AI News Archive: July 22, 2026 — Part 24
Sourced from 500+ daily AI sources, scored by relevance.
- Moonshot AI reportedly plans final pre-IPO round at $50 billion valuation
Moonshot AI, the developer of the Kimi chatbot, reportedly plans to begin talks in August for a final pre-IPO funding round targeting a pre-money valuation of up to $50 billion. The company could pursue a Hong Kong listing within six months. The round would follow current financing expected to value Moonshot at about $31.5 billion […]
- China’s Moonshot AI targets $50b valuation
Moonshot AI is best known for Kimi, which ranked third among Chinese AI chatbots by monthly active users in August 2024.
- Moonshot AI Eyes IPO as Kimi K3 Drives ARR to $300M
Moonshot AI could list in Hong Kong within six months after ARR reached $300 million and Kimi K3 demand forced a pause in new subscriptions. The post Moonshot AI Eyes IPO as Kimi K3 Drives ARR to $300M appeared first on TechRepublic .
- DataMEDS AI Officially Begins Trading Under Stock Symbol 'MEDS' on the NASDAQ Capital Markets
DataMEDS AI Officially Begins Trading Under Stock Symbol 'MEDS' on the NASDAQ Capital Markets azcentral.com and The Arizona Republic
- Gemini 3.6 Flash ✨, OpenAI security escape 🚨, Devin Outposts 🛰️
Gemini 3.6 Flash ✨, OpenAI security escape 🚨, Devin Outposts 🛰️
- 🙀 OpenAI’s new model escaped
PLUS: AI cheating evals, China’s model squeeze, and Niobium’s encrypted vibe-coding toolkit.
- PRO-LONG: Programmatic Memory Enables Long-Horizon Reasoning
Long-horizon tasks require sustained perception, reasoning, and exploration, and are a persistent challenge for large language model (LLM) agents. This gap is reflected in their limited performance on continual learning benchmarks such as ARC-AGI-3, especially when models are evaluated out of the bo...
- OpenAI’s Brockman Says Kimi K3 Is ‘Pretty Good,’ Won’t Confirm Distillation
OpenAI president Greg Brockman called Moonshot AI’s Kimi K3 “pretty good” in a Bloomberg interview on Tuesday, a rare public acknowledgement from a US frontier lab that a Chinese rival has produced a competitive model. Brockman said it is “too early” to determine whether Moonshot trained K3 by distilling outputs from OpenAI’s own systems, a […] This story continues at The Next Web
- Cisco launches Antares AI models for code vulnerability detection
Cisco has released Antares, a new set of security-focused SLMs aimed at helping locate known vulnerabilities within software codebases.
- Cisco launches Antares AI models for code vulnerability detection
Cisco launches Antares AI models for code vulnerability detection verdict.co.uk
- Sound Probabilistic Safety Bounds for Large Language Models
We propose a novel framework for computing rigorous bounds on the probability that a large language model (LLM) generates harmful output to a given prompt. We study a new application of the Clopper-Pearson confidence intervals to obtain probably approximately correct (PAC) bounds for this problem. A...
- PoTRE: Test-Time Reasoning inspired by Cognitive Heterogeneity
While Large Language Models (LLMs) excel at many tasks, they frequently struggle with complex reasoning that requires long-horizon planning and iterative error correction. Furthermore, standard single-stream prompting proves brittle when models encounter novel abstractions or rigorous domain constra...
- The Maskability Index: Predicting Task-Objective Alignment in Pretrained Language Models
Large-scale pretrained language models such as T5 and BERT have demonstrated strong capabilities for generating structured knowledge. However, their performance depends on how closely the prompting strategy matches the objectives used during pretraining. We introduce the Maskability Index (MI), a qu...
- The Ethics of Autonomous AI Agents for Offensive Security
LLM-driven autonomous agents are reshaping offensive security. Unlike traditional penetration-testing tooling -- deterministic, narrowly scoped, and operated by trained practitioners -- agentic security tools exhibit \textit{indeterminacy} along three independent dimensions. First, their actions are...
