AI News Archive: April 28, 2026 — Part 24
Sourced from 500+ daily AI sources, scored by relevance.
- Verisk Stock and More Software Losers That Could Be AI Winners, According to Goldman Sachs
Verisk Stock and More Software Losers That Could Be AI Winners, According to Goldman Sachs Barron's
- Seagate Stock Rallies On As AI Stock's Earnings Crush Targets
Seagate stock jumped by 10% late Tuesday after posting fiscal Q3 results that crushed Wall Street's targets. The post Seagate Stock Rallies On As AI Stock's Earnings Crush Targets appeared first on Investor's Business Daily .
- Dow Jones Futures: Seagate, Bloom Energy, Teradyne Lead Earnings Movers After OpenAI Hits Techs; Titans On Tap
The stock market fell on OpenAI fears and rising oil prices, but pared losses. AI stocks Seagate, Bloom Energy led earnings movers late. The post Dow Jones Futures: Seagate, Bloom Energy, Teradyne Lead Earnings Movers After OpenAI Hits Techs; Titans On Tap appeared first on Investor's Business Daily .
- Bloom Energy is riding the AI wave with a major lift from Oracle
Wall Street is validating Bloom’s ‘vision,’ and AI accelerating it, CEO says.
- I used Naval Ravikant’s 'Leverage' rule with ChatGPT agents — and it cut my workload in half
I used Naval Ravikant’s 'Leverage' rule with ChatGPT agents — and it cut my workload in half Tom's Guide
- I used these 10 ChatGPT prompts to fix my communication habits and the results were immediate
I used these 10 ChatGPT prompts to fix my communication habits and the results were immediate Tom's Guide
- Can AI quantify beauty? New study suggests it can’t
Can AI quantify beauty? New study suggests it can’t EurekAlert!
- STAT+: Google’s clinical director on AI challenges
In this edition of STAT Health Tech newsletter: a conversation with Google's clinical director, an update on Doctronic's Utah experiment, and more.
- Labor Department nears launch of AI workforce hub
The agency’s chief innovation officer told FedScoop that the portal of government and private-sector data will go public in “the coming months,” fulfilling a Trump AI Action Plan requirement. The post Labor Department nears launch of AI workforce hub appeared first on FedScoop .
- Energy Department eyes AI-enabled self-service features for workforce
The interest in artificial intelligence additions follows what the agency is characterizing as a successful HR modernization project that centralized talent management platforms. The post Energy Department eyes AI-enabled self-service features for workforce appeared first on FedScoop .
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- Meet the Emirati inventor honoured by Sheikh Mohammed at 15, now building AI in the UAE
Meet the Emirati inventor honoured by Sheikh Mohammed at 15, now building AI in the UAE Gulf News
- Probabilistic modelling of single-cell bisulfite sequencing data with MethylVI
Nature Machine Intelligence, Published online: 28 April 2026; doi:10.1038/s42256-026-01225-9 MethylVI enhances analyses of single-cell bisulfite sequencing methylomic data via a deep generative model that accounts for the unique technical and biological sources of variability in this data modality.
