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Qwen-Scope paper is an interesting shift in how we handle mechanistic interpretability. The core idea here is moving sparse autoencoders from being just a post-hoc inspection tool to an actual interface for building and fixing language models. The team open-sourced 14 groups of SAEs for Qwen3 and Qwen3.5 architectures and demonstrated four practical ways to use them directly in the development pipeline.

First up is inference steering. Instead of just looking at what features activate when a model messes up, you can actively suppress or amplify those latent features to fix the output on the fly without updating any model weights. They showed an example where suppressing a specific Chinese language feature stopped the model from randomly mixing languages during an English prompt. They also proved you can trigger a classical literary style transfer just by turning on the right feature direction.

The evaluation finding is probably the most immediately useful for saving compute. They found that tracking the footprint of SAE features activated by a benchmark gives you a highly accurate proxy for dataset redundancy. If a bunch of reasoning problems activate the exact same micro-capability features, you can just sample a tiny subset of the benchmark and still get the exact same model ranking. Measuring feature overlap is also a reliable way to figure out if two different benchmarks are actually just testing the exact same capabilities before you waste time running full evaluations.

On the data curation side they proved you do not even need to train a classification head for things like toxicity. A simple logical rule over a few toxic-biased SAE features acts as a classifier and achieves an F1 score above 0.90. These toxic features discovered in English actually transfer quite well to other European languages. They also used this representation-level view for synthetic data generation by identifying safety features that were missing from the training distribution and prompting the model to generate examples that specifically trigger those missing internal directions.

Finally they integrated these latent features directly into supervised fine-tuning and reinforcement learning. In the fine-tuning stage they added an auxiliary loss to suppress language-specific features which heavily reduced unexpected code-switching. For reinforcement learning they intentionally amplified repetition features to force the policy model to generate endless loops. This gives the RL pipeline rare negative samples that are otherwise incredibly hard to encounter naturally and provides an explicit training signal against repetitive loops.

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So Jerboa won't work on certified Android devices anymore or rather only on lineageos or grapheneos. Due to various reasons I can't use those OS, is there another way of continuing to use Jerboa or is my best option to access Lemmygrad through the browser?

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New technology developed by Chinese scientists achieves higher energy efficiency than burning while eliminating carbon dioxide emissions

Chinese scientists have developed a way to generate electricity and achieve higher energy efficiency than conventional burning methods, while producing zero carbon dioxide emissions, by placing coal inside a “battery”.

“Coal-fired power” conjures images of heavy pollution, steep carbon footprints and modest efficiency. But a novel, direct coal power technology challenges that stereotype by eliminating combustion entirely and sidestepping the carbon dioxide emissions that have long defined coal use.

A team led by Xie Heping, a member of the Chinese Academy of Sciences with Shenzhen University, has for the first time built what they call a zero-carbon-emission direct coal fuel cell, or ZC-DCFC.

Full article

In this system, coal is pulverised, dried, purified and subjected to surface pre-treatment before being fed into the anode chamber of the cell.

Oxygen is supplied to the cathode, and within the cell, the fine coal powder undergoes electrochemical oxidation across an oxide membrane, yielding electricity directly – without any intermediate steam cycle or mechanical turbine.

At the anode outlet, the high-purity carbon dioxide generated by the reaction is captured in situ and catalytically converted into valuable chemical feedstocks such as synthesis gas or mineralised into compounds like sodium bicarbonate. The entire process is silent and clean.

Conventional coal power relies on burning coal to produce heat, which then boils water into steam to spin a turbine generator – a chain of conversions that remains hostage to the Carnot efficiency limit of internal combustion engines.

“This process is bound by the Carnot cycle, capping energy efficiency at around 40 per cent. In the ZC-DCFC, by avoiding the efficiency losses associated with combustion and thermal engines, it enables substantially higher theoretical efficiency,” Xie noted in his paper, which appeared in the peer-reviewed journal Energy Reviews.

Since 2018, Xie’s team has pushed the technology forward step by step, solving problems in materials, cell durability, fuel treatment and continuous coal feeding along the way.

Earlier generations of direct carbon fuel cells were plagued by low power density and short operational lifetimes. The newly developed cell, however, incorporates improvements in stack scalability, long-term stability, carbon conversion efficiency and overall system integration – areas the team has targeted in their paper.

“This concept can also be extended to deep coal seams located 2km (1.2 miles) underground,” Xie said.

Traditional mining of coal from such depths is prohibitively expensive. This technology could convert the coal to electricity on site, with only the power needing transmission to the surface. This approach could help ease pressure as shallow coal reserves gradually dwindle.

Xie’s group is also spearheading a landmark project under the National Science and Technology Major Project for Deep Earth Probe and Mineral Resources Exploration, launched in 2025.

Adapting the ZC-DCFC to withstand high temperatures, pressures and corrosive environments would enable the fuel cell to serve the deep-earth exploration initiative directly.

The research aligns squarely with China’s goal of achieving carbon neutrality by 2060. Yet expecting this laboratory-scale innovation to displace the nation’s existing coal-fired power fleet any time soon would be unrealistic.

Wei Zhijiang, a senior engineer at HBIS Group Xuansteel, said that by the end of 2025, coal power made up about 45 per cent of China’s total installed capacity but still supplied nearly 60 per cent of the nation’s electricity.

Meanwhile, half of those coal plants had been running for just 15 years – still young in industrial terms.

Pointing out the practical hurdles, Wei said moving the direct coal fuel cell from the lab to wide commercial use would take time and careful cost planning. Therefore, he believed the technology would not be cost-competitive until after 2045.

If you're interested to learn more about how the technology works in detail, here's the paper referenced in the article from the peer-reviewed journal Energy Reviews:

Towards zero-carbon-emission direct coal fuel cells for power generation

Abstract

Carbon neutrality has become an international consensus under the requirements established by the Paris Agreement. Accordingly, countries worldwide, especially developing nations, have formulated their own carbon neutrality policies. Owing to differences in regional development histories and resource endowments, as well as the intermittency of new energy, developing countries will continue to rely on coal to meet their energy demands for sustainable economic and social development in the near future. However, conventional coal-fired power generation technologies can hardly achieve low-carbon or even negative-carbon emissions. It is therefore urgent to develop novel carbon-free coal power technologies. This perspective proposes the concept of Zero-carbon-emission direct coal fuel cells (ZC-DCFC) for power generation as a disruptive technological paradigm for efficient coal utilization. The technological architecture of ZC-DCFC is discussed, including fuel supply, key materials, and in-situ CO2 conversion. The technical challenges and future development directions are also identified. ZC-DCFC is expected to open up a new pathway for near-zero-emission coal utilization, transforming coal from a traditional fossil fuel into a feasible clean energy source in the global low-carbon transition.

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At Intel (disjunctionsmag.com)
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