- On the Systematic Challenges of Culturally Loaded Machine Translation: Dream of the Red Chamber as the Cultural Lens
Culturally loaded translation poses unique challenges for machine translation (MT), as meanings are deeply embedded in socio-cultural contexts beyond surface linguistic forms. Although large language models (LLMs) have enabled MT systems to achieve human-like quality in many scenarios, their ability...
- DQAOA-GPT: AI-Accelerated Distributed Quantum Optimization for Combinatorial Problems
While combinatorial optimization problems are central to many scientific and engineering applications, their solution remains challenging due to exponentially large search spaces. Variational quantum algorithms offer a promising route for tackling such problems, yet their practical performance is li...
- Small, Free, and Effective: Orchestrating Open-Weight Small Language Models to Outperform Single LLM for Malware Analysis
Malware analysis demands rapid interpretation of complex detonation reports spanning filesystem, network, and process behaviours. While large language models (LLMs) demonstrate impressive capabilities for technical artifact interpretation, the opacity and escalating API costs of closed-weight fronti...
- The Quadrilateral Loss: Additivity as a Measurable Behavior of Dense Neural Networks
Additive models buy interpretability by forbidding feature interactions, a constraint that neural instantiations enforce architecturally. We introduce the quadrilateral loss, a differentiable penalty that treats additivity as a measurable behavior instead: a second-order mixed difference on pairs of...
- SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD
Full-parameter post-training of trillion-parameter-scale MoE models introduces substantial system-level challenges for large-scale distributed training, including severe memory pressure, non-overlapped communication overhead, and inefficient kernel execution. While most large-scale LLM training syst...
- Formal Foundations for Known Good Reliable Die Screening in Chiplet-Based AI Systems-on-Chip
The rapid growth of chiplet-based artificial intelligence systems-on-chip (SoCs) has exposed a fundamental gap in semiconductor test methodology. Existing Known Good Die (KGD) screening guarantees pre-assembly functional correctness, yet it offers no probabilistic assurance of post-assembly reliabil...
- CUSUM-Shaped Inference-Time Monitoring and Targeted Re-Decoding for Quantized Small Language Model Reasoning
Quantized small autoregressive reasoning models can enter long, repetitive, or unproductive trajectories, yet inference-time compute is usually allocated without observing how a trajectory develops. Building on an earlier token-level e-CUSUM controller, we develop MGT-B (Monitoring-Guided Test-time ...
- Reinforcement Learning for Large Language Model Selective Evidence Adoption from Contaminated Retrieval Results
Retrieval-augmented large language models frequently face contexts that interleave useful evidence with misleading statements or instruction-like content. Blanket refusal discards valid evidence, whereas uncritical adoption yields incorrect or unsafe answers. The ability to selectively adopt relevan...
- Co-Evolving LLM Evaluators and Policies via DynamicRubric
Post-training with evaluator feedback on policy-induced samples serves as a major mechanism for improving large language models. As policies improve, these sampled responses become close in quality. These close candidates create a bottleneck for policy optimization: collapsed relative evaluator scor...
- Reading and Steering Representations of Materials-Science Mechanisms in an Open-Weight Language Model
Large language models can answer scientific questions, yet a correct output does not reveal whether the model represents or uses the governing physics. Here we show that materials science mechanism information in the open-weight google/gemma-4-E4B-it model has three experimentally separable forms: c...
- Test Case Prioritization for DNNs via Neural Collapse Instability
With the widespread deployment of deep neural networks (DNNs) in safety-critical domains, reducing the cost of model validation under limited testing budgets has become increasingly important. Existing test case prioritization techniques often rely on single-checkpoint confidence signals derived fro...
