A Locality Radius Framework for Understanding Relational Inductive Bias in Database Learning
arXiv:2602.17092v1 Announce Type: new Abstract: Foreign key discovery and related schema-level prediction tasks are often modeled using graph neural networks (GNNs), implicitly assuming that relational inductive bias improves performance. However, it remains unclear when multi-hop structural reasoning is actually necessary....
FLoRG: Federated Fine-tuning with Low-rank Gram Matrices and Procrustes Alignment
arXiv:2602.17095v1 Announce Type: new Abstract: Parameter-efficient fine-tuning techniques such as low-rank adaptation (LoRA) enable large language models (LLMs) to adapt to downstream tasks efficiently. Federated learning (FL) further facilitates this process by enabling collaborative fine-tuning across distributed clients without sharing...
Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy
Effectual Contract Management and Analysis with AI-Powered Technology: Reducing Errors and Saving Time in Legal Document
Examining the revolutionary effects of AI-powered tools in the field of contract analysis and management for legal document inspection is the focus of this study. The purpose of this research is to experimentally explore the likelihood of efficiency benefits and...
AI-Driven Legal Automation to Enhance Legal Processes with Natural Language Processing
The legal sector often faces delays and inefficiencies due to the overwhelming volume of information, the labor-intensive nature of research, and high service costs. This paper introduces a novel framework for AI-driven legal automation, which employs Natural Language Processing (NLP)...
Justices to consider constitutionality of tax foreclosure sales
The argument next week in Pung v Isabella County asks the court to consider the constitutionality of the longstanding practice of tax foreclosures sales. This is one of those cases […]The postJustices to consider constitutionality of tax foreclosure salesappeared first...
SCOTUStoday for Friday, February 20
Good morning, and welcome to what is likely to be the first opinion day of the month, as the justices reconvene after their winter recess. We will be live blogging […]The postSCOTUStoday for Friday, February 20appeared first onSCOTUSblog.
FCC asks stations for "pro-America" programming, like daily Pledge of Allegiance
Brendan Carr wants "patriotic" shows for Trump's yearlong America 250 celebration.
Supreme Court blocks Trump's emergency tariffs, billions in refunds may be owed
Economists estimated more than $175 billion may need to be refunded.
The creator economy’s ad revenue problem and India’s AI ambitions
The creator economy is evolving fast, and ad revenue alone isn’t cutting it anymore. YouTubers are launching product lines, acquiring startups, and building actual business empires. In fact, MrBeast’s company bought fintech startup Step, and his chocolate business is outearning...
Why creators are ditching ad revenue for chocolate bars and fintech acquisitions
The creator economy is evolving fast, and ad revenue alone isn’t cutting it anymore. YouTubers are launching product lines, acquiring startups, and building actual business empires. In fact, MrBeast’s company bought fintech startup Step, and his chocolate business is out-earning...
TechCrunch Disrupt 2026 Super Early Bird rates end in 1 week
The lowest ticket rates of the year for TechCrunch Disrupt 2026 end next Friday, February 27. Save up to $680 on your pass. Register now before prices increase.
OpenAI says 18- to 24-year-olds account for nearly 50% of ChatGPT usage in India
The company said on Friday that users between 18 and 24 years of age account for nearly 50% of all messages sent by Indians to ChatGPT, and users under 30 account for 80% of usage in the country.
General Catalyst commits $5B to India over five years
The pledge marks a sharp jump from General Catalyst's earlier $500 million–$1 billion India earmark.
Resp-Agent: An Agent-Based System for Multimodal Respiratory Sound Generation and Disease Diagnosis
arXiv:2602.15909v1 Announce Type: cross Abstract: Deep learning-based respiratory auscultation is currently hindered by two fundamental challenges: (i) inherent information loss, as converting signals into spectrograms discards transient acoustic events and clinical context; (ii) limited data availability, exacerbated by severe class...
KD4MT: A Survey of Knowledge Distillation for Machine Translation
arXiv:2602.15845v1 Announce Type: new Abstract: Knowledge Distillation (KD) as a research area has gained a lot of traction in recent years as a compression tool to address challenges related to ever-larger models in NLP. Remarkably, Machine Translation (MT) offers a...
Multi-source Heterogeneous Public Opinion Analysis via Collaborative Reasoning and Adaptive Fusion: A Systematically Integrated Approach
arXiv:2602.15857v1 Announce Type: new Abstract: The analysis of public opinion from multiple heterogeneous sources presents significant challenges due to structural differences, semantic variations, and platform-specific biases. This paper introduces a novel Collaborative Reasoning and Adaptive Fusion (CRAF) framework that systematically...
