AI News Archive: April 30, 2026 — Part 19
Sourced from 500+ daily AI sources, scored by relevance.
- Gemini app rolls out notebooks to Android as iOS gets Liquid Glass
Following the launch earlier this month , Gemini notebooks are now available on Android and iOS, while the iPhone has been updated with Liquid Glass. more…
- Aurora to deliver 500 self-driving trucks to refrigerated freight firm
The refrigerated freight company will purchase the Aurora Driver-powered vehicles as the autonomous vehicle firm prepares to launch a fleet without observers.
- AI-assisted approach identifies IRS4 as a promising drug target in multiple solid tumors [IMAGE]
AI-assisted approach identifies IRS4 as a promising drug target in multiple solid tumors [IMAGE] EurekAlert!
- 'It’s very unusual for business to grow this fast': Even Amazon CEO Andy Jassy is surprised at AWS cloud success — and AI domination is next
AWS reports its best growth rate in 15 quarters, and at 28%, it's propping up total Amazon revenue by quite a margin.
- Amazon tops cloud expectations on strong AI demand
Amazon.com last night reported cloud sales growth above Wall Street expectations, driven by strong enterprise spending as companies continue to devote tremendous resources to their artificial intelligence efforts.
- Cambricon revenue more than doubles on strong demand
Net income increased to 1 billion yuan (US$148 million) from 356 million yuan (US$52.1 million).
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- Latent-GRPO: Group Relative Policy Optimization for Latent Reasoning
Latent reasoning offers a more efficient alternative to explicit reasoning by compressing intermediate reasoning into continuous representations and substantially shortening reasoning chains. However, existing latent reasoning methods mainly focus on supervised learning, and reinforcement learning i...
- DPN-LE: Dual Personality Neuron Localization and Editing for Large Language Models
With the widespread adoption of large language models (LLMs), understanding their personality representation mechanisms has become critical. As a novel paradigm in Personality Editing, most existing methods employ neuron-editing to locate and modify LLM neurons, requiring changes to numerous neurons...
- TwinGate: Stateful Defense against Decompositional Jailbreaks in Untraceable Traffic via Asymmetric Contrastive Learning
Decompositional jailbreaks pose a critical threat to large language models (LLMs) by allowing adversaries to fragment a malicious objective into a sequence of individually benign queries that collectively reconstruct prohibited content. In real-world deployments, LLMs face a continuous, untraceable ...
- ZipCCL: Efficient Lossless Data Compression of Communication Collectives for Accelerating LLM Training
Communication has emerged as a critical bottleneck in the distributed training of large language models (LLMs). While numerous approaches have been proposed to reduce communication overhead, the potential of lossless compression has remained largely underexplored since compression and decompression ...
- Linguistically Informed Multimodal Fusion for Vietnamese Scene-Text Image Captioning: Dataset, Graph Framework, and Phonological Attention
Scene-text image captioning requires fusing three information streams -- visual features, OCR-detected text, and linguistic knowledge -- to generate descriptions that faithfully integrate text visible in images. Existing fusion approaches treat text as language-agnostic, which fails for Vietnamese: ...
- Contextual Agentic Memory is a Memo, Not True Memory
Current agentic memory systems (vector stores, retrieval-augmented generation, scratchpads, and context-window management) do not implement memory: they implement lookup. We argue that treating lookup as memory is a category error with provable consequences for agent capability, long-term learning, ...
- One Single Hub Text Breaks CLIP: Identifying Vulnerabilities in Cross-Modal Encoders via Hubness
The hubness problem, in which hub embeddings are close to many unrelated examples, occurs often in high-dimensional embedding spaces and may pose a practical threat for purposes such as information retrieval and automatic evaluation metrics. In particular, since cross-modal similarity between text a...
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- RHyVE: Competence-Aware Verification and Phase-Aware Deployment for LLM-Generated Reward Hypotheses
Large language models (LLMs) make reward design in reinforcement learning substantially more scalable, but generated rewards are not automatically reliable training objectives. Existing work has focused primarily on generating, evolving, or selecting reward candidates, while paying less attention to...
- Collaborative Agent Reasoning Engineering (CARE): A Three-Party Design Methodology for Systematically Engineering AI Agents with Subject Matter Experts, Developers, and Helper Agents
We present Collaborative Agent Reasoning Engineering (CARE), a disciplined methodology for engineering Large Language Model (LLM) agents in scientific domains. Unlike ad-hoc trial-and-error approaches, CARE specifies behavior, grounding, tool orchestration, and verification through reusable artifact...
