AI × Crypto News Feed

Sequential Feature Selection for Efficient Landslide Segmentation from Multi-Spectral Data

Researchers present a Sequential Forward Floating Selection (SFFS) framework for identifying the minimal set of satellite imagery channels needed for accurate landslide detection, demonstrating that 8 carefully selected channels match or exceed the performance of models using 30 channels. The work addresses computational efficiency and model interpretability in Earth observation machine learning by moving beyond conventional approaches that simply include all available data.

Adaptive DNN Partitioning and Offloading in Heterogeneous Edge-Cloud Continuum

Researchers propose an adaptive framework for dynamically partitioning deep neural networks across edge-cloud infrastructure, addressing limitations of static approaches. Testing on real hardware demonstrates 27-35% energy reductions and 6-23% latency improvements compared to static baselines, validating the effectiveness of runtime-adaptive strategies for heterogeneous computing environments.

Any2Any 3D Diffusion Models with Knowledge Transfer: A Radiotherapy Planning Study

Researchers introduced DiffKT3D, a 3D diffusion model framework that applies knowledge transfer from video diffusion models to radiotherapy dose prediction. The approach achieves state-of-the-art results by reducing prediction error by 7% compared to previous benchmarks while maintaining clinical alignment through reinforcement learning post-training.

Learning the Preferences of a Learning Agent

Researchers present a theoretical framework for inferring the preferences and reward functions of learning agents through observation, extending inverse reinforcement learning beyond its traditional assumption that observed agents act optimally. The work establishes mathematical guarantees for preference learning algorithms when agents are either no-regret learners or converge to optimal Boltzmann policies.

Causal Parametric Drift Simulation: A Digital Twin Framework for Classifier Robustness Evaluation

Researchers propose Causal Parametric Drift Simulation, a framework using Structural Causal Models as digital twins to evaluate machine learning classifier robustness against concept drift in dynamic environments. The method preserves causal dependencies in tabular data and identifies vulnerabilities that conventional statistical tests miss, demonstrated on mental health datasets.

Understanding Asynchronous Inference Methods for Vision-Language-Action Models

Researchers present a systematic comparison of four asynchronous inference methods designed to reduce latency issues in Vision-Language-Action robot control models. The study benchmarks A2C2, IT-RTC, TT-RTC, and VLASH across standardized conditions, finding that A2C2's residual correction approach performs most consistently across varying delay scenarios.

Digital Image Forgery Detection Using Transfer Learning

Researchers present a transfer learning framework for detecting digitally forged images by combining RGB data with compression-difference features and optimized thresholds. Testing across multiple CNN architectures on the CASIA v2.0 dataset shows DenseNet121 achieves highest accuracy while ResNet50 provides most reliable predictions, addressing critical forensic security needs.

parHSOM: A novel parallel Hierarchical Self-Organizing Map implementation

Researchers have developed parHSOM, a parallel implementation of Hierarchical Self-Organizing Maps designed to accelerate training for cybersecurity intrusion detection systems. Testing across multiple datasets and configurations demonstrates faster training times without performance degradation compared to sequential HSOM approaches.

WATCH: Wide-Area Archaeological Site Tracking for Change Detection

Researchers introduce WATCH, a satellite-based framework using foundation models to detect disturbances at archaeological sites across months and years. The system combines three approaches—temporal embedding distance, self-supervised change detection, and weakly supervised learning—achieving up to 92.5% accuracy within three-month tolerance windows when monitoring 1,943 Afghan sites and cross-validating in Syria, Turkey, Pakistan, and Egypt.

Guaranteed Jailbreaking Defense via Disrupt-and-Rectify Smoothing

Phoenix-VL 1.5 Medium Technical Report

Do multimodal models imagine electric sheep?

Researchers demonstrate that large multimodal models develop internal visual representations when solving spatial reasoning tasks, improving puzzle-solving accuracy from 83% to 89% by integrating visual tokens into chain-of-thought reasoning. The findings suggest AI systems spontaneously form world models without explicit visual supervision, with practical applications for enhancing spatial reasoning capabilities.

Agent-X: Full Pipeline Acceleration of On-device AI Agents

Researchers introduce Agent-X, a software framework that accelerates LLM-based agents running on edge devices by optimizing both prefill and decode stages through prompt rewriting and LLM-free speculative decoding. The framework achieves 1.61x end-to-end speedup with no accuracy loss, addressing a critical performance bottleneck in on-device AI deployments.

TRACE: Distilling Where It Matters via Token-Routed Self On-Policy Alignment

Researchers introduce TRACE, a novel training method that improves AI model performance by selectively applying different optimization techniques to critical versus routine tokens in reasoning tasks. The approach addresses inefficiencies in standard self-distillation by concentrating training effort on important decision points, achieving 2.76 percentage point improvements over baseline methods while better preserving out-of-distribution generalization.

