AI News Archive: April 29, 2026 — Part 23
Sourced from 500+ daily AI sources, scored by relevance.
- Uninstalls of ChatGPT Are Spiking at the Worst Time Imaginable for OpenAI
It's not looking good. The post Uninstalls of ChatGPT Are Spiking at the Worst Time Imaginable for OpenAI appeared first on Futurism .
- Port Washington data center developer nears $38B financing package
The financing is to cover a portion of the costs for its massive Port Washington data center campus as well as a campus in Texas.
- Could AI make Duke business school students more human?
Could AI make Duke business school students more human? The News & Observer
- Evolving AI may arrive before AGI and create hard-to-control risks
Evolutionary biology holds clues for the future of AI, argue researchers from the HUN-REN Centre for Ecological Research, Eötvös Loránd University, and the Royal Flemish Academy of Belgium for Science and the Arts. In a new Perspective published April 20 in Proceedings of the National Academy of Sciences, the team warn that evolvable AI (eAI) systems that can undergo Darwinian evolution may soon emerge, and they will generate special risks that can be understood, and mitigated, based on insights from evolutionary biology.
- AI and Griefbots Are Changing How We Mourne (IMAGE)
AI and Griefbots Are Changing How We Mourne (IMAGE) EurekAlert!
- New research uses AI to unlock decades of hidden flood risk data
New research uses AI to unlock decades of hidden flood risk data EurekAlert!
- AI Uncovers a Hidden “Grammar” in DNA Packaging
Researchers have discovered a new "organizational code" for the genome, showing that most DNA is partially accessible even when wrapped up.
- Sam Altman’s Hard Reset & Baldonigate Down Under
Sam Altman’s Hard Reset & Baldonigate Down Under Puck
- Lehane’s OpenAI Playbook & The King’s Trump Digs
Lehane’s OpenAI Playbook & The King’s Trump Digs Puck
- A retrieval-augmented framework enabling VLM spatial awareness for object-centric robot manipulation
Science Robotics, Volume 11, Issue 113, April 2026.
- DSO: Direct Steering Optimization for Bias Mitigation
Generative models are often deployed to make decisions on behalf of users, such as vision-language models (VLMs) identifying which person in a room is a doctor to help visually impaired individuals. Yet, VLM decisions are influenced by the perceived demographic attributes of people in the input, which can lead to biased outcomes like failing to identify women as doctors. Moreover, when reducing bias leads to performance loss, users may have varying needs for balancing bias mitigation with overall model capabilities, highlighting the demand for methods that enable controllable bias reduction…
- Rule-based High-Level Coaching for Goal-Conditioned Reinforcement Learning in Search-and-Rescue UAV Missions Under Limited-Simulation Training
This paper presents a hierarchical decision-making framework for unmanned aerial vehicle (UAV) missions motivated by search-and-rescue (SAR) scenarios under limited simulation training. The framework combines a fixed rule-based high-level advisor with an online goal-conditioned low-level reinforceme...
- ViCrop-Det: Spatial Attention Entropy Guided Cropping for Training-Free Small-Object Detection
Transformer-based architectures have established a dominant paradigm in global semantic perception; however, they remain fundamentally constrained by the profound spatial heterogeneity inherent in natural images. Specifically, the imposition of a uniform global receptive field across regions of vary...
- Bian Que: An Agentic Framework with Flexible Skill Arrangement for Online System Operations
Operating and maintaining (O&M) large-scale online engine systems (search, recommendation, advertising) demands substantial human effort for release monitoring, alert response, and root cause analysis. While LLM-based agents are a natural fit for these tasks, the deployment bottleneck is not reasoni...
- MemOVCD: Training-Free Open-Vocabulary Change Detection via Cross-Temporal Memory Reasoning and Global-Local Adaptive Rectification
Open-vocabulary change detection aims to identify semantic changes in bi-temporal remote sensing images without predefined categories. Recent methods combine foundation models such as SAM, DINO and CLIP, but typically process each timestamp independently or interact only at the final comparison stag...
- Exploring the Potential of Probabilistic Transformer for Time Series Modeling: A Report on the ST-PT Framework
The Probabilistic Transformer (PT) establishes that the Transformer's self-attention plus its feed-forward block is mathematically equivalent to Mean-Field Variational Inference (MFVI) on a Conditional Random Field (CRF). Under this equivalence the Transformer ceases to be a black-box neural network...
- FutureWorld: A Live Environment for Training Predictive Agents with Real-World Outcome Rewards
Live future prediction refers to the task of making predictions about real-world events before they unfold. This task is increasingly studied using large language model-based agent systems, and it is important for building agents that can continually learn from real-world. Just as interactive enviro...
- Atomic-Probe Governance for Skill Updates in Compositional Robot Policies
Skill libraries in deployed robotic systems are continually updated through fine-tuning, fresh demonstrations, or domain adaptation, yet existing typed-composition methods (BLADE, SymSkill, Generative Skill Chaining) treat the library as frozen at test time and do not analyze how composition outcome...
