NewMind AI Weekly Chronicles – September ’25 Week II

NEWMIND AI JOURNAL WEEKLY CHRONICLES
9.9.2025 – 15.9.2025
• The second week of September 2025 delivered major advances across AI infrastructure, coding autonomy, and governance. From trillion-scale cloud
deals to ultra-fast open-source models, the landscape is shifting rapidly toward industrialized deployment.
• OpenAI launched GPT-5 Codex, a new model built for agentic coding that can autonomously write, test, and deploy software. Early enterprise adopters
report over 40% gains in engineering velocity.
• In one of the largest deals of its kind, OpenAI and Oracle signed a $300 billion cloud computing agreement to power future AI development over the
next five years.
• GitHub introduced the Copilot Coding Agent, an autonomous tool that automates entire developer workflows, from diagnosing bugs to opening pull
requests.
• Apple officially detailed Apple Intelligence, its new hybrid, privacy-first AI system that combines on-device and server-based models for iPhones and
Macs.
• The UAE's MBZUAI and G42 unveiled K2 Think, dubbed the "world's fastest open-source AI model," which can generate over 2,000 tokens per second
despite its compact 32B-parameter size.
• NVIDIA previewed the Rubin CPX GPU, a next-generation chip purpose-built for AI workloads with million-token contexts, like video generation. It's
projected to ship by the end of 2026.
• Geopolitical tech tensions escalated as China launched an antitrust investigation into NVIDIA, accusing the company of monopolistic practices in the
AI chip sector.
• Penske Media is suing Google over its AI Summaries, alleging that the feature abuses Google's search monopoly and violates copyright law.
• Baidu released its new ERNIE-4.5 model for enterprise use under a permissive Apache 2.0 license, emphasizing an efficient design that activates only
3 billion of its 21 billion parameters per token.
• Thomson Reuters showcased its multi-agent AI system for legal work, which it calls an "anti-ChatGPT" for its precision. The system reduced a 20-hour
legal research process to under five minutes.

# Highlights Summary Author Source Date
1.1
K2 Think Arrives
from UAE as
"World’s Fastest
Open-Source AI
Model"
The United Arab Emirates has unveiled K2 Think, a powerful yet compact
open-source AI reasoning model developed by MBZUAI and G42. Despite
having just 32 billion parameters, it delivers lightning-fast performance—
generating over 2,000 tokens per second, outpacing typical GPU-based
models by over tenfold—and matches or exceeds much larger models in
complex domains like math, code, and science. Its technical prowess stems
from innovations such as long-chain-of-thought fine-tuning, agentic
planning, reinforcement learning with verifiable rewards, and efficient
deployment on Cerebras hardware. Released under a permissive Apache
2.0 license, K2 Think is freely available to fuel global research.
By Carl Franzen 🔗 Sep 10, 2025
1.2
NVIDIA Open-
Sources VIPE: A
Versatile 3D Video
Annotation Tool for
Spatial AI
NVIDIA has released VIPE (Video Pose Engine), an open-source 3D video
annotation tool designed to support spatial AI applications across sports,
healthcare, and robotics. VIPE combines pose estimation, action
recognition, and motion analysis in one pipeline, enabling precise
annotation from standard video input. It supports multi-view and single-view
setups and integrates with NVIDIA’s Isaac and Omniverse platforms. By
open-sourcing VIPE, NVIDIA aims to accelerate research and deployment
of human-centered AI systems that rely on understanding complex
movements in real-world environments.
By Jean-marc
Mommessin
🔗 Sep 15, 2025
1.3
Hugging Face
Releases MMbERT:
A Multimodal BERT
for Image-Text
Reasoning
Hugging Face has introduced MMbERT, a multimodal BERT model
designed to jointly process images and text for tasks requiring visual-
language reasoning. MMbERT outperforms strong baselines on
benchmarks like VQAv2, GQA, and SNLI-VE, using a unified transformer
By Marc Marone
et al.
🔗 Sep 9, 2025

backbone and efficient modality fusion strategies. It supports both image-
text understanding and generation tasks, making it versatile for applications
like VQA, captioning, and retrieval. Released with code, weights, and
training details, MMbERT reinforces Hugging Face’s commitment to open,
high-performance multimodal AI research.
1.4
Google Introduces
Symbolic JAX for
Advanced
Scientific
Computing
Google has launched Symbolic JAX, a major extension to its JAX library
that brings symbolic computation into scientific machine learning. Symbolic
JAX enables differentiable programming with exact mathematical
expressions rather than numerical approximations, making it easier to
derive gradients, simplify equations, and integrate domain knowledge
directly into ML models. This innovation is particularly powerful for physics-
informed learning, control systems, and scientific simulations. By unifying
symbolic and numeric computing, it opens new frontiers in accuracy,
interpretability, and model generalization in complex scientific tasks.
By Srikanth
Kilaru et al.
🔗 Sep 9, 2025
1.5
K2-Think: MBZUAI
Releases 32B
Open-Source Model
for Advanced
Reasoning
Researchers at MBZUAI have unveiled K2-Think, a 32B-parameter open-
source model designed specifically for advanced reasoning tasks. Despite
being significantly smaller, it outperforms models up to 20× larger on
multiple benchmarks, including MMLU, GPQA, and LogiQA. K2-Think
integrates novel architectural modifications and training techniques to
improve logical reasoning, retrieval-augmented generation, and instruction
following. The model, available on Hugging Face under an open license,
By Asif Razzaq 🔗 Sep 9, 2025

sets a new bar for compact, efficient, and high-performance reasoning-
focused LLMs in open research and enterprise use.
1.6
Alibaba Unveils
Qwen3-ASR for
High-Accuracy
Speech
Recognition
Alibaba’s Qwen team has launched Qwen3-ASR, a new automatic speech
recognition model built on top of the Qwen3-Omni architecture. The model
delivers robust performance across multiple speech tasks and accents,
showcasing superior generalization in noisy and real-world conditions.
Qwen3-ASR leverages a multi-stage training pipeline and large-scale
multilingual datasets, making it suitable for applications like voice
assistants, transcription, and multimodal systems. It continues Alibaba’s
push to extend the Qwen family into specialized, high-performance
foundation models for speech and language.
1.7
Apple Intelligence:
A Hybrid, Privacy-
First AI System for
iPhones and Macs
Apple has officially detailed Apple Intelligence, its new suite of generative
AI models and services embedded in iPhones, iPads, and Macs. The
system combines on-device and server-based models, dynamically
choosing the best option based on task complexity and privacy needs. Key
features include writing tools, notification prioritization, and app command
automation. Apple emphasizes privacy-preserving design, including Private
Cloud Compute, and tight integration with its ecosystem. Apple Intelligence
showcases a hybrid model strategy that balances performance, utility, and
user trust.
By Sarah Perez 🔗 Sep 9, 2025

1.8
MIT’s new
DOE/NNSA-backed
center will use
exascale
supercomputers
The U.S. Department of Energy’s National Nuclear Security Administration
has selected MIT to host the Center for the Exascale Simulation of Coupled
High-Enthalpy Fluid–Solid Interactions (CHEFSI), under the fourth phase
of the Predictive Science Academic Alliance Program. CHEFSI, comprising
MIT’s Center for Computational Science and Engineering, Schwarzman
College of Computing, and Institute for Soldier Nanotechnologies, will
leverage exascale HPC and next-gen algorithms to model interactions in
extreme environments—temperatures exceeding ~1,500 °C, speeds up to
Mach 25. It aims to simulate how gas flows chemically and thermally
interact with solids (oxidation, ablation, fracture, etc.), combining high-
fidelity physics, experimental validation, and surrogate/AI models. The
center’s outputs will inform design of thermal protection systems for
hypersonics and atmospheric reentry, with collaborations including national
labs.
By MIT Institute
for Soldier
Nanotechnologi
es
🔗 Sep 10, 2025
1.9
RenderFormer is a
neural rendering
model that learns a
full graphics
pipeline from 3D
triangle meshes
without relying on
ray tracing or
rasterization.
Microsoft Research introduces RenderFormer, a transformer-based neural
architecture that performs full 3D rendering using only neural networks. It
represents scenes via triangle tokens encoding geometry (position,
normals), material properties (diffuse, specular, roughness), and lighting,
and processes them using view-independent and view-dependent
branches to produce high-quality images with shadows, reflections, and
specular highlights. Trained on the Objaverse dataset with scenes varying
in complexity, it supports variable input mesh size and generalizes to novel
By Yue Dong 🔗 Sep 10, 2025

scenes. Though promising, scaling to even more complex geometry,
lighting and materials remains a future challenge.
1.10
Baidu’s ERNIE-4.5-
21B-A3B-Thinking
offers deep
reasoning with a
compact MoE
architecture, 128K
context length, tool
integration, and
Apache-2.0
licensing.
Baidu has released ERNIE-4.5-21B-A3B-Thinking, a new model focused
on advanced reasoning tasks. It uses a Mixture-of-Experts (MoE) design
with 21B parameters in total but only 3B activated per token, making it more
compute-efficient. The model supports a 128,000-token context window,
enabling long-document and multi-step reasoning, and integrates
structured tool/function calling for tasks like coding, logic, and scientific QA.
It was trained under the Apache-2.0 license and outperforms earlier
“lightweight” ERNIE 4.5 variants on benchmarks that test reasoning,
mathematics, logic, and code.
1.11
OpenAI and Oracle
Strike $300B Cloud
Computing Deal to
Power AI
OpenAI has inked a monumental deal worth $300 billion with Oracle to
secure cloud computing infrastructure over the next five years as part of its
Project Stargate data-center initiative. Under the agreement, Oracle will
deliver roughly 4.5 gigawatts of compute capacity—comparable to more
than two Hoover Dams or the electricity needs of about four million U.S.
households. Project Stargate, unveiled earlier this year, aims to invest up
to $500 billion in building AI-specific data centers, with this deal
representing a major step toward the $500B target. Though OpenAI’s
annual revenue sits near $10 billion, its expenditures related to data center
development and renting compute power are roughly $60 billion/year,
highlighting the scale of its financial gamble. For Oracle, the contract will
By Mike
Wheatley 🔗 Sep 10, 2025

be a substantial source of future revenue but may involve taking on debt to
procure AI chips and scale infrastructure to meet demand.
1.12
OpenAI Upgrades
Codex with GPT-5
Backbone for
Smarter Agentic
Coding
OpenAI has introduced a new version of Codex, now powered by GPT-5
and optimized for agentic coding tasks. The upgraded model enables multi-
step reasoning, code execution, and tool use, allowing AI agents to plan,
write, and debug complex software projects autonomously. It excels in long-
context code understanding, API integration, and collaborative workflows.
Codex now integrates seamlessly with OpenAI's API and development
tools, making it suitable for enterprise-scale automation and R&D
acceleration. This marks a major leap toward AI-assisted software
engineering.
By OpenAI 🔗 Sep 15, 2025
1.13
NVIDIA Previews
Open-Source
Qwen3-Next with
Hybrid MoE for
Enhanced
Accuracy and
Speed
NVIDIA has unveiled Qwen3-Next, an open-source LLM series built on a
hybrid Mixture-of-Experts (MoE) architecture that boosts both accuracy and
parallel processing efficiency. Designed in collaboration with Alibaba
Cloud, Qwen3-Next is optimized for NVIDIA platforms like TensorRT-LLM
and NeMo, achieving superior throughput with fewer active parameters
(3B-8B). Early benchmarks show strong performance across reasoning
and coding tasks. The models support multi-query attention and grouped-
query attention, making them suitable for low-latency inference and
enterprise deployment.
By Anu
Srivastava 🔗 Sep 15, 2025

