Large Language Models (LLMs) deliver powerful reasoning and generation capabilities but incur substantial run-time costs when operating in agentic workflows that chain together lengthy prompts and process rich data streams. We introduce CompactPrompt, an end-to-end pipeline that merges hard prompt compression with lightweight file-level data compression. CompactPrompt first prunes low-information tokens from prompts using self-information scoring and dependency-based phrase grouping. In parallel, it applies n-gram abbreviation to recurrent textual patterns in attached documents and uniform quantization to numerical columns, yielding compact yet semantically faithful representations. Integrated into standard LLM agents, CompactPrompt reduces total token usage and inference cost by up to 60% on benchmark dataset like TAT-QA and FinQA, while preserving output quality (Results in less than 5% accuracy drop for Claude-3.5-Sonnet, and GPT-4.1-Mini) CompactPrompt helps visualize real-time compression decisions and quantify cost-performance trade-offs, laying the groundwork for leaner generative AI pipelines.

Preprint repositories become central infrastructures for scholarly communication. Their expansion transforms how research is circulated and evaluated before journal publication. Generative large language models (LLMs) introduce a further potential disruption by altering how manuscripts are written. While speculation abounds, systematic evidence of whether and how LLMs reshape scientific publishing remains limited. This paper addresses the gap through a large-scale analysis of more than 2.1 million preprints spanning 2016--2025 (115 months) across four major repositories (i.e., arXiv, bioRxiv, medRxiv, SocArXiv). We introduce a multi-level analytical framework that integrates interrupted time-series models, collaboration and productivity metrics, linguistic profiling, and topic modeling to assess changes in volume, authorship, style, and disciplinary orientation. Our findings reveal that LLMs have accelerated submission and revision cycles, modestly increased linguistic complexity, and disproportionately expanded AI-related topics, while computationally intensive fields benefit more than others. These results show that LLMs act less as universal disruptors than as selective catalysts, amplifying existing strengths and widening disciplinary divides. By documenting these dynamics, the paper provides the first empirical foundation for evaluating the influence of generative AI on academic publishing and highlights the need for governance frameworks that preserve trust, fairness, and accountability in an AI-enabled research ecosystem.

Rapid advancement in generative AI and large language models (LLMs) has enabled the generation of highly realistic and contextually relevant digital content. LLMs such as ChatGPT with DALL-E integration and Stable Diffusion techniques can produce images that are often indistinguishable from those created by humans, which poses challenges for digital content authentication. Verifying the integrity and origin of digital data to ensure it remains unaltered and genuine is crucial to maintaining trust and legality in digital media. In this paper, we propose an embedding-based AI image detection framework that utilizes image embeddings and a vector similarity to distinguish AI-generated images from real (human-created) ones. Our methodology is built on the hypothesis that AI-generated images demonstrate closer embedding proximity to other AI-generated content, while human-created images cluster similarly within their domain. To validate this hypothesis, we developed a system that processes a diverse dataset of AI and human-generated images through five benchmark embedding models. Extensive experimentation demonstrates the robustness of our approach, and our results confirm that moderate to high perturbations minimally impact the embedding signatures, with perturbed images maintaining close similarity matches to their original versions. Our solution provides a generalizable framework for AI-generated image detection that balances accuracy with computational efficiency.

Large Language Models (LLMs) have rapidly transitioned from academic research to core components of real-world applications, especially since the emergence of high-profile foundation models such as OpenAI's GPT series [17, 140], Google Gemini [9], Meta Llama [175, 176], Anthropic Claude [12], Alibaba Qwen [11, 210, 209], and Doubao [172]. Today, LLMs are deployed across an unprecedented range of sectors--from web search and code assistants to legal, educational, and healthcare domains--reaching hundreds of millions of end users globally. The rapid adoption of LLMs has ushered in a new era of AI-powered services, but it also brings serious safety and security risks. These risks manifest in multiple forms, ranging from misinformation and privacy leaks to adversarial attacks that exploit model vulnerabilities. In particular, a growing body of work shows that carefully crafted jailbreak prompts can bypass alignment constraints, inducing models to produce sensitive, illegal, or harmful content. Alarmingly, recent studies report that such attacks achieve success rates exceeding 90% even on flagship models such as GPT-4, Claude 3, and DeepSeek-R1 [124, 42, 154, 118]. The outputs generated through these attacks could be used for malicious purposes, underscoring the urgent need for close attention and mitigation.

