Results for "prompting"

arXiv:2603.28555v1 Announce Type: cross Abstract: Large pre-trained vision-language models like CLIP have transformed computer vision by aligning images and tex

arXiv:2506.21138v2 Announce Type: replace-cross Abstract: High-quality labeled datasets are fundamental for training and evaluating machine learning models, yet

arXiv:2603.25944v1 Announce Type: cross Abstract: Large language models show promise for legal applications, but deploying frontier models raises concerns about

arXiv:2603.25960v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly deployed in medical settings, yet their sensitivity to prompt fo

arXiv:2603.26244v1 Announce Type: cross Abstract: Domain-driven design (DDD) is a powerful design technique for architecting complex software systems. This pape

arXiv:2405.00181v3 Announce Type: replace-cross Abstract: Video anomaly understanding (VAU) aims to automatically comprehend unusual occurrences in videos, ther

arXiv:2603.25379v1 Announce Type: new Abstract: Does structured intent representation generalize across languages and models? We study PPS (Prompt Protocol Spec

arXiv:2603.25613v1 Announce Type: cross Abstract: Multimodal Large Language Models (MLLMs) have recently been explored as face verification systems that determi

arXiv:2507.20423v4 Announce Type: replace-cross Abstract: Recent studies have explored various approaches for treating candidate named entity spans as both sour

arXiv:2603.06663v2 Announce Type: replace-cross Abstract: Recent advances in training-free visual prompting, such as Set-of-Mark, have emerged as a promising di

arXiv:2603.11583v2 Announce Type: replace-cross Abstract: The success of a Large Language Model (LLM) task depends heavily on its prompt. Most use-cases specify

Another iPhone update has just landed: is it so good you should install it immediately, or hold off for a while?

You asked Claude to build something. It worked. You shipped it. Three weeks later, something breaks in a way that's weirdly hard to debug. The code is technical

arXiv:2603.24065v1 Announce Type: new Abstract: Current prompting paradigms for large language models (LLMs), including Chain-of-Thought (CoT) and Tree-of-Thoug

arXiv:2506.06303v5 Announce Type: replace-cross Abstract: Reinforcement learning (RL) is a framework for solving sequential decision-making problems. In this wo

arXiv:2603.09643v3 Announce Type: replace-cross Abstract: Current evaluation frameworks and benchmarks for LLM powered agents focus on text chat driven agents,

Nearly half of publisher search traffic has disappeared post-AI Overviews, raising urgent questions about how content gets funded next. The post Half Your Traff

AI cyberattacks are rising fast, exposing limits of software security. Here is why institutions are shifting to hardware and what Ledger’s move signals next

arXiv:2603.22619v1 Announce Type: new Abstract: LLMs often generate seemingly valid answers to flawed or ill-posed inputs. This is not due to missing knowledge:

arXiv:2603.22288v1 Announce Type: cross Abstract: Prompting strategies affect LLM reasoning performance, but their role in chart-based QA remains underexplored.

arXiv:2603.23269v1 Announce Type: cross Abstract: Large Language Models(LLMs) are widely deployed, yet are vulnerable to jailbreak prompts that elicit policy-vi

Consider the stark contrast between two modern developers approaching the exact same engineering problem. The first developer casually types into their artifici

Research finds that persona prompts "reliably damage" factual accuracy in certain kinds of tasks but work well in others. The post Research Shows Where Persona

arXiv:2603.19531v1 Announce Type: cross Abstract: Open-Vocabulary Semantic Segmentation (OVSS) assigns pixel-level labels from an open set of text-defined categ