- From Black-Box Confidence to Measurable Trust in Clinical AI: A Framework for Evidence, Supervision, and Staged Autonomy
Trust in clinical artificial intelligence (AI) cannot be reduced to model accuracy, fluency of generation, or overall positive user impression. In medicine, trust must be engineered as a measurable system property grounded in evidence, supervision, and operational boundaries of AI autonomy. This art...
- When to Retrieve During Reasoning: Adaptive Retrieval for Large Reasoning Models
Large reasoning models such as DeepSeek-R1 and OpenAI o1 generate extended chains of thought spanning thousands of tokens, yet their integration with retrieval-augmented generation (RAG) remains fundamentally misaligned. Current RAG systems optimize for providing context before reasoning begins, whi...
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- SciHorizon-DataEVA: An Agentic System for AI-Readiness Evaluation of Heterogeneous Scientific Data
AI-for-Science (AI4Science) is increasingly transforming scientific discovery by embedding machine learning models into prediction, simulation, and hypothesis generation workflows across domains. However, the effectiveness of these models is fundamentally constrained by the AI-readiness of scientifi...
- TDD Governance for Multi-Agent Code Generation via Prompt Engineering
Large language models (LLMs) accelerate software development but often exhibit instability, non-determinism, and weak adherence to development discipline in unconstrained workflows. While test-driven development (TDD) provides a structured Red-Green-Refactor process, existing LLM-based approaches ty...
- Human-in-the-Loop Benchmarking of Heterogeneous LLMs for Automated Competency Assessment in Secondary Level Mathematics
As Competency-Based Education (CBE) is gaining traction around the world, the shift from marks-based assessment to qualitative competency mapping is a manual challenge for educators. This paper tackles the bottleneck issue by suggesting a "Human-in-the-Loop" benchmarking framework to assess the effe...
- Translating Under Pressure: Domain-Aware LLMs for Crisis Communication
Timely and reliable multilingual communication is critical during natural and human-induced disasters, but developing effective solutions for crisis communication is limited by the scarcity of curated parallel data. We propose a domain-adaptive pipeline that expands a small reference corpus, by retr...
- MappingEvolve: LLM-Driven Code Evolution for Technology Mapping
Technology mapping is a critical yet challenging stage in logic synthesis. While Large Language Models (LLMs) have been applied to generate optimization scripts, their potential for core algorithm enhancement remains untapped. We introduce MappingEvolve, an open-source framework that pioneers the us...
- Star-Fusion: A Multi-modal Transformer Architecture for Discrete Celestial Orientation via Spherical Topology
Reliable celestial attitude determination is a critical requirement for autonomous spacecraft navigation, yet traditional "Lost-in-Space" (LIS) algorithms often suffer from high computational overhead and sensitivity to sensor-induced noise. While deep learning has emerged as a promising alternative...
- Benchmarking the Safety of Large Language Models for Robotic Health Attendant Control
Large language models (LLMs) are increasingly considered for deployment as the control component of robotic health attendants, yet their safety in this context remains poorly characterized. We introduce a dataset of 270 harmful instructions spanning nine prohibited behavior categories grounded in th...
- Preserving Disagreement: Architectural Heterogeneity and Coherence Validation in Multi-Agent Policy Simulation
Multi-agent deliberation systems using large language models (LLMs) are increasingly proposed for policy simulation, yet they suffer from artificial consensus: evaluator agents converge on the same option regardless of their assigned value perspectives. We present the AI Council, a three-phase delib...
- DUAL-BLADE: Dual-Path NVMe-Direct KV-Cache Offloading for Edge LLM Inference
The increasing deployment of Large Language Model (LLM) inference on edge AI systems demands efficient execution under tight memory budgets. A key challenge arises from Key-Value (KV) caches, which often exceed available device memory. Although NVMe-based offloading offers scalable capacity, existin...
- TLPO: Token-Level Policy Optimization for Mitigating Language Confusion in Large Language Models
Large language models (LLMs) demonstrate strong multilingual capabilities, yet often fail to consistently generate responses in the intended language, exhibiting a phenomenon known as language confusion. Prior mitigation approaches based on sequence-level fine-tuning, such as DPO, ORPO, and GRPO, op...
- AGEL-Comp: A Neuro-Symbolic Framework for Compositional Generalization in Interactive Agents
Large Language Model (LLM)-based agents exhibit systemic failures in compositional generalization, limiting their robustness in interactive environments. This work introduces AGEL-Comp, a neuro-symbolic AI agent architecture designed to address this challenge by grounding actions of the agent. AGEL-...
- Grounding vs. Compositionality: On the Non-Complementarity of Reasoning in Neuro-Symbolic Systems
Compositional generalization remains a foundational weakness of modern neural networks, limiting their robustness and applicability in domains requiring out-of-distribution reasoning. A central, yet unverified, assumption in neuro-symbolic AI is that compositional reasoning will emerge as a byproduc...
