CRISPR Gene Therapy Patents: The Legal Battle Reshaping Biotechnology
The ongoing patent disputes surrounding CRISPR gene editing technology have profound implications for biotech innovation, patient access, and IP strategy.
Zero-Day Vulnerabilities in Enterprise AI Systems: Legal and Technical Implications
The discovery of critical zero-day vulnerabilities in widely deployed AI systems raises urgent questions about cybersecurity liability and disclosure obligations.
Towards Better RL Training Data Utilization via Second-Order Rollout
arXiv:2602.22765v1 Announce Type: new Abstract: Reinforcement Learning (RL) has empowered Large Language Models (LLMs) with strong reasoning capabilities, but vanilla RL mainly focuses on generation capability improvement by training with only first-order rollout (generating multiple responses for a question), and...
Test-Time Scaling with Diffusion Language Models via Reward-Guided Stitching
arXiv:2602.22871v1 Announce Type: new Abstract: Reasoning with large language models often benefits from generating multiple chains-of-thought, but existing aggregation strategies are typically trajectory-level (e.g., selecting the best trace or voting on the final answer), discarding useful intermediate work from partial...
Toward Automatic Filling of Case Report Forms: A Case Study on Data from an Italian Emergency Department
arXiv:2602.23062v1 Announce Type: new Abstract: Case Report Forms (CRFs) collect data about patients and are at the core of well-established practices to conduct research in clinical settings. With the recent progress of language technologies, there is an increasing interest in...
Quantity Convergence, Quality Divergence: Disentangling Fluency and Accuracy in L2 Mandarin Prosody
arXiv:2602.23071v1 Announce Type: new Abstract: While second language (L2) learners may acquire target syntactic word order, mapping this syntax onto appropriate prosodic structures remains a persistent challenge. This study investigates the fossilization and stability of the L2 syntax-prosody interface by...
Causal Direction from Convergence Time: Faster Training in the True Causal Direction
arXiv:2602.22254v1 Announce Type: new Abstract: We introduce Causal Computational Asymmetry (CCA), a principle for causal direction identification based on optimization dynamics in which one neural network is trained to predict $Y$ from $X$ and another to predict $X$ from $Y$,...
AutoQRA: Joint Optimization of Mixed-Precision Quantization and Low-rank Adapters for Efficient LLM Fine-Tuning
arXiv:2602.22268v1 Announce Type: new Abstract: Quantization followed by parameter-efficient fine-tuning has emerged as a promising paradigm for downstream adaptation under tight GPU memory constraints. However, this sequential pipeline fails to leverage the intricate interaction between quantization bit-width and LoRA rank....
CQSA: Byzantine-robust Clustered Quantum Secure Aggregation in Federated Learning
arXiv:2602.22269v1 Announce Type: new Abstract: Federated Learning (FL) enables collaborative model training without sharing raw data. However, shared local model updates remain vulnerable to inference and poisoning attacks. Secure aggregation schemes have been proposed to mitigate these attacks. In this...
Positional-aware Spatio-Temporal Network for Large-Scale Traffic Prediction
arXiv:2602.22274v1 Announce Type: new Abstract: Traffic flow forecasting has emerged as an indispensable mission for daily life, which is required to utilize the spatiotemporal relationship between each location within a time period under a graph structure to predict future flow....
BrepCoder: A Unified Multimodal Large Language Model for Multi-task B-rep Reasoning
arXiv:2602.22284v1 Announce Type: new Abstract: Recent advancements in deep learning have actively addressed complex challenges within the Computer-Aided Design (CAD) domain.However, most existing approaches rely on task-specifi c models requiring structural modifi cations for new tasks, and they predominantly focus...
OmniZip: Learning a Unified and Lightweight Lossless Compressor for Multi-Modal Data
arXiv:2602.22286v1 Announce Type: new Abstract: Lossless compression is essential for efficient data storage and transmission. Although learning-based lossless compressors achieve strong results, most of them are designed for a single modality, leading to redundant compressor deployments in multi-modal settings. Designing...
Structure and Redundancy in Large Language Models: A Spectral Study via Random Matrix Theory
arXiv:2602.22345v1 Announce Type: new Abstract: This thesis addresses two persistent and closely related challenges in modern deep learning, reliability and efficiency, through a unified framework grounded in Spectral Geometry and Random Matrix Theory (RMT). As deep networks and large language...
