Decoder-based Sense Knowledge Distillation
arXiv:2602.22351v1 Announce Type: new Abstract: Large language models (LLMs) learn contextual embeddings that capture rich semantic information, yet they often overlook structured lexical knowledge such as word senses and relationships. Prior work has shown that incorporating sense dictionaries can improve...
Scaling In, Not Up? Testing Thick Citation Context Analysis with GPT-5 and Fragile Prompts
arXiv:2602.22359v1 Announce Type: new Abstract: This paper tests whether large language models (LLMs) can support interpretative citation context analysis (CCA) by scaling in thick, text-grounded readings of a single hard case rather than scaling up typological labels. It foregrounds prompt-sensitivity...
Causality $\neq$ Invariance: Function and Concept Vectors in LLMs
arXiv:2602.22424v1 Announce Type: new Abstract: Do large language models (LLMs) represent concepts abstractly, i.e., independent of input format? We revisit Function Vectors (FVs), compact representations of in-context learning (ICL) tasks that causally drive task performance. Across multiple LLMs, we show...
A Fusion of context-aware based BanglaBERT and Two-Layer Stacked LSTM Framework for Multi-Label Cyberbullying Detection
arXiv:2602.22449v1 Announce Type: new Abstract: Cyberbullying has become a serious and growing concern in todays virtual world. When left unnoticed, it can have adverse consequences for social and mental health. Researchers have explored various types of cyberbullying, but most approaches...
Mind the Gap in Cultural Alignment: Task-Aware Culture Management for Large Language Models
arXiv:2602.22475v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly deployed in culturally sensitive real-world tasks. However, existing cultural alignment approaches fail to align LLMs' broad cultural values with the specific goals of downstream tasks and suffer from cross-culture...
Sydney Telling Fables on AI and Humans: A Corpus Tracing Memetic Transfer of Persona between LLMs
arXiv:2602.22481v1 Announce Type: new Abstract: The way LLM-based entities conceive of the relationship between AI and humans is an important topic for both cultural and safety reasons. When we examine this topic, what matters is not only the model itself...
Efficient Dialect-Aware Modeling and Conditioning for Low-Resource Taiwanese Hakka Speech Processing
arXiv:2602.22522v1 Announce Type: new Abstract: Taiwanese Hakka is a low-resource, endangered language that poses significant challenges for automatic speech recognition (ASR), including high dialectal variability and the presence of two distinct writing systems (Hanzi and Pinyin). Traditional ASR models often...
Iterative Prompt Refinement for Dyslexia-Friendly Text Summarization Using GPT-4o
arXiv:2602.22524v1 Announce Type: new Abstract: Dyslexia affects approximately 10% of the global population and presents persistent challenges in reading fluency and text comprehension. While existing assistive technologies address visual presentation, linguistic complexity remains a substantial barrier to equitable access. This...
Ruyi2 Technical Report
arXiv:2602.22543v1 Announce Type: new Abstract: Large Language Models (LLMs) face significant challenges regarding deployment costs and latency, necessitating adaptive computing strategies. Building upon the AI Flow framework, we introduce Ruyi2 as an evolution of our adaptive model series designed for...
Search-P1: Path-Centric Reward Shaping for Stable and Efficient Agentic RAG Training
arXiv:2602.22576v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) enhances large language models (LLMs) by incorporating external knowledge, yet traditional single-round retrieval struggles with complex multi-step reasoning. Agentic RAG addresses this by enabling LLMs to dynamically decide when and what to...
Towards Faithful Industrial RAG: A Reinforced Co-adaptation Framework for Advertising QA
arXiv:2602.22584v1 Announce Type: new Abstract: Industrial advertising question answering (QA) is a high-stakes task in which hallucinated content, particularly fabricated URLs, can lead to financial loss, compliance violations, and legal risk. Although Retrieval-Augmented Generation (RAG) is widely adopted, deploying it...
dLLM: Simple Diffusion Language Modeling
arXiv:2602.22661v1 Announce Type: new Abstract: Although diffusion language models (DLMs) are evolving quickly, many recent models converge on a set of shared components. These components, however, are distributed across ad-hoc research codebases or lack transparent implementations, making them difficult to...
