AI News Archive: June 15, 2026 — Part 12
Sourced from 500+ daily AI sources, scored by relevance.
- Courts cracking down on error-strewn AI-assisted legal briefs
When a U.S. judge found fabricated quotes in a lawyer's brief earlier this year, the attorney admitted he had used Claude, an artificial intelligence chatbot, to write the document.
- OpenAI Under Investigation by Group of State Attorneys General, Source Says
A coalition of U.S. state attorneys general has opened a sweeping investigation into OpenAI, a source familiar with the matter said on Friday. The ChatGPT maker was served on Friday with a subpoena seeking documents related to a wide range …
- KPMG Allegedly Published AI Report Filled With Hallucinations
KPMG Allegedly Published AI Report Filled With Hallucinations PCMag Australia
- Climate crisis is changing when plants flower, artificial intelligence study finds
A global study using AI to analyse eight million digitalised plant specimens dating back a century revealed flowering has shifted by 2.5 days earlier or later per decade on average
- Hate talking to AI customer support? 70% of Americans say help from a real human should be a legal right
Three-quarters of Americans want to be told when they’re interacting with AI
- AI hiring in Ireland doubles as adoption accelerates
New research shows that AI is rapidly reshaping the skills employers want most from workers - increasing the emphasis on human skills such as judgement, creativity and leadership.
- Trump tried to block state AI regulations, but some states are forging ahead
Trump tried to block state AI regulations, but some states are forging ahead Austin American-Statesman
- As AI cameras scan for wildfires human lookouts still stand guard
As AI cameras scan for wildfires human lookouts still stand guard azcentral.com and The Arizona Republic
- A 13-word Reddit comment can trick AI search into recommending scams, researchers find
A 13-word Reddit comment can trick AI search into recommending scams, researchers find Tom's Guide
- Google’s Android coding tests reveal an unexpected Gemini 3.5 Flash weakness
Google's new Gemini 3.5 Flash is a pricey downgrade for Android devs.
- Gemini suddenly can’t make calls on Android and Android Auto for some
The transition to Gemini on Android Auto has been a bit rough for a number of reasons, but a current bug has left some users unable to make calls due to a strange error, and it’s not just an issue behind the wheel.
- AI schools like Alpha promise efficiency, but can't replicate the messy process that helps kids learn
A child at a playground tries to climb, jump or negotiate with a peer, and the attempt does not work. They fall, get left out of a game or reach another impasse. Then they try again.
- OpenAI hit with multistate probe into possible user harm as its IPO looms
OpenAI received a subpoena from several states as part of a probe into the safety of users of its chatbot as it prepares to offer stock to the public for the first time.
- ETRI develops autonomous 6G core network powered by AI
ETRI develops autonomous 6G core network powered by AI EurekAlert!
- Robotic pet rabbit created that recognizes who hugs it by their voice
Robotic pet rabbit created that recognizes who hugs it by their voice EurekAlert!
- AI and digitisation transform fight against global extinction, landmark report reveals
AI and digitisation transform fight against global extinction, landmark report reveals EurekAlert!
- Young People Turn to AI for Mental Health Support
A new study demonstrates that 18% of college students use generative AI for mental health support, with usage rates doubling among students suffering from severe anxiety, depression, and suicidality.
- Exploring Extrinsic and Intrinsic Properties for Effective Reasoning with Code Interpreter
Reasoning with a Code Interpreter (CI) has emerged as an effective paradigm for enhancing the reasoning capabilities of large language models (LLMs) through executable computation and iterative verification. Despite its growing adoption, the behavioral properties underlying effective code reasoning ...
- Speaking the Language of Science: Toward a General-Purpose Generative Foundation Model for the Natural Sciences
In this report, we present LOGOS (Language Of Generative Objects in Science), a scientific generative language model that unifies heterogeneous tasks across the natural sciences within a single autoregressive framework based on a shared scientific grammar. It encodes diverse scientific objects and t...
- Contrastive-Difference CKA Reveals Concept-Specific Structural Alignment Across Language Model Architectures
Do different LLM architectures encode high-level concepts in structurally compatible ways? We systematically characterize a geometric-functional universality dissociation: across multiple concept domains and architectural families, moderate geometric convergence coexists with near-perfect functional...
- Compositional Reasoning Depth Predicts Clinical AI Failure: Empirical Evidence Consistent with Transformer Compositionality Limits in Electronic Health Record Question Answering
Aggregate accuracy benchmarks conceal a systematic structure in how large language models fail at electronic health record (EHR) question answering: questions requiring more inferential steps produce disproportionately more errors. Motivated by theoretical results on transformer compositionality lim...
- Revisiting the Systematicity in Negation in the Era of In-Context Learning
Understanding the meaning of negated sentences remains one of the challenges for language models, even in the era of large language models (LLMs). We analyze systematicity regarding LLM understanding of negation from two perspectives: behavioral systematicity and representational systematicity. For ...
- Follow the Latent Roadmap: Navigating Revocable Decoding for Diffusion LLMs with Anchor Tokens
Diffusion Large Language Models (dLLMs) offer a promising avenue for parallel generation but face a trade-off between decoding speed and quality. While revocable decoding strategies attempt to mitigate errors by verifying and remasking tokens, they typically operate within a mixed-quality context. T...
- Robust Dual-Signal Fusion: Hybrid Neuro-Symbolic Gating with Compressed Chain-of-Thought Refinement for Irony Detection in Social Media Texts
Large Language Models (LLMs) natively default to literal semantic interpretations, making zero-shot irony detection a persistent challenge. We introduce the Robust Dual-Signal (RDS) Fusion framework, a hybrid neuro-symbolic architecture that compresses Chain-of-Thought (CoT) reasoning trajectories w...
- Data-Driven Decoding of Russell's Circumplex Model of Affect
Affective computing increasingly relies on deep learning to represent emotions, yet latent spaces often remain opaque, high-dimensional black boxes. This paper investigates whether Transformers' embeddings recover the geometric regularities of Russell's circumplex model. We unify two complementary e...
- Tying the Loop -- Tied Expert Layers in Mixture-of-Experts Language Models
Mixture-of-Experts (MoE) architectures efficiently scale Large Language Models (LLMs) by activating only a small fraction of their experts per token, yet the full parameter count - dominated by the expert parameters - must be held in training and inference memory. To address this, we introduce Exper...
- How Much Can We Trust LLM Search Agents? Measuring Endorsement Vulnerability to Web Content Manipulation
Large language model (LLM)-based search agents synthesize open-web content into actionable recommendations on behalf of users, creating a risk that attacker-published pages are transformed into endorsed claims. We introduce SearchGEO, a controlled evaluation framework for measuring endorsement corru...
- Understanding the Behaviors of Environment-aware Information Retrieval
Recent retrieval-augmented generation (RAG) approaches have demonstrated strong capability in handling complex queries, yet current research overlooks a critical challenge: different retrievers require fundamentally different query formulation strategies for optimal performance. In this work, we pre...
- Scaling LLM Reasoning from Minimal Labels: A Semi-Supervised Framework with a Lightweight Verifier
For the development of Large language models (LLMs), recent approaches to generating pseudo intermediate reasoning have shown remarkable progress. But they typically rely on large numbers of correctly annotated answers to assess reasoning quality. This paper presents a semi-supervised framework that...
- LLM-based Visual Code Completion for Aerospace Geometric Design
Recent advances in both Large Language Models (LLMs) and Vision Language Models (VLMs) have seen a step change in their ability to perform visual code completion, but the aerospace industry, which prioritizes safety and explainabilty over rapid LLM adoption, currently has no publicly announced LLM-b...
- The Art of Mixology: Mixup-based Obfuscation for Privacy-Preserving Split Learning in Large Language Models
Split learning provides a practical paradigm for resource-constrained users to train Large Language Models (LLMs) by offloading computation-intensive layers to a server while keeping raw data local. However, existing privacy-preserving split learning methods still face a difficult trade-off among ut...
- OpenClaw-Skill: Collective Skill Tree Search for Agentic Large Language Models
Equipping Large Language Model (LLM) agents with effective skills is crucial for solving complex tasks in real-world systems like OpenClaw. In this work, we aim to develop a framework that automatically constructs such reusable skills to enhance LLMs in tool use, multi-step reasoning, and dynamic en...
- P3B3: A Multi-Turn Conversational Benchmark for Measuring European and Brazilian Portuguese Variety Bias in LLMs
As Large Language Models (LLMs) become embedded in everyday communication, capturing regional linguistic variation is essential for reliable and equitable language use. In Portuguese, European (pt-PT) and Brazilian (pt-BR) varieties remain unevenly represented, with pt-BR dominating in data quantity...
- Misinformation Propagation in Benign Multi-Agent Systems
Multi-agent systems, in which multiple large language model agents solve problems through turn-based interaction, are increasingly deployed in high-stakes settings such as medical diagnosis, legal analysis, and forensic decision-making. Their reliability can be at risk when single agents reason from...
- Progressive Knowledge-Guided Large Language Model Framework for Bearing Fault Diagnosis
Vibration-based bearing fault diagnosis requires resolving three interrelated measurement challenges, including the trade-off between global statistical feature efficiency and local transient signal fidelity, insufficient traceability of measurement features to underlying fault physics, and ineffect...
- Multimodal Evaluator Preference Collapse: Cross-Modal Contagion in Self-Evolving Agents
When AI agents use language models to evaluate their own outputs in a feedback loop, systematic biases emerge. We show that Evaluator Preference Collapse (EPC) is dramatically amplified in multimodal settings. Using GPT-4o to evaluate DeepSeek-chat across text and visual tasks, we find that a single...
- SCAR: Semantic Continuity-Aware Retrieval for Efficient Context Expansion in RAG
Fixed-length chunking in Retrieval-Augmented Generation (RAG) often leads to boundary fragmentation, where critical evidence is split across segments, degrading retrieval recall. While static windowing and parent retrieval improve recall, they introduce significant token overhead. We propose SCAR (S...
- Islamic Large Language Models: From Knowledge Acquisition to Trustworthy and Hallucination-Resistant AI
Large language models (LLMs) are increasingly used for knowledge-intensive question answering, including religious and legal questions. Islamic knowledge is a particularly demanding setting: answers are expected to be grounded in authoritative sources, citations must be exact, Arabic varieties diffe...
- Relay
Paste a site & get an AI receptionist that learns from calls
- Sycophancy as Material Failure under Pushback Loading: A Multi-Axis Characterization Across Three Loading Cases and up to Seventeen Material Charges
Sycophancy in LLMs is documented across 70+ papers, but expert agreement on construct boundaries remains low (ICC=.184; Ye et al., 2026). The construct fragments because behavioral classification depends on which surface form is privileged. We adopt a materials-science framing: conversation as test ...
- VeriGraph: Towards Verifiable Data-Analytic Agents
LLM-based agents have demonstrated strong capabilities in data-intensive analytical tasks, yet their outputs are rarely verifiable: a reliance on linear text trajectories makes their reasoning difficult to audit. In particular, deterministic computations over raw data and semantic deductions over na...
- SING: Synthetic Intention Graph for Scalable Active Tool Discovery in LLM Agents
Large language model (LLM) agents increasingly rely on agent harnesses that manage context, tools, and multi-turn execution, making tools a central interface for acting in realistic digital environments. As harness-connected tool ecosystems expand to hundreds or thousands of APIs, services, and task...
- Can LLM Agents Infer World Models? Evidence from Agentic Automata Learning
We propose agentic automata learning to evaluate the extent to which tool-calling LLM agents can uncover hidden environments through interaction. In our setup, an agent should uncover a hidden deterministic finite automaton (DFA) by interacting with an oracle through (1) membership queries ("Does th...
- Can LLM Coding Agents Reason About Time Series?
Large language models (LLMs) are increasingly being used for automated decision-making systems in finance, healthcare, or environmental monitoring. Time series data are ubiquitous in these fields, yet hard to process automatically. Can time series be analyzed by LLM agents? We examine three approach...
- DoubtProbe: Black-Box Jailbreak Defense via Structural Verification and Semantic Auditing
As large language models (LLMs) are increasingly deployed in user-facing systems, black-box jailbreak defense has become an important practical problem. Existing defenses often rely on known-attack coverage, prompt-level semantic judgment, or local runtime control, yet these paths can become unstabl...
- daVinci-kernel: Co-Evolving Skill Selection, Summarization, and Utilization via RL for GPU Kernel Optimization
GPU kernel optimization represents a paradigm where functional correctness is assumed and execution efficiency is the objective. We present daVinci-kernel, a reinforcement learning framework that couples skill discovery with skill exploitation through a dynamically evolving skill library. daVinci-ke...
- REFLEX: Reflective Evolution from LLM Experience
Large multimodal language models (LLMs) have emerged as powerful tools for guiding evolutionary search toward interpretable programmatic policies. However, existing frameworks rely on a monolithic model call to simultaneously interpret visual behavioral evidence and synthesize corrective code. This ...
- Lost at the End: Primacy Bias in Multimodal Retrieval-Augmented Question Answering
Knowledge-based visual question answering (KB-VQA) lets vision-language systems answer questions that exceed their parametric knowledge by conditioning a reader on passages retrieved from a Wikipedia-scale knowledge base. In pure-text long-context LLMs, retrieved-context use follows the U-shaped "lo...
- ACCORD: Action-Conditioned Contextual Grounding for Language Agents
User instructions are often underspecified because humans rely on implicit assumptions about the surrounding environment. For large language model (LLM) agents operating in information-rich digital and physical environments, these assumptions cannot be inferred from the instruction alone; they must ...
- PathRouter: Aligning Rewards with Retrieval Quality in Agentic Graph Retrieval-Augmented Generation
Agentic GraphRAG trains language-model agents to iteratively retrieve and reason over graph-structured evidence, enabling more accurate and context-aware decision-making by efficiently navigating complex information networks. However, outcome-only reinforcement learning suffers from \textit{\textbf{...