A Learning-Based Hybrid Decision Framework for Matching Systems with User Departure Detection
arXiv:2602.22412v1 Announce Type: new Abstract: In matching markets such as kidney exchanges and freight exchanges, delayed matching has been shown to improve overall market efficiency. The benefits of delay are highly sensitive to participants' sojourn times and departure behavior, and...
From Bias to Balance: Fairness-Aware Paper Recommendation for Equitable Peer Review
arXiv:2602.22438v1 Announce Type: new Abstract: Despite frequent double-blind review, systemic biases related to author demographics still disadvantage underrepresented groups. We start from a simple hypothesis: if a post-review recommender is trained with an explicit fairness regularizer, it should increase inclusion...
Announcement of opinions for Wednesday, March 4
We will be live blogging as the court potentially releases opinions in one or more argued cases from the current term. Click here for a list of FAQs about opinion […]The postAnnouncement of opinions for Wednesday, March 4appeared first onSCOTUSblog.
Supreme Court to consider whether freight brokers can be held liable for negligent hiring
In Montgomery v. Caribe Transport II, to be argued on Wednesday, March 4, the court will consider whether a federal law initially designed to deal with state trucking regulations supersedes […]The postSupreme Court to consider whether freight brokers can be...
Justices appear dubious of challenge to constitutionality of foreclosure sales
The argument yesterday in Pung v Isabella County had two distinct threads. On the one hand, the justices who discussed the question presented seemed to have no doubt that they […]The postJustices appear dubious of challenge to constitutionality of foreclosure...
United States v. Hemani: an animated explainer
SCOTUSblog is thrilled to introduce the first in a series of animated videos, done in partnership with Briefly, on some of the most important upcoming cases of the 2025-26 term. Today’s […]The postUnited States v. Hemani: an animated explainerappeared first...
SCOTUStoday for Friday, February 27
We’re thrilled to introduce the first in a series of animated videos, done in partnership with Briefly, on some of the most important upcoming cases of the current term. This first […]The postSCOTUStoday for Friday, February 27appeared first onSCOTUSblog.
Anthropic vs. the Pentagon: What’s actually at stake?
Anthropic and the Pentagon are clashing over AI use in autonomous weapons and surveillance, raising high-stakes questions about national security, corporate control, and who sets the rules for military AI.
Who’s really running AI? Inside the billion-dollar battle over regulation with Alex Bores
The Pentagon is playing chicken with Anthropic over who gets to control how the military uses AI while communities across the country are blocking data center construction. As the AI debate has been flattened to “doomers versus boomers,” one state...
RADAR: Reasoning as Discrimination with Aligned Representations for LLM-based Knowledge Graph Reasoning
arXiv:2602.21951v1 Announce Type: new Abstract: Knowledge graph reasoning (KGR) infers missing facts, with recent advances increasingly harnessing the semantic priors and reasoning abilities of Large Language Models (LLMs). However, prevailing generative paradigms are prone to memorizing surface-level co-occurrences rather than...
DLT-Corpus: A Large-Scale Text Collection for the Distributed Ledger Technology Domain
arXiv:2602.22045v1 Announce Type: new Abstract: We introduce DLT-Corpus, the largest domain-specific text collection for Distributed Ledger Technology (DLT) research to date: 2.98 billion tokens from 22.12 million documents spanning scientific literature (37,440 publications), United States Patent and Trademark Office (USPTO)...
On the Structural Non-Preservation of Epistemic Behaviour under Policy Transformation
arXiv:2602.21424v1 Announce Type: new Abstract: Reinforcement learning (RL) agents under partial observability often condition actions on internally accumulated information such as memory or inferred latent context. We formalise such information-conditioned interaction patterns as behavioural dependency: variation in action selection with...
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...
Mamba Meets Scheduling: Learning to Solve Flexible Job Shop Scheduling with Efficient Sequence Modeling
arXiv:2602.21546v1 Announce Type: new Abstract: The Flexible Job Shop Problem (FJSP) is a well-studied combinatorial optimization problem with extensive applications for manufacturing and production scheduling. It involves assigning jobs to various machines to optimize criteria, such as minimizing total completion...
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...
ABM-UDE: Developing Surrogates for Epidemic Agent-Based Models via Scientific Machine Learning
arXiv:2602.21588v1 Announce Type: new Abstract: Agent-based epidemic models (ABMs) encode behavioral and policy heterogeneity but are too slow for nightly hospital planning. We develop county-ready surrogates that learn directly from exascale ABM trajectories using Universal Differential Equations (UDEs): mechanistic SEIR-family...
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,...