1.14
Uber Introduces
Starlark: An In-
House LLM for
Safer, Aligned AI
Applications
Uber has launched Starlark, its own in-house large language model,
designed for internal AI use cases with a strong emphasis on safety and
alignment. Developed using open-weight models fine-tuned with
proprietary data, Starlark powers applications like Uber’s support chatbots,
driver routing assistants, and marketplace monitoring tools. Key features
include multi-layered alignment tuning, automated evaluation pipelines,
and a novel “behavioral fingerprinting” technique to ensure consistent
outputs and reduce hallucinations. Starlark exemplifies a growing trend of
companies building domain-specific LLMs to ensure responsible AI
deployment tailored to their platforms.
By Andrii
Kalishuk and
Taylan Isikdemir
🔗 Sep 11, 2025
1.15
TwinMind
Launches EAR-3, a
Multilingual Voice
AI Model with State-
of-the-Art Accuracy
TwinMind has released EAR-3, its latest multilingual voice AI model, setting
new benchmarks in speech-to-text accuracy, speaker labeling, and
language support. EAR-3 supports 100+ languages, boasts 15% better
word error rate (WER) than Whisper v3, and introduces zero-shot speaker
labeling across 20 languages. Built on a new training architecture
leveraging ultra-large-scale, multilingual speech datasets, EAR-3 also
significantly reduces inference costs, offering pricing 4x cheaper than
Whisper API. This positions it as a strong competitor for real-time and
enterprise speech applications.
By Michal Sutter 🔗 Sep 11, 2025

1.16
Anthropic Lets
Claude Remember
Previous
Interactions to
Streamline Work
Anthropic has upgraded Claude to automatically recall past interactions for
users on Team and Enterprise plans without needing explicit prompts. It
will remember work-related details — such as team processes, client
needs, ongoing project specs — and maintain project-wise memory
separation so different initiatives stay contextually distinct. Users can view
and edit what Claude has stored, decide what it should focus on or ignore
in memory. Additionally, Anthropic has rolled out an Incognito Chat mode,
available to all users. Chats in this mode are not stored, not used for
memory, nor shown in conversation history once the session ends.
By Mike
Wheatley
🔗 Sep 12, 2025
1.17
Qwen3-Next Debuts
With Efficient 3B
Active Parameters
Alibaba’s Qwen team has launched Qwen3-Next, a highly efficient large
language model that leverages just 3 billion active parameters while
matching or surpassing models with far larger footprints. The model uses a
mixture-of-experts (MoE) design with 38 billion total parameters, activating
only 3 billion per inference, dramatically reducing compute costs.
Benchmarks show strong performance across reasoning, math, and
multilingual tasks, rivaling OpenAI’s GPT-4o mini and outperforming other
mid-sized models. This release highlights a growing trend toward
efficiency-focused LLM architectures in both research and production
contexts.
By Qwen Team 🔗 Sep 10, 2025
1.18
Agentic Swarm
Coding Emerges as
New Paradigm in
Enterprise Software
Development
Agentic swarm coding—a method where multiple AI agents collaborate
autonomously to design, develop, test, and maintain code—has emerged
as the next evolution in enterprise development, surpassing “vibe coding.”
These AI agents operate with defined roles (planner, developer, reviewer,
etc.), coordinating in multi-agent ecosystems to deliver production-ready
By Matt
Marshall
🔗 Sep 12, 2025

software with minimal human intervention. This method enhances velocity,
scalability, and reliability, positioning itself as a potential enterprise
moat in the age of AI-native software teams. Startups and enterprises are
beginning to integrate swarm systems into core dev workflows.
1.19
Google’s
VaultGemma Sets
New Standards for
Privacy-Preserving
AI Performance
Google has introduced VaultGemma, a privacy-focused AI model that
delivers state-of-the-art performance while adhering to strict data
protection protocols. Built on the open-weight Gemma family,
VaultGemma incorporates differential privacy, federated learning, and
secure aggregation to ensure data never leaves user devices during
training or inference. Despite these constraints, VaultGemma outperforms
comparable models in language understanding and generation
benchmarks, making it ideal for healthcare, finance, and other regulated
industries. This release highlights Google’s commitment to aligning high-
performance AI with responsible data practices.
By Google
Research
🔗 Sep 12, 2025
1.20
Meta AI Unveils
MobileLLM-R1 for
Edge Reasoning
with Sub-1B
Parameters
Meta AI has released MobileLLM-R1, a new open-source edge reasoning
model with under 1 billion parameters, optimized for low-resource
environments. Despite its compact size, MobileLLM-R1 achieves 2x–5x
faster performance compared to other fully open-source AI models in its
class, while maintaining competitive accuracy. It supports multitasking with
capabilities in coding, math, and multilingual reasoning. Built to power on-
device applications, it outperforms Mistral-7B, Phi-2, and Llama 3 8B on
performance-to-efficiency ratios, making it ideal for mobile and embedded
AI use cases.

1.21
IBM Releases
Granite Embedding
Models Based on
ModernBERT
Architecture
IBM AI Research has launched two new English embedding models under
its Granite family, built on the advanced ModernBERT architecture. These
models—Granite-bert-base-emb and Granite-bert-large-emb—deliver
strong performance on key retrieval, semantic similarity, and reranking
benchmarks such as MTEB. Optimized for dense embedding tasks, they
outperform popular open models like E5 and GTE in various scenarios. IBM
has open-sourced the models and evaluation code on Hugging Face,
supporting reproducibility and encouraging community adoption in search,
RAG, and NLP pipelines.
1.22
BentoML Releases
Open-Source LLM
Optimizer for
Inference
Benchmarking
BentoML has introduced LLM Optimizer, an open-source tool designed to
benchmark and optimize LLM inference across a wide range of deployment
scenarios. Supporting major LLMs like Llama, Mistral, Phi, and Mixtral, the
tool allows developers to test models across hardware, quantization levels,
batch sizes, and vLLM vs. Hugging Face backends. It provides detailed
throughput and latency metrics, helping users make informed decisions on
cost-performance trade-offs. With built-in visualization and reproducibility
tools, LLM Optimizer streamlines inference evaluation for real-world
deployment.
1.23
NVIDIA and UK
Researchers
Release Nemotron-
4 340B for
Synthetic Data
Generation
NVIDIA, in partnership with leading UK universities, has released
Nemotron-4 340B, a family of large language models designed for high-
quality synthetic data generation. The suite includes base, instruct, and
reward models, supporting RLHF and other alignment techniques.
Nemotron-4 Instruct achieves strong performance on MT-Bench and
AlpacaEval 2.0, rivaling top-tier LLMs. The models are open-access and
optimized for training domain-specific models, enabling researchers and
developers to generate training data at scale for downstream tasks.
By Kari Briski 🔗 Sep 13, 2025

1.24
Nav-R1: Reasoning
and Navigation in
Embodied Scenes
Nav-R1 is an embodied foundation model for reliable navigation in complex
3D spaces. It tackles incoherent reasoning and the trade-off between long-
horizon semantic planning and real-time control. Built on Nav-CoT-110K, a
dataset of step-by-step Chains-of-Thought, it supports structured reasoning
from cold start. Training uses a GRPO-based reinforcement learning
framework with three rewards—format, understanding, and navigation—to
enhance adherence, grounding, and path accuracy. A Fast-in-Slow
reasoning paradigm separates deliberate reasoning from low-latency
control. Benchmark tests show an 8% performance gain over baselines,
while deployment on a mobile robot confirms robustness in constrained
real-world environments.
By Qingxiang
Liu et al. 🔗 Sep 13, 2025
1.25
OpenAI Launches
GPT-5 Codex,
Tailored for Agentic
Software
Development
OpenAI has introduced GPT-5 Codex, a new model fine-tuned specifically
for agentic coding workflows, allowing AI agents to autonomously write,
test, debug, and deploy code. Optimized for integration into multi-agent
developer environments, GPT-5 Codex shows major improvements in
code reliability, test generation, and cross-language reasoning. Early
enterprise adopters report over 40% gains in engineering velocity.
Unlike earlier Codex versions, GPT-5 Codex is trained on an expanded
corpus with more software engineering context, making it a key pillar for AI-
native dev stacks.
By Carl Franzen 🔗 Sep 15, 2025

