AI News Archive: June 17, 2026 — Part 16
Sourced from 500+ daily AI sources, scored by relevance.
- AP Exclusive: Bernie Sanders unveils plan to give the public direct ownership of AI companies
AP Exclusive: Bernie Sanders unveils plan to give the public direct ownership of AI companies Houston Chronicle
- Bernie Sanders unveils plan to give the public direct ownership of AI companies
Bernie Sanders unveils plan to give the public direct ownership of AI companies Boston Herald
- Banks face $170 billion profit squeeze while AI challengers reset finance
Banks face $170 billion profit squeeze while AI challengers reset finance Gulf News
- Half of AI job cuts will be reversed by 2027 — and it reveals the biggest mistake companies are making
Half of AI job cuts will be reversed by 2027 — and it reveals the biggest mistake companies are making Tom's Guide
- OpenAI launches scheduled tasks in ChatGPT, details here
OpenAI is updating ChatGPT with a new scheduled tasks feature. The new ability is rolling out starting today.
- Brain-inspired phototransistor could cut AI energy use by sensing and storing data
Inspired by the human brain, Oregon State University researchers have developed a new light-sensitive device that combines sensing and memory while controlling how digital memories strengthen or fade over time. The research was published in Advanced Functional Materials.
- Databricks targets AI-driven threat detection with Panther deal
Databricks targets AI-driven threat detection with Panther deal verdict.co.uk
- AMD acquires MEXT to boost AI-driven memory optimisation
US-based semiconductor firm AMD has announced the acquisition of MEXT, which specialises in AI-driven memory optimisation technology.
- Nvidia's Huang pledges AI will boost manufacturing jobs. A test will come in Texas
Nvidia's Huang pledges AI will boost manufacturing jobs. A test will come in Texas Austin American-Statesman
- Stop making boring slides because Google Vids just made AI avatars free for everyone
Google rolled out free AI avatars in Vids along with extended Veo video clips, multilingual voiceovers, and an upcoming emotion steering feature for personalized delivery.
- Google now lets you try its custom AI avatars without signing up for a paid plan
Your next Google Vids pitch could be in 24 languages without you saying a word.
- Millions of hours of Bay Area police body-camera footage go unwatched. Stanford says AI could change that
Millions of hours of Bay Area police body-camera footage go unwatched. Stanford says AI could change that East Bay Times
- UBP2: Uncertainty-Balanced Preference Planning for Efficient Preference-based Reinforcement Learning
Preference-based RL provides an approach to learning reward models from pairwise comparisons of behaviors, bypassing the need for explicit reward design. However, existing methods typically rely on passive data collection and suffer from poor sample efficiency, especially during the early stages of ...
- Rethinking Reward Supervision: Rubric-Conditioned Self-Distillation
Post-training of reasoning language models is commonly driven by supervised distillation and reinforcement learning with verifiable rewards. Distillation often relies on chain-of-thought annotations that are expensive to obtain and may themselves be noisy, incomplete, or partially incorrect; even wh...
- Explaining Attention with Program Synthesis
A longstanding goal of research on interpretable deep learning is to replace opaque neural computations with human-meaningful symbolic descriptions. In this paper, we propose an approach for approximating the behavior of components of deep networks with executable programs. We focus on attention hea...
- Trade-offs in Medical LLM Adaptation: An Empirical Study in French QA
The development of large language models (LLMs) has led to an increased focus on their adaptation to specialized domains and languages, yet the effectiveness of domain adaptation strategies remains unclear. We present a study of medical domain adaptation using French medical question-answering (QA) ...
- A Multi-Domain Benchmark for Detecting AI-Generated Text-Rich Images from GPT-Image-2
Text-rich images often contain privacy-sensitive, transactional, or decision-relevant information. As recent multimodal image generation models become increasingly capable of synthesizing realistic textual content and structured visual designs, detecting AI-generated text-rich images has become an i...
- X+Slides: Benchmarking Audience-Conditioned Slide Generation
Automatically generating slide decks from source documents is an important application of large language models (LLMs). Existing benchmarks primarily assess slide completeness and technical depth, while overlooking the target audience as a critical real-world factor. For instance, specialists demand...
- TxBench-PP: Analyzing AI Agent Performance on Small-Molecule Preclinical Pharmacology
Artificial intelligence (AI) agents promise to accelerate drug discovery by compressing interpretation and decision-making loops, but practical deployment requires trusted evaluation on realistic program decisions. We introduce TherapeuticsBench Preclinical Pharmacology (TxBench-PP), a verifiable be...
- STARE: Surprisal-Guided Token-Level Advantage Reweighting for Policy Entropy Stability
Reinforcement Learning with Verifiable Rewards algorithms like GRPO have emerged as the dominant post-training paradigm for complex reasoning in LLMs, yet commonly suffer from policy entropy collapse during training. We conduct a first-order gradient analysis of token-level entropy dynamics under GR...