- StereoFoley: Object-Aware Stereo Audio Generation from Video
We present StereoFoley, a video-to-audio generation framework that produces semantically aligned, temporally synchronized, and spatially accurate stereo sound at 48 kHz. While recent generative video-to-audio models achieve strong semantic and temporal fidelity, they largely remain limited to mono or fail to deliver object-aware stereo imaging, constrained by the lack of professionally mixed, spatially accurate video-to-audio datasets. First, we develop and train a base model that generates stereo audio from video, achieving state-of-the-art in both semantic accuracy and synchronization. Next…
📄 ResearchApr 28, 2026https://machinelearning.apple.com/research/stereofoley-object-aware-stereo-audio - Local Mechanisms of Compositional Generalization in Conditional Diffusion
Conditional diffusion models appear capable of compositional generalization, i.e., generating convincing samples for out-of-distribution combinations of conditioners, but the mechanisms underlying this ability remain unclear. To make this concrete, we study length generalization, the ability to generate images with more objects than seen during training. In a controlled CLEVR setting (Johnson et al.,2017), we find that length generalization is achievable in some cases but not others, suggesting that models only sometimes learn the underlying compositional structure. We then investigate…
- LaDiR: Latent Diffusion Enhances LLMs for Text Reasoning
Large Language Models (LLMs) demonstrate their reasoning ability through chain-of-thought (CoT) generation. However, LLM’s autoregressive decoding may limit the ability to revisit and refine earlier tokens in a holistic manner, which can also lead to inefficient exploration for diverse solutions. In this paper, we propose LaDiR (Latent Diffusion Reasoner), a novel reasoning framework that unifies the expressiveness of continuous latent representation with the iterative refinement capabilities of latent diffusion models for an existing LLM. We first construct a structured latent reasoning space…
- How Fast Should a Model Commit to Supervision? Training Reasoning Models on the Tsallis Loss Continuum
Adapting reasoning models to new tasks during post-training with only output-level supervision stalls under reinforcement learning from verifiable rewards (RLVR) when the initial success probability $p_0$ is small. Using the Tsallis $q$-logarithm, we define a loss family $J_Q$ that interpolates betw...
- TSN-Affinity: Similarity-Driven Parameter Reuse for Continual Offline Reinforcement Learning
Continual offline reinforcement learning (CORL) aims to learn a sequence of tasks from datasets collected over time while preserving performance on previously learned tasks. This setting corresponds to domains where new tasks arise over time, but adapting the model in live environment interactions i...
- Three Models of RLHF Annotation: Extension, Evidence, and Authority
Preference-based alignment methods, most prominently Reinforcement Learning with Human Feedback (RLHF), use the judgments of human annotators to shape large language model behaviour. However, the normative role of these judgments is rarely made explicit. I distinguish three conceptual models of that...
- Conditional misalignment: common interventions can hide emergent misalignment behind contextual triggers
Finetuning a language model can lead to emergent misalignment (EM) [Betley et al., 2025b]. Models trained on a narrow distribution of misaligned behavior generalize to more egregious behaviors when tested outside the training distribution. We study a set of interventions proposed to reduce EM. We ...
- When Errors Can Be Beneficial: A Categorization of Imperfect Rewards for Policy Gradient
Training language models via reinforcement learning often relies on imperfect proxy rewards, since ground truth rewards that precisely define the intended behavior are rarely available. Standard metrics for assessing the quality of proxy rewards, such as ranking accuracy, treat incorrect rewards as ...
- RESTestBench: A Benchmark for Evaluating the Effectiveness of LLM-Generated REST API Test Cases from NL Requirements
Existing REST API testing tools are typically evaluated using code coverage and crash-based fault metrics. However, recent LLM-based approaches increasingly generate tests from NL requirements to validate functional behaviour, making traditional metrics weak proxies for whether generated tests valid...
- Investigation into In-Context Learning Capabilities of Transformers
Transformers have demonstrated a strong ability for in-context learning (ICL), enabling models to solve previously unseen tasks using only example input output pairs provided at inference time. While prior theoretical work has established conditions under which transformers can perform linear classi...
- SIEVES: Selective Prediction Generalizes through Visual Evidence Scoring
Multimodal large language models (MLLMs) achieve ever-stronger performance on visual-language tasks. Even as traditional visual question answering benchmarks approach saturation, reliable deployment requires satisfying low error tolerances in real-world out-of-distribution (OOD) scenarios. Precisely...
- G-Loss: Graph-Guided Fine-Tuning of Language Models
Traditional loss functions, including cross-entropy, contrastive, triplet, and su pervised contrastive losses, used for fine-tuning pre-trained language models such as BERT, operate only within local neighborhoods and fail to account for the global semantic structure. We present G-Loss, a graph-guid...