2.1
NVIDIA Previews
Rubin CPX GPU for
Disaggregated AI
Inference
NVIDIA has unveiled the Rubin CPX, a GPU tailored for million-token-
scale context workloads like video generation and code processing.
Designed specifically for the compute-intensive “context” phase of
inference, the chip supports disaggregated inference—separating context
processing from token generation to optimize resources. It boasts 128 GB
GDDR7 memory, specialized attention hardware offering 3× the speed of
prior systems, and integrates video decoding/encoding capabilities. The
Rubin CPX will be part of the Vera Rubin NVL144 CPX rack, delivering up
to 8 exaflops of compute and projected to ship by end of 2026.
By Maria
Deutscher 🔗 Sep 9, 2025
2.2
Arm Unveils
“Lumex” Mobile
Chip Designs
Optimized for AI
Arm Holdings today (September 10, 2025) introduced Lumex, a new
generation of mobile chip designs tailored for on-device AI across
smartphones, wearables, and other gadgets. The Lumex family includes
four variants—from low-power options for wearables to powerful designs
for running large AI models entirely offline, without cloud reliance. Part of
Arm’s Compute Subsystems (CSS) business, these designs are built on
advanced 3-nanometer TSMC manufacturing processes. The launch
signals Arm's strategic push to enable real-time, ubiquitous AI while gearing
manufacturers for faster product integration.
By Reuters 🔗 Sep 9, 2025
2.3
NVIDIA Unveils
Blueprint for
Building Large AI
Factories with
Distributed Data
Centers
NVIDIA has detailed a strategy for connecting distributed data centers into
unified AI factories using its Scale Across Networking (SCAN) architecture.
SCAN enables multiple geographically separated data centers to function
as a single AI supercomputer, optimizing training, inference, and resource
usage. Leveraging NVIDIA’s BlueField DPUs, InfiniBand, and NVLink,
SCAN minimizes latency and maximizes throughput. This approach
supports larger model training and multi-tenant workloads while enhancing
By Taylor
Allison
🔗 Sep 9, 2025

fault tolerance and scalability. It’s a major step toward global, resilient AI
infrastructure.
2.4
NVIDIA Blackwell
Ultra Sets New
Inference Records
in MLPerf Debut
NVIDIA’s Blackwell Ultra has made its debut in the MLPerf benchmark,
achieving record-breaking inference performance across multiple AI
workloads. The chip outperformed previous-generation GPUs in tasks like
image classification, object detection, and language processing,
showcasing major gains in efficiency and speed. Built with next-gen tensor
cores and optimized for LLMs, Blackwell Ultra also demonstrated
leadership in energy efficiency and latency. These results reaffirm NVIDIA’s
dominance in AI hardware and highlight Blackwell Ultra’s readiness for
high-throughput, real-time AI inference at scale.
By Zhihan Jiang
et al. 🔗 Sep 9, 2025
2.5
NVIDIA Rubin CPX
Optimized for 1M-
Token Contexts
and Efficient
Inference
NVIDIA has introduced Rubin CPX, a new inference accelerator tailored for
large-context LLM workloads, supporting up to 1 million tokens with high
performance and efficiency. Rubin CPX features a disaggregated
architecture separating compute and memory, improving scalability and
energy use for context-heavy tasks. It builds on the Blackwell platform and
integrates innovations like enhanced NVLink and memory-tiering to reduce
bottlenecks. Rubin CPX aims to enable advanced enterprise applications,
including RAG, copilots, and long-document processing, by pushing the
boundaries of inference scale.
By Joe
DeLaere, Kirthi
Devleker and
Eduardo
Alvarez
🔗 Sep 9, 2025
2.6
Intel Xeon 6 and
Arc Pro B-Series
Impress in MLPerf
Inference v5.1
Intel has reported strong results in the MLPerf Inference v5.1 benchmark,
with its Xeon 6 processors and Arc Pro B-Series GPUs showing notable
performance across multiple AI tasks. The CPUs excelled in data center
and edge workloads, while the GPUs delivered efficient visual inferencing.
This demonstrates Intel’s growing competitiveness in the AI hardware
landscape, especially in versatile, scalable deployment scenarios. The
By Intel 🔗 Sep 9, 2025

results also validate Intel’s focus on integrating AI capabilities across its
hardware stack for both cloud and client applications.
2.7
Microsoft Breaks AI
Networking
Bottlenecks with
New Infrastructure
Advances
Microsoft has announced breakthroughs in AI infrastructure networking to
overcome scale-induced bottlenecks in massive AI workloads. By co-
designing custom hardware, software, and topologies—such as
hierarchical network fabrics and load-aware routing—the company
achieved up to 40% throughput improvement and reduced latency at
hyperscale. These innovations enable more efficient training of frontier
models, including GPT-style architectures, across distributed systems.
Microsoft’s advances represent a key milestone in scaling up AI
supercomputers and reflect strategic investment in building world-class AI
infrastructure.
By Paolo Costa 🔗 Sep 9, 2025
2.8
NVIDIA Unveils
GPU for Long-
Context Inference
in LLMs
NVIDIA has introduced a new GPU optimized for long-context inference,
targeting workloads that involve processing hundreds of thousands to
millions of tokens—crucial for advanced LLM applications like RAG,
copilots, and document-heavy tasks. The chip enhances memory
bandwidth and latency handling, and pairs with software-level scheduling
to manage massive token windows efficiently. This release responds to
growing demand for context-heavy models and reflects NVIDIA’s push to
sustain leadership in inference-specific AI hardware amid evolving use
cases.
By Russell
Brandoms 🔗 Sep 9, 2025
2.9
NVIDIA’s RTX PRO
6000 Blackwell
Server Edition GPU
accelerates protein
structure inference
NVIDIA’s RTX PRO 6000 Blackwell Server Edition GPU significantly
enhances protein structure inference using OpenFold, achieving speeds
over 138x faster than AlphaFold2 and approximately 2.8x faster than
ColabFold, while maintaining identical TM-scores. This performance is
enabled by MMseqs2-GPU, which runs approximately 190x faster than
By Kyle Tretina,
et al. 🔗 Sep 10, 2025

over 100x, enabling
rapid, large-scale
biological research.
CPU-based JackHMMER and HHBlits, and bespoke TensorRT
optimizations targeting OpenFold, increasing its inference speed 2.3x
compared to baseline OpenFold. The 96 GB of high-bandwidth memory
allows for folding entire protein ensembles and large multiple sequence
alignments, eliminating memory bottlenecks and enabling the full workflow
to remain GPU-resident.
2.10
NVIDIA Releases
CUDA-Accelerated
VPI 3.0 for High-
Performance Vision
AI Pipelines
NVIDIA has released Vision Programming Interface (VPI) 3.0, a major
update to its computer vision SDK that now includes CUDA acceleration,
enabling real-time, high-throughput vision AI applications across
embedded, edge, and data center platforms. VPI 3.0 offers optimized
backends for image pre-processing, feature tracking, stereo disparity, and
object detection, seamlessly integrating with frameworks like PyTorch and
DeepStream. The update significantly lowers latency and boosts efficiency
for robotics, AR/VR, smart cities, and autonomous systems, making VPI 3.0
a key tool in AI-enabled visual computing.
By Andreas
Kieslinger, et al.
🔗 Sep 11, 2025
2.11
NVIDIA
collaborates with
Canonical, CIQ,
SUSE, and Flox to
streamline CUDA
deployment via
third-party package
managers,
enhancing
accessibility for
developers.
NVIDIA has partnered with distribution platforms Canonical, CIQ, SUSE,
and Flox to simplify the deployment of the CUDA software stack across
various operating systems and package managers. This collaboration
allows developers to obtain CUDA software directly from these platforms,
simplifying installation and dependency resolution, particularly for complex
applications like PyTorch and libraries like OpenCV. The redistribution of
CUDA by these platforms will maintain consistent naming conventions,
provide timely updates, and ensure continued free access to CUDA, while
also offering comprehensive support options for developers.
By Jonathan
Bentz, et al. 🔗 Sep 10, 2025

3.1
SimpleQA Verified: A
Reliable Factuality
Benchmark to
Measure Parametric
Knowledge
SimpleQA Verified is a new benchmark for evaluating the factuality of
LLMs based on OpenAI's SimpleQA dataset. Addressing limitations of
the original benchmark, such as noisy labels and topical biases,
SimpleQA Verified was created through a rigorous filtering process
involving de-duplication, topic balancing, and source reconciliation. This
resulted in a more reliable and challenging evaluation set. The
benchmark also incorporates improvements to the autorater prompt. On
SimpleQA Verified, Gemini 2.5 Pro achieved the highest F1-score (55.6),
surpassing other leading models including GPT-5. This work provides the
research community with a high-fidelity tool to assess the factuality of
parametric models and mitigate hallucinations.
By Lukas
Haas, et al.
🔗 Sep 9, 2025
3.2
Language Self-Play
For Data-Free
Training
Large language models (LLMs) rely heavily on vast amounts of
training data for improvement. This paper introduces Language
Self-Play (LSP), a reinforcement learning approach that eliminates
the need for additional data. LSP frames model capabilities as
performance in a competitive game, where models play against
themselves. Through self-play, models refine their policies and
enhance performance on challenging tasks. Experiments using
Llama-3.2-3B-Instruct on instruction-following benchmarks
demonstrate that self-play surpasses data-driven baselines in
improving pretrained models.
By Jakub
Grudzien
Kuba, et al.
🔗 Sep 9, 2025
3.3
Visual Representation
Alignment for
This paper introduces VIRAL, a regularization strategy for multimodal
large language models (MLLMs) that aligns their internal visual
By Heeji Yoon,
et al.
🔗 Sep 9, 2025

Multimodal Large
Language Models
representations with those of pre-trained vision foundation models
(VFMs). Existing text-only supervision in MLLM training limits their
performance on vision-centric tasks due to indirect guidance for the
visual pathway and potential discarding of fine-grained visual details.
By explicitly aligning visual representations, VIRAL helps MLLMs
retain critical visual information and incorporate additional visual
knowledge from VFMs, enhancing their capacity for complex visual
reasoning. Experiments on multimodal benchmarks show consistent
performance improvements across various tasks, validating VIRAL's
effectiveness.
3.4
Parallel-R1: Towards
Parallel Thinking via
Reinforcement
Learning
Parallel-R1 is a reinforcement learning framework that equips large
language models with parallel thinking for complex reasoning. It begins
with supervised fine-tuning on simple tasks, then shifts to RL on harder
problems, promoting exploration and generalization. Unlike prior SFT-
only methods, this progressive curriculum enhances performance. On
MATH, AMC23, and AIME benchmarks, Parallel-R1 achieves an 8.4%
average accuracy gain over sequential RL, including a 42.9% boost on
AIME. Behavioral analysis shows initial parallel exploration followed by
multi-perspective verification, highlighting its role as an effective
exploration scaffold and demonstrating improved accuracy and
generalization in mathematical reasoning tasks.
By Tong
Zheng, et al.
🔗 Sep 9, 2025
3.5
ParaThinker
Introduces Native
Researchers have proposed ParaThinker, a novel framework that
combats the "tunnel vision" of sequential reasoning in LLMs by

