce Type: new
Abstract: Multi-objective learning (MOL) aims to optimize multiple objectives simultaneously. The multi-gradient descent algorithm (MGDA) is a workhorse that iteratively updates along a common descent or conflict-avoidant (CA) direction across objectives. In st...
LVSum: A Benchmark for Timestamp-Aware Long Video Summarization
Long video summarization presents significant challenges for multimodal large language models (MLLMs), particularly in maintaining temporal fidelity over extended durations and producing summaries that are both semantically and temporally grounded. We introduce LVSum, a human-annotated benchmark for...
RayRoPE: Projective Ray Positional Encoding for Multi-View Attention
We study positional encodings for multi-view transformers that process tokens from a set of posed input images, and seek a mechanism that encodes patches uniquely, allows SE(3)-invariant attention with multi-frequency similarity, and can be adaptive to the geometry of the underlying scene. We find t...
Certified Domain Consistency for Multi-Domain Retrieval: Label-Free Per-Domain Contamination Control with Conformal Risk Guarantees
arXiv:2607.14157v1 Announce Type: new
Abstract: Retrieval over corpora that mix several domains often returns relevant but wrong-domain evidence that ranking metrics miss and that conformal risk control bounds only marginally, under-covering the worst domains. This work introduces C3R, a drop-in co...
Explainable Geospatial AI for Satellite Ground Station Siting Using LiDAR-Derived Terrain Intelligence
arXiv:2607.14127v1 Announce Type: new
Abstract: Representative clutter height (RCH) is a key parameter in radio propagation and interference analysis because it captures the dominant height of local obstructions that drive terminal clutter loss. Current practice often relies on fixed clutter height...
Berlin, Paris, London: how Europe's AI hubs are diverging
Europe's AI scene used to get lumped together as one story. In 2026, Berlin, Paris, and London are running three different plays, and the gap between them is widening fast...
What Your Model Threw Away and Why You'll Want It Back: Masking, Fingerprinting, and Privacy from Discarded Geometry
arXiv:2607.13046v1 Announce Type: new
Abstract: We develop a framework for the information discarded by machine learning models whose inputs carry a Lie group action. Given a representation $\pi$ of a Lie group $G$ on a space $V$ and a learned function $f\colon V \to \mathbb{R}$, we define two obje...
Interactive Proofs for General Distribution Properties
Suppose Alice has collected a small number of samples from an unknown distribution, and would like to learn about the distribution. Bob, an untrusted data analyst, claims to have run a sophisticated data analysis on the distribution and makes assertions about its properties. When and how is it possi...
Location-Invariant Properties of Functions Versus Properties of Distributions: United in Testing but Separated in Verification
A property of functions is called location-invariant (or symmetric) if it can be characterized in terms of the frequencies in which each value occurs in the function, regardless of the locations in which each value occurs. It is known that the (query) complexity of testing location-invariant propert...