NLP Privacy Risk Identification in Social Media (NLP-PRISM): A Survey
arXiv:2602.15866v1 Announce Type: cross Abstract: Natural Language Processing (NLP) is integral to social media analytics but often processes content containing Personally Identifiable Information (PII), behavioral cues, and metadata raising privacy risks such as surveillance, profiling, and targeted advertising. To systematically...
Test-Time Adaptation for Tactile-Vision-Language Models
arXiv:2602.15873v1 Announce Type: cross Abstract: Tactile-vision-language (TVL) models are increasingly deployed in real-world robotic and multimodal perception tasks, where test-time distribution shifts are unavoidable. Existing test-time adaptation (TTA) methods provide filtering in unimodal settings but lack explicit treatment of modality-wise...
FUTURE-VLA: Forecasting Unified Trajectories Under Real-time Execution
arXiv:2602.15882v1 Announce Type: cross Abstract: General vision-language models increasingly support unified spatiotemporal reasoning over long video streams, yet deploying such capabilities on robots remains constrained by the prohibitive latency of processing long-horizon histories and generating high-dimensional future predictions. To bridge...
Doc-to-LoRA: Learning to Instantly Internalize Contexts
arXiv:2602.15902v1 Announce Type: cross Abstract: Long input sequences are central to in-context learning, document understanding, and multi-step reasoning of Large Language Models (LLMs). However, the quadratic attention cost of Transformers makes inference memory-intensive and slow. While context distillation (CD) can...
An order-oriented approach to scoring hesitant fuzzy elements
arXiv:2602.16827v1 Announce Type: new Abstract: Traditional scoring approaches on hesitant fuzzy sets often lack a formal base in order theory. This paper proposes a unified framework, where each score is explicitly defined with respect to a given order. This order-oriented...
Narrow fine-tuning erodes safety alignment in vision-language agents
arXiv:2602.16931v1 Announce Type: new Abstract: Lifelong multimodal agents must continuously adapt to new tasks through post-training, but this creates fundamental tension between acquiring capabilities and preserving safety alignment. We demonstrate that fine-tuning aligned vision-language models on narrow-domain harmful datasets induces...
LLM4Cov: Execution-Aware Agentic Learning for High-coverage Testbench Generation
arXiv:2602.16953v1 Announce Type: new Abstract: Execution-aware LLM agents offer a promising paradigm for learning from tool feedback, but such feedback is often expensive and slow to obtain, making online reinforcement learning (RL) impractical. High-coverage hardware verification exemplifies this challenge due...
Retaining Suboptimal Actions to Follow Shifting Optima in Multi-Agent Reinforcement Learning
arXiv:2602.17062v1 Announce Type: new Abstract: Value decomposition is a core approach for cooperative multi-agent reinforcement learning (MARL). However, existing methods still rely on a single optimal action and struggle to adapt when the underlying value function shifts during training, often...
How AI Coding Agents Communicate: A Study of Pull Request Description Characteristics and Human Review Responses
arXiv:2602.17084v1 Announce Type: new Abstract: The rapid adoption of large language models has led to the emergence of AI coding agents that autonomously create pull requests on GitHub. However, how these agents differ in their pull request description characteristics, and...
Agentic Wireless Communication for 6G: Intent-Aware and Continuously Evolving Physical-Layer Intelligence
arXiv:2602.17096v1 Announce Type: new Abstract: As 6G wireless systems evolve, growing functional complexity and diverse service demands are driving a shift from rule-based control to intent-driven autonomous intelligence. User requirements are no longer captured by a single metric (e.g., throughput...
Evaluating Monolingual and Multilingual Large Language Models for Greek Question Answering: The DemosQA Benchmark
arXiv:2602.16811v1 Announce Type: new Abstract: Recent advancements in Natural Language Processing and Deep Learning have enabled the development of Large Language Models (LLMs), which have significantly advanced the state-of-the-art across a wide range of tasks, including Question Answering (QA). Despite...
Claim Automation using Large Language Model
arXiv:2602.16836v1 Announce Type: new Abstract: While Large Language Models (LLMs) have achieved strong performance on general-purpose language tasks, their deployment in regulated and data-sensitive domains, including insurance, remains limited. Leveraging millions of historical warranty claims, we propose a locally deployed...
ConvApparel: A Benchmark Dataset and Validation Framework for User Simulators in Conversational Recommenders
arXiv:2602.16938v1 Announce Type: new Abstract: The promise of LLM-based user simulators to improve conversational AI is hindered by a critical "realism gap," leading to systems that are optimized for simulated interactions, but may fail to perform well in the real...
ReIn: Conversational Error Recovery with Reasoning Inception
arXiv:2602.17022v1 Announce Type: new Abstract: Conversational agents powered by large language models (LLMs) with tool integration achieve strong performance on fixed task-oriented dialogue datasets but remain vulnerable to unanticipated, user-induced errors. Rather than focusing on error prevention, this work focuses...