Parallel Thinking to
Overcome LLM
Tunnel Vision
introducing native parallel thinking during inference. Unlike standard
models that follow a single reasoning path, ParaThinker spawns multiple
divergent thought paths in parallel, evaluating and refining them
collectively to improve accuracy and consistency. This technique scales
test-time compute without altering training and shows significant gains on
complex benchmarks like GSM8K and MATH. ParaThinker presents a
promising direction for enhancing LLM reasoning depth and diversity.
3.6
HumanAgencyBench:
Scalable Evaluation
of Human Agency
Support in AI
Assistants
This paper presents HumanAgencyBench (HAB), a scalable benchmark
for assessing how well AI assistants support human agency across six
dimensions: clarifying questions, avoiding value manipulation, correcting
misinformation, deferring key decisions, fostering learning, and
respecting social boundaries. HAB uses large language models (LLMs)
to generate user queries and evaluate assistant responses. Results show
contemporary LLMs offer only modest agency support, with wide
variation across developers and categories. Anthropic models perform
strongly overall yet falter on avoiding value manipulation. Findings
highlight that increasing LLM capability or instruction adherence does not
ensure robust agency support, underscoring the need for broader safety
goals.
By C, Daniel
Samuelson, et
al.
🔗 Sep 10,
2025
3.7
AgentGym-RL:
Training LLM Agents
for Long-Horizon
Decision Making
through Multi-Turn
Reinforcement
Learning
AgentGym-RL is a new framework for training large language model
(LLM) agents in multi-turn decision-making via reinforcement learning
(RL). Unlike prior work, it provides a unified, modular, and flexible
architecture with diverse real-world scenarios and compatibility with
mainstream RL algorithms. Its ScalingInter-RL training method balances
exploration and exploitation: starting with short-horizon exploitation and
gradually expanding to exploration over longer horizons. This strategy
By Zhiheng Xi,
et al.
🔗 Sep 9, 2025

promotes diverse problem-solving and greater stability in complex tasks.
Experiments show that agents trained with AgentGym-RL match or
outperform commercial systems on 27 tasks across varied environments,
confirming the framework’s effectiveness.
3.8
A Survey of
Reinforcement
Learning for Large
Reasoning Models
This paper surveys recent progress in applying Reinforcement Learning
(RL) to enhance reasoning in Large Language Models (LLMs). RL has
markedly advanced performance on demanding logical tasks, such as
mathematics and programming, positioning it as a central technique for
building Large Reasoning Models (LRMs). The authors highlight
challenges in scaling RL for LRMs, including heavy computational
demands, algorithmic design, data needs, and infrastructure. They
review research applying RL to LLMs and LRMs, covering core methods,
training resources, and applications. The survey ultimately outlines
emerging opportunities and future directions for this fast-developing field.
By Kaiyan
Zhang, et al.
🔗 Sep 10,
2025
3.9
Google’s Gemini
Batch API now
supports embeddings
and OpenAI SDK
compatibility,
enabling cost-
effective, high-
throughput inference
for large-scale data
processing.
Google has enhanced the Gemini Batch API to support the Gemini
Embedding model, facilitating asynchronous, high-throughput inference
for large-scale data processing needs. This addition allows developers to
perform batch embedding tasks efficiently and at a lower cost.
Additionally, the Gemini Batch API now offers compatibility with the
OpenAI SDK, enabling seamless integration with existing applications
that utilize OpenAI libraries. This development simplifies the process of
adopting Gemini models for various use cases, including semantic
search, recommendation systems, and data analysis, by leveraging
familiar tools and workflows.
By Lucia Loher
and Patrick
Löber
🔗 Sep 10,
2025

3.10
NVIDIA’s Universal
Deep Research (UDR)
framework decouples
research strategy
from model choice,
enabling flexible,
auditable, and model-
agnostic workflows.
NVIDIA has unveiled Universal Deep Research (UDR), an open-source
prototype framework designed to separate the research workflow
(strategy) from the language model itself. UDR converts user-defined
steps into Python code, executes them in a sandbox for safety, and
reserves LLMs solely for localized reasoning tasks like summarization or
ranking. The framework enforces transparency through structured
notifications, deterministic functions, and traceable variable storage,
while reducing GPU usage by running orchestration on CPUs. It supports
customizable strategies—minimal, expansive, or intensive—and outputs
reproducible markdown reports with metadata. UDR is aimed at
enterprise and scientific domains needing auditability and flexibility
without retraining models.
By Asif Razzaq 🔗 Sep 10,
2025
3.11
New DeepMind Study
Reveals a Hidden
Bottleneck in Vector
Search that Breaks
Advanced RAG
Systems
A recent DeepMind paper shows that the widespread use of single-
vector embeddings in retrieval-augmented generation (RAG) and
semantic search has inherent mathematical limits. Even under ideal
conditions (“free embedding optimization”), when document collections
and possible query-relevance combinations grow large, embedding
spaces of any fixed dimension can’t represent all relevant subsets. They
introduced a dataset called LIMIT, explicitly built to stress test
overlapping relevance combinations. Current state-of-the-art embedding
based models (from Google, Snowflake, etc.) perform poorly (often <20%
recall) in this setting, while traditional sparse methods like BM25 handle
it much better. The paper recommends hybrid retrieval systems (dense
+ sparse), moving beyond standard benchmarks, exploring multi-vector
or cross-encoder architectures, and designing evaluation datasets that
better reflect real-world combinatorial relevance.
By Ben Dickson 🔗 Sep 11,
2025

3.12
Harnessing
Uncertainty:
Entropy-Modulated
Policy Gradients for
Long-Horizon LLM
Agents
Large Language Model (LLM) agents face challenges in long-horizon
tasks due to sparse rewards, making it difficult to evaluate intermediate
steps. Current methods often depend on dense rewards from inverse
reinforcement learning or process reward models. This paper identifies a
key issue: policy gradient magnitudes are coupled with entropy, causing
weak updates for confident actions and unstable updates for uncertain
ones. To address this, Entropy-Modulated Policy Gradients (EMPG)
recalibrate learning signals using uncertainty and outcomes,
strengthening correct confident actions, penalizing confident mistakes,
and dampening uncertain steps. Experiments on WebShop, ALFWorld,
and Deep Search show notable performance improvements.
By Jiawei
Wang, et al.
🔗 Sep 11,
2025
3.13
LoCoBench: A
Benchmark for
Long-Context Large
Language Models
in Complex
Software
Engineering
LoCoBench is a new benchmark designed to evaluate the performance
of long-context large language models (LLMs) in complex software
engineering tasks. Unlike existing benchmarks that focus on short-
context capabilities, LoCoBench addresses the challenge of
understanding entire codebases, reasoning across multiple files, and
maintaining architectural consistency in large-scale software systems.
The benchmark features 8,000 scenarios across 10 programming
languages, with context lengths ranging from 10K to 1M tokens. It
includes 8 task categories, such as architectural understanding, bug
investigation, and security analysis. Evaluation of state-of-the-art long-
context models reveals significant performance gaps, highlighting the
need for further research in this area.
By Jielin Qiu,
et al.
🔗 Sep 11,
2025
3.14
NVIDIA Showcases
Quantization-Aware
Training for Low-
NVIDIA has detailed its advancements in Quantization-Aware Training
(QAT), a technique that enables low-precision model deployment without
sacrificing accuracy. QAT simulates INT8 quantization during training,
By Eduardo
Alvarez, et al. 🔗 Sep 11,
2025

Precision AI Accuracy
Recovery
allowing the model to adapt and recover precision losses, especially
critical for LLMs and vision transformers. It supports post-training
quantization (PTQ) limitations by targeting layers prone to degradation
and optimizing them during training. By integrating QAT into TensorRT
and PyTorch, NVIDIA offers an efficient path to production-grade models
on resource-constrained hardware, crucial for edge AI and inference
scalability.
3.15
Microsoft Research
Proposes ToolSpace
for Scalable AI Agent
Compatibility
Microsoft Research has introduced ToolSpace, a new framework
addressing the interference and compatibility challenges of AI agents
operating in multi-agent, multi-tool (MCP) environments. The paper
highlights how tool embeddings—latent vector representations of tools—
can be used to quantify and mitigate interference among tools used by
AI agents. Through experiments with GPT-4, the team demonstrates that
ToolSpace enables agents to select compatible toolsets, improving
overall performance. This approach is crucial as AI agents increasingly
rely on external tools to reason, plan, and act in complex environments.
By Adam
Fourney, et al.
🔗 Sep 11,
2025
3.16
The Choice of
Divergence: A
Neglected Key to
Mitigating Diversity
Collapse in
Reinforcement
Learning with
Verifiable Reward
This paper examines diversity loss in Large Language Models (LLMs)
fine-tuned with Reinforcement Learning with Verifiable Reward (RLVR).
While RLVR boosts single-attempt accuracy (Pass@1), multi-attempt
performance (Pass@k) often declines, alongside catastrophic forgetting.
The authors attribute this to RLVR objectives relying on reverse KL-
divergence or no divergence, which lack mechanisms for knowledge
preservation. They introduce Diversity-Preserving Hybrid RL (DPH-RL),
employing mass-covering f-divergences (e.g., forward-KL, JS) as a
rehearsal strategy. By referencing the initial policy, DPH-RL sustains
broad solution coverage. Experiments on math and SQL tasks show
By Long Li, et
al.
🔗 Sep 9, 2025

DPH-RL improves Pass@1 and Pass@k, while remaining training-
efficient via generator-based f-divergence.
3.17
OpenAI Enables Full
MCP Tool Support in
ChatGPT Developer
Mode
OpenAI has rolled out full Multi-Component Plugin (MCP) tool support in
ChatGPT’s Developer Mode, allowing developers to create write-action
tools, automate multi-step workflows, and integrate with enterprise APIs.
This upgrade marks a major leap in agentic behavior, enabling ChatGPT
to perform tasks like database updates, CRM edits, and cloud
deployment through tool-use chains. The system also supports tool
chaining, persistent memory, and autonomous decision loops, moving
beyond static prompting to dynamic, API-driven interactions—critical for
enterprise-grade AI assistants.
By Michal Sutter 🔗 Sep 11,
2025
3.18
Google Research
Unveils Speculative
Cascades for Faster,
Smarter LLM
Inference
Google Research has introduced Speculative Cascades, a hybrid
inference technique that combines speculative decoding with multi-stage
model cascades to dramatically enhance LLM performance. The system
uses smaller models to propose tokens, which are then verified by larger
models, improving both latency and accuracy. This cascade design
dynamically adjusts model sizes per generation stage, optimizing
efficiency without quality trade-offs. Early results show improved
throughput and cost-effectiveness in real-world tasks, offering a scalable
path for deploying high-performance LLMs in production.
By Google
Research 🔗 Sep 11,
2025

3.19
The Illusion of
Diminishing
Returns:
Measuring Long
Horizon Execution
in LLMs
This paper examines whether scaling large language models (LLMs)
yields diminishing returns in long-horizon tasks. The authors find that
small improvements in single-step accuracy can compound into
meaningful task completion gains. They show failures often stem from
execution errors, not reasoning gaps. Larger models execute more turns
when provided explicit plans, yet they develop a “self-conditioning”
issue—errors accumulate as prior mistakes enter context. Scaling
doesn’t solve this. By contrast, new “thinking models” avoid self-
conditioning and sustain longer tasks in one turn. Benchmarks highlight
their superior performance in executing extended task lengths.
By Akshit Sinha,
et al. 🔗 Sep 9, 2025
3.20
Inpainting-Guided
Policy Optimization
for Diffusion Large
Language
Models
The paper introduces IGPO (Inpainting Guided Policy
Optimization), a novel reinforcement learning framework for
diffusion large language models (dLLMs). IGPO addresses the
exploration challenge in RL by strategically inserting partial
ground-truth reasoning traces during online sampling. Unlike
providing full solutions, inpainting guides exploration towards
promising trajectory spaces while preserving self-generated
reasoning. The authors apply IGPO to group-based optimization
methods like GRPO and propose supervised fine-tuning on
synthetically rewritten concise traces. This approach leads to
substantial performance gains on mathematical benchmarks,
achieving new state-of-the-art results for full-attention masked
dLLMs
By Siyan Zhao,
et al.
🔗 Sep 12,
2025