- SpecVQA: A Benchmark for Spectral Understanding and Visual Question Answering in Scientific Images
Spectra are a prevalent yet highly information-dense form of scientific imagery, presenting substantial challenges to multimodal large language models (MLLMs) due to their unstructured and domain-specific characteristics. Here we introduce SpecVQA, a professional scientific-image benchmark for evalu...
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- Claude Mythos Found 271 Zero-Days in Firefox as Bundesbank Demands EU Access
Claude Mythos found 271 zero-days in Firefox as Bundesbank demands EU access.
- Anthropic's Mythos Has Landed: Here's What Comes Next for Cyber
In this latest installment of the Reporters' Notebook video series, we discuss how the new AI model threatens to completely upend cybersecurity, and what industry leaders are telling the press.
- Claude Mythos Fears Startle Japan's Financial Services Sector
Global financial institutions are panicked over Anthropic's new superhacker AI model. Cyber experts aren't quite as worried.
- Europe’s finance ministers are about to discuss an AI model none of them can access
Euro-area finance ministers will discuss Anthropic’s Mythos AI model with banking supervisors on Monday, according to a senior EU official. The technology that will be on the agenda is one that no government in the European Union has access to, built by a company that the United States Pentagon has designated a national security supply […] This story continues at The Next Web
- The Mythos AI preview is here. Corporate war rooms are mobilizing to deal with the fallout
Anthropic’s new AI model is expected to reveal an exponential surge in system vulnerabilities that bad actors could exploit
- MIFair: A Mutual-Information Framework for Intersectionality and Multiclass Fairness
Fairness in machine learning remains challenging due to its ethical complexity, the absence of a universal definition, and the need for context-specific bias metrics. Existing methods still struggle with intersectionality, multiclass settings, and limited flexibility and generality. To address these...
- Reliable Answers for Recurring Questions: Boosting Text-to-SQL Accuracy with Template Constrained Decoding
Large language models (LLMs) have revolutionized Text-to-SQL generation, allowing users to query structured data using natural language with growing ease. Yet, real-world deployment remains challenging, especially in complex or unseen schemas, due to inconsistent accuracy and the risk of generating ...
- Learning from Disagreement: Clinician Overrides as Implicit Preference Signals for Clinical AI in Value-Based Care
We reframe clinician overrides of clinical AI recommendations as implicit preference data - the same signal structure exploited by reinforcement learning from human feedback (RLHF), but richer: the annotator is a domain expert, the alternatives carry real consequences, and downstream outcomes are ob...
- A Pattern Language for Resilient Visual Agents
Integrating multimodal foundation models into enterprise ecosystems presents a fundamental software architecture challenge. Architects must balance competing quality attributes: the high latency and non-determinism of vision language action (VLA) models versus the strict determinism and real-time pe...
- Exploring Interaction Paradigms for LLM Agents in Scientific Visualization
This paper examines how different types of large language model (LLM) agents perform on scientific visualization (SciVis) tasks, where users generate visualization workflows from natural-language instructions. We compare three primary interaction paradigms, including domain-specific agents with stru...
- From LLM-Driven Trading Card Generation to Procedural Relatedness: A Pokémon Case Study
Since the dawn of Trading Card Games, the genre has grown into a multi-billion-dollar industry engaging millions of analog and digital players worldwide. Popular TCGs rely on regular updates, balance adjustments, and rotating constraints to sustain engagement. Yet, as metagames stabilize, predictabl...
- From Mirage to Grounding: Towards Reliable Multimodal Circuit-to-Verilog Code Generation
Multimodal large language models (MLLMs) are increasingly used to translate visual artifacts into code, from UI mockups into HTML to scientific plots into Python scripts. A circuit diagram can be viewed as a visual domain-specific language for hardware: it encodes timing, topology, and bit level sem...
- Language Models Refine Mechanical Linkage Designs Through Symbolic Reflection and Modular Optimisation
Designing mechanical linkages involves combinatorial topology selection and continuous parameter fitting. We show that language models can systematically improve linkage designs through symbolic representations. Language model agents explore discrete topologies while numerical optimisers fit continu...
- LLMs as ASP Programmers: Self-Correction Enables Task-Agnostic Nonmonotonic Reasoning
Recent large language models (LLMs) have achieved impressive reasoning milestones but continue to struggle with high computational costs, logical inconsistencies, and sharp performance degradation on high-complexity problems. While neuro-symbolic methods attempt to mitigate these issues by coupling ...
