DeepSeek

Background: Hypospadias is a common congenital malformation requiring surgery. Caregivers face substantial perioperative information needs, and large language models (LLMs) offer a potential health education channel, but their performance in pediatric urology and the relation between citation accuracy and clinical content safety lack systematic evaluation. Objective: This study aimed to evaluate 5 LLMs (ChatGPT-4o, Gemini-2.5-Pro, OpenEvidence, Zhipu Qingyan, and DeepSeek) for pediatric hypospad

Background: Although thyroid nodules are detected in up to 60% of adults on ultrasound, the vast majority are benign, creating a substantial decision-making burden compounded by heterogeneous practice guidelines. Large language models (LLMs) show promise in processing unstructured medical text and are emerging as tools for report interpretation among both clinicians and patients. However, their reliability across distinct clinical tasks in thyroid ultrasound interpretation remains poorly charact

We investigate how well large language models (LLMs) can assist scientific project planning and proposal evaluation. One-page project plans were independently generated for eight expert-conceived research projects in physics, astrophysics, and cosmology by human researchers and three contemporary LLMs (ChatGPT, Claude, and DeepSeek; mid-2025 models, used with their default tool access). The resulting 32 proposals were blindly evaluated by four human reviewers and two newer frontier LLMs (Claude

arXiv:2604.27618v2 Announce Type: replace-cross Abstract: Understanding the impact of large language models (LLMs) on mathematics education requires data on LLMs' mathematical performance and biases. To this end, we introduce Math Education Digital Shadows (MEDS), a dataset mapping how LLMs reason about mathematics across human- and AI-like personifications. MEDS comprises 28,000 runs from 14 LLMs (i.e., Mistral, Qwen, DeepSeek, IBM Granite, Microsoft Phi, and xAI Grok) generated under human-sha

In an era dominated by aggressive tech founders who chase billion-dollar valuations and maximal profits while curating loud public profiles, Liang Wenfeng stands out for his insistence on staying in the background. With only a couple of photographs of him circulating online, the founder of Chinese artificial intelligence start-up DeepSeek and quantitative hedge fund High-Flyer Quant has long been a reclusive figure. But last week, a leaked transcript of a closed-door meeting with potential...

DeepSeek has suspended its second fundraising round after comments made by founder Liang Wenfeng during private investor meetings were leaked and went viral on Chinese social media, Bloomberg reported on Friday. Liang verbally informed prospective backers to hold off on the round, which had been targeting a pre-money valuation of roughly 480 billion yuan, equivalent […] This story continues at The Next Web

Chinese AI startup DeepSeek has told prospective ​investors in its second fundraising round that ‌it is suspending the deal for now, Bloomberg News reported on Saturday, citing people familiar ​with ...

On the surface, China’s cutting-edge tech sector – from the algorithmic breakthroughs of DeepSeek and Zhipu AI to the hardware of Unitree Robotics and ChangXin Memory Technologies (CXMT) – mirrors Silicon Valley’s venture capital-backed ecosystem. But a closer look at their financing histories reveals a common investor: the Chinese state. Beijing’s strong presence underscores a more profound structural shift in how China’s frontier technology is being funded. As Western venture capital and...

Transformer Weekly: Trahan and Obernolte bill, OpenAI-Hugging Face hack, and CAISI chief resigns

Social media sentiment analysis has become one of the most significant instruments for understanding the opinion of the population in the spheres of healthcare, politics, and education. Yet, large language models (LLMs) remain unevenly distributed in their linguistic coverage, failing to adequately serve a large portion of the world's languages. This study evaluates five state-of-the-art LLMs: GPT-4o, Gemini 2.0 Flash, DeepSeek-V3, Mistral Large, and Claude 3.7 Sonnet on three-class sentiment cl

In a leaked call transcript, we learn what DeepSeek's CEO thinks are China's weaknesses and strengths in the AI race with the United States | Edition #309

According to a report by IT Home, DeepSeek, the Chinese AI developer behind the open-source R1 reasoning model, is prioritizing artificial general intelligence (AGI) research over building a consumer platform or maximizing near-term revenue. The report is based on a circulated transcript of a four-hour investor meeting involving founder Liang Wenfeng. Why it matters: The […]

Chinese artificial intelligence unicorn Moonshot AI, whose release of its Kimi K3 model last week sparked talk of another “DeepSeek moment”, has accelerated its fundraising ahead of a planned initial public offering (IPO) in Hong Kong, eyeing a new financing round as early as next month, a source says. The Beijing-based company was set to close its current funding round at a US$30 billion valuation later this month or early next month, according to a person familiar with the matter. It planned..

