arXiv:2507.17731v2 Announce Type: replace-cross Abstract: Over the past decade, advances in generative modeling, such as generative adversarial networks, masked autoencoders, and diffusion models, have significantly transformed biological research and discovery, enabling breakthroughs in molecule design, protein generation, catalysis discovery, drug discovery, and beyond. At the same time, biological applications have served as valuable testbeds for […]
ABD: Default Exception Abduction in Finite First Order Worlds
arXiv:2602.18843v2 Announce Type: replace Abstract: We introduce ABD, a benchmark for default-exception abduction over finite first-order worlds. Given a background theory with an abnormality predicate and a set of relational structures, a model must output a first-order formula that defines exceptions, restoring satisfiability while keeping exceptions sparse. We formalize three observation regimes (closed-world, existential completion, […]
FOR-Prompting: From Objection to Revision via an Asymmetric Prompting Protocol
arXiv:2510.01674v2 Announce Type: replace-cross Abstract: Reasoning protocols such as Chain of Thought (CoT) and Tree of Thought (ToT) organize internal deliberation but lack an explicit mechanism for external questioning that elicits self-revision. We present FOR-Prompting (From Objection to Revision Prompting), an asymmetric protocol where a Defender proposes an answer, an Debater (Questioner) raises question-style objections […]
Variational Learning of Gaussian Process Latent Variable Models through Stochastic Gradient Annealed Importance Sampling
arXiv:2408.06710v3 Announce Type: replace-cross Abstract: Gaussian Process Latent Variable Models (GPLVMs) have become increasingly popular for unsupervised tasks such as dimensionality reduction and missing data recovery due to their flexibility and non-linear nature. An importance-weighted version of the Bayesian GPLVMs has been proposed to obtain a tighter variational bound. However, this version of the approach […]
SwiftEmbed: Ultra-Fast Text Embeddings via Static Token Lookup for Real-Time Applications
arXiv:2510.24793v3 Announce Type: replace-cross Abstract: We present SwiftEmbed, a production-oriented serving system for static token embeddings that achieves 1.12,ms p50 latency for single-text requests while maintaining a 60.6 MTEB average score across 8 representative tasks. Built around the open-source Potion-base-8M distilled model from MinishLab and implemented in Rust, the system delivers 50,000 requests per second […]
UNBOX: Unveiling Black-box visual models with Natural-language
arXiv:2603.08639v1 Announce Type: cross Abstract: Ensuring trustworthiness in open-world visual recognition requires models that are interpretable, fair, and robust to distribution shifts. Yet modern vision systems are increasingly deployed as proprietary black-box APIs, exposing only output probabilities and hiding architecture, parameters, gradients, and training data. This opacity prevents meaningful auditing, bias detection, and failure analysis. […]
ForamDeepSlice: A High-Accuracy Deep Learning Framework for Foraminifera Species Classification from 2D Micro-CT Slices
arXiv:2512.00912v2 Announce Type: replace-cross Abstract: This study presents a comprehensive deep learning pipeline for the automated classification of foraminifera species using 2D micro-CT slices derived from 3D scans. We curated a scientifically rigorous dataset of 97 micro-CT scanned specimens spanning 27 species, from which we selected 12 representative species with sufficient specimen counts (at least […]
Your Agent May Misevolve: Emergent Risks in Self-evolving LLM Agents
arXiv:2509.26354v2 Announce Type: replace Abstract: Advances in Large Language Models (LLMs) have enabled a new class of self-evolving agents that autonomously improve through interaction with the environment, demonstrating strong capabilities. However, self-evolution also introduces novel risks overlooked by current safety research. In this work, we study the case where an agent’s self-evolution deviates in unintended […]
Optical Manipulation of Erythrocytes via Evanescent Waves: Assessing Glucose-Induced Mobility Variations
arXiv:2601.15502v2 Announce Type: replace-cross Abstract: This study investigates the dynamics of red blood cells (RBCs) under the influence of evanescent waves generated by total internal reflection (TIR). Using a 1064 nm laser system and a dual-chamber prism setup, we quantified the mobility of erythrocytes in different glucose environments. Our methodology integrates automated tracking via TrackMatecopyright […]
ERP-RiskBench: Leakage-Safe Ensemble Learning for Financial Risk
arXiv:2603.06671v1 Announce Type: cross Abstract: Financial risk detection in Enterprise Resource Planning (ERP) systems is an important but underexplored application of machine learning. Published studies in this area tend to suffer from vague dataset descriptions, leakage-prone pipelines, and evaluation practices that inflate reported performance. This paper presents a rebuilt experimental framework for ERP financial risk […]
TrasMuon: Trust-Region Adaptive Scaling for Orthogonalized Momentum Optimizers
arXiv:2602.13498v2 Announce Type: replace-cross Abstract: Muon-style optimizers leverage Newton-Schulz (NS) iterations to orthogonalize updates, yielding update geometries that often outperform Adam-series methods. However, this orthogonalization discards magnitude information, rendering training sensitive to step-size hyperparameters and vulnerable to high-energy bursts. To mitigate this, we introduce TrasMuon (textbfTrust textbfRegion textbfAdaptive textbfScaling textbfMuon). TrasMuon preserves the near-isometric geometry […]
Learning When to Cooperate Under Heterogeneous Goals
arXiv:2603.07253v1 Announce Type: cross Abstract: A significant element of human cooperative intelligence lies in our ability to identify opportunities for fruitful collaboration; and conversely to recognise when the task at hand is better pursued alone. Research on flexible cooperation in machines has left this meta-level problem largely unexplored, despite its importance for successful collaboration in […]