The Poly Problem in Zoning: Redefining “Family” for a Changing Society lawreview - Minnesota Law Review
By ARIC SHORT & TANYA PIERCE. Full Text. Single-family zoning has long dictated not only where people may live but also with whom. Although extensively critiqued for perpetuating racial and economic exclusion, these laws also privilege relationships defined by blood,...
The Innocence Trap lawreview - Minnesota Law Review
By CAITLIN GLASS & JULIAN GREEN. Full Text. What makes a conviction wrongful? Developments in DNA science have led to a wave of exonerations over the past thirty years, revealing sources of error in the criminal legal process. Innocence organizations...
The Skidmore Compromise: Interpreting Skidmore as a Tiebreaker to Preserve Judicial Wisdom in the Era of Loper Bright lawreview - Minnesota Law Review
By MITCHELL ZAIC. Full Text. 'Law must be stable, and yet it cannot stand still.' Here is the great antinomy confronting us at every turn. Rest and motion, unrelieved and unchecked, are equally destructive. The law, like human kind, if...
Trump moves to ban Anthropic from the US government
The Defense Department pressured Anthropic to drop restrictions on how its AI can be used by the military.
ESG Investing Under Scrutiny: Legal and Regulatory Developments in 2026
ESG investing faces both increased regulatory support in some jurisdictions and political backlash in others, creating a complex compliance landscape.
The Rise of AI-Powered Legal Research: Transforming How Lawyers Work
AI-powered legal research tools are fundamentally changing the practice of law, offering unprecedented efficiency while raising questions about quality and oversight.
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.
Digital Sovereignty: How Nations Are Asserting Control Over Technology Infrastructure
Countries worldwide are implementing digital sovereignty measures to control data flows, technology standards, and digital infrastructure within their borders.
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.
Reinforcing Real-world Service Agents: Balancing Utility and Cost in Task-oriented Dialogue
arXiv:2602.22697v1 Announce Type: new Abstract: The rapid evolution of Large Language Models (LLMs) has accelerated the transition from conversational chatbots to general agents. However, effectively balancing empathetic communication with budget-aware decision-making remains an open challenge. Since existing methods fail to...
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...
Imagination Helps Visual Reasoning, But Not Yet in Latent Space
arXiv:2602.22766v1 Announce Type: new Abstract: Latent visual reasoning aims to mimic human's imagination process by meditating through hidden states of Multimodal Large Language Models. While recognized as a promising paradigm for visual reasoning, the underlying mechanisms driving its effectiveness remain...
Probing for Knowledge Attribution in Large Language Models
arXiv:2602.22787v1 Announce Type: new Abstract: Large language models (LLMs) often generate fluent but unfounded claims, or hallucinations, which fall into two types: (i) faithfulness violations - misusing user context - and (ii) factuality violations - errors from internal knowledge. Proper...
TARAZ: Persian Short-Answer Question Benchmark for Cultural Evaluation of Language Models
arXiv:2602.22827v1 Announce Type: new Abstract: This paper presents a comprehensive evaluation framework for assessing the cultural competence of large language models (LLMs) in Persian. Existing Persian cultural benchmarks rely predominantly on multiple-choice formats and English-centric metrics that fail to capture...
Improving Neural Argumentative Stance Classification in Controversial Topics with Emotion-Lexicon Features
arXiv:2602.22846v1 Announce Type: new Abstract: Argumentation mining comprises several subtasks, among which stance classification focuses on identifying the standpoint expressed in an argumentative text toward a specific target topic. While arguments-especially about controversial topics-often appeal to emotions, most prior work...
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...
Where Vision Becomes Text: Locating the OCR Routing Bottleneck in Vision-Language Models
arXiv:2602.22918v1 Announce Type: new Abstract: Vision-language models (VLMs) can read text from images, but where does this optical character recognition (OCR) information enter the language processing stream? We investigate the OCR routing mechanism across three architecture families (Qwen3-VL, Phi-4, InternVL3.5)...
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...