TIDES: Implicit Time-Awareness in Selective State Space Models

Researchers introduce TIDES, a new selective state space model architecture that combines the expressivity of input-dependent models like Mamba with the native irregular time-series handling of continuous-time models like S5. By moving input-dependence to the state matrix rather than the discretization step, TIDES maintains the physical meaning of time intervals while preserving per-token expressivity, achieving state-of-the-art results on time-series benchmarks.

Learning the Interaction Prior for Protein-Protein Interaction Prediction: A Model-Agnostic Approach

Researchers propose L3-PPI, a biologically-informed machine learning approach for predicting protein-protein interactions by leveraging the L3 rule—the principle that multiple length-3 paths between proteins indicate interaction likelihood. The method integrates a lightweight graph prompt learning module into existing PPI predictors as a plug-and-play component, demonstrating superior performance over conventional approaches that rely on generic aggregation methods.

The Metacognitive Probe: Five Behavioural Calibration Diagnostics for LLMs

Researchers introduce the Metacognitive Probe, a diagnostic tool measuring five dimensions of LLM confidence behavior including calibration, epistemic vigilance, and reasoning validation. Testing on eight frontier models and 69 humans reveals significant within-model disparities—exemplified by Gemini 2.5 Flash scoring 88 on confidence calibration but only 41 on difficulty prediction—suggesting composite benchmarks mask pockets of overconfidence.

How Much is Brain Data Worth for Machine Learning?

Researchers present a mathematical framework quantifying the value of brain imaging data for training machine learning models, deriving scaling laws that establish exchange rates between neural recordings and task samples. The work identifies specific conditions where brain data improves model performance and robustness, providing theoretical foundations for when neural data collection is economically justified.

Separate First, Fuse Later: Mitigating Cross-Modal Interference in Audio-Visual LLMs Reasoning with Modality-Specific Chain-of-Thought

Researchers propose SFFL, a framework that mitigates cross-modal interference in audio-visual language models by enforcing separate reasoning chains for each modality before fusion. The approach uses modality-preference labels and reinforcement learning to reduce hallucinations and achieves 5-11% performance improvements on benchmarks.

Yield Curve Forecasting using Machine Learning and Econometrics: A Comparative Analysis

A comprehensive study comparing machine learning, deep learning, and traditional econometric methods for forecasting U.S. Treasury yield curves reveals that classical ARIMA models and naive benchmarks generally outperform advanced algorithms, though TimeGPT and RNNs show promise among machine learning approaches. The research challenges assumptions about deep learning's universal superiority in financial forecasting.

Fairness of Explanations in Artificial Intelligence (AI): A Unifying Framework, Axioms, and Future Direction toward Responsible AI

Researchers present a unified framework addressing a critical gap between algorithmic fairness and explainable AI (XAI): models can produce fair outputs while employing biased reasoning processes. The study introduces the concept of 'procedural bias' and proposes a conditional invariance framework to formalize and audit explanation fairness, establishing the first comprehensive taxonomy and evaluation workflow for this emerging field.

VT-Bench: A Unified Benchmark for Visual-Tabular Multi-Modal Learning

Researchers introduce VT-Bench, the first comprehensive benchmark for visual-tabular multi-modal learning, aggregating 14 datasets with 756K samples across 9 domains. The benchmark evaluates 23 models and reveals significant gaps in current approaches for combining image and tabular data, particularly in high-stakes sectors like healthcare.

TMAS: Scaling Test-Time Compute via Multi-Agent Synergy

Researchers introduce TMAS, a multi-agent framework that improves test-time compute scaling for large language models by enabling specialized agents to collaborate through hierarchical memory systems. The approach balances exploration and exploitation more effectively than existing methods, achieving stronger iterative scaling on challenging reasoning benchmarks.

Marrying Generative Model of Healthcare Events with Digital Twin of Social Determinants of Health for Disease Reasoning

Researchers develop a generative AI model that integrates social determinants of health (SDoH) with multi-organ sensor data and medical events to improve disease prediction and personalized clinical decision support. Tested on UK Biobank data spanning nearly 500,000 medical histories, the model outperforms existing autoregressive disease prediction systems by explicitly modeling socioeconomic factors alongside imaging and biomarker data.

ChaosNetBench: Benchmarking Spatio-Temporal Graph Neural Networks on Chaotic Lattice Dynamics

Researchers introduce ChaosNetBench, a synthetic benchmark framework for evaluating spatio-temporal graph neural networks (STGNNs) on chaotic dynamical systems. The framework reveals that STGNNs outperform traditional baselines (TCN, N-BEATS, Transformers) in high-chaos regimes, while non-graph methods remain competitive in low-chaos conditions.