AI News Archive: July 16, 2026 — Part 22
Sourced from 500+ daily AI sources, scored by relevance.
- AI startup Thinking Machines launches an open-weight AI model
Named Inkling, the model is open-weight, meaning users can download, run and customize the underlying systems, unlike proprietary, closed-source models.
- Mira Murati's startup Thinking Machines launches an open-weight AI model
Named Inkling, the open-weight model allows developers to download, run and customise its underlying systems; the 975-bn-parameter model is the first general-purpose AI release from Thinking Machines
- Thinking Machines Rolls Out Broad but Efficient Model
The AI startup, founded by OpenAI's former CTO, released Inkling, a general-purpose model that keeps token use in mind.
- AI startup Thinking Machines launches an open-weight AI model
Named Inkling, the model is open-weight, meaning users can download, run and customize the underlying systems, unlike proprietary, closed-source models.
- OpenAI’s Former CTO Just Dropped a Very Different Kind of AI Model
Mira Murati’s Thinking Machines released its first model, Inkling. It might be perfect for your business.
- What is Thinking Machines’ first AI model ‘Inkling’, and how is it different from ChatGPT, Claude?
What is Thinking Machines’ first AI model ‘Inkling’, and how is it different from ChatGPT, Claude?
- America's best open-weight model yet
Explores the latest open-weight AI model leading the market.
- Mira Murati’s AI start-up unveils customisable model Inkling
Inkling is Thinking Machines Lab's first big AI model launch, and comes a little more than a year after the start-up was founded. Read more: Mira Murati’s AI start-up unveils customisable model Inkling
- Former OpenAI CTO does what Altman won't, releases a frontier AI model that's actually open
Thinking Machines' first open weights model is a 975 billion parameter alternative to Chinese LLMs
- SpaceXAI Open-Sources Grok Build After Privacy Backlash
SpaceXAI open-sourced Grok Build under Apache 2.0 after privacy backlash over broad directory uploads from its terminal AI coding agent. The post SpaceXAI Open-Sources Grok Build After Privacy Backlash appeared first on TechRepublic .
- OpenAI Unveils GPT-Red to Test AI Model Safety
While red teaming is standard practice, using humans and AI to test the security of new models is novel. Enterprises should still ensure the model they use aligns with their business and security workflows.
- When AI Blurs the Boundaries of Contribution: An Empirical Study of Authorship Calibration
The broad adoption of Artificial Intelligence (AI), especially Generative AI, raises pressing questions about how users interact with these systems to produce new content. In this paper, we introduce the concept of authorship calibration, defined as users awareness of their actual authorship when in...
- The Download: OpenAI unveils GPT-Red and heat pumps rise in the US
This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology. Meet GPT-Red: an LLM super-hacker OpenAI built to make its models safer OpenAI has built an LLM super-hacker called GPT-Red that it uses as a sparring partner to help its other…
- NVIDIA Nemotron open models draw enterprise partners citing up to 20x inference cost advantage over closed AI
NVIDIA has used its Nemotron Labs blog series to highlight a growing roster of enterprise partners building specialized AI agents on its open Nemotron model family, with documented inference cost reductions of up to 20 times compared with leading closed alternatives, underscoring a widening divide between open and proprietary AI economics. Open weights as competitive ... Read more
- MM-IssueLoc: A Controlled Benchmark for Evaluating Visual Evidence in Multimodal Repository-Level Issue Localization
Real repository issues routinely include visual evidence such as screenshots, error dialogs, rendered UI states, and logs, yet repository-level issue localization is evaluated mostly as a text-only task. Existing multimodal SE benchmarks evaluate end-to-end repair, entangling localization with patch...
- Self-Evolving Human-Centered Framework for Explainable Depression Symptom Annotation
Annotation quality is a major bottleneck in building reliable and explainable artificial intelligence (XAI) systems for mental health research. In depression-related datasets, labels are often assigned without structured evidence, symptom-level justification, or traceable alignment with the criteria...
- Mask-Aware Policy Gradients for Diffusion Language Models
Reinforcement learning has proven effective for improving reasoning in large language models, but extending it to Masked Diffusion Language Models (MDLMs) remains challenging due to the intractability of the log-likelihood estimation. Existing approaches approximate this log-likelihood by modeling o...
- Plover: Steering GUI Agents through Plan-Centric Interaction
Graphical user interface (GUI) automation remains challenging in real-world environments, where dynamic layouts, unexpected dialogs, and evolving interface states can cause autonomous agents to drift from user intent. Recent vision-based multimodal agents improve flexibility by operating directly ov...
- Benchmarking Multimodal Large Language Models for Scientific Visualization Literacy
Multimodal large language models (MLLMs) are increasingly used to interpret visualizations, yet current evaluations remain largely chart-centric and provide limited evidence of understanding of scientific visualization (SciVis). We benchmark six MLLMs on the scientific visualization literacy assessm...