Poolside has released Laguna S 2.1, a 118-billion-parameter open-weight model built for agentic coding that the San Francisco startup says matches or exceeds models several times its size. The model uses a mixture-of-experts architecture with eight billion active parameters per token, is compact enough to run on a single Nvidia DGX Spark desktop system, and […] This story continues at The Next Web

The British AI Security Institute warns that open-weight models like GLM-5.2 and DeepSeek V4-Pro now trail closed frontier models in cyber capabilities by four to seven months. At the start of 2025, the gap was still six to ten months. It also found that safety measures on open models are largely ineffective, leaving defenders less time to prepare. The article Open-weight models now match frontier cyber performance from just four months ago at a fraction of the cost appeared first on The Decoder

On a video conference call from Hangzhou earlier this year, one of China's hottest startups held a four-hour pitch meeting with potential venture investors that was unusual by almost any measure. Von Saritha Rai und Olivia Poh ( Deepseek , KI )

As soon as ChangXin Memory Technologies opened its blockbuster initial public offering to public subscription on Thursday morning, Luo Yi applied for 86,000 shares – despite acknowledging that she knew little about the semiconductor industry. The 60-year-old stock investor from southwestern Sichuan province was encouraged by her securities account manager, who told her that CXMT’s unusually large share sale could produce a higher allotment rate than most Chinese mainland IPOs. Luo’s application.

Nur Wochen nach einer milliardenschweren Finanzierungsrunde soll DeepSeek die nächste Runde und parallel einen Börsengang planen.

Chinese frontier artificial intelligence start-up DeepSeek is in talks with investors to raise a new financing round at around US$70 billion pre-investment valuation, shortly after closing a landmark first round, reflecting unquenched investor enthusiasm for the company. The plan comes on the heels of the Chinese AI darling’s recently closed Series A funding round in June – its first external funding round – which brought in about US$7 billion and valued the Hangzhou-based firm at nearly US$60..

DeepSeek, the Hangzhou-based AI model developer, has begun preparing for a mainland IPO and may file its listing application as soon as this year, according to Bloomberg, citing people familiar with the matter. The company is targeting a 2027 debut. DeepSeek is in talks with accounting and banking advisers and is seeking additional private funding […]

DeepSeek is already raising again. The Chinese AI lab just closed its first funding round and needs capital for its own data centers and chips to keep its aggressive pricing strategy going. The article DeepSeek needs more cash just weeks after closing its first $7 billion round appeared first on The Decoder .

Self-interested agents, left unconstrained, tend toward defection in repeated social dilemmas, causing cooperative gains from trade to collapse. This paper investigates what formal mechanisms, layered on top of unrestricted communication, are sufficient for a society of such agents to maintain market stability, and how resilient those mechanisms are to adversarial attack. We instantiate the research question as a multi-agent marketplace simulation where 18 LLM agents (DeepSeek-V3) with complemen

When large language models serve as evaluators in multi-agent systems, their strategy preferences -- whether induced by explicit prompts or by shared architectural priors -- propagate through the agent network. We introduce Contagion Networks, a formal framework for measuring how evaluator preferences spread across interacting LLM agents. In a controlled 3-agent experiment using DeepSeek-chat with three distinct evaluator preference profiles (structured, balanced, evidence-based), we measure the

This study investigates cross-lingual distributional skew (the Shibboleth Effect) in frontier large language models (LLMs) subjected to sustained adversarial conditions. We develop a multi-agent geopolitical wargame, the Cerulean Sea Crisis, a synthetic maritime territorial dispute designed to mirror the structural dynamics of Eastern Mediterranean conflicts. Six frontier models (GPT-4o, Llama-4, Mistral-Large, Gemini-3.1-Pro, Qwen3.6-Plus, and DeepSeek-R1) participate in a between-groups experi

Public trust in generative artificial intelligence exhibits increasingly divergent patterns across national contexts, yet prevailing research largely overlooks the macro-structural forces underlying this divergence. This study argues that trust in AI is not merely a technical response to performance but a product of institutional refraction. We propose an ``Institutional Prism'' framework to demonstrate how institutional trust shapes user trust in domestic (DeepSeek) and global (ChatGPT) large l

AI models are already deployed in societies affected by armed conflict, and journalists, humanitarian workers, governments and ordinary citizens rely on them for information or for their work processes. No established practice exists for checking whether their outputs can make those conflicts worse. We tested nine model configurations from four providers (OpenAI, Anthropic, DeepSeek, xAI) on 90 multi-turn scenarios designed to surface misaligned behaviour in conflict contexts: false equivalence

We evaluate whether enabling provider-exposed reasoning mode changes moral judgments within the same model checkpoint. Across 100 moral-judgment scenarios and five frontier reasoning-trained LLMs (Claude Sonnet 4.6, GPT 5.5, Gemini 3 Flash, DeepSeek V3.1, and Qwen3.5 397B), aggregate binary-verdict agreement remains high and statistically indistinguishable between instant and thinking modes (Krippendorff's alpha = 0.78 vs. 0.79). However, disagreement is concentrated in 21 model-disputed scenari

Emergency department triage assigns patients an acuity score that determines treatment priority, and clinical evidence documents persistent gender disparities in human acuity assessment. As hospitals pilot large language models (LLMs) as triage decision support, a critical question is whether these models reproduce or mitigate known biases. We present EQUITRIAGE, a fairness audit of LLM-based ESI assignment evaluating five models (Gemini-3-Flash, Nemotron-3-Super, DeepSeek-V3.1, Mistral-Small-3.