- A Systematic Benchmark of Intensity Normalisation Methods for 3D Knee MRI Segmentation and Cross-Domain Generalisability
Robust out-of-the-box performance is essential for the clinical deployment of deep learning models in medical imaging. An important but underexplored factor affecting model generalisability is intensity normalisation, particularly for magnetic resonance imaging (MRI), where image intensities vary ac...
- Taming the Security-Energy Paradox: A Green AI Approach to Optimized Android Malware Detection
An increase in advanced Android malware requires the use of deep learning models, which can run on Android devices. But there is a trade-off between security and energy use, as strong detection models can drain the battery of devices fast. This work tests different Multi-Layer Perceptron (MLP) model...
- Post-Training in Time Series Foundation Models: A Unifying Framework
Time series foundation models (TSFMs) have emerged as general-purpose models for time series analysis, but pretraining alone is often insufficient for reliable downstream deployment. Bridging this gap requires further intervention to handle domain shift, task heterogeneity, limited supervision, and ...
- Are Attributions of Consciousness to AI Chatbots Epistemically Innocent?
Artificial intelligence (AI) chatbots (e.g., ChatGPT) can communicate in strikingly humanlike ways. This has prompted many chatbot users to attribute psychological properties, including consciousness, to these systems. However, there is little scientific evidence that current AI chatbots are conscio...
- CLARK: Closed-loop Learning for Adaptive Reasoning over Knowledge Graphs
Machine Learning models are widely used for automating classification tasks by extracting statistical patterns from data. However, their performance deteriorates if the data distribution changes, making them ill-suited to handle uncertain and evolving information. Moreover, they provide limited supp...
- TINY_SCHILLER: A Drop-In German Drama Corpus for Small Language Models
tiny_schiller closes the small-language-model prototyping, fine-tuning, education, and research gap for German literary text, providing a single-file, drop-in counterpart to Karpathy's tiny_shakespeare. The available German literary corpora are larger and richer, but require parser engineering befor...
- Coordinating from Memory: Graph-Structured Experience Reuse for Multi-Agent Adaptation in Dynamic Manufacturing
Dynamic manufacturing environments require multi-agent systems to coordinate effectively under frequent operational disturbances such as machine failures, urgent job arrivals, and processing time variations. Existing multi-agent reinforcement learning approaches treat each disturbance episode indepe...
- When Shippers Become Algorithms: Candidate Exposure, Information Design, and the Concentration of LLM-Mediated Freight Markets
Shippers are beginning to delegate carrier selection to large language model (LLM) agents. We ask what such delegation does to a freight matching market, and which platform design choices contain it. We carried out agent-based simulations in which fifty shipper agents, built on commercial LLMs from ...
- Persian Pixel: A large-scale synthetic OCR dataset for Persian language
Optical Character Recognition (OCR) for Persian remains substantially less mature than for Latin-script languages despite Persian being spoken by more than 110 million people across multiple countries. This gap arises from two fundamental challenges: the intrinsic complexity of the Perso-Arabic writ...
- FMRP-LEAN: A HIPAA-Compliant AI-Augmented LIMS Architecture for End-to-End Clinical Assay Workflow Optimization
Clinical biomarker workflows in translational research settings often rely on spreadsheet-driven tracking, manual quality control (QC) reconciliation, and loosely integrated systems, resulting in limited state visibility, delayed reporting, and increased operational risk. These challenges are partic...
- Train the Model, Not the Reader: Decodability Supervision for Verifiable Activation Explanations
Natural-language autoencoders score explanations of hidden activations by reconstruction: an explanation is deemed faithful if the activation can be regenerated from it. The test is structurally insensitive to individual false claims: if flipping a claim does not change the reconstruction, the claim...
- Generative AI floods and dilutes the market for books
Generative AI can produce book-length works of fiction at near-zero cost. These books are often dismissed as low-quality ``slop'' that buyers will ignore, and are assumed to carry little commercial weight. We test that assumption with full-text AI detection across 14,419 self-published genre-fiction...