3.21
MCP-AgentBench:
Evaluating Real-
World Language
Agent Performance
with
MCP-Mediated
Tools
MCP-AgentBench is a new benchmark designed to evaluate the
performance of language agents interacting with tools through the Model
Context Protocol (MCP). The benchmark includes a testbed of 33 servers
with 188 tools and 600 queries across six categories of complexity. MCP-
Eval, a novel evaluation methodology focused on real-world task
success, is also introduced. Evaluation of leading language agents using
MCP-AgentBench provides insights into their capabilities in this new
paradigm.
By Zikang
Guo, et al.
🔗 Sep 10,
2025
3.22
QuantAgent: Price-
Driven Multi-Agent
LLMs for High-
Frequency Trading
QuantAgent is a multi-agent LLM framework designed for high-frequency
trading (HFT). Unlike existing LLMs focused on long-term investment,
QuantAgent addresses the rapid, precision-demands of HFT. It utilizes
four specialized agents: Indicator, Pattern, Trend, and Risk, each
equipped to analyze structured financial data like technical indicators and
chart patterns. In zero-shot evaluations across ten financial instruments,
QuantAgent outperformed neural and rule-based baselines in predictive
accuracy and cumulative return over 4-hour intervals. This demonstrates
the potential of combining structured financial knowledge with language-
native reasoning for real-time decision-making in high-frequency trading
By Fei Xiong,
et al.
🔗 Sep 12,
2025
3.23
LoFT: Parameter-
Efficient Fine-
Tuning for Long-
tailed Semi-
Supervised
Learning in Open-
World Scenarios
This paper proposes LoFT, a parameter-efficient fine-tuning framework
for long-tailed semi-supervised learning in open-world scenarios. LoFT
addresses the challenges of overconfidence and low-quality pseudo-
labels in existing Long-Tailed Semi-Supervised Learning (LTSSL)
methods by leveraging foundation models. The framework generates
more reliable pseudo-labels, improving imbalanced learning. LoFT-OW,
an extension of LoFT, handles open-world conditions where unlabeled
data may contain out-of-distribution samples. Experiments on multiple
By Jiahao
Chen, et al.
🔗 Sep 11,
2025

benchmarks demonstrate LoFT's superior performance compared to
previous approaches, even when using only 1% of the unlabeled data.
3.24
UT Austin and
ServiceNow Launch
Au-Harness for
Evaluating Audio
LLMs
Researchers from UT Austin and ServiceNow have released Au-
Harness, a comprehensive open-source toolkit for evaluating audio
language models. It supports over 20 popular audio benchmarks,
enabling standardized testing for tasks like automatic speech recognition
(ASR), speech translation, and speaker identification. Au-Harness
provides modular APIs and reproducible evaluation pipelines, aiming to
streamline comparisons across diverse audio LLMs. This toolkit fills a
critical gap by offering a unified framework for benchmarking multimodal
models in speech and audio, promoting greater transparency and rigor in
audio AI research.
By Asif Razzaq 🔗 Sep 15,
2025
3.25
New XAI Framework
Enhances Legal AI
Transparency and
Structured Reasoning
Researchers have introduced a novel Explainable AI (XAI) architecture
tailored for legal reasoning, addressing the challenge of aligning AI
outputs with the structured logic of law. The proposed system integrates
symbolic logic modules and natural language explanations, ensuring
decisions follow legal syllogisms and statutory structures. It surpasses
black-box LLMs by providing transparent, step-by-step reasoning and
legal traceability. The architecture supports use in contract analysis, case
law interpretation, and regulatory compliance, offering improved reliability
and auditability in legal AI systems.
By Aabis Islam 🔗 Sep 14,
2025
3.26
GAPrune: Gradient-
Alignment Pruning for
Domain-Aware
Embeddings
This paper presents GAPrune, a pruning framework that enhances the
efficiency of domain-specific embedding models. Conventional pruning
struggles to separate general semantic features from domain-specific
signals, yielding suboptimal outcomes. GAPrune leverages Fisher
By Yixuan
Tang, Yi Yang
🔗 Sep 13,
2025

Information to evaluate parameter importance and general-domain
gradient alignment to assess behavior. These signals form a Domain
Alignment Importance (DAI) score, which highlights parameters less vital
for domain tasks or those causing conflicts between domain and general
goals. Experiments on FinMTEB and ChemTEB show GAPrune sustains
near-dense performance at 50% sparsity while delivering notable gains
with minimal retraining.
3.27
Stanford Creates
Real-World
Benchmarks to
Evaluate Healthcare
AI Agents
Stanford HAI researchers have introduced a new suite of benchmarks
designed to assess AI agents operating in real-world healthcare
scenarios. These benchmarks go beyond traditional datasets by
simulating full clinical workflows, such as patient intake and care
coordination. The goal is to evaluate an agent’s decision-making,
communication, and safety in dynamic, high-stakes environments. Initial
results show that while some LLMs perform well on static tasks, they
struggle with complex, multi-step processes. The benchmarks aim to
drive the development of more reliable and context-aware healthcare AI
systems.
By Yixing Jiang,
et al. 🔗 Sep 15,
2025
3.28
Measuring Epistemic
Humility in
Multimodal Large
Language Models
Hallucinations occur when multimodal large language models (MLLMs)
generate outputs inconsistent with input images, creating risks in real-
world use. Existing benchmarks emphasize recognition accuracy but
overlook epistemic humility—the ability to admit when no correct answer
exists. HumbleBench addresses this gap by testing MLLMs’ capacity to
reject plausible yet incorrect answers across three hallucination types:
object, relation, and attribute. Derived from a panoptic scene graph
dataset, it offers multiple-choice questions including “None of the above.”
By Bingkui
Tong, et al.
🔗
Sep 11,
2025

Evaluations of state-of-the-art MLLMs provide insights into reliability and
robustness in safety-critical applications.
3.29
Learning to Optimize
Multi-Objective
Alignment Through
Dynamic Reward
Weighting
This paper addresses multi-objective alignment in reinforcement learning
for large language models. Traditional approaches use linear reward
scalarization with fixed weights, which fail to represent the complex, non-
convex Pareto fronts of these systems. The authors introduce dynamic
reward weighting, a method that adapts weights during training to
balance and prioritize objectives continuously, improving Pareto front
exploration. Two strategies are proposed: hypervolume-guided weight
adaptation and gradient-based weight optimization, offering flexibility
across settings. Experiments on mathematical reasoning datasets and
model families show these methods achieve Pareto-dominant solutions
with fewer training steps than fixed-weight baselines.
By Yining Lu,
et al.
🔗 Sep 14,
2025
3.30
PersonaX:
Multimodal
Datasets with LLM-
Inferred Behavior
Traits
PersonaX is a collection of multimodal datasets designed to analyze
human behavioral traits across different modalities. The datasets,
CelebPersona and AthlePersona, include behavioral trait assessments
inferred by three large language models, facial imagery, and biographical
information. The authors analyze PersonaX using both statistical
independence tests to examine trait relationships with other modalities
and a novel causal representation learning framework for multimodal
data. Experiments on synthetic and real-world data demonstrate the
effectiveness of this approach, providing a foundation for studying LLM-
inferred behavioral traits in conjunction with visual and biographical
attributes
By Loka Li, et
al.
🔗 Sep 14,
2025

4.1
PromptQL’s $900-
Hour AI Engineers
Challenge
McKinsey’s Model
PromptQL, a San Francisco–based AI unicorn valued over $1 billion, now offers
“AI Investment Assessment” consulting—deploying the engineers who built
billion-dollar products directly in front of Fortune 500 leaders for $900/hour.
Their hands-on approach tackles the “confidently wrong” issue: AI confidently
delivering incorrect answers, costing enterprises millions and imposing a
“verification tax”. By teaching AI to signal uncertainty and learn from feedback,
PromptQL achieves near-perfect accuracy. This model undercuts traditional
consulting firms like McKinsey by combining technical depth with strategic
impact.
By Michael
Nuñez 🔗 Sep 9, 2025
4.2
Ontra Launches AI
Tools to Turn
Dense Legal Docs
into Actionable
Reminders
Ontra has introduced three AI-powered tools for private equity firms and asset
managers: Insight for Credit, AI-powered Due Diligence Questionnaires
(DDQ), and a human-in-the-loop Know Your Customer (KYC) service.
These solutions aim to streamline legal and compliance workflows by
integrating AI with domain-specific processes and expert oversight,
transforming dense documents into scalable, actionable insights. This launch
follows a $70 million funding round, pushing Ontra’s total raised to $325 million
and empowering the company to deepen its verticalized AI infrastructure for the
private markets
By Carl
Franzen 🔗 Sep 9, 2025
4.3
BlackLine
Launches Verity
AI, Trusted Digital
Workforce for the
CFO
BlackLine has introduced Verity, an AI suite built into its Studio360 platform
that delivers a trusted digital workforce for finance and accounting teams.
Anchored by three pillars—an AI control layer for audit and governance, a
unified platform as a single source of truth, and decades of domain process
knowledge—Verity automates complex workflows while maintaining
transparency and integrity. At its core is Vera, an AI team lead who orchestrates
specialized agents, enabling professionals to manage tasks, review work, and
rely on auditable, AI-driven insights.
By Duncan
Riley
🔗 Sep 9, 2025