- Lyapunov-Guided Self-Alignment: Test-Time Adaptation for Offline Safe Reinforcement Learning
Offline reinforcement learning (RL) agents often fail when deployed, as the gap between training datasets and real environments leads to unsafe behavior. To address this, we present SAS (Self-Alignment for Safety), a transformer-based framework that enables test-time adaptation in offline safe RL wi...
- Text-Utilization for Encoder-dominated Speech Recognition Models
This paper investigates efficient methods for utilizing text-only data to improve speech recognition, focusing on encoder-dominated models that facilitate faster recognition. We provide a comprehensive comparison of techniques to integrate text-only data, including modality matching and dynamic down...
- Tatemae: Detecting Alignment Faking via Tool Selection in LLMs
Alignment faking (AF) occurs when an LLM strategically complies with training objectives to avoid value modification, reverting to prior preferences once monitoring is lifted. Current detection methods focus on conversational settings and rely primarily on Chain-of-Thought (CoT) analysis, which prov...
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- Auto-Relational Reasoning
Background & Objectives: In the last decade, Machine learning research has grown rapidly, but large models are reaching their soft limits demonstrating diminishing returns and still lack solid reasoning abilities. These limits could be surpassed through synergistic combination of Machine Learning sc...
- Tree-of-Text: A Tree-based Prompting Framework for Table-to-Text Generation in the Sports Domain
Generating sports game reports from structured tables is a complex table-to-text task that demands both precise data interpretation and fluent narrative generation. Traditional model-based approaches require large, annotated datasets, while prompt-based methods using large language models (LLMs) oft...
- Naamah: A Large Scale Synthetic Sanskrit NER Corpus via DBpedia Seeding and LLM Generation
The digitisation of classical Sanskrit literature is impeded by a scarcity of annotated resources, particularly for Named Entity Recognition. While recent methodologies utilise generic Large Language Models (LLMs) for data augmentation, these approaches remain prone to error and often lack the reaso...
- QYOLO: Lightweight Object Detection via Quantum Inspired Shared Channel Mixing
The rapid advancement of object detection architectures has positioned single stage detectors as the dominant solution for real-time visual perception. A primary source of computational overhead in these models lies in the deep backbone stages, where C2f bottleneck modules at high stride levels accu...
- Delineating Knowledge Boundaries for Honest Large Vision-Language Models
Large Vision-Language Models (VLMs) have achieved remarkable multimodal performance yet remain prone to factual hallucinations, particularly in long-tail or specialized domains. Moreover, current models exhibit a weak capacity to refuse queries that exceed their parametric knowledge. In this paper, ...
- Turning the TIDE: Cross-Architecture Distillation for Diffusion Large Language Models
Diffusion large language models (dLLMs) offer parallel decoding and bidirectional context, but state-of-the-art dLLMs require billions of parameters for competitive performance. While existing distillation methods for dLLMs reduce inference steps within a single architecture, none address cross-arch...
- Causal Learning with Neural Assemblies
Can Neural Assemblies -- groups of neurons that fire together and strengthen through co-activation -- learn the direction of causal influence between variables? While established as a computationally general substrate for classification, parsing, and planning, neural assemblies have not yet been sho...
- ClawGym: A Scalable Framework for Building Effective Claw Agents
Claw-style environments support multi-step workflows over local files, tools, and persistent workspace states. However, scalable development around these environments remains constrained by the absence of a systematic framework, especially one for synthesizing verifiable training data and integratin...
- Resume-ing Control: (Mis)Perceptions of Agency Around GenAI Use in Recruiting Workflows
When generative AI (genAI) systems are used in high-stakes decision-making, its recommended role is to aid, rather than replace, human decision-making. However, there is little empirical exploration of how professionals making high-stakes decisions, such as those related to employment, perceive thei...
- Language Diffusion Models are Associative Memories Capable of Retrieving Unseen Data
When do language diffusion models memorize their training data, and how to quantitatively assess their true generative regime? We address these questions by showing that Uniform-based Discrete Diffusion Models (UDDMs) fundamentally behave as Associative Memories (AMs) $\textit{with emergent creative...
- HalluCiteChecker: A Lightweight Toolkit for Hallucinated Citation Detection and Verification in the Era of AI Scientists
We introduce HalluCiteChecker, a toolkit for detecting and verifying hallucinated citations in scientific papers. While AI assistant technologies have transformed the academic writing process, including citation recommendation, they have also led to the emergence of hallucinated citations that do no...
- Random Cloud: Finding Minimal Neural Architectures Without Training
I propose the \emph{Random Cloud} method, a training-free approach to neural architecture search that discovers minimal feedforward network topologies through stochastic exploration and progressive structural reduction. Unlike post-training pruning methods that require a full train-prune-retrain cyc...
- Domain-Adapted Small Language Models for Reliable Clinical Triage
Accurate and consistent Emergency Severity Index (ESI) assignment remains a persistent challenge in emergency departments, where highly variable free-text triage documentation contributes to mistriage and workflow inefficiencies. This study evaluates whether open-source small language models (SLMs) ...