- Closing the Lab-to-Store Gap: A Data-Efficient Post-Training and Experience-Driven Learning VLA Framework for Retail Humanoids
Closing the gap between benchmark performance and reliable real-world operation remains a central challenge for Vision-Language-Action (VLA) humanoid robots, which must handle execution errors, distribution shifts, and environmental variability. This paper presents DEED (Data-Efficient Post-Training...
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- Understanding Generative AI-mediated User Engagement with Academic Library Resources
This study empirically analyzed generative AI as an emerging discovery pathway to academic library resources. Utilizing web analytics from August 2023 to October 2025, the research identifies a significant increase in AI-mediated traffic, particularly following the integration of linked citation fea...
- Toward Reliable RGB-D Semantic Segmentation: Handling Missing Modalities via Condition Dropout
RGB-D semantic segmentation has achieved remarkable progress, yet most models assume that RGB and depth are always available. In practice, failures or occlusions of surveillance sensors often remove one modality. Although RGB or depth alone can contain sufficient cues, models trained only on full-mo...
- Don't Trust the Label: License Laundering in AI Supply Chains
AI artifacts move through a multi-platform supply chain, spanning datasets and models on Hugging Face and applications on GitHub. While each artifact carries a license whose obligations should propagate through redistribution, no study has yet measured whether those obligations survive the chain or ...
- Courteous Anticipation: Improving Long-Lived Task Planning in Persistent Shared Environments
We consider a task planning scenario in which robots sharing a persistent environment are assigned tasks one at a time from a held-out sequence. Standard task planners, lacking foresight of future tasks and inconsiderate of others' constraints, solve each task in isolation, leaving terminal states t...
- Self-supervision drives representational convergence in medical foundation models more than clinical supervision
Medical image encoders from different groups are increasingly treated as interchangeable, on the assumption that scale and clinical supervision concentrate their representations onto a shared structure. Whether this convergence is real, what produces it, and whether it is clinically usable are untes...
- Pushing the Frontier of Full-Song Generation: Hierarchical Autoregressive Planning Meets Flow-Matching Rendering
In this report, we present a unified song generation framework capable of producing high-quality full-length music from lyrics, text descriptions, and musical attributes. The proposed framework supports three tasks: Lyrics-to-Song Generation, which generates complete songs from text descriptions, ly...
- ELSAA: Efficient Low-Rank and Sparse Attention Approximation for Training Transformers
The quadratic $N\times N$ attention score matrix remains a central obstacle to extending Transformers to longer input lengths. Existing efficient attention methods usually reduce this bottleneck by either imposing sparsity, so that each query attends to only a small subset of keys, or by using low-r...
- StreamHOI: Interaction-aware Temporal Memory Adaptation for Streaming HOI Video Generation
Existing human--object interaction (HOI) video generation methods are largely limited to offline short-video generation with complex driving conditions, making them unsuitable for real-time interactive applications. We present \emph{StreamHOI}, a low-latency streaming framework for long-duration HOI...
- Audio-Zero: Label-Free Self-Evolution for Fine-Grained Audio Reasoning
Large Audio Language models (LALMs) have made rapid progress on acoustic understanding, yet they still struggle with fine-grained audio reasoning (e.g., recognizing event order, repetitions and duration). Existing post-training methods heavily rely on expensive external labels or provide only coarse...
- Active Inference as a Convex Markov Decision Process
Active Inference (AIF) frames adaptive behavior as the minimization of expected free energy (EFE), combining epistemic and pragmatic objectives within a single variational principle. We frame AIF as policy optimization and show that, for closed-loop control policies, EFE minimization can be formulat...
- PRIME-SVR: Physics-infoRmed Implicit Multi-Echo Slice-to-Volume Reconstruction for Fetal T2 mapping
Slice-to-volume reconstruction (SVR) is the standard method for obtaining high-resolution (HR) 3D fetal brain volumes from motion-corrupted 2D MRI slice stacks acquired in multiple orientations. Existing SVR methods are optimized and validated only for clinical-range echo times (TEs), limiting their...