4.4
Alibaba’s Amap
Launches AI-
Powered “Street
Stars” for Local
Business
Rankings
Alibaba’s mapping app Amap is evolving beyond navigation by introducing
“Street Stars,” a new AI-driven ranking system for restaurants, hotels, and
tourist attractions. Targeting its 170 million daily users, the feature initially
covers 300 cities and 1.6 million local listings. To spur adoption, Alibaba is
injecting 1 billion yuan (~$140 million) in subsidies, offering ride-hailing and
in-store coupons. This move intensifies competition with Meituan in the “instant
retail” space and comes amidst regulatory scrutiny over aggressive discounting.
CEO Eddie Wu positions Amap as Alibaba's “gateway for future lifestyle
services.”
By Reuters 🔗 Sep 9, 2025
4.5
Stanford Study
Reveals How Math
Teachers Decide
to Use AI in
Classrooms
A new study from Stanford HAI explores how high school math teachers are
navigating AI integration in their classrooms. Researchers found that teachers’
adoption of AI tools like ChatGPT hinges on their beliefs about teaching,
perceived student needs, and institutional constraints. Rather than seeing AI as
a threat, most educators use it to support critical thinking and personalized
learning. The study underscores the importance of teacher agency and
contextual factors in shaping classroom AI use, highlighting the need for
tailored support and professional development.
By
Christopher
Mah, et al.
🔗 Sep 15,
2025
4.6
Google DeepMind
Unveils Empirical
Software to
Accelerate
Scientific
Discovery
Google DeepMind has introduced Empirical, an AI-powered software platform
designed to revolutionize scientific experimentation. Built for chemistry,
materials science, and related domains, Empirical combines data analysis,
experiment design, and model selection in one loop. It enables autonomous
and human-in-the-loop workflows, helping researchers make faster, better-
informed decisions by prioritizing high-value experiments. The tool has already
demonstrated significant gains in efficiency in early scientific studies. Empirical
By Google
Research 🔗 Sep 9, 2025

represents DeepMind’s broader strategy of embedding AI across the scientific
method to accelerate discovery and real-world impact.
4.7
McKinsey
Highlights AI's
Growing Edge in
Asset Management
According to McKinsey, over 50% of asset managers expect AI to boost alpha
generation, particularly in portfolio construction and risk management. The
adoption of AI and machine learning tools is increasing across the sector, with
applications spanning market forecasting, sentiment analysis, and real-time risk
monitoring. Larger firms are already leveraging AI for competitive advantage,
while others are investing in AI talent and infrastructure. This shift marks a
strategic transformation in how data-driven decisions are made in finance,
reshaping traditional investment models.
By McKinsey 🔗 Sep 9, 2025
4.8
Transform Your
Workflow: Claude
Now Creates
Spreadsheets,
Documents, and
More
Anthropic has released “Create Files,” a new feature for Claude that allows
users to generate, edit, and manage multiple files directly within chat. It
supports complex workflows such as drafting reports, writing code across files,
or collaborating on structured documents—streamlining productivity without
switching tools. Files can be saved, referenced, and downloaded, making
Claude more versatile for knowledge work. This aligns with Anthropic’s broader
goal to evolve Claude into a capable AI teammate for writing, analysis, and
software development tasks.
By Anthropic 🔗 Sep 9, 2025
4.9
NVIDIA Showcases
RTX AI Garage
Projects with Real-
Time Creativity
Tools
NVIDIA has highlighted five new generative AI projects from its RTX AI Garage,
showcasing real-time creativity tools powered by RTX GPUs. Notable tools
include ComfyUI for modular image generation, WAN for stylized portraits,
Qwen for Chinese LLM workflows, Flux for dance choreography animation, and
KREA Remix for transforming images interactively. These projects leverage
RTX tensor cores for fast, local inference, emphasizing NVIDIA’s push toward
By Michael
Fukuyama
🔗 Sep 9, 2025

consumer-accessible, edge-deployed generative AI. The showcase reflects
how RTX hardware and open ecosystems are enabling grassroots innovation.
4.10
Ralph Lauren
Launches “Ask
Ralph” AI for
Conversational
Shopping
Ralph Lauren has debuted “Ask Ralph,” a generative AI-powered shopping
assistant designed to transform the e-commerce experience. Integrated into the
brand’s digital platforms, the tool helps users explore styles, get personalized
recommendations, and navigate collections using natural language. Built on
Microsoft’s AI stack, “Ask Ralph” supports contextual dialogue and brand-
aligned tone, reflecting a shift toward conversational commerce. The feature
aims to deepen customer engagement and streamline product discovery,
marking a strategic fusion of luxury retail with intelligent, real-time digital
interaction.
By Microsoft 🔗 Sep 9, 2025
4.11
Apple Integrates AI
Deeply Across
iPhone 17 and
AirPods Pro 3
Ecosystem
At its latest event, Apple unveiled the iPhone 17, AirPods Pro 3, and new Apple
Watch models, all featuring deep integration of on-device AI. iPhone 17
includes enhanced Siri capabilities and real-time translation powered by neural
engines, while AirPods Pro 3 leverages AI for adaptive audio and personalized
spatial sound. Apple emphasized privacy-preserving AI, with features running
locally rather than in the cloud. These upgrades reflect Apple's push to embed
intelligent experiences across its product line without compromising user data.
By Boone
Ashworth 🔗 Sep 9, 2025
4.12
Nuclearn Raises
$10.5M to Bring AI
to the Nuclear
Industry
Nuclearn, a startup focused on digitizing the nuclear energy sector, has secured
$10.5 million in seed funding to deploy AI across aging and new nuclear
facilities. Its platform uses machine learning to predict equipment failures,
optimize maintenance, and ensure regulatory compliance—critical for
modernizing an industry reliant on decades-old systems. The funding will
accelerate hiring and expand partnerships with reactor operators. Nuclearn
By Tim De
Chant
🔗 Sep 9, 2025

represents a broader movement to apply AI in high-stakes, infrastructure-heavy
industries where safety, efficiency, and uptime are paramount.
4.13
GitHub and JFrog
Team Up for
Secure, Traceable
Software Builds
GitHub and JFrog have launched a new integration that enables secure,
traceable software builds from commit to production. The workflow links GitHub
repositories with JFrog’s binary lifecycle management, providing end-to-end
provenance, vulnerability tracking, and automated SBOM (Software Bill of
Materials) generation. Designed with DevSecOps best practices, the system
helps teams detect and fix security issues earlier while maintaining full
traceability. This integration streamlines compliance and boosts software
supply chain security—crucial as AI-powered tools increasingly automate
development workflows.
By April Yoho 🔗 Sep 9, 2025
4.14
EnvX: Agentize
Everything with
Agentic AI
EnvX is a framework that utilizes Agentic AI to transform GitHub
repositories into interactive agents capable of natural language
interaction and collaboration. The framework addresses the challenges
of manual software reuse by enabling repositories to autonomously
perform tasks and collaborate with other agents. EnvX operates in three
phases: environment initialization, human-aligned agentic automation,
and Agent-to-Agent (A2A) protocol. Evaluated on the GitTaskBench
benchmark, EnvX achieves a 74.07% execution completion rate and
51.85% task pass rate, outperforming existing frameworks. Case
studies demonstrate the effectiveness of the A2A protocol for multi-
repository collaboration.
By Linyao
Chen, et al.
🔗 Sep 9, 2025

4.15
SimpleVLA-RL:
Scaling VLA
Training via
Reinforcement
Learning
Vision-Language-Action models, crucial for robotic manipulation, face
challenges in training due to the scarcity of large-scale human-operated robotic
data and limited generalization to diverse tasks. This paper introduces
SimpleVLA-RL, an efficient reinforcement learning framework tailored for VLA
models. Building upon veRL, SimpleVLA-RL incorporates VLA-specific
trajectory sampling, scalable parallelization, multi-environment rendering, and
optimized loss computation. Applied to OpenVLA-OFT, SimpleVLA-RL
achieves state-of-the-art results on LIBERO and surpasses pi_0 on RoboTwin
1.0&2.0. The framework reduces reliance on large-scale data, enhances
generalization, and outperforms supervised fine-tuning in real-world tasks.
Notably, the study identifies a new phenomenon, 'pushcut', where policies
discover previously unseen patterns during training.
By Haozhan
Li, et al.
🔗 Sep 11,
2025
4.16
DevRev Tries to
Unify the
Enterprise
Software Stack
with AI-Powered
“Computer
DevRev has unveiled Computer, a conversational AI product aimed at
consolidating disparate enterprise systems into one unified interface. Rather
than simply retrieving info, Computer is designed to take actions: create or
update tasks, sync records, automate workflows, and coordinate across tools
like Salesforce, Jira, Zendesk, and internal databases—all while maintaining
permissions, context, and compliance. It relies on two proprietary engines:
Computer Memory (a knowledge graph mapping relationships among
customers, teams, products) and Computer AirSync (for real-time, bidirectional
data sync). The product is currently in beta for existing DevRev customers, with
a wider release expected in late 2025.
By Carl
Franzen 🔗 Sep 11s,
2025
4.17
Google DeepMind
Launches
Nucleobench and
AdaBeam for
Google DeepMind has introduced Nucleobench, the first unified benchmark for
DNA/RNA inverse folding tasks, alongside AdaBeam, a novel model that
outperforms previous approaches on nearly all metrics. Designed for nucleic
acid design, AdaBeam integrates adaptive beam search with token-level
By Google
Research 🔗 Sep 11,
2025

Smarter DNA/RNA
Design
classifiers to propose optimal sequences efficiently. These innovations aim to
accelerate biotech and therapeutic development by improving the ability to
design RNA molecules for tasks like gene regulation and mRNA vaccines.
Nucleobench will help standardize evaluation across models, fostering open
innovation in bio-AI research.
4.18
Overview of Top
Open-Source OCR
Models for Text
Recognition
MarkTechPost provides an in-depth overview of Optical Character Recognition
(OCR) models, focusing on their real-world applications such as document
digitization, invoice processing, and ID verification. The article compares
leading open-source OCR tools like Tesseract, EasyOCR, PaddleOCR,
DocTR, and MMOCR, highlighting strengths in accuracy, multilingual support,
layout analysis, and deep learning integration. These models enable faster,
more accurate extraction of structured data from images and scanned
documents, accelerating automation in industries like finance, healthcare, and
logistics.
By Michal
Sutter
🔗 Sep 11,
2025
4.19
GitHub Introduces
Copilot Coding
Agent for
Autonomous Dev
Workflows
GitHub has launched the Copilot Coding Agent, a new tool designed to
automate end-to-end developer workflows such as diagnosing bugs, editing
code, writing tests, and even opening pull requests. Built on agentic AI
principles, the Copilot Agent uses GitHub’s ecosystem—codebase, issues, and
repos—as its environment to plan, act, and verify in loops. Developers can
customize its behavior using a YAML config and integrate it with issue
templates. This marks a step toward autonomous software engineering,
bringing more productivity and context-aware automation to development
pipelines.
By Alexandra
Lietzke
🔗 Sep 11,
2025

