Causal Decoding for Hallucination-Resistant Multimodal Large Language Models
arXiv:2602.21441v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) deliver detailed responses on vision-language tasks, yet remain susceptible to object hallucination (introducing objects not present in the image), undermining reliability in practice. Prior efforts often rely on heuristic penalties,...
MINAR: Mechanistic Interpretability for Neural Algorithmic Reasoning
arXiv:2602.21442v1 Announce Type: new Abstract: The recent field of neural algorithmic reasoning (NAR) studies the ability of graph neural networks (GNNs) to emulate classical algorithms like Bellman-Ford, a phenomenon known as algorithmic alignment. At the same time, recent advances in...
When Learning Hurts: Fixed-Pole RNN for Real-Time Online Training
arXiv:2602.21454v1 Announce Type: new Abstract: Recurrent neural networks (RNNs) can be interpreted as discrete-time state-space models, where the state evolution corresponds to an infinite-impulse-response (IIR) filtering operation governed by both feedforward weights and recurrent poles. While, in principle, all parameters...
Asymptotically Fast Clebsch-Gordan Tensor Products with Vector Spherical Harmonics
arXiv:2602.21466v1 Announce Type: new Abstract: $E(3)$-equivariant neural networks have proven to be effective in a wide range of 3D modeling tasks. A fundamental operation of such networks is the tensor product, which allows interaction between different feature types. Because this...
Learning Recursive Multi-Scale Representations for Irregular Multivariate Time Series Forecasting
arXiv:2602.21498v1 Announce Type: new Abstract: Irregular Multivariate Time Series (IMTS) are characterized by uneven intervals between consecutive timestamps, which carry sampling pattern information valuable and informative for learning temporal and variable dependencies. In addition, IMTS often exhibit diverse dependencies across...
WaterVIB: Learning Minimal Sufficient Watermark Representations via Variational Information Bottleneck
arXiv:2602.21508v1 Announce Type: new Abstract: Robust watermarking is critical for intellectual property protection, whereas existing methods face a severe vulnerability against regeneration-based AIGC attacks. We identify that existing methods fail because they entangle the watermark with high-frequency cover texture, which...
Extending Sequence Length is Not All You Need: Effective Integration of Multimodal Signals for Gene Expression Prediction
arXiv:2602.21550v1 Announce Type: new Abstract: Gene expression prediction, which predicts mRNA expression levels from DNA sequences, presents significant challenges. Previous works often focus on extending input sequence length to locate distal enhancers, which may influence target genes from hundreds of...
From Basis to Basis: Gaussian Particle Representation for Interpretable PDE Operators
arXiv:2602.21551v1 Announce Type: new Abstract: Learning PDE dynamics for fluids increasingly relies on neural operators and Transformer-based models, yet these approaches often lack interpretability and struggle with localized, high-frequency structures while incurring quadratic cost in spatial samples. We propose representing...
Training-free Composition of Pre-trained GFlowNets for Multi-Objective Generation
arXiv:2602.21565v1 Announce Type: new Abstract: Generative Flow Networks (GFlowNets) learn to sample diverse candidates in proportion to a reward function, making them well-suited for scientific discovery, where exploring multiple promising solutions is crucial. Further extending GFlowNets to multi-objective settings has...
Copyright’s Invisible Hand: Subsidizing America’s Cultural Institutions
The doctrine of copyright exhaustion conceals a substantial and underappreciated subsidy at the heart of American copyright law. For more than a century, it has operated as a deliberate congressional scheme transferring billions of dollars in value to cultural institutions,...
The Beginnings Of The One Big Beautiful Bill Act: Placing The 2017 Tax Cuts And Jobs Act In Historical Perspective
On July 4, 2025, President Donald J. Trump signed into law the One Big Beautiful Bill Act (OBBBA). This new law was built on the foundations of its immediate predecessor,the 2017 Tax Cuts and Jobs Act (TCJA). This Essay examines...
Court to hear argument on whether and when drug users may possess firearms
The Supreme Court will hear oral arguments on Monday in United States v. Hemani, the second gun-rights case of the 2025-26 term. In January, the Trump administration supported Hawaii gun […]The postCourt to hear argument on whether and when drug...
Court rules criminal defendants may be prohibited from discussing ongoing testimony with counsel during an overnight recess
When a trial court recesses a criminal trial during a defendant’s testimony, the court may order the defendant and his lawyer not to discuss that testimony during the break except […]The postCourt rules criminal defendants may be prohibited from discussing...
How can the Supreme Court protect electoral integrity?
Justice, Democracy, and Law is a recurring series by Edward B. Foley that focuses on election law and the relationship of law and democracy. The court has already confronted cases […]The postHow can the Supreme Court protect electoral integrity?appeared first...
Third Time’s the Charm? The Fate of the EU–U.S. Data Privacy Framework
Ksheeraja Satish, LL.M. Class of 2026 Transatlantic transfers of personal data are fundamental to the global digital economy. However, the legal history of these transfer mechanisms has been one of successive invalidations. Each time the European Union (EU) and the...