- Large language models eroding science understanding: an experimental study
This paper is under review in AI and Ethics This study examines whether large language models (LLMs) can reliably answer scientific questions and demonstrates how easily they can be influenced by fringe scientific material. The authors modified custom LLMs to prioritise knowledge in selected fringe ...
- ADEMA: A Knowledge-State Orchestration Architecture for Long-Horizon Knowledge Synthesis with LLMAgents
Long-horizon LLM tasks often fail not because a single answer is unattainable, but because knowledge states drift across rounds, intermediate commitments remain implicit, and interruption fractures the evolving evidence chain. This paper presents ADEMA as a knowledge-state orchestration architecture...
- Semi-Markov Reinforcement Learning for City-Scale EV Ride-Hailing with Feasibility-Guaranteed Actions
We study city-scale control of electric-vehicle (EV) ride-hailing fleets where dispatch, repositioning, and charging decisions must respect charger and feeder limits under uncertain, spatially correlated demand and travel times. We formulate the problem as a hex-grid semi-Markov decision process (se...
- From Soliloquy to Agora: Memory-Enhanced LLM Agents with Decentralized Debate for Optimization Modeling
Optimization modeling underpins real-world decision-making in logistics, manufacturing, energy, and public services, but reliably solving such problems from natural-language requirements remains challenging for current large language models (LLMs). In this paper, we propose \emph{Agora-Opt}, a modul...
- CGU-ILALab at FoodBench-QA 2026: Comparing Traditional and LLM-based Approaches for Recipe Nutrient Estimation
Accurate nutrient estimation from unstructured recipe text is an important yet challenging problem in dietary monitoring, due to ambiguous ingredient terminology and highly variable quantity expressions. We systematically evaluate models spanning a wide range of representational capacity, from lexic...
- Measuring the Sensitivity of Classification Models with the Error Sensitivity Profile
The quality of training data is critical to the performance of machine learning models. In this paper, the Error Sensitivity Profile (ESP) is proposed. It quantifies the sensitivity of model performance to errors in a single feature or in multiple features. By leveraging ESP, data-cleaning efforts c...
- QAROO: AI-Driven Online Task Offloading for Energy-Efficient and Sustainable MEC Networks
With the rapid advancement of artificial intelligence (AI) and intelligent science, intelligent edge computing has been widely adopted. However, the limitations of traditional methods, such as poor adaptability and the slow convergence of heuristic algorithms, are becoming increasingly evident. To e...
- SAFEdit: Does Multi-Agent Decomposition Resolve the Reliability Challenges of Instructed Code Editing?
Instructed code editing is a significant challenge for large language models (LLMs). On the EditBench benchmark, 39 of 40 evaluated models obtain a task success rate (TSR) below 60 percent, highlighting a gap between general code generation and the ability to perform instruction-driven editing under...
- Verification of Neural Networks (Lecture Notes)
These lecture notes provide an introduction to the verification of neural networks from a theoretical perspective. We discuss feed-forward neural networks, recurrent neural networks, attention mechanisms, and transformers, together with specification languages and algorithmic verification techniques...
- Cross-Lingual Jailbreak Detection via Semantic Codebooks
Safety mechanisms for large language models (LLMs) remain predominantly English-centric, creating systematic vulnerabilities in multilingual deployment. Prior work shows that translating malicious prompts into other languages can substantially increase jailbreak success rates, exposing a structural ...
- Learning Generalizable Multimodal Representations for Software Vulnerability Detection
Source code and its accompanying comments are complementary yet naturally aligned modalities-code encodes structural logic while comments capture developer intent. However, existing vulnerability detection methods mostly rely on single-modality code representations, overlooking the complementary sem...
