LogicGraph : Benchmarking Multi-Path Logical Reasoning via Neuro-Symbolic Generation and Verification
arXiv:2602.21044v1 Announce Type: new Abstract: Evaluations of large language models (LLMs) primarily emphasize convergent logical reasoning, where success is defined by producing a single correct proof. However, many real-world reasoning problems admit multiple valid derivations, requiring models to explore diverse...
Decomposing Physician Disagreement in HealthBench
arXiv:2602.22758v1 Announce Type: new Abstract: We decompose physician disagreement in the HealthBench medical AI evaluation dataset to understand where variance resides and what observable features can explain it. Rubric identity accounts for 15.8% of met/not-met label variance but only 3.6-6.9%...
Certified Circuits: Stability Guarantees for Mechanistic Circuits
arXiv:2602.22968v1 Announce Type: new Abstract: Understanding how neural networks arrive at their predictions is essential for debugging, auditing, and deployment. Mechanistic interpretability pursues this goal by identifying circuits - minimal subnetworks responsible for specific behaviors. However, existing circuit discovery methods...
Copyright Protection for AI-Generated Works
Since the 2010s, artificial intelligence (AI) has quickly grown from another subset of machine learning (ie deep learning) in particular with recent advances in generative AI, such as ChatGPT. The use of generative AI has gone beyond leisure purposes. It...
Multi-dimensional Assessment and Explainable Feedback for Counselor Responses to Client Resistance in Text-based Counseling with LLMs
arXiv:2602.21638v1 Announce Type: new Abstract: Effectively addressing client resistance is a sophisticated clinical skill in psychological counseling, yet practitioners often lack timely and scalable supervisory feedback to refine their approaches. Although current NLP research has examined overall counseling quality and...
GradAlign: Gradient-Aligned Data Selection for LLM Reinforcement Learning
arXiv:2602.21492v1 Announce Type: new Abstract: Reinforcement learning (RL) has become a central post-training paradigm for large language models (LLMs), but its performance is highly sensitive to the quality of training problems. This sensitivity stems from the non-stationarity of RL: rollouts...
CARE: An Explainable Computational Framework for Assessing Client-Perceived Therapeutic Alliance Using Large Language Models
arXiv:2602.20648v1 Announce Type: new Abstract: Client perceptions of the therapeutic alliance are critical for counseling effectiveness. Accurately capturing these perceptions remains challenging, as traditional post-session questionnaires are burdensome and often delayed, while existing computational approaches produce coarse scores, lack interpretable...
Quantitative Approximation Rates for Group Equivariant Learning
arXiv:2602.20370v1 Announce Type: new Abstract: The universal approximation theorem establishes that neural networks can approximate any continuous function on a compact set. Later works in approximation theory provide quantitative approximation rates for ReLU networks on the class of $\alpha$-H\"older functions...
CITED: A Decision Boundary-Aware Signature for GNNs Towards Model Extraction Defense
arXiv:2602.20418v1 Announce Type: new Abstract: Graph neural networks (GNNs) have demonstrated superior performance in various applications, such as recommendation systems and financial risk management. However, deploying large-scale GNN models locally is particularly challenging for users, as it requires significant computational...
Nonparametric Teaching of Attention Learners
arXiv:2602.20461v1 Announce Type: new Abstract: Attention learners, neural networks built on the attention mechanism, e.g., transformers, excel at learning the implicit relationships that relate sequences to their corresponding properties, e.g., mapping a given sequence of tokens to the probability of...
Online decoding of rat self-paced locomotion speed from EEG using recurrent neural networks
arXiv:2602.18637v1 Announce Type: new Abstract: $\textit{Objective.}$ Accurate neural decoding of locomotion holds promise for advancing rehabilitation, prosthetic control, and understanding neural correlates of action. Recent studies have demonstrated decoding of locomotion kinematics across species on motorized treadmills. However, efforts to...
Neural Synchrony Between Socially Interacting Language Models
arXiv:2602.17815v1 Announce Type: new Abstract: Neuroscience has uncovered a fundamental mechanism of our social nature: human brain activity becomes synchronized with others in many social contexts involving interaction. Traditionally, social minds have been regarded as an exclusive property of living...
Information-Theoretic Storage Cost in Sentence Comprehension
arXiv:2602.18217v1 Announce Type: new Abstract: Real-time sentence comprehension imposes a significant load on working memory, as comprehenders must maintain contextual information to anticipate future input. While measures of such load have played an important role in psycholinguistic theories, they have...
SPQ: An Ensemble Technique for Large Language Model Compression
arXiv:2602.18420v1 Announce Type: new Abstract: This study presents an ensemble technique, SPQ (SVD-Pruning-Quantization), for large language model (LLM) compression that combines variance-retained singular value decomposition (SVD), activation-based pruning, and post-training linear quantization. Each component targets a different source of inefficiency:...
COMBA: Cross Batch Aggregation for Learning Large Graphs with Context Gating State Space Models
arXiv:2602.17893v1 Announce Type: new Abstract: State space models (SSMs) have recently emerged for modeling long-range dependency in sequence data, with much simplified computational costs than modern alternatives, such as transformers. Advancing SMMs to graph structured data, especially for large graphs,...
S-PRESSO: Ultra Low Bitrate Sound Effect Compression With Diffusion Autoencoders And Offline Quantization
arXiv:2602.15082v1 Announce Type: cross Abstract: Neural audio compression models have recently achieved extreme compression rates, enabling efficient latent generative modeling. Conversely, latent generative models have been applied to compression, pushing the limits of continuous and discrete approaches. However, existing methods...
