Jensen Huang just put Nvidia’s Blackwell and Vera Rubin sales projections into the $1 trillion stratosphere
Nvidia CEO Jensen Huang said he expects $1 trillion worth of orders for the chips.
Warren presses Pentagon over decision to grant xAI access to classified networks
Sen. Elizabeth Warren noted that Grok, xAI's controversial chatbot, has created harmful outputs for users and poses a potential national security risk.
Elon Musk’s xAI faces child porn lawsuit from minors Grok allegedly undressed
The three plaintiffs are seeking to represent anyone who had real images of them as a minor altered into sexual content by Grok.
How to watch Jensen Huang’s Nvidia GTC 2026 keynote — and what to expect
GTC is Nvidia's flagship annual event, where the chipmaker typically announces new products, partnerships, and its vision for the future of computing. Huang's keynote will focus on Nvidia's role in the future of computing and AI.
Another deep tech chip startup becomes a unicorn: Frore hits $1.64B
At Nvidia CEO Jensen Huang's urging, Frore developed liquid-cooling tech for chips. That shift helped it raise $143 million.
Fuse raises $25M to disrupt aging loan origination systems used by US credit unions
The startup also announced a $5 million "rescue fund" to help credit unions ditch legacy software for its AI-native platform.
Generating Expressive and Customizable Evals for Timeseries Data Analysis Agents with AgentFuel
arXiv:2603.12483v1 Announce Type: new Abstract: Across many domains (e.g., IoT, observability, telecommunications, cybersecurity), there is an emerging adoption of conversational data analysis agents that enable users to "talk to your data" to extract insights. Such data analysis agents operate on...
VQQA: An Agentic Approach for Video Evaluation and Quality Improvement
arXiv:2603.12310v1 Announce Type: cross Abstract: Despite rapid advancements in video generation models, aligning their outputs with complex user intent remains challenging. Existing test-time optimization methods are typically either computationally expensive or require white-box access to model internals. To address this,...
Efficient Reasoning with Balanced Thinking
arXiv:2603.12372v1 Announce Type: new Abstract: Large Reasoning Models (LRMs) have shown remarkable reasoning capabilities, yet they often suffer from overthinking, expending redundant computational steps on simple problems, or underthinking, failing to explore sufficient reasoning paths despite inherent capabilities. These issues...
An ethical framework for conversational AI in higher education: toward an evidence-based ethical governance
AI Model Modulation with Logits Redistribution
arXiv:2603.12755v1 Announce Type: new Abstract: Large-scale models are typically adapted to meet the diverse requirements of model owners and users. However, maintaining multiple specialized versions of the model is inefficient. In response, we propose AIM, a novel model modulation paradigm...
Aligning Language Models from User Interactions
arXiv:2603.12273v1 Announce Type: cross Abstract: Multi-turn user interactions are among the most abundant data produced by language models, yet we lack effective methods to learn from them. While typically discarded, these interactions often contain useful information: follow-up user messages may...
Prompt Injection as Role Confusion
arXiv:2603.12277v1 Announce Type: cross Abstract: Language models remain vulnerable to prompt injection attacks despite extensive safety training. We trace this failure to role confusion: models infer roles from how text is written, not where it comes from. We design novel...
Optimizing Task Completion Time Updates Using POMDPs
arXiv:2603.12340v1 Announce Type: cross Abstract: Managing announced task completion times is a fundamental control problem in project management. While extensive research exists on estimating task durations and task scheduling, the problem of when and how to update completion times communicated...
Operationalising Cyber Risk Management Using AI: Connecting Cyber Incidents to MITRE ATT&CK Techniques, Security Controls, and Metrics
arXiv:2603.12455v1 Announce Type: cross Abstract: The escalating frequency of cyber-attacks poses significant challenges for organisations, particularly small enterprises constrained by limited in-house expertise, insufficient knowledge, and financial resources. This research presents a novel framework that leverages Natural Language Processing to...
The Perfection Paradox: From Architect to Curator in AI-Assisted API Design
arXiv:2603.12475v1 Announce Type: cross Abstract: Enterprise API design is often bottlenecked by the tension between rapid feature delivery and the rigorous maintenance of usability standards. We present an industrial case study evaluating an AI-assisted design workflow trained on API Improvement...
One-Step Flow Policy: Self-Distillation for Fast Visuomotor Policies
arXiv:2603.12480v1 Announce Type: cross Abstract: Generative flow and diffusion models provide the continuous, multimodal action distributions needed for high-precision robotic policies. However, their reliance on iterative sampling introduces severe inference latency, degrading control frequency and harming performance in time-sensitive manipulation....