- Machine Unlearning for the XGBoost Model with Network Intrusion Datasets
Machine Unlearning (MU) has emerged as an important technique for removing specific data points from trained models without requiring full retraining. However, most existing MU research focuses on deep learning and image data, leaving a gap in the domain of network intrusion detection, which relies ...
- Forecasting what Matters: Decision-Focused RL for Controlled EV Charging with Unknown Departure Times
The recent growth of EV adoption poses challenges for power systems, including increased peak demand and potential grid instability. Smart control of EV charging -- e.g., based on reinforcement learning (RL) -- can alleviate these issues by learning temporal and contextual patterns from historical d...
- Language Models as Interfaces, Not Oracles: A Hybrid LLM-ML System for Pediatric Appendicitis
Large language models (LLMs) can make clinical decision support more accessible by interpreting free-text documentation, but their direct use as diagnostic engines is limited by sensitivity to prompts, information order, and plausible but incorrect outputs. Structured machine-learning models offer m...
- User as Engram: Internalizing Per-User Memory as Local Parametric Edits
Personal memory in a language model is two problems: content and reasoning skill. The brain keeps the two apart (a sparse, local engram in the hippocampus for each episode, a slow neocortex for the shared skills that interpret it), so a new fact need not overwrite everything else. Most personalizati...
- Beyond Safe Data: Pretraining-Stage Alignment with Regular Safety Reflection
To achieve deeper safety alignment for large language models (LLMs), recent efforts have studied how to push safety interventions earlier into the pretraining stage, primarily by filtering unsafe data or rewriting it into safer forms. We argue that pretraining-stage alignment should go beyond making...
- A Technical Taxonomy of LLM Agent Communication Protocols
As large language models (LLMs) advance and multi-agent systems aim to overcome the limits of standalone agents, robust communication protocols are becoming essential infrastructure for distributed agent networks. Nonetheless, the fragmented protocol landscape presents a significant interoperability...
- Pareto Q-Learning with Reward Machines
We present Pareto Q-Learning with Reward Machines (PQLRM), a multi-objective reinforcement learning algorithm for tasks whose reward structure is specified by a set of reward machines (RMs). PQLRM combines Pareto Q-Learning (PQL), which maintains sets of vector-valued Q-estimates to approximate the ...
- Equivariant Graph Neural Networks Improve Optical Spectra Prediction for Materials Screening
Scalable prediction of optical spectra is a critical component of high-throughput materials screening for optoelectronic applications such as solar cells. Existing surrogate models are trained on spectra computed from lower levels of theory or rely on rotation-invariant scalar features, limiting the...
- Analysing drivers and interdependencies in European electricity markets using XAI
Electricity markets are inherently complex systems characterised by strong nonlinearities, high-dimensional interactions, and increasing interdependence across regions. While deep neural networks (DNNs) have demonstrated strong predictive capabilities for electricity prices, their lack of interpreta...
- Leadership as Coordination Control: Behavioral Signatures and the Recovery-Advantage Boundary in Multi-Agent LLM Teams
Team science holds that leadership is contingent: it helps only under specific conditions, and capable, autonomous teams may need none at all. We ask the analogous question for multi-agent LLM teams: under what measurable conditions does process-level coordination control add value, and do those con...
- ARIADNE: Agnostic Routing for Inference-time Adapter DyNamic sElection
The increasing deployment of parameter-efficient fine-tuning (PEFT) has led to model ecosystems in which a single backbone is paired with many task-specialized adapters. In this setting, inference-time queries often arrive without task labels, requiring the system to automatically select the most ap...
- Where Did the Variability Go? From Vibe Coding to Product Lines by Regeneration
In vibe coding, an emerging AI-driven paradigm, an LLM generates an entire program from a natural language prompt, but what happens to the variability that traditional software engineering carefully builds into code? To answer this question, we conducted an exploratory analysis on 10 vibe coded C/C+...
- A Hybrid LSTM--Vision Transformer Architecture for Predicting HRRR Forecast Errors
Forecast errors in high-resolution numerical weather prediction (NWP) systems are often linked to unresolved planetary boundary layer (PBL) processes, convection, terrain-induced circulations, and other vertically structured atmospheric phenomena. Previous work demonstrated that Long Short-Term Memo...
- FoMoE: Breaking the Full-Replica Barrier with a Federation of MoEs
Pre-training Large Language Models (LLMs) typically demands large-scale infrastructure with tightly coupled hardware accelerators. While increasing model and dataset scale remains the dominant driver of performance, Mixture-of-Experts (MoEs) architectures have recently achieved state-of-the-art resu...
- Spotlight: Synergizing Seed Exploration and Spot GPUs for DiT RL Post-Training
Reinforcement learning (RL) post-training of Diffusion Transformers (DiTs) is prohibitively expensive, requiring thousands of high-end GPUs. Existing works explore two directions to reduce cost: seed exploration improves training convergence by selecting high-contrast samples, yet adds compute to th...