PolyNODE: Variable-dimension Neural ODEs on M-polyfolds
arXiv:2602.15128v1 Announce Type: cross Abstract: Neural ordinary differential equations (NODEs) are geometric deep learning models based on dynamical systems and flows generated by vector fields on manifolds. Despite numerous successful applications, particularly within the flow matching paradigm, all existing NODE...
ExpertWeaver: Unlocking the Inherent MoE in Dense LLMs with GLU Activation Patterns
arXiv:2602.15521v1 Announce Type: new Abstract: Mixture-of-Experts (MoE) effectively scales model capacity while preserving computational efficiency through sparse expert activation. However, training high-quality MoEs from scratch is prohibitively expensive. A promising alternative is to convert pretrained dense models into sparse MoEs....
Beyond Static Pipelines: Learning Dynamic Workflows for Text-to-SQL
arXiv:2602.15564v1 Announce Type: new Abstract: Text-to-SQL has recently achieved impressive progress, yet remains difficult to apply effectively in real-world scenarios. This gap stems from the reliance on single static workflows, fundamentally limiting scalability to out-of-distribution and long-tail scenarios. Instead of...
LLM-to-Speech: A Synthetic Data Pipeline for Training Dialectal Text-to-Speech Models
arXiv:2602.15675v1 Announce Type: new Abstract: Despite the advances in neural text to speech (TTS), many Arabic dialectal varieties remain marginally addressed, with most resources concentrated on Modern Spoken Arabic (MSA) and Gulf dialects, leaving Egyptian Arabic -- the most widely...
Towards Efficient Constraint Handling in Neural Solvers for Routing Problems
arXiv:2602.16012v1 Announce Type: new Abstract: Neural solvers have achieved impressive progress in addressing simple routing problems, particularly excelling in computational efficiency. However, their advantages under complex constraints remain nascent, for which current constraint-handling schemes via feasibility masking or implicit feasibility...
Causally-Guided Automated Feature Engineering with Multi-Agent Reinforcement Learning
arXiv:2602.16435v1 Announce Type: new Abstract: Automated feature engineering (AFE) enables AI systems to autonomously construct high-utility representations from raw tabular data. However, existing AFE methods rely on statistical heuristics, yielding brittle features that fail under distribution shift. We introduce CAFE,...
Epistemology of Generative AI: The Geometry of Knowing
arXiv:2602.17116v1 Announce Type: new Abstract: Generative AI presents an unprecedented challenge to our understanding of knowledge and its production. Unlike previous technological transformations, where engineering understanding preceded or accompanied deployment, generative AI operates through mechanisms whose epistemic character remains obscure,...
Sink-Aware Pruning for Diffusion Language Models
arXiv:2602.17664v1 Announce Type: new Abstract: Diffusion Language Models (DLMs) incur high inference cost due to iterative denoising, motivating efficient pruning. Existing pruning heuristics largely inherited from autoregressive (AR) LLMs, typically preserve attention sink tokens because AR sinks serve as stable...
Machine Learning Argument of Latitude Error Model for LEO Satellite Orbit and Covariance Correction
arXiv:2602.16764v1 Announce Type: new Abstract: Low Earth orbit (LEO) satellites are leveraged to support new position, navigation, and timing (PNT) service alternatives to GNSS. These alternatives require accurate propagation of satellite position and velocity with a realistic quantification of uncertainty....
Neural Proposals, Symbolic Guarantees: Neuro-Symbolic Graph Generation with Hard Constraints
arXiv:2602.16954v1 Announce Type: new Abstract: We challenge black-box purely deep neural approaches for molecules and graph generation, which are limited in controllability and lack formal guarantees. We introduce Neuro-Symbolic Graph Generative Modeling (NSGGM), a neurosymbolic framework that reapproaches molecule generation...
AdvSynGNN: Structure-Adaptive Graph Neural Nets via Adversarial Synthesis and Self-Corrective Propagation
arXiv:2602.17071v1 Announce Type: new Abstract: Graph neural networks frequently encounter significant performance degradation when confronted with structural noise or non-homophilous topologies. To address these systemic vulnerabilities, we present AdvSynGNN, a comprehensive architecture designed for resilient node-level representation learning. The proposed...
CLAA: Cross-Layer Attention Aggregation for Accelerating LLM Prefill
arXiv:2602.16054v1 Announce Type: new Abstract: The prefill stage in long-context LLM inference remains a computational bottleneck. Recent token-ranking heuristics accelerate inference by selectively processing a subset of semantically relevant tokens. However, existing methods suffer from unstable token importance estimation, often...
Distributed physics-informed neural networks via domain decomposition for fast flow reconstruction
arXiv:2602.15883v1 Announce Type: new Abstract: Physics-Informed Neural Networks (PINNs) offer a powerful paradigm for flow reconstruction, seamlessly integrating sparse velocity measurements with the governing Navier-Stokes equations to recover complete velocity and latent pressure fields. However, scaling such models to large...
MolCrystalFlow: Molecular Crystal Structure Prediction via Flow Matching
arXiv:2602.16020v1 Announce Type: new Abstract: Molecular crystal structure prediction represents a grand challenge in computational chemistry due to large sizes of constituent molecules and complex intra- and intermolecular interactions. While generative modeling has revolutionized structure discovery for molecules, inorganic solids,...