DeepSeek v3, developed in China, was released in December 2024, followed by Alibaba’s Qwen 2.5 Max in January 2025 and Qwen3 235B in April 2025. These free and open-source models offer significant potential for academic writing and content creation. This study evaluates their academic writing performance by comparing them with ChatGPT, Gemini, Llama, Mistral, and Gemma. There is a critical gap in the literature concerning how extensively these tools can be utilized and their potential to generat

To address the unsustainable rise in public health expenditures, the Hong Kong SAR Government is shifting its strategic focus to primary healthcare and encouraging citizens to use community resources to self-manage their health. However, official clinical guidelines are fragmented across disparate departments and formats, creating significant access barriers. While general-purpose Large Language Models (LLMs) such as ChatGPT and DeepSeek offer potential solutions for information accessibility, t

Large language models (LLMs) are increasingly applied in computer science education for tasks such as tutoring, content generation, and code assessment. However, systematic evaluations aligned with formal curricula and certification standards remain limited. This study benchmarked four recent models, including GPT-5, DeepSeek-R1, Qwen-Plus, and Llama-3.3-70B-Instruct, using a dataset of 1,068 questions derived from six certification exams covering networking, office applications, and Java progra

The "AI singularity" is often miscast as a monolithic, godlike mind. Evolution suggests a different path: intelligence is fundamentally plural, social, and relational. Recent advances in agentic AI reveal that frontier reasoning models, such as DeepSeek-R1, do not improve simply by "thinking longer". Instead, they simulate internal "societies of thought," spontaneous cognitive debates that argue, verify, and reconcile to solve complex tasks. Moreover, we are entering an era of human-AI centaurs:

Natural language prompts often suffer from intent transmission loss: the gap between what users actually need and what they communicate to AI systems. We evaluate PPS (Prompt Protocol Specification), a 5W3H-based framework for structured intent representation in human-AI interaction. In a controlled three-condition study across 60 tasks in three domains (business, technical, and travel), three large language models (DeepSeek-V3, Qwen-Max, and Kimi), and three prompt conditions - (A) simple promp

Public leaderboards increasingly suggest that large language models (LLMs) surpass human experts on benchmarks spanning academic knowledge, law, and programming. Yet most benchmarks are fully public, their questions widely mirrored across the internet, creating systematic risk that models were trained on the very data used to evaluate them. This paper presents three complementary experiments forming a rigorous multi-method contamination audit of six frontier LLMs: GPT-4o, GPT-4o-mini, DeepSeek-R

Despite the impressive performance of general-purpose large language models (LLMs), they often require fine-tuning or post-training to excel at specific tasks. For instance, large reasoning models (LRMs), such as the DeepSeek-R1 series, demonstrate strong reasoning capabilities after post-training different general large language models on diverse chain-of-thought (CoT) datasets. However, this additional training frequently comes at the cost of reduced safety, as the fine-tuned or post-trained m

Abstract There is much discussion of the false outputs that generative AI systems such as ChatGPT, Claude, Gemini, DeepSeek, and Grok create. In popular terminology, these have been dubbed “AI hallucinations”. However, deeming these AI outputs “hallucinations” is controversial, with many claiming this is a metaphorical misnomer. Nevertheless, in this paper, I argue that when viewed through the lens of distributed cognition theory, we can better see the dynamic ways in which inaccurate beliefs, d

The integration of generative AI tools, such as ChatGPT, Gemini, and DeepSeek, into higher education offers transformative opportunities for personalised learning and academic productivity. However, their unregulated use raises concerns about academic integrity, critical thinking, and educational equity. This systematic review synthesises insights from 96 peer-reviewed articles, identifying six key intervention themes, namely, curriculum integration, policy and governance, faculty development, s

Abstract General reasoning represents a long-standing and formidable challenge in artificial intelligence (AI). Recent breakthroughs, exemplified by large language models (LLMs) 1,2 and chain-of-thought (CoT) prompting 3 , have achieved considerable success on foundational reasoning tasks. However, this success is heavily contingent on extensive human-annotated demonstrations and the capabilities of models are still insufficient for more complex problems. Here we show that the reasoning abilitie