From Transcripts to AI Agents: Knowledge Extraction, RAG Integration, and Robust Evaluation of Conversational AI Assistants
arXiv:2602.15859v1 Announce Type: new Abstract: Building reliable conversational AI assistants for customer-facing industries remains challenging due to noisy conversational data, fragmented knowledge, and the requirement for accurate human hand-off - particularly in domains that depend heavily on real-time information. This...
Reranker Optimization via Geodesic Distances on k-NN Manifolds
arXiv:2602.15860v1 Announce Type: new Abstract: Current neural reranking approaches for retrieval-augmented generation (RAG) rely on cross-encoders or large language models (LLMs), requiring substantial computational resources and exhibiting latencies of 3-5 seconds per query. We propose Maniscope, a geometric reranking method...
CheckIfExist: Detecting Citation Hallucinations in the Era of AI-Generated Content
arXiv:2602.15871v1 Announce Type: new Abstract: The proliferation of large language models (LLMs) in academic workflows has introduced unprecedented challenges to bibliographic integrity, particularly through reference hallucination -- the generation of plausible but non-existent citations. Recent investigations have documented the presence...
P-RAG: Prompt-Enhanced Parametric RAG with LoRA and Selective CoT for Biomedical and Multi-Hop QA
arXiv:2602.15874v1 Announce Type: new Abstract: Large Language Models (LLMs) demonstrate remarkable capabilities but remain limited by their reliance on static training data. Retrieval-Augmented Generation (RAG) addresses this constraint by retrieving external knowledge during inference, though it still depends heavily on...
Quality-constrained Entropy Maximization Policy Optimization for LLM Diversity
arXiv:2602.15894v1 Announce Type: new Abstract: Recent research indicates that while alignment methods significantly improve the quality of large language model(LLM) outputs, they simultaneously reduce the diversity of the models' output. Although some methods have been proposed to enhance LLM output...
Every Little Helps: Building Knowledge Graph Foundation Model with Fine-grained Transferable Multi-modal Tokens
arXiv:2602.15896v1 Announce Type: new Abstract: Multi-modal knowledge graph reasoning (MMKGR) aims to predict the missing links by exploiting both graph structure information and multi-modal entity contents. Most existing works are designed for a transductive setting, which learns dataset-specific embeddings and...
Mitigating Gradient Inversion Risks in Language Models via Token Obfuscation
arXiv:2602.15897v1 Announce Type: new Abstract: Training and fine-tuning large-scale language models largely benefit from collaborative learning, but the approach has been proven vulnerable to gradient inversion attacks (GIAs), which allow adversaries to reconstruct private training data from shared gradients. Existing...
DocSplit: A Comprehensive Benchmark Dataset and Evaluation Approach for Document Packet Recognition and Splitting
arXiv:2602.15958v1 Announce Type: new Abstract: Document understanding in real-world applications often requires processing heterogeneous, multi-page document packets containing multiple documents stitched together. Despite recent advances in visual document understanding, the fundamental task of document packet splitting, which involves separating a...
Language Statistics and False Belief Reasoning: Evidence from 41 Open-Weight LMs
arXiv:2602.16085v1 Announce Type: new Abstract: Research on mental state reasoning in language models (LMs) has the potential to inform theories of human social cognition--such as the theory that mental state reasoning emerges in part from language exposure--and our understanding of...
Updating Parametric Knowledge with Context Distillation Retains Post-Training Capabilities
arXiv:2602.16093v1 Announce Type: new Abstract: Post-training endows pretrained LLMs with a variety of desirable skills, including instruction-following, reasoning, and others. However, these post-trained LLMs only encode knowledge up to a cut-off date, necessitating continual adaptation. Unfortunately, existing solutions cannot simultaneously...
Missing-by-Design: Certifiable Modality Deletion for Revocable Multimodal Sentiment Analysis
arXiv:2602.16144v1 Announce Type: new Abstract: As multimodal systems increasingly process sensitive personal data, the ability to selectively revoke specific data modalities has become a critical requirement for privacy compliance and user autonomy. We present Missing-by-Design (MBD), a unified framework for...
Balancing Faithfulness and Performance in Reasoning via Multi-Listener Soft Execution
arXiv:2602.16154v1 Announce Type: new Abstract: Chain-of-thought (CoT) reasoning sometimes fails to faithfully reflect the true computation of a large language model (LLM), hampering its utility in explaining how LLMs arrive at their answers. Moreover, optimizing for faithfulness and interpretability in...
Beyond Learning: A Training-Free Alternative to Model Adaptation
arXiv:2602.16189v1 Announce Type: new Abstract: Despite the continuous research and evolution of language models, they sometimes underperform previous versions. Existing approaches to overcome these challenges are resource-intensive, highlighting the need for alternatives that enable immediate action. We assume that each...