What Makes a Good Doctor Response? An Analysis on a Romanian Telemedicine Platform
arXiv:2602.17194v1 Announce Type: new Abstract: Text-based telemedicine has become a common mode of care, requiring clinicians to deliver medical advice clearly and effectively in writing. As platforms increasingly rely on patient ratings and feedback, clinicians face growing pressure to maintain...
Same Meaning, Different Scores: Lexical and Syntactic Sensitivity in LLM Evaluation
arXiv:2602.17316v1 Announce Type: new Abstract: The rapid advancement of Large Language Models (LLMs) has established standardized evaluation benchmarks as the primary instrument for model comparison. Yet, their reliability is increasingly questioned due to sensitivity to shallow variations in input prompts....
Learning to Stay Safe: Adaptive Regularization Against Safety Degradation during Fine-Tuning
arXiv:2602.17546v1 Announce Type: new Abstract: Instruction-following language models are trained to be helpful and safe, yet their safety behavior can deteriorate under benign fine-tuning and worsen under adversarial updates. Existing defenses often offer limited protection or force a trade-off between...
Intent Laundering: AI Safety Datasets Are Not What They Seem
arXiv:2602.16729v1 Announce Type: cross Abstract: We systematically evaluate the quality of widely used AI safety datasets from two perspectives: in isolation and in practice. In isolation, we examine how well these datasets reflect real-world attacks based on three key properties:...
PETS: A Principled Framework Towards Optimal Trajectory Allocation for Efficient Test-Time Self-Consistency
arXiv:2602.16745v1 Announce Type: new Abstract: Test-time scaling can improve model performance by aggregating stochastic reasoning trajectories. However, achieving sample-efficient test-time self-consistency under a limited budget remains an open challenge. We introduce PETS (Principled and Efficient Test-TimeSelf-Consistency), which initiates a principled...
Spatio-temporal dual-stage hypergraph MARL for human-centric multimodal corridor traffic signal control
arXiv:2602.17068v1 Announce Type: new Abstract: Human-centric traffic signal control in corridor networks must increasingly account for multimodal travelers, particularly high-occupancy public transportation, rather than focusing solely on vehicle-centric performance. This paper proposes STDSH-MARL (Spatio-Temporal Dual-Stage Hypergraph based Multi-Agent Reinforcement Learning),...
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...
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...
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...
Helpful to a Fault: Measuring Illicit Assistance in Multi-Turn, Multilingual LLM Agents
arXiv:2602.16346v1 Announce Type: new Abstract: LLM-based agents execute real-world workflows via tools and memory. These affordances enable ill-intended adversaries to also use these agents to carry out complex misuse scenarios. Existing agent misuse benchmarks largely test single-prompt instructions, leaving a...
Verifier-Constrained Flow Expansion for Discovery Beyond the Data
arXiv:2602.15984v1 Announce Type: new Abstract: Flow and diffusion models are typically pre-trained on limited available data (e.g., molecular samples), covering only a fraction of the valid design space (e.g., the full molecular space). As a consequence, they tend to generate...
Axle Sensor Fusion for Online Continual Wheel Fault Detection in Wayside Railway Monitoring
arXiv:2602.16101v1 Announce Type: new Abstract: Reliable and cost-effective maintenance is essential for railway safety, particularly at the wheel-rail interface, which is prone to wear and failure. Predictive maintenance frameworks increasingly leverage sensor-generated time-series data, yet traditional methods require manual feature...
Deep TPC: Temporal-Prior Conditioning for Time Series Forecasting
arXiv:2602.16188v1 Announce Type: new Abstract: LLM-for-time series (TS) methods typically treat time shallowly, injecting positional or prompt-based cues once at the input of a largely frozen decoder, which limits temporal reasoning as this information degrades through the layers. We introduce...
Training-Free Adaptation of Diffusion Models via Doob's $h$-Transform
arXiv:2602.16198v1 Announce Type: new Abstract: Adaptation methods have been a workhorse for unlocking the transformative power of pre-trained diffusion models in diverse applications. Existing approaches often abstract adaptation objectives as a reward function and steer diffusion models to generate high-reward...
ChartEditBench: Evaluating Grounded Multi-Turn Chart Editing in Multimodal Language Models
arXiv:2602.15758v1 Announce Type: new Abstract: While Multimodal Large Language Models (MLLMs) perform strongly on single-turn chart generation, their ability to support real-world exploratory data analysis remains underexplored. In practice, users iteratively refine visualizations through multi-turn interactions that require maintaining common...
ScrapeGraphAI-100k: A Large-Scale Dataset for LLM-Based Web Information Extraction
arXiv:2602.15189v1 Announce Type: cross Abstract: The use of large language models for web information extraction is becoming increasingly fundamental to modern web information retrieval pipelines. However, existing datasets tend to be small, synthetic or text-only, failing to capture the structural...