Na\"ive PAINE: Lightweight Text-to-Image Generation Improvement with Prompt Evaluation
arXiv:2603.12506v1 Announce Type: cross Abstract: Text-to-Image (T2I) generation is primarily driven by Diffusion Models (DM) which rely on random Gaussian noise. Thus, like playing the slots at a casino, a DM will produce different results given the same user-defined inputs....
Red-Teaming Vision-Language-Action Models via Quality Diversity Prompt Generation for Robust Robot Policies
arXiv:2603.12510v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models have significant potential to enable general-purpose robotic systems for a range of vision-language tasks. However, the performance of VLA-based robots is highly sensitive to the precise wording of language instructions, and it...
TERMINATOR: Learning Optimal Exit Points for Early Stopping in Chain-of-Thought Reasoning
arXiv:2603.12529v1 Announce Type: cross Abstract: Large Reasoning Models (LRMs) achieve impressive performance on complex reasoning tasks via Chain-of-Thought (CoT) reasoning, which enables them to generate intermediate thinking tokens before arriving at the final answer. However, LRMs often suffer from significant...
TASTE-Streaming: Towards Streamable Text-Aligned Speech Tokenization and Embedding for Spoken Language Modeling
arXiv:2603.12350v1 Announce Type: new Abstract: Text-speech joint spoken language modeling (SLM) aims at natural and intelligent speech-based interactions, but developing such a system may suffer from modality mismatch: speech unit sequences are much longer than text tokens. Prior work reduces...
Interpreting Negation in GPT-2: Layer- and Head-Level Causal Analysis
arXiv:2603.12423v1 Announce Type: new Abstract: Negation remains a persistent challenge for modern language models, often causing reversed meanings or factual errors. In this work, we conduct a causal analysis of how GPT-2 Small internally processes such linguistic transformations. We examine...
CSE-UOI at SemEval-2026 Task 6: A Two-Stage Heterogeneous Ensemble with Deliberative Complexity Gating for Political Evasion Detection
arXiv:2603.12453v1 Announce Type: new Abstract: This paper describes our system for SemEval-2026 Task 6, which classifies clarity of responses in political interviews into three categories: Clear Reply, Ambivalent, and Clear Non-Reply. We propose a heterogeneous dual large language model (LLM)...
LMEB: Long-horizon Memory Embedding Benchmark
arXiv:2603.12572v1 Announce Type: new Abstract: Memory embeddings are crucial for memory-augmented systems, such as OpenClaw, but their evaluation is underexplored in current text embedding benchmarks, which narrowly focus on traditional passage retrieval and fail to assess models' ability to handle...
EvolveCoder: Evolving Test Cases via Adversarial Verification for Code Reinforcement Learning
arXiv:2603.12698v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) is a promising approach for improving code generation in large language models, but its effectiveness is limited by weak and static verification signals in existing coding RL datasets. In...
A Method for Learning Large-Scale Computational Construction Grammars from Semantically Annotated Corpora
arXiv:2603.12754v1 Announce Type: new Abstract: We present a method for learning large-scale, broad-coverage construction grammars from corpora of language use. Starting from utterances annotated with constituency structure and semantic frames, the method facilitates the learning of human-interpretable computational construction grammars...
Rethinking Multiple-Choice Questions for RLVR: Unlocking Potential via Distractor Design
arXiv:2603.12826v1 Announce Type: new Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) significantly enhances the reasoning capabilities of Large Language Models. When applied to RLVR, Multiple-Choice Questions (MCQs) offer a scalable source of verifiable data but risk inducing reward hacking, where...
Learning from Child-Directed Speech in Two-Language Scenarios: A French-English Case Study
arXiv:2603.12906v1 Announce Type: new Abstract: Research on developmentally plausible language models has largely focused on English, leaving open questions about multilingual settings. We present a systematic study of compact language models by extending BabyBERTa to English-French scenarios under strictly size-matched...
HMS-BERT: Hybrid Multi-Task Self-Training for Multilingual and Multi-Label Cyberbullying Detection
arXiv:2603.12920v1 Announce Type: new Abstract: Cyberbullying on social media is inherently multilingual and multi-faceted, where abusive behaviors often overlap across multiple categories. Existing methods are commonly limited by monolingual assumptions or single-task formulations, which restrict their effectiveness in realistic multilingual...
Long-form RewardBench: Evaluating Reward Models for Long-form Generation
arXiv:2603.12963v1 Announce Type: new Abstract: The widespread adoption of reinforcement learning-based alignment highlights the growing importance of reward models. Various benchmarks have been built to evaluate reward models in various domains and scenarios. However, a significant gap remains in assessing...