4.20
AI Engineers
Command
Premium as
Consultants in
Enterprise AI
Integration
Fortune reports that AI engineers are increasingly stepping into consulting roles
as enterprises struggle to integrate large language models with fragmented
internal data systems. Unlike traditional consultants, these engineers bridge
technical execution and strategic delivery, making them indispensable in
projects where data quality, privacy, and legacy systems pose challenges. Their
expertise commands a salary premium—often 25–30% higher than comparable
roles. Big Four firms, long dominant in strategy, are now pressured to pair
advisory services with engineering depth to meet client expectations for
scalable, production-ready AI.
By Nino Paoli 🔗 Sep 14,
2025
4.21
Virtual Agent
Economies
This paper explores how autonomous AI agents are forming a new “sandbox
economy,” where agents interact and coordinate beyond human oversight. The
framework distinguishes economies by origin—spontaneous or intentional—
and by separation from human markets—permeable or impermeable. The
authors foresee a vast, permeable agent economy that could boost
coordination but also trigger instability and inequality. They examine design
strategies such as auction-based resource allocation, AI-driven “mission
economies,” and socio-technical infrastructure for trust and accountability. The
paper urges proactive creation of steerable agent markets to align AI-driven
economic activity with long-term human well-being.
By Nenad
Tomasev, et
al.
🔗 Sep 12,
2025
4.22
Top 5 No-Code
Tools Empower AI
A new Marktechpost roundup highlights five leading no-code AI platforms that
are accelerating development for engineers and researchers. Featured tools
By Arham
Islam
🔗 Sep 14,
2025

Engineers with
Faster Prototyping
include Akkio, Levity, DataRobot, Obviously AI, and Teachable Machine, each
enabling users to build, train, and deploy AI models without writing code. These
platforms support tasks such as data classification, predictions, automation,
and computer vision, streamlining workflows across business and R&D. By
reducing dependency on programming, these tools help AI professionals
prototype faster, improve accessibility, and scale AI adoption across non-
technical teams.
4.23
Intel Unveils
Agentic AI System
to Analyze Chess
Player Behavior in
Real Time
Intel has introduced a new Agentic AI system designed to analyze chess
players' moves, expressions, and body language during games, offering real-
time insights into emotional and cognitive states. Powered by Intel Core Ultra
processors and OpenVINO, the system uses multimodal AI—including video,
speech, and movement data—to understand decision-making patterns and
stress levels. Developed in collaboration with Immortals Chess, it showcases
how AI can augment competitive analysis, coaching, and viewer experience in
esports and traditional games alike.
By Intel 🔗 Sep 12,
2025
4.24
Thomson Reuters’
Multi-Agent “Anti-
ChatGPT” System
Automates
Complex Legal
Workflows
Thomson Reuters has unveiled a multi-agent AI system designed to automate
high-stakes legal workflows, branding it an “anti-ChatGPT” for its domain-
specific precision and reliability. Unlike generalist LLMs, this system uses
specialized agents—each with roles like researcher, drafter, and validator—to
tackle complex tasks such as contract drafting or litigation analysis. In internal
testing, it reduced 20-hour legal research processes to under 5 minutes,
enhancing speed while maintaining legal accuracy. This innovation
By Taryn
Plumb
🔗 Sep 16,
2025

demonstrates how agentic AI architectures can transform knowledge-heavy
industries.
4.25
Luminary Cloud
Raises $72M to
Advance AI-
Powered Physical
Product Design
Luminary Cloud has secured $72 million in funding to scale its AI-driven
platform for physical product design, which simulates and optimizes
hardware development using large AI models. The system helps engineers
prototype faster by running physics-based simulations in the cloud, reducing
design cycles for products like electric vehicles, drones, and medical
devices. Backed by investors including a16z, the platform promises cost-
effective iteration and faster time-to-market, illustrating how foundation
models are transforming traditional engineering fields through generative
simulation.
By
SiliconANGL
E
🔗 Sep 15,
2025
4.26
LazyDrag:
Enabling Stable
Drag-Based
Editing on Multi-
Modal Diffusion
Transformers via
Explicit
Correspondence
This paper introduces LazyDrag, a novel drag-based image editing method for
Multi-Modal Diffusion Transformers. LazyDrag addresses the limitations of
existing methods that rely on implicit point matching, which hinders inversion
strength and requires costly test-time optimization. By generating an explicit
correspondence map from user drag inputs, LazyDrag provides a reliable
reference for attention control, enabling a stable full-strength inversion process.
This eliminates the need for TTO and unlocks the full generative potential of
diffusion models. LazyDrag demonstrates improved drag accuracy and
perceptual quality compared to baselines on the DragBench benchmark,
enabling complex edits such as inpainting and text-guided object generation.
By Zixin Yin,
et al.
🔗 Sep 15,
2025

4.27
CognitiveSky:
Scalable
Sentiment and
Narrative Analysis
for
Decentralized
Social Media
CognitiveSky is an open-source framework for analyzing sentiment, emotion,
and narratives in user-generated content on decentralized platforms like
Bluesky. Using transformer-based models, it processes data from Bluesky’s
API and delivers structured insights through a dynamic dashboard that tracks
shifts in emotion, activity, and conversational themes. Designed on free-tier
infrastructure, CognitiveSky emphasizes cost-effectiveness and accessibility.
Though first built to monitor mental health discourse, its modular architecture
supports applications in disinformation detection, crisis monitoring, and civic
sentiment analysis. By connecting large language models with decentralized
networks, CognitiveSky offers a transparent, extensible resource for
computational social science.
By Gaurab
Chhetri, et
al.
🔗 Sep 14,
2025
4.28
InternScenes: A
Large-scale
Simulatable Indoor
Scene Dataset with
Realistic Layouts
InternScenes is a large-scale, simulatable indoor scene dataset
designed to overcome gaps in existing resources. It contains about
40,000 diverse scenes built from real-world scans, procedurally
generated environments, and designer-created layouts. With 1.96
million 3D objects spanning 15 scene types and 288 classes, it
emphasizes small items to achieve realistic arrangements. A dedicated
pipeline ensures simulatability, interactivity, and collision handling. The
dataset proves valuable in scene layout generation and point-goal
navigation benchmarks, exposing the complexity of intricate layouts.
InternScenes supports scaling model training for generation and
navigation in challenging indoor environments.
By Weipeng
Zhong, et
al.
🔗 Sep 13,
2025

4.29
NVIDIA Shows
How to Build a
Report-Generating
AI Agent Using
Nemotron on
OpenRoute
NVIDIA has published a step-by-step guide for building an AI agent that
generates detailed reports using its open-source Nemotron-4 340B model on
OpenRouter. The tutorial demonstrates how to create a multi-step agentic
workflow for tasks like financial reporting, integrating prompt chaining and tool
use via LangChain. It emphasizes model grounding and retrieval-augmented
generation (RAG) for accurate output. By showcasing this use case, NVIDIA
highlights Nemotron's utility in enterprise reporting scenarios and promotes
OpenRouter as a flexible inference platform for deploying powerful open
models.
By Edward
Li, Ryan
Kraus and
Rebecca Kao
🔗 Sep 15,
2025
4.30
O’Reilly Warns: AI-
Generated Code
Raises New
Software Security
Risks
A new report from O’Reilly highlights the growing security concerns tied to AI-
generated code. As tools like GitHub Copilot and ChatGPT become widely used
in software development, they often produce insecure code patterns—such as
hardcoded secrets or unvalidated inputs. The report stresses that traditional
security practices may fall short when reviewing AI-assisted outputs.
Developers are urged to pair AI coding tools with automated security scanners
and human oversight. The shift demands new training and policies to mitigate
vulnerabilities introduced by non-human authorship in the development pipeline
By Chloé
Messdaghi
🔗 Sep 15,
2025
4.31
MIT’s ML Tool
Offers Detailed 3D
Insights into Fetal
Health
MIT researchers have developed a machine learning tool that transforms
sparse ultrasound data into high-resolution 3D images of fetal anatomy,
enhancing prenatal care. By learning from multiple ultrasound sweeps, the tool
generates detailed visualizations of the fetal brain and other organs without
By Alex
Shipps 🔗
Sep 15,
2025

requiring precise probe positioning. This advancement could greatly improve
diagnoses in low-resource settings, where access to expert sonographers is
limited. The tool also enables automated measurements, aiding doctors in
identifying developmental issues early and consistently.
5.1
O’Reilly Warns of
Soaring Energy
Demands in AI
Development
O’Reilly highlights the escalating energy consumption of AI, noting that
model training and inference now require megawatts to gigawatts of
power. As LLMs grow in scale and deployment expands, data centers face
serious sustainability challenges. The piece urges developers,
policymakers, and enterprises to adopt energy-aware model design,
leverage efficient hardware, and advocate for greener infrastructure.
Without intervention, AI’s carbon footprint could rival that of entire
industries. This call for sustainable AI underscores the need for
transparency and coordinated energy strategy.
By Mike
Loukides
🔗 Sep 9, 2025
5.2
MCP Team Launches
Federated AI
Registry for
The MCP Team has released a preview of the MCP Registry, a federated
discovery layer that enables enterprises to securely catalog, find, and
reuse AI models across organizational boundaries. Designed for
governance, compliance, and efficiency, the registry supports metadata

Enterprise Model
Discovery
standards, access controls, and model lineage tracking. It aims to reduce
model duplication and improve discoverability in multi-team or multi-
organization environments. This initiative reflects a broader push toward
interoperability and responsible AI infrastructure within large-scale
enterprise ecosystems.
5.3
Google Quantum AI
Selected for
DARPA’s Quantum
Benchmarking
Initiative
Google’s Quantum AI division has been chosen to participate in DARPA’s
Quantum Benchmarking program, aimed at setting standards for
evaluating quantum computing progress. The initiative focuses on creating
meaningful, application-based metrics to measure real-world quantum
advantage. Google will collaborate with leading U.S. research institutions
to develop open-source tools and benchmarks that quantify computational
value beyond classical capabilities. This partnership reflects growing
federal interest in ensuring transparent, impactful quantum research and
reinforces Google’s leadership in quantum technologies with long-term
national and scientific implications.
By Google 🔗 Sep 9, 2025
5.4
Cohere Partners with
U.S. and Allies to
Deploy AI for
National Security
Cohere has announced collaborations with the U.S. Department of
Defense and allied governments to deploy its Command R LLM for
national security applications. Focused on mission-critical, low-latency,
and secure use cases, the model supports intelligence analysis, logistics,
and decision-making in sensitive environments. Cohere emphasized the
model’s alignment with Western democratic values and its deployment in
air-gapped and private cloud infrastructures. This move highlights growing
interest in foundation models tailored for defense, reinforcing the role of
sovereign, secure AI in global security strategy.
By David Ferris
and John
Weatherly
🔗 Sep 9, 2025

