The IDF is moving to curb sensitive military information leaking onto