Signed Symmetric Quantization for Few-Bit Integers
arXiv:2607.08779v1 Announce Type: new
Abstract: The signed integer alphabet contains one more negative representable value than positive. Yet, by convention, the standard symmetric integer quantizer fixes its scale to be strictly positive, which assigns this extra representable value to the negativ...
iLENS: Interpretable LLM-Guided Mixture-of-Experts for Neuroimaging Survival Analysis
arXiv:2607.08778v1 Announce Type: new
Abstract: Alzheimer's Disease (AD) is a complex neurodegenerative disorder that continues to impact millions of people worldwide. Predicting AD conversion during the prodromal stage remains critical for disease understanding and patient care. As such, survival ...
Reward Transport: Property Control in Flow Matching via Noise-Space Alignment
arXiv:2607.08781v1 Announce Type: new
Abstract: The coupling in flow matching -- the rule pairing noise vectors with data points -- is typically treated as a computational choice. We show that this coupling can instead serve as an alignment interface: by matching noise and data according to a targe...
Who Gets Missed in the Tail? Thresholded Subgroup Underdiagnosis in Long-Tailed Chest X-ray Classification
arXiv:2607.07717v1 Announce Type: new
Abstract: In chest X-ray (CXR) classification, acceptable ranking performance can still leave rare-positive patients below threshold, especially within subgroups. We study this pre-deployment fairness problem as an audit question: after a long-tailed multi-labe...
LLT: Local Linear Transformer for PDE Operator Learning
arXiv:2607.07718v1 Announce Type: new
Abstract: Neural operators have become a common approach for learning PDE solution maps and accelerating numerical simulations. Transformer-based neural operators are of particular interest, since attention can learn long-range dependencies in the computational...
AgentLens: Production-Assessed Trajectory Reviews for Coding Agent Evaluation
arXiv:2607.06624v1 Announce Type: new
Abstract: We present AgentLens, a production-assessed benchmark for interactive code agents. Most code-agent benchmarks reduce a run to a single bit -- did the task pass? -- but the people who actually use these agents experience the entire trajectory: how the ...
Deep Reinforcement Learning for Reliability Based Bi-Objective Portfolio Optimization
arXiv:2607.06610v1 Announce Type: new
Abstract: Portfolio optimization under uncertainty is inherently a multi-objective decision problem involving complex interactions among return, risk, market dynamics, and practical investment constraints. Existing reliability based portfolio optimization appro...
NEST: Tackling Dataset-Level Distribution Shifts via Regime-Oriented Mixture-of-Experts
arXiv:2607.06607v1 Announce Type: new
Abstract: Accurate long-term forecasting in complex systems is frequently compromised by dataset-level distribution shifts, where diverse underlying behavioral modes and evolving system states drive the dynamic multivariate time-series. While existing methods p...
From Graphs to Gradients: Physics-Inspired Structural Attribution for Cyber-Physical IoT Systems and Beyond
arXiv:2607.05563v1 Announce Type: new
Abstract: Interpretable explanation methods in Artificial Intelligence aim to uncover the underlying causes and their effects, enabling a deeper understanding of why a system behaves in a certain way under different inputs. Unlike traditional explainability met...
Geometry-Aware Infrastructure-Anchored Denoiser for UWB Sensing and Work-Zone Reconstruction
arXiv:2607.05449v1 Announce Type: new
Abstract: Accurate work-zone geometry perception is critical for intelligent transportation systems, and ultra-wideband sensing offers a low-cost approach for infrastructure-aided reconstruction. However, outdoor UWB ranging is often degraded by non-line-of-sig...
The Granularity Paradox: How Temporal Disaggregation Inflates In-Sample Fit and Compounds Out-of-Sample Error
arXiv:2607.05450v1 Announce Type: new
Abstract: This paper explores the "Granularity Paradox" in time-series forecasting, wherein finer temporal disaggregation (e.g., Monthly to Weekly/Daily) improves in-sample diagnostics and dataset size (N), but degrades out-of-sample accuracy due to recursive e...
arXiv:2607.02542v1 Announce Type: new
Abstract: General-purpose embodied agents must understand multimodal instructions, anticipate how their environment will evolve, and produce precise control actions over extended horizons. Existing approaches typically specialize in visual-language reasoning, v...
Scaling Laws for Grid-Based Approximate Nearest Neighbor Search in High Dimensions
arXiv:2607.01283v1 Announce Type: new
Abstract: Grid-based approaches to approximate nearest neighbor (ANN) search have been absent from modern scaling analyses. We present a systematic characterization of a multiprobe grid algorithm with respect to dataset size $N$ and dimensionality $d$. Our expe...
Constructive Alignment: Governing Preference Dynamics in Human-AI Interaction
arXiv:2607.00001v1 Announce Type: new
Abstract: Most approaches to AI alignment treat human preferences as fixed targets to be inferred and optimized. This assumption conflicts with extensive empirical evidence showing that preferences are layered, dynamic, and constructed through interaction--part...
Solution space path planning for supporting en-route air traffic control
arXiv:2607.00064v1 Announce Type: new
Abstract: As technology advances, many path-planning algorithms have been proposed for Air Traffic Management, yet their operational adoption in tactical control remains limited, revealing a misalignment between algorithmic design priorities and air traffic con...
SemiScope: Disentangling Classifier Tuning and Joint Optimization in Semi-Supervised Security Classification
arXiv:2607.00113v1 Announce Type: new
Abstract: Background. Labeled data for security classification is scarce. Semi-supervised learning (SSL) propagates labels from a small labeled pool to larger unlabeled pools. Yet security applications often use SSL as a black box: default parameters, a fixed c...
How Can AI Find My Model? A Model-Finding Experimental Study Considering Data Formats, Embeddings, and Retrieval Strategies
arXiv:2606.30846v1 Announce Type: new
Abstract: Discovering simulation models for reuse remains a fundamental challenge in Modeling and Simulation (M&S). When many models coexist, identifying those that align with a given modeling intent remains difficult. Recent advances in Artificial Intelligence...
arXiv:2606.30709v1 Announce Type: new
Abstract: Hierarchical Global Attention (HGA) is a drop-in replacement for dense causal attention in pretrained long-context transformers. HGA preserves the original checkpoint parameters: the pretrained $W_Q$, $W_K$, $W_V$, and $W_O$ projections remain unchang...
Joint discovery of governing partial differential equations from multi-source datasets by competitive optimization
arXiv:2606.30699v1 Announce Type: new
Abstract: Discovering governing equations directly from observational data is a key step towards interpretable scientific machine learning. Current data-driven approaches typically operate on a single dataset, inherently limiting their performance when faced wi...
Why Do Few-Step Text Latents Fail When Image Latents Work? Non-Commitment at Sharp Categorical Readouts
arXiv:2606.30705v1 Announce Type: new
Abstract: Deterministic few-step generation succeeds on continuous image latents but collapses to incoherent text on continuous text latents, and we show the cause is geometric rather than a training or scaling deficiency: a smooth, regularity-limited determini...