- MedFailBench: A Clinician-Built Open-Source Benchmark for Medical AI Safety Boundary Inspection
Most medical AI benchmarks measure whether a model knows the correct answer. MedFailBench asks a different question: which safety boundary failed? We present a clinician-built synthetic benchmark and failure atlas that labels medical AI errors by severity (1--5) and safety gate type (missed urgent e...
- The Industrialization of Research ; On AI-Driven Science and Its Consequences
Artificial intelligence is transforming scientific research - not merely as a more powerful instrument, but as an autonomous participant in the research cycle itself. This transition constitutes, in the most precise sense of the term, the industrialization of research: a shift from a craft model, in...
- Scaling Behavior Foundation Model for Humanoid Robots
Humanoid control requires natural whole-body coordination, precise real-time responses to control signals, and robust generalization across diverse environmental contexts, making it a cornerstone for generalist embodied agents. Behavior Foundation Models (BFMs) have recently emerged as a promising s...
- Concept-Guided Spatial Regularization for World Models in Atari Pong
World models are usually evaluated as components of model-based reinforcement learning (MBRL) systems, while the world models themselves are rarely studied in isolation. We examine five representative visual world-model agents in Atari Pong: DreamerV3, DIAMOND, TWISTER, Simulus, and STORM. After r...
- NIFA: Nonlinear IMC enhanced FPGA for efficient ML inference
Recent FPGAs have improved deep learning (DL) inference efficiency through dedicated tensor blocks and in-BRAM computation. ReRAM-based analog in-memory computing (IMC) pushes efficiency further, offering an order-of-magnitude improvement in compute density and energy efficiency over conventional di...
- Long-Context Fine-Tuning with Limited VRAM
Parameter-efficient fine-tuning reduces model and optimizer memory, but dense attention still makes long training sequences expensive. We combine Hierarchical Global Attention (HGA) with segment-wise backpropagation and tiered KV storage. Only the active segment remains differentiable in VRAM; older...
- Digital Pantheon: Simulating and Auditing Coalition Formation with LLM Agents
The formation of political coalitions is a complex negotiation driven by both concrete policy objectives and deep-seated ideological convictions. While Large Language Models (LLMs) open new avenues for computational political science, the neutrality and helpfulness biases instilled by Reinforcement ...
- Parameter-efficient Prompt Tuning of Vision Foundation Model With Adaptive Focal Loss for Interpretable MCI Screening
Mild Cognitive Impairment is a critical early stage of cognitive decline that frequently precedes Alzheimer's disease, yet its automated detection from neuropsychological drawing tests remains fundamentally constrained by data scarcity, class imbalance, and diagnostic ambiguity near clinical boundar...
- Codex Micro
Tactile controls for your Codex agents
- SMC-ES: Automated synthesis of formally verified control policies
The deployment of autonomous cyber-physical systems in safety-critical environments requires closed-loop control strategies (i.e., policies) that are not only performant but also provably safe and robust. While learning-based methodologies such as Reinforcement Learning offer flexible and scalable a...
- CFM-Bench: A Unified Multi-Domain, Multi-Task Benchmark for Channel Foundation Models
Channel foundation models (CFMs) are developing rapidly, with recent studies reporting benefits from pretraining across downstream wireless tasks. Yet CFMs are commonly evaluated in model-specific pipelines with different data, radio configurations, partitions, adaptation procedures, task definition...
- Multi-Axis Max@K Reinforcement Learning for Representative Diversity in Text-to-Image Generation
Text-to-image (T2I) models can synthesize realistic, prompt-aligned images, yet samples generated for the same prompt often cover only a small subset of visually distinct modes. This limits the diversity of images, and for person-centric prompts, can reflect or amplify demographic skew. We formalize...
- Contextualized Early Detection of Online Firestorms: A Sequential LLM-Based Approach
Online firestorms are rapid collective escalations of highly negative user-generated content and may cause substantial reputational and economic damage. Existing detectors usually work with volume signals, sentiment scores, or predefined linguistic features. Such signals are useful, but they capture...
- Random Logit Scaling: Defending Deep Neural Networks Against Black-Box Score-Based Adversarial Example Attacks
Machine learning models are increasingly adapted in various domains. However, adversarial examples pose a significant threat to the reliable deployment of these models. In recent years, some powerful adversarial example attacks have been proposed for the fast and query-efficient generation of advers...
- Show Me How You Reason and I'll Tell You Who You Are: Reasoning Graphs for Robust LLM Authorship Attribution
Given the current trend to employ large language models (LLMs) in almost any imaginable context, LLM-generated text detection and authorship attribution have become a pressing issue. Prior work has primarily focused on surface-level linguistic features, an approach shown to be susceptible to paraphr...