Mistral AI inks a deal with global consulting giant Accenture
Mistral AI lands a partnership with Accenture, the consultant that has also recently announced partnerships with rivals OpenAI and Anthropic.
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...
CAMEL: Confidence-Gated Reflection for Reward Modeling
arXiv:2602.20670v1 Announce Type: new Abstract: Reward models play a fundamental role in aligning large language models with human preferences. Existing methods predominantly follow two paradigms: scalar discriminative preference models, which are efficient but lack interpretability, and generative judging models, which...
Don't Ignore the Tail: Decoupling top-K Probabilities for Efficient Language Model Distillation
arXiv:2602.20816v1 Announce Type: new Abstract: The core learning signal used in language model distillation is the standard Kullback-Leibler (KL) divergence between the student and teacher distributions. Traditional KL divergence tends to be dominated by the next tokens with the highest...
Blackbird Language Matrices: A Framework to Investigate the Linguistic Competence of Language Models
arXiv:2602.20966v1 Announce Type: new Abstract: This article describes a novel language task, the Blackbird Language Matrices (BLM) task, inspired by intelligence tests, and illustrates the BLM datasets, their construction and benchmarking, and targeted experiments on chunking and systematicity. BLMs are...
Evaluating Proactive Risk Awareness of Large Language Models
arXiv:2602.20976v1 Announce Type: new Abstract: As large language models (LLMs) are increasingly embedded in everyday decision-making, their safety responsibilities extend beyond reacting to explicit harmful intent toward anticipating unintended but consequential risks. In this work, we introduce a proactive risk...
Prompt-Level Distillation: A Non-Parametric Alternative to Model Fine-Tuning for Efficient Reasoning
arXiv:2602.21103v1 Announce Type: new Abstract: Advanced reasoning typically requires Chain-of-Thought prompting, which is accurate but incurs prohibitive latency and substantial test-time inference costs. The standard alternative, fine-tuning smaller models, often sacrifices interpretability while introducing significant resource and operational overhead. To...
On Data Engineering for Scaling LLM Terminal Capabilities
arXiv:2602.21193v1 Announce Type: new Abstract: Despite rapid recent progress in the terminal capabilities of large language models, the training data strategies behind state-of-the-art terminal agents remain largely undisclosed. We address this gap through a systematic study of data engineering practices...
Graph Modelling Analysis of Speech-Gesture Interaction for Aphasia Severity Estimation
arXiv:2602.20163v1 Announce Type: cross Abstract: Aphasia is an acquired language disorder caused by injury to the regions of the brain that are responsible for language. Aphasia may impair the use and comprehension of written and spoken language. The Western Aphasia...
MedCLIPSeg: Probabilistic Vision-Language Adaptation for Data-Efficient and Generalizable Medical Image Segmentation
arXiv:2602.20423v1 Announce Type: cross Abstract: Medical image segmentation remains challenging due to limited annotations for training, ambiguous anatomical features, and domain shifts. While vision-language models such as CLIP offer strong cross-modal representations, their potential for dense, text-guided medical image segmentation...
GATES: Self-Distillation under Privileged Context with Consensus Gating
arXiv:2602.20574v1 Announce Type: cross Abstract: We study self-distillation in settings where supervision is unreliable: there are no ground truth labels, verifiable rewards, or external graders to evaluate answers. We focus on document-grounded question answering with asymmetric context, where a single...
RMIT-ADM+S at the MMU-RAG NeurIPS 2025 Competition
arXiv:2602.20735v1 Announce Type: cross Abstract: This paper presents the award-winning RMIT-ADM+S system for the Text-to-Text track of the NeurIPS~2025 MMU-RAG Competition. We introduce Routing-to-RAG (R2RAG), a research-focused retrieval-augmented generation (RAG) architecture composed of lightweight components that dynamically adapt the retrieval...
Multimodal MRI Report Findings Supervised Brain Lesion Segmentation with Substructures
arXiv:2602.20994v1 Announce Type: cross Abstract: Report-supervised (RSuper) learning seeks to alleviate the need for dense tumor voxel labels with constraints derived from radiology reports (e.g., volumes, counts, sizes, locations). In MRI studies of brain tumors, however, we often involve multi-parametric...
Generative Pseudo-Labeling for Pre-Ranking with LLMs
arXiv:2602.20995v1 Announce Type: cross Abstract: Pre-ranking is a critical stage in industrial recommendation systems, tasked with efficiently scoring thousands of recalled items for downstream ranking. A key challenge is the train-serving discrepancy: pre-ranking models are trained only on exposed interactions,...
Tensor Network Generator-Enhanced Optimization for Traveling Salesman Problem
arXiv:2602.20175v1 Announce Type: new Abstract: We present an application of the tensor network generator-enhanced optimization (TN-GEO) framework to address the traveling salesman problem (TSP), a fundamental combinatorial optimization challenge. Our approach employs a tensor network Born machine based on automatically...