COMPOT: Calibration-Optimized Matrix Procrustes Orthogonalization for Transformers Compression
arXiv:2602.15200v1 Announce Type: new Abstract: Post-training compression of Transformer models commonly relies on truncated singular value decomposition (SVD). However, enforcing a single shared subspace can degrade accuracy even at moderate compression. Sparse dictionary learning provides a more flexible union-of-subspaces representation,...
MAVRL: Learning Reward Functions from Multiple Feedback Types with Amortized Variational Inference
arXiv:2602.15206v1 Announce Type: new Abstract: Reward learning typically relies on a single feedback type or combines multiple feedback types using manually weighted loss terms. Currently, it remains unclear how to jointly learn reward functions from heterogeneous feedback types such as...
BindCLIP: A Unified Contrastive-Generative Representation Learning Framework for Virtual Screening
arXiv:2602.15236v1 Announce Type: new Abstract: Virtual screening aims to efficiently identify active ligands from massive chemical libraries for a given target pocket. Recent CLIP-style models such as DrugCLIP enable scalable virtual screening by embedding pockets and ligands into a shared...
Scaling Laws for Masked-Reconstruction Transformers on Single-Cell Transcriptomics
arXiv:2602.15253v1 Announce Type: new Abstract: Neural scaling laws -- power-law relationships between loss, model size, and data -- have been extensively documented for language and vision transformers, yet their existence in single-cell genomics remains largely unexplored. We present the first...
Fast and Effective On-policy Distillation from Reasoning Prefixes
arXiv:2602.15260v1 Announce Type: new Abstract: On-policy distillation (OPD), which samples trajectories from the student model and supervises them with a teacher at the token level, avoids relying solely on verifiable terminal rewards and can yield better generalization than off-policy distillation....
Hybrid Federated and Split Learning for Privacy Preserving Clinical Prediction and Treatment Optimization
arXiv:2602.15304v1 Announce Type: new Abstract: Collaborative clinical decision support is often constrained by governance and privacy rules that prevent pooling patient-level records across institutions. We present a hybrid privacy-preserving framework that combines Federated Learning (FL) and Split Learning (SL) to...
A Scalable Curiosity-Driven Game-Theoretic Framework for Long-Tail Multi-Label Learning in Data Mining
arXiv:2602.15330v1 Announce Type: new Abstract: The long-tail distribution, where a few head labels dominate while rare tail labels abound, poses a persistent challenge for large-scale Multi-Label Classification (MLC) in real-world data mining applications. Existing resampling and reweighting strategies often disrupt...
Directional Reasoning Trajectory Change (DRTC): Identifying Critical Trace Segments in Reasoning Models
arXiv:2602.15332v1 Announce Type: new Abstract: Understanding how language models carry out long-horizon reasoning remains an open challenge. Existing interpretability methods often highlight tokens or spans correlated with an answer, but they rarely reveal where the model makes consequential reasoning turns,...
FedPSA: Modeling Behavioral Staleness in Asynchronous Federated Learning
arXiv:2602.15337v1 Announce Type: new Abstract: Asynchronous Federated Learning (AFL) has emerged as a significant research area in recent years. By not waiting for slower clients and executing the training process concurrently, it achieves faster training speed compared to traditional federated...
ER-MIA: Black-Box Adversarial Memory Injection Attacks on Long-Term Memory-Augmented Large Language Models
arXiv:2602.15344v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly augmented with long-term memory systems to overcome finite context windows and enable persistent reasoning across interactions. However, recent research finds that LLMs become more vulnerable because memory provides extra...
Doubly Stochastic Mean-Shift Clustering
arXiv:2602.15393v1 Announce Type: new Abstract: Standard Mean-Shift algorithms are notoriously sensitive to the bandwidth hyperparameter, particularly in data-scarce regimes where fixed-scale density estimation leads to fragmentation and spurious modes. In this paper, we propose Doubly Stochastic Mean-Shift (DSMS), a novel...
Fairness over Equality: Correcting Social Incentives in Asymmetric Sequential Social Dilemmas
arXiv:2602.15407v1 Announce Type: new Abstract: Sequential Social Dilemmas (SSDs) provide a key framework for studying how cooperation emerges when individual incentives conflict with collective welfare. In Multi-Agent Reinforcement Learning, these problems are often addressed by incorporating intrinsic drives that encourage...
Logit Distance Bounds Representational Similarity
arXiv:2602.15438v1 Announce Type: new Abstract: For a broad family of discriminative models that includes autoregressive language models, identifiability results imply that if two models induce the same conditional distributions, then their internal representations agree up to an invertible linear transformation....
Benchmarking IoT Time-Series AD with Event-Level Augmentations
arXiv:2602.15457v1 Announce Type: new Abstract: Anomaly detection (AD) for safety-critical IoT time series should be judged at the event level: reliability and earliness under realistic perturbations. Yet many studies still emphasize point-level results on curated base datasets, limiting value for...
POP: Prior-fitted Optimizer Policies
arXiv:2602.15473v1 Announce Type: new Abstract: Optimization refers to the task of finding extrema of an objective function. Classical gradient-based optimizers are highly sensitive to hyperparameter choices. In highly non-convex settings their performance relies on carefully tuned learning rates, momentum, and...
Evaluating Federated Learning for Cross-Country Mood Inference from Smartphone Sensing Data
arXiv:2602.15478v1 Announce Type: new Abstract: Mood instability is a key behavioral indicator of mental health, yet traditional assessments rely on infrequent and retrospective reports that fail to capture its continuous nature. Smartphone-based mobile sensing enables passive, in-the-wild mood inference from...
Approximation Theory for Lipschitz Continuous Transformers
arXiv:2602.15503v1 Announce Type: new Abstract: Stability and robustness are critical for deploying Transformers in safety-sensitive settings. A principled way to enforce such behavior is to constrain the model's Lipschitz constant. However, approximation-theoretic guarantees for architectures that explicitly preserve Lipschitz continuity...
The Obfuscation Atlas: Mapping Where Honesty Emerges in RLVR with Deception Probes
arXiv:2602.15515v1 Announce Type: new Abstract: Training against white-box deception detectors has been proposed as a way to make AI systems honest. However, such training risks models learning to obfuscate their deception to evade the detector. Prior work has studied obfuscation...
CEPAE: Conditional Entropy-Penalized Autoencoders for Time Series Counterfactuals
arXiv:2602.15546v1 Announce Type: new Abstract: The ability to accurately perform counterfactual inference on time series is crucial for decision-making in fields like finance, healthcare, and marketing, as it allows us to understand the impact of events or treatments on outcomes...
Law of informational cosmic spacetime: E = i mc2
Combining mass and energy with information in the inseparable quantum equivalence of E=i mc2 has opened a new informational medicine to help patients maintain truth, good health and beauty (Klimek R., Threefold Material-InformationalEnergetic Reality:E=i mc2 ,Biocosmology–neo-Aristotelism, Vol. 4(4), 2014: 408-409;...
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