- GUI Agents with Reinforcement Learning: Toward Digital Inhabitants
Graphical User Interface (GUI) agents have emerged as a promising paradigm for intelligent systems that perceive and interact with graphical interfaces visually. Yet supervised fine-tuning alone cannot handle long-horizon credit assignment, distribution shifts, and safe exploration in irreversible e...
- MM-StanceDet: Retrieval-Augmented Multi-modal Multi-agent Stance Detection
Multimodal Stance Detection (MSD) is crucial for understanding public discourse, yet effectively fusing text and image, especially with conflicting signals, remains challenging. Existing methods often face difficulties with contextual grounding, cross-modal interpretation ambiguity, and single-pass ...
- Taming the Centaur(s) with LAPITHS: a framework for a theoretically grounded interpretation of AI performances
We introduce a framework called LAPITHS (Language model Analysis through Paradigm grounded Interpretations of Theses about Human likenesS) and use it to show that several major claims advanced by models such as CENTAUR, proposed as an artificial Unified Model of Cognition, are not theoretically or e...
- Can AI Be a Good Peer Reviewer? A Survey of Peer Review Process, Evaluation, and the Future
Peer review is a multi-stage process involving reviews, rebuttals, meta-reviews, final decisions, and subsequent manuscript revisions. Recent advances in large language models (LLMs) have motivated methods that assist or automate different stages of this pipeline. In this survey, we synthesize techn...
- Beyond Semantics: Measuring Fine-Grained Emotion Preservation in Small Language Model-Based Machine Translation
Preserving affective nuance remains a challenge in Machine Translation (MT), where semantic equivalence often takes precedence over emotional fidelity. This paper evaluates the performance of three state-of-the-art Small Language Models (SLMs) -- EuroLLM, Aya Expanse, and Gemma -- in maintaining fin...
- Simulating clinical interventions with a generative multimodal model of human physiology
Understanding how human health changes over time, and why responses to interventions vary between individuals, remains a central challenge in medicine. Here we present HealthFormer, a decoder-only transformer that models the human physiological trajectory generatively, by training on data from the H...
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- Graph World Models: Concepts, Taxonomy, and Future Directions
As one of the mainstream models of artificial intelligence, world models allow agents to learn the representation of the environment for efficient prediction and planning. However, classical world models based on flat tensors face several key problems, including noise sensitivity, error accumulation...
- In-Context Prompting Obsoletes Agent Orchestration for Procedural Tasks
Agent orchestration frameworks -- LangGraph, CrewAI, Google ADK, OpenAI Agents SDK, and others -- place an external orchestrator above the LLM, tracking state and injecting routing instructions at every turn. We present a controlled comparison showing that for procedural tasks, this architecture is ...
- Modeling Clinical Concern Trajectories in Language Model Agents
Large language model (LLM) agents deployed in clinical settings often exhibit abrupt, threshold-driven behavior, offering little visibility into accumulating risk prior to escalation. In real-world care, however, clinicians act on gradually rising concern rather than instantaneous triggers. We study...
- Rethinking Agentic Reinforcement Learning In Large Language Models
Reinforcement Learning (RL) has traditionally focused on training specialized agents to optimize predefined reward functions within narrowly defined environments. However, the advent of powerful Large Language Models (LLMs) and increasingly complex, open-ended tasks has catalyzed a paradigm shift to...
- NeocorRAG: Less Irrelevant Information, More Explicit Evidence, and More Effective Recall via Evidence Chains
Although precise recall is a core objective in Retrieval-Augmented Generation (RAG), a critical oversight persists in the field: improvements in retrieval performance do not consistently translate to commensurate gains in downstream reasoning. To diagnose this gap, we propose the Recall Conversion R...
- CastFlow: Learning Role-Specialized Agentic Workflows for Time Series Forecasting
Recently, large language models (LLMs) have shown great promise in time series forecasting. However, most existing LLM-based forecasting methods still follow a static generative paradigm that directly maps historical observations to future values in a single pass. Under this paradigm, forecasting is...
- ObjectGraph: From Document Injection to Knowledge Traversal -- A Native File Format for the Agentic Era
Every document format in existence was designed for a human reader moving linearly through text. Autonomous LLM agents do not read - they retrieve. This fundamental mismatch forces agents to inject entire documents into their context window, wasting tokens on irrelevant content, compounding state ac...