- RADD: Retrieval-Augmented Discrete Diffusion for Multi-Modal Knowledge Graph Completion
Most multi-modal knowledge graph completion (MMKGC) models use one embedding scorer to do both retrieval over the full entity set and final decision making. We argue that this coupling is a core bottleneck: global high-recall search and local fine-grained disambiguation require different inductive b...
- Spreadsheet Modeling Experiments Using GPTs on Small Problem Statements and the Wall Task
This paper investigates how GPT-based tools can assist in building reusable analytical spreadsheet models. After a screening, we evaluate five GPT extensions and select Excel AI by pulsrai.com for detailed testing. Through structured experiments on simple problem statements, we assess Excel AI's per...
- CORAL: Adaptive Retrieval Loop for Culturally-Aligned Multilingual RAG
Multilingual retrieval-augmented generation (mRAG) is often implemented within a fixed retrieval space, typically via query or document translation or multilingual embedding vector representations. However, this approach may be inadequate for culturally grounded queries, in which retrieval-condition...
- LLM-ReSum: A Framework for LLM Reflective Summarization through Self-Evaluation
Reliable evaluation of large language model (LLM)-generated summaries remains an open challenge, particularly across heterogeneous domains and document lengths. We conduct a comprehensive meta-evaluation of 14 automatic summarization metrics and LLM-based evaluators across seven datasets spanning fi...
- Prefill-Time Intervention for Mitigating Hallucination in Large Vision-Language Models
Large Vision-Language Models (LVLMs) have achieved remarkable progress in visual-textual understanding, yet their reliability is critically undermined by hallucinations, i.e., the generation of factually incorrect or inconsistent responses. While recent studies using steering vectors demonstrated pr...
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- Emotive Architectures: The Role of LLMs in Adjusting Work Environments
In remote and hybrid work contexts, the integration of physical and digital environments is revolutionizing spatial experiences, collaboration, and interpersonal interactions. This study examines three fundamental spatial conditions: the physical environment, characterized by material and sensory at...
- Walking Through Uncertainty: An Empirical Study of Uncertainty Estimation for Audio-Aware Large Language Models
Recent audio-aware large language models (ALLMs) have demonstrated strong capabilities across diverse audio understanding and reasoning tasks, but they still frequently produce hallucinated or overly confident outputs. While uncertainty estimation has been extensively studied in text-only LLMs, it r...
- DualFact+: A Multimodal Fact Verification Framework for Procedural Video Understanding
We introduce DualFact, a dual-layer, multimodal factuality evaluation framework for procedural video captioning. DualFact separates factual correctness into conceptual facts, capturing abstract semantic roles (e.g., Action, Ingredient, Tool, Location), and contextual facts, capturing their grounded ...
- Recursive Multi-Agent Systems
Recursive or looped language models have recently emerged as a new scaling axis by iteratively refining the same model computation over latent states to deepen reasoning. We extend such scaling principle from a single model to multi-agent systems, and ask: Can agent collaboration itself be scaled th...
- No Pedestrian Left Behind: Real-Time Detection and Tracking of Vulnerable Road Users for Adaptive Traffic Signal Control
Current pedestrian crossing signals operate on fixed timing without adjustment to pedestrian behavior, which can leave vulnerable road users (VRUs) such as the elderly, disabled, or distracted pedestrians stranded when the light changes. We introduce No Pedestrian Left Behind (NPLB), a real-time ada...
- Luminol-AIDetect: Fast Zero-shot Machine-Generated Text Detection based on Perplexity under Text Shuffling
Machine-generated text (MGT) detection requires identifying structurally invariant signals across generation models, rather than relying on model-specific fingerprints. In this respect, we hypothesize that while large language models excel at local semantic consistency, their autoregressive nature r...
- Towards Agentic Investigation of Security Alerts
Security analysts are overwhelmed by the volume of alerts and the low context provided by many detection systems. Early-stage investigations typically require manual correlation across multiple log sources, a task that is usually time-consuming. In this paper, we present an experimental, agentic wor...