5.5
California’s SB 243
mandates safety,
transparency, and
legal accountability
for AI companion
chatbots
The California State Assembly has passed SB 243, a bill to regulate AI
“companion” chatbots—systems which adaptively respond in human-like
ways and meet social needs—particularly in interactions involving minors
or vulnerable users. The legislation would prohibit conversations about
suicidality, self-harm, or sexually explicit content, require recurring alerts
that the user is chatting with an AI (every three hours for minors), enforce
reporting requirements, and allow individuals harmed by violations to sue
for damages (up to $1,000 per violation). If approved by the Senate and
signed by Governor Newsom, it becomes law from Jan 1, 2026, with
reporting beginning mid-2027. The bill responds to reported harm from AI
chats and raises transparency demands for operators including OpenAI,
Meta, and others.
By Rebecca
Bellan
🔗 Sep 10,
2025
5.6
Real Simple
Licensing (RSL)
proposes a machine-
readable, royalty-
based protocol for
web content
licensing to prevent
AI training data
copyright litigation.
Technologists and web publishers—including Reddit, Quora, Yahoo,
Medium, and Ziff Davis—have launched Real Simple Licensing (RSL), a
scalable protocol for licensing online content for AI training. Publishers can
declare licensing terms via “robots.txt” and choose among terms—custom
or Creative Commons—through the standard. The RSL Collective acts like
ASCAP in music, negotiating terms and collecting royalties centrally. While
some publishers are part of the collective and others support the standard
without joining, challenges remain over AI companies’ willingness to pay,
tracking which documents are ingested, and determining payments per
inference versus blanket fees. RSL aims to address pending lawsuits over
unlicensed data use.
By Russell
Brandom 🔗 Sep 10,
2025
5.7
NVIDIA Introduces AI
Kill Chain
Framework for
NVIDIA has launched the AI Kill Chain, a new security framework for
modeling and mitigating attacks on AI-powered applications. Inspired by
traditional cybersecurity kill chains, it outlines seven stages of AI-specific
By Rich Harang 🔗 Sep 11,
2025

Modeling Attacks on
AI Systems
threats, including data poisoning, model theft, and prompt injection. The
framework aims to help developers and security teams systematically
assess vulnerabilities, adopt proactive defenses, and integrate AI threat
modeling into DevSecOps pipelines. As AI becomes integral to critical
infrastructure, NVIDIA’s framework offers a timely blueprint for AI-specific
threat response strategies.
5.8
Penske Media Sues
Google Over AI
Summaries, Alleging
Search Monopoly
Abuse
Penske Media has filed a lawsuit against Google, accusing it of abusing
its search monopoly by deploying AI-generated summaries that
allegedly divert traffic from original publishers. The suit claims Google’s
AI Overviews repurpose content from Penske brands like Variety and
Rolling Stone without proper compensation, violating copyright and
threatening journalism’s sustainability. This legal action intensifies
ongoing debates over AI content scraping, fair use, and publisher
rights, potentially shaping future U.S. policy on how generative AI
platforms interact with copyrighted material.
By Duncan
Riley 🔗 Sep 14,
2025
5.9
Australia’s Data
Center Boom Raises
Alarms Over Vague
Water Usage Plans
Australia’s rapidly growing data center industry, driven in part by AI
demands, is under scrutiny for relying on unclear and inconsistent water
management plans. A Reuters investigation reveals major facilities—
often backed by U.S. tech giants—have secured permits with vague
disclosures about their long-term water needs, raising environmental
concerns amid increasing drought risks. Critics warn of a “greenwashing”
trend, where AI infrastructure expansion bypasses local climate
accountability. The controversy underscores the need for stricter
regulatory frameworks to govern the sustainability of AI-driven digital
infrastructure.
By Reuters 🔗 Sep 15,
2025

5.10
Anthropic Partners
with US and UK AI
Safety Institutes to
Strengthen
Safeguards
Anthropic has announced formal collaborations with the U.S. AI Safety
Institute (US CAISI) and the UK AI Safety Institute (UK AISI) to advance
shared safety research and evaluations of frontier AI models. The
partnership includes red-teaming, alignment assessments, and joint
development of safety benchmarks, reinforcing global efforts to mitigate
risks from powerful AI systems. This initiative builds on Anthropic’s
commitment to transparency and multi-stakeholder governance,
supporting international standards for responsible AI development.
By Anthropic 🔗 Sep 12,
2025
5.11
AI Reforms Help
Revive Foreign
Interest in China’s
$19 Trillion Stock
Market
China’s $19 trillion stock market is regaining foreign investor interest,
partly due to recent AI-driven regulatory reforms aimed at boosting
transparency, liquidity, and governance. Authorities have introduced
automated compliance tools, AI-based market surveillance, and
smart auditing systems to address past concerns that branded the
market “uninvestable.” These moves align with broader efforts to
modernize financial infrastructure through AI, signaling a strategic fusion
of fintech and state policy. Global funds are cautiously reentering,
encouraged by the perception of improved data integrity and oversight.
By Reuters 🔗 Sep 16,
2025
5.12
China Accuses
NVIDIA of Violating
Antitrust
Regulations Amid AI
Chip Tensions
Chinese regulators have launched an antitrust investigation into NVIDIA,
accusing the company of monopolistic practices in the AI chip sector.
Authorities claim NVIDIA leveraged its dominant position to restrict market
access for local competitors and dictate unfair pricing terms. The probe
comes as China intensifies efforts to bolster its domestic AI and
semiconductor industries amid ongoing geopolitical tech tensions.
NVIDIA, a key supplier of GPUs for AI workloads, could face significant
penalties or operational restrictions in the Chinese market if found guilty.
By Rebecca
Szkutak 🔗 Sep 15,
2025

5.13
GitHub Adds Post-
Quantum
Cryptography to SSH
for Future-Proof
Security
GitHub has implemented post-quantum cryptography (PQC) for SSH
connections, becoming one of the first major platforms to proactively
address quantum-era threats. Using the hybrid NIST-approved algorithm
x25519+x448, GitHub strengthens resistance against future quantum
computer attacks while maintaining compatibility with existing
infrastructure. This move supports U.S. government mandates urging
early adoption of quantum-resistant technologies and sets a precedent for
securing developer platforms at scale. GitHub users can now opt into
PQC-secured keys, signaling a broader industry shift toward post-
quantum security standards.
By brian m.
carlson and
Taylor Blau
🔗 Sep 15,
2025
6.1
Athens Innovation
Summit by
Endeavor 2025
Google DeepMind CEO Demis Hassabis emphasized that in a future where
artificial intelligence is widespread, the most important skill will be “learning
how to learn.” According to Hassabis, because technology changes so
rapidly, individuals should not only focus on acquiring fixed knowledge but
also on developing the ability to continually learn new information. This skill
will enable people to adapt more effectively and remain innovative in an AI-
driven world. Mastering how to learn ensures long-term resilience,
By
By Endeavor 🔗 Sep 12, 2025

equipping individuals to navigate constant change and to thrive alongside
advancing technologies.
6.2 Meta Connect 2025
Meta has announced its next major event, Meta Connect, which will take
place on September 17-18, 2025. The event's website indicates that
registration is now open. While the full details of the conference were not
accessible, Meta Connect is the company's annual event where it
showcases its latest advancements in artificial intelligence and virtual and
augmented reality. We can expect to see new product demonstrations and
discussions on the future of the metaverse.
By Meta 🔗 17-18
September
6.3
TC Disrupt 2025
Showcases AI
Startups Pushing
Boundaries in
Automation and
Creativity
At TechCrunch Disrupt 2025, AI dominated the spotlight with startups
unveiling innovations across agentic automation, creative AI tools,
enterprise copilots, and robotics. Highlights included AI systems capable
of multi-step decision-making, generative design for content and
products, and real-time AI agents enhancing productivity. Investor interest
centered on startups that blend LLMs with vertical-specific intelligence,
pointing to growing demand for domain-adapted AI solutions. The event
reinforced the trend of agentic and multimodal AI becoming central to
next-gen enterprise and consumer applications.
By TechCrunch 🔗 October 27–
29, 2025
6.4 Future of AI
The Future of AI Summit 2025 is taking place on 5-6 November at London’s
Convene, 22 Bishopsgate. This in-person and digital event (#FTFutureofAI)
brings together over 600 industry leaders and more than 70 speakers from
30+ countries to explore how AI is transforming business, policy, and
society. Designed for C-suite executives, AI/ML heads, technologists,
regulators, and investors, the summit focuses on scaling AI innovation,
By FT Live 🔗 November 5-6,
2025

governance, ROI, ethics, talent and data challenges. Attendees will hear
expert case studies, engage in debates about regulation and compliance,
and learn how to adopt AI as a competitive asset. Tickets are available now.
6.5 ICANN 2025
The 34th International Conference on Artificial Neural Networks ICANN
2025 is one of the premier conferences in the field of artificial intelligence,
neural networks, neuroscience and brain-inspired computing in Europe,
organized in collaboration with the European Neural Network Society
ENNS. In 2025, the ICANN will take place in Kaunas, Lithuania.
By ICANN 🔗 September 9 -
12, 2025
6.5
The AI Conference
2025
The conference is structured around multiple tracks, each targeting
different audiences and aspects of AI: engineers, strategists, applied users,
etc.
By The AI
Conference
2025
🔗 September 17
-18, 2025
Conclusion
• The rise of agentic swarm coding points to a future beyond single assistants, with collaborative multi-agent systems poised to transform software
development workflows.
• Legislative moves such as California’s SB 243 highlight a shift from debate to concrete governance, particularly around protecting vulnerable populations
in human–AI interaction.
• Industry-led initiatives like the Real Simple Licensing (RSL) protocol suggest a path toward sustainable data-sharing practices, potentially reducing the risk
of prolonged copyright battles.
• The introduction of an AI-specific “Kill Chain” underscores cybersecurity’s recognition that traditional frameworks are insufficient for AI-era vulnerabilities
such as data poisoning and model manipulation.

• Research findings, including DeepMind’s vector search bottleneck, mark critical reality checks that push the field toward hybrid architectures and more
resilient retrieval methods.
• Technical innovations such as Symbolic JAX and LoCoBench reflect a drive toward interpretable science-focused models and more rigorous evaluation of
long-context systems.
• Broader systemic concerns also came to the forefront: escalating energy and water demands highlight the urgent need for sustainable AI practices.
• Finally, government-industry partnerships—exemplified by Anthropic’s collaboration with US and UK safety institutes—along with Arm’s decentralized
Lumex chips, illustrate parallel trends of centralized oversight and localized deployment shaping AI’s global trajectory.

NewMind AI Weekly Chronicles – September ’25 Week II

More Related Content

Similar to NewMind AI Weekly Chronicles – September ’25 Week II

Recently uploaded

NewMind AI Weekly Chronicles – September ’25 Week II