We present FinAI Data Assistant, a practical approach for natural-language querying over financial databases that combines large language models (LLMs) with the OpenAI Function Calling API. Rather than synthesizing complete SQL via text-to-SQL, our system routes user requests to a small library of vetted, parameterized queries, trading generative flexibility for reliability, low latency, and cost efficiency. We empirically study three questions: (RQ1) whether LLMs alone can reliably recall or extrapolate time-dependent financial data without external retrieval; (RQ2) how well LLMs map company names to stock ticker symbols; and (RQ3) whether function calling outperforms text-to-SQL for end-to-end database query processing. Across controlled experiments on prices and fundamentals, LLM-only predictions exhibit non-negligible error and show look-ahead bias primarily for stock prices relative to model knowledge cutoffs. Ticker-mapping accuracy is near-perfect for NASDAQ-100 constituents and high for S\&P~500 firms. Finally, FinAI Data Assistant achieves lower latency and cost and higher reliability than a text-to-SQL baseline on our task suite. We discuss design trade-offs, limitations, and avenues for deployment.

Despite their strong performance on reasoning tasks, large reasoning models (LRMs) often suffer from overthinking, producing unnecessarily long outputs and incurring high end-to-end latency, a significant limitation to their real-world deployment. To address overthinking, early-exit mechanisms have been proposed to terminate reasoning before typical completion, showing that this approach can effectively shorten generation length with minimal impact on accuracy. However, their reliance on probing mechanisms introduces a detection overhead that limits their end-to-end latency gains and compromises their generalizability across diverse problems. Inspired by the use of hidden states in speculative decoding, we propose SpecExit, a novel framework that predicts both future tokens and an early-exit signal directly from a lightweight draft model without probing overhead. Our method offers significant improvements, reducing average generation length by 66% and achieving a 2.5x speedup in end-to-end latency compared to the speculative decoding baseline, without compromising accuracy. Our method leverages the inherent signals from hidden states to provide effective early-exit signals, suggesting broader use of hidden states for efficient reasoning. Large reasoning models (LRMs) such as OpenAI-o1 (OpenAI, 2024), DeepSeek-R1 (DeepSeek-AI et al., 2025) and Qwen (Qwen et al., 2025) have recently achieved state-of-the-art performance in complex tasks.

OpenAI has unveiled ChatGPT Atlas, a long-anticipated artificial intelligence-powered web browser built around its popular chatbot, in a direct challenge to Google Chrome's dominance. OpenAI on Tuesday unveiled ChatGPT Atlas, a long-anticipated artificial intelligence-powered web browser built around its popular chatbot, in a direct challenge to Google Chrome's dominance. The launch marks OpenAI's latest move to capitalize on 800 million weekly active ChatGPT users, as it expands into more aspects of users' online lives by collecting data about consumers' browser behavior. It could accelerate a broader shift toward AI-driven search, as users increasingly turn to conversational tools that synthesize information instead of relying on traditional keyword-based results from Google -- intensifying competition between OpenAI and Google. Shares of Alphabet, which owns the Chrome browser, were down 1.8% in afternoon trading.

OpenAI's Atlas Browser Takes Direct Aim at Google Chrome The new ChatGPT-powered web browser is OpenAI's boldest play yet to reinvent how people use the web. OpenAI announced on Tuesday it's rolling out a new internet browser called Atlas that integrates directly with ChatGPT . Atlas includes features like a sidebar window people can use to ask ChatGPT questions about the web pages they visit. "We think that AI represents a rare, once a decade opportunity to rethink what a browser can be about," OpenAI CEO Sam Altman said during a livestream announcing Atlas. "Tabs were great, but we haven't seen a lot of browser innovation since then."

OpenAI's CEO, Sam Altman, testifies on Capitol Hill in Washington DC on 8 May. OpenAI's CEO, Sam Altman, testifies on Capitol Hill in Washington DC on 8 May. Company's AI-powered browser built around marquee bot is designed to provide more personalized web experience OpenAI on Tuesday launched an AI-powered web browser built around its marquee chatbot. The browser is designed to provide a more personalized web experience and includes a ChatGPT sidebar that enables users to asks questions about or engage with various aspects of each website they visit, as demonstrated in a video posted alongside the announcement. Atlas is now available globally on Apple's Mac operating system and will soon be made available on Windows, iOS and Android, according to OpenAI's announcement.

This holiday season, more shoppers are expected to use chatbots to figure out what to buy. This holiday season, rather than searching on Google, more Americans will likely be turning to large language models to find gifts, deals, and sales. Retailers could see up to a 520 percent increase in traffic from chatbots and AI search engines this year compared to 2024, according to a recent shopping report from Adobe . OpenAI is already moving to capitalize on the trend: Last week, the ChatGPT maker announced a major partnership with Walmart that will allow users to buy goods directly within the chat window. As people start relying on chatbots to discover new products, retailers are having to rethink their approach to online marketing.