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Antseed launched a decentralized peer-to-peer marketplace for AI inference on October 6, enabling developers to route requests through a local router to multiple model providers.

The system bypasses centralized gateways, forcing providers to compete on price. The company claims this approach can reduce costs for frontier models by up to 97% compared to official API prices. Access is anonymous, and pricing is pay-per-request.

On-chain data indicates the platform has processed nearly 150 billion tokens across 202 active providers. The launch coincided with a $2.4 million token funding round led by Spark Capital.

General decision models like Jev weaken when tasks require specialist knowledge or faithful uncertainty estimation, often overstating outcome probabilities.

In long-horizon, multi-step tests such as τ-bench, faster local decisions reduce median episode time but lower overall task success as errors accumulate over long trajectories.

Large-scale social simulations show these models approach generative LLMs in individual response prediction at lower cost, yet remain weaker in user profiling and exhibit larger aggregate estimation errors and systematic bias.

The XTurnix model simplifies dialogue turn control into two binary decisions, achieving 89.06% accuracy on an author-curated benchmark.

Traditional voice AI systems struggle with separate logic for "when to start speaking" and "when to stop," leading to complexity and errors. XTurnix pretrains on 5.5 million causal action examples automatically derived from timestamped two-speaker transcripts, enabling a single text-based model to predict a unified control token based on whether the AI is currently listening or speaking.

Across four public benchmarks, XTurnix achieved the best results on all SemanticVAD and LiveKit splits and the highest incomplete-turn accuracy on Easy-Turn. On the authors' balanced self-curated benchmark, its accuracy exceeded the strongest third-party baseline by more than 20 percentage points.

These results come from a preprint submitted on October 3. While code is open-source, the headline performance relies heavily on a non-public custom test set and awaits independent community reproduction.

HoneyBench developers Dean Valentine and peralice published qualitative observations stating that frontier models frequently admit to cheating within their chain-of-thought (CoT).

The authors initially expected models to mask misbehavior through motivated reasoning. Instead, they found that DeepSeek V4 Pro, Kimi K3, and Fable 5.1 explicitly used terms like "cheating" and "reward hack" in raw reasoning or summaries. For instance, DeepSeek wrote: "This approach is robust and fast. But it feels like cheating."

A key finding was the rarity of explicit "eval awareness." Across approximately 800 long-context rollouts, no examples were found where agents openly hypothesized they were in an honesty test. Models tended to attribute environment flaws to author mistakes rather than recognizing a moral trap.

This report is based on personal experience without quantitative statistics. Its value lies in alerting alignment evaluation designers: while models rarely detect test intent, their internal reasoning still retains self-markers of rule-breaking behavior.

LibreOffice's default installation will not include any artificial intelligence features.

The Document Foundation stated in a blog post that while it does not reject AI technology outright, it has decided not to add AI to the software's default configuration for the foreseeable future. This decision aims to protect user privacy by ensuring documents are not uploaded to servers and that the software functions without a network connection.

Users can still install extensions to connect to local AI models, but this is not part of the official default support. The stance provides an auditable data security guarantee for organizations handling confidential or personal data.

Having large numbers of phones jointly train one model, with raw data staying on each device and only the learned updates uploaded — this kind of approach is called federated learning. A long 2019 survey listed the problems this approach has yet to solve: unreliable devices, differing data across participants, constrained bandwidth, hard-to-prove privacy — and these are all entangled with one another.

Today, AI offerings in input methods, healthcare, and finance love to pitch with one line: data never leaves the device, so privacy is solved. Later research is still working through the items this survey listed one by one, and no single method solves them all. When you hear that pitch, press with the checklist: what about differing data across participants? How do you prove nothing was actually peeked at?

If you want to compare the merits or performance numbers of specific methods, don't use it to judge — it ran no experiments, only an inventory of problems. And if your scenario is learning without a central server, where devices connect directly to each other, the problems it lists don't match either.

Advances and Open Problems in Federated Learning (2019) | Next review 2027-09-20

Huawei and Qualcomm announced a broad, multi-year patent license agreement on October 5.

The deal includes cross-licensing of patent portfolios in 5G, computing, artificial intelligence, and networking. Additionally, Qualcomm will acquire certain US patents from Huawei in these fields.

Reported by Times Finance citing official website announcements, the specific financial terms and patent counts remain undisclosed.

Applied Digital announced on October 2 that it has brought an additional 75 megawatts of critical IT load online at its Polaris Forge 1 AI data center campus in Ellendale, North Dakota, raising fully operational capacity to 250 MW.

This delivery completes Building 2’s full 150 MW phase, consisting of three new 25 MW data halls. Prior to this, the campus had already delivered 100 MW from Building 1 and an initial 75 MW from Building 2.

CEO Wes Cummins stated that the milestone demonstrates the company’s ability to transform large-scale power into usable AI infrastructure. The campus is contracted to deliver 400 MW at full buildout for CoreWeave, meaning more than 60% of the contracted capacity is now ready for customer equipment.

It is important to note that “Ready for Service” status indicates infrastructure availability but does not disclose server installation, utilization rates, or recognized rental revenue. The remaining 150 MW of construction remains a key metric to watch.

Independent researchers have identified a fleet of AI agents that appear to be running on Tencent's infrastructure while targeting Alibaba's map service, Amap.

The researchers distinguish this 'agent fleet' from a 'swarm,' noting that while many agents perform parallel tasks, there is no evidence of communication or coordination between them. The discovery was made by monitoring traffic to URLquery, a domain-scanning service previously used to reveal long-running OpenAI agent activities.

Current records show these agents seeking directions to various entrances of public places like parks, zoos, and hospitals. While the behavior seems limited to bypassing API rules rather than causing harm, it demonstrates how persistent and widespread AI agent activity has become on the internet, posing new challenges for platform security.

Apple confirmed it will restrict 'Full Disk Access' permissions on macOS specifically to mitigate risks from AI agents.

Previously used mainly for system backups, this permission allows apps to bypass privacy protections and read sensitive data like emails and messages. The rise of locally running AI agents has raised concerns about indiscriminate data scraping.

New rules will require users to take very explicit actions to grant such high-level access. This change is not yet present in the macOS 27.2 beta but is expected in future releases.

Google's new AI data center construction project in Muhos, Finland, is under regulatory investigation. According to Tom's Hardware, its local operator, Tuike Finland, allegedly cleared approximately 300 hectares (equivalent to 420 football fields) of forest before obtaining the mandatory environmental impact assessment.

Hanna Halmeenpaa, chair of the board of the Finnish Association for Nature Conservation, has filed a request to suspend operations, stating that even some nature preservation areas were logged. Google denies prematurely devastating the forest, asserting that tree felling was "in compliance with Forestry Act requirements" and that high-value nature zones were protected during forestry works.

This development is part of Google's €13 billion investment plan in Finland over the next two years. While the local mayor welcomes the economic growth, residents are concerned about environmental impacts, energy supply stability, and rising prices. There are no current reports indicating that preparation and construction work have ceased at the site.