- ThinkDeception: A Progressive Reinforcement Learning Framework for Interpretable Multimodal Deception Detection
Multimodal deception detection is critical for identifying fraudulent intentions, yet existing approaches predominantly rely on end to end black--box paradigms. These methods suffer from a severe lack of interpretability failing to provide transparent reasoning trajectories and struggling to explici...
- Beyond Tokenization: Direct Timestep Embedding and Contrastive Alignment for Time-Series Question Answering
Recent advances in large language models (LLMs) have given rise to time-series question answering (TSQA), which formulates time-series analysis as natural-language question answering. However, directly feeding raw numerical series into LLMs suffers from a tokenization bottleneck: Byte Pair Encoding ...
- CAPRA: Scaling Feedback on Software Architecture Deliverables with a Multi-Agent LLM System
Automated assessment in software engineering education has advanced significantly for code grading and essay scoring. However, reviewing software architecture deliverables, which requires analyzing structural completeness and requirements traceability, has not yet been fully automated. Applying Larg...
- A Controlled Benchmark of Quantum-Latent GAN Augmentation for Brain MRI
Medical image classification is often constrained by limited labeled data, motivating generative augmentation; recently, quantum generative models have been proposed for this purpose, frequently reporting accuracy gains. However, such claims are typically based on single training runs, do not match ...
- PaneFlow
Let AI agents build real animated slideshows
- Decoupling Search from Reasoning: A Vendor-Agnostic Grounding Architecture for LLM Agents
Production LLM agents increasingly depend on real-time search, yet native search grounding bundles retrieval policy, provider choice, evidence injection, cost, latency, and generation behavior behind a single model-provider boundary. This coupling makes grounding hard to inspect, tune, reuse, or por...
- SciRisk-Bench: A Risk-Dimension-Aware Benchmark for AI4Science Safety
Large language models (LLMs) are increasingly embedded in AI for Science (AI4Science) workflows, from scientific question answering and literature analysis to laboratory planning and autonomous discovery. This progress creates an urgent need for safety benchmarks that evaluate not only scientific co...
- Reference-Driven Multi-Speaker Audio Scene Generation from In-the-Wild Priors
Existing multi-speaker dialogue systems bind speakers to utterances through structured supervision: per-turn tags, multi-stream transcriptions, or learnable speaker embeddings. These systems operate within speech-only pipelines that produce clean vocal sequences without the ambient texture of real c...
- Data Intelligence Agents: Interpreting, Modeling, and Querying Enterprise Data via Autonomous Coding Agents
Production data integration is bottlenecked by repeated, lossy handoffs between data owners, engineers, and analysts who must collaboratively discover, structure, and query enterprise data. We present Data Intelligence Agents (DIA), a system of three agents (Data Interpreter, Schema Creator, and Que...
- Correct Yourself, Keep My Trust: How Self-Correction and Social Connection Shape Credibility in Social Chatbots
When social chatbots make mistakes, and they do, how they recover determines whether users trust them again. Social chatbots are increasingly integrated into everyday life, yet they remain prone to generating convincing but inaccurate information. The social connection they build with users makes su...
- NeSyCat Torch: A Differentiable Tensor Implementation of Categorical Semantics for Neurosymbolic Learning
Neurosymbolic semantics is fragmented: classical, fuzzy, probabilistic and neural systems each define truth by their own inductive rules. NeSyCat, extending ULLER, subsumes them under a single inductive definition of truth, parametric in a strong monad and an aggregation structure on truth-values. N...
- Mechanism-Guided Selective Unlearning for RLVR-Induced Reasoning
We propose MAST (Mechanism-Aligned Selective Targeting), a mechanism-guided method for unlearning RLVR-induced reasoning with substantially lower collateral damage than standard full-parameter updates. In matched SFT/RLVR checkpoints on Qwen2.5-Math-1.5B and Qwen3-1.7B-Base, the SFT-to-RLVR incremen...
- Compute Efficiency and Serial Runtime Tradeoffs for Stochastic Momentum Methods
Stochastic momentum methods such as heavy ball (HB), Nesterov momentum, and variants of Accelerated SGD (ASGD) [Kidambi et al., 2018] are widely used in modern training, but their stochastic benefits depend on two distinct quantities: serial runtime, the number of iterations needed to reach a target...
- Hardware- and Vision-in-the-Loop Validation of Deep Monocular Pose Estimation for Autonomous Maritime UAV Flight
Autonomous UAV operations on ships require reliable vision-based relative pose estimation, yet at-sea validation is costly, weather-dependent, and risky. This paper presents a hardware-validated vision-in-the-loop framework that enables fully autonomous indoor flight while emulating photorealistic m...
- A Clinician-Centered Pipeline for Annotation and Evaluation in Ultrasound AI Studies
Clinician-centered evaluation is critical for validating medical AI systems, especially in ultrasound imaging where quantitative metrics do not always capture clinical usability. Existing medical image platforms primarily focus on dataset labeling. They lack integrated support for blinded model comp...