- StructureClaw: Traceable LLM Agents and an Executable Benchmark for Structural Engineering Workflows
Addressing a structural-engineering request requires more than a single answer; it requires a chain of interdependent artifacts: interpreted requirements, a computable model, validation records, solver outputs, code-check records, and a final report. Evaluations centered on question answering or scr...
- Innocuous-Seeming Data, Latent Ideology: Ideological Generalisation in Finetuned LLMs
Finetuning language models on small, curated datasets is standard practice for adapting them to specific policies or domains. We show that finetuning on narrow, factually-defensible, moderation-passing data can cause broad ideological shifts across unrelated domains, while preserving general capabil...
- Reachability-Aware Pretraining for Efficient Target-Oriented Path Exploration in Temporal Knowledge Graph Reasoning
Temporal Knowledge Graph (TKG) reasoning under the extrapolation setting focuses on forecasting future time-stamped events (facts) from historical data in a temporal knowledge graph. Existing approaches, reinforcement learning (RL)-based multi-hop reasoning methods are prominent for TKG reasoning be...
- Can LLMs Build a MaxSAT Solver from Papers? The CoreForge Experience
We report on CoreForge, an experience in using large language models (LLMs) to build an unweighted MaxSAT solver from research papers rather than from an existing solver codebase. The project focuses on unsatisfiability-based MaxSAT algorithms and follows an iterative workflow that combines paper di...
- Large Language Models for Code Generation from Multilingual Prompts: A Curated Benchmark and a Study on Code Quality
Large Language Models (LLMs) perform differently on identical programming tasks when prompted in different natural languages, a phenomenon known as language bias. While this behavior has been widely studied for general text generation, its impact on code generation quality and programming convention...
- Can We Trust Item Response Theory for AI Evaluation?
AI benchmarks increasingly leverage item-level statistical models, particularly item response theory (IRT), to estimate model capabilities, rank systems, select informative examples, and diagnose benchmark quality. However, AI benchmark data often departs from the data regime of human testing, for w...
- T^2MLR: Transformer with Temporal Middle-Layer Recurrence
Transformer reasoning is limited by autoregressive decoding, which repeat edly compresses rich hidden computation through token space and makes it difficult for intermediate reasoning states to persist across time. We in troduce Transformers with Temporal Middle-Layer Recurrence (T2MLR), a transform...
- Towards Hierarchical Structure Understanding of Newspaper Images
Understanding newspaper images remains a challenging task due to their complex, nested hierarchical structures and dense, heterogeneous layouts. In this paper, we explore two complementary approaches for newspaper structure understanding. First, we present a modular bottom-up pipeline that combines ...
- BrainPilot: Automating Brain Discovery with Agentic Research
Understanding the brain increasingly depends on integrating evidence across scales, modalities, and disciplines. Addressing a single research question therefore requires a coordinated sequence of operations, from surveying prior work to executing analyses and interpreting results in light of domain ...
- River
AI account executives that demo and close B2B deals
- ANet Patu-1: The Value of Connection in the Agent Network
The Internet taught us that the value of a network depends on \emph{how} its nodes connect: broadcast stars scale as $V\!\propto\!N$ (Sarnoff), fully-connected meshes as $N^2$ (Metcalfe), and group-forming networks as $2^{N}$ (Reed). We ask the analogous question for networks of AI agents. We model ...
- Man, Machine, and Masterpiece: Artistic Ownership in the AI Era
The integration of AI-driven systems in creative work has sparked debates among artists and legal communities about notions of ownership. Yet there remains little consensus on how ownership should be defined and attributed when human and AI contributions are intertwined. To provoke critical reflecti...
- Moral Attitudes of Sentient ASI towards Humanity and Implications for AGI Development
This paper suggests the adoption of a novel inversion in AI ethics: instead of asking how humans should treat artificial superintelligence (ASI), it examines how future sentient ASI may morally consider and evaluate humanity. We are not only designing intelligent systems but also shaping the initial...
- OmniaBench: Benchmarking General AI Agents Across Diverse Scenarios
Large language models are increasingly evolving from text generators into general agents capable of understanding user requests, invoking external tools, and completing complex tasks through interaction. However, existing agent benchmarks often focus on limited scenarios, tool ecosystems, or interac...
- Demographically-Conditioned Synthetic Medical Images for Bias Mitigation and Bias Detection in Disease Classifiers
Per-subgroup fairness audits of medical image classifiers face a sample-size problem: minority subgroups in held-out test sets have so few samples that the resulting confidence intervals on per-subgroup performance are wider than the bias the audit is meant to detect. We argue that a demographically...
- Explaining Process Control Optimisation Recommendations via GradientSHAP and Implicit Differentiation
Automated optimisation is increasingly adopted in industrial processes, yet a trust gap persists between engineers who design these algorithms and operators who must act on their recommendations. Explainable AI methods like SHAP (SHapley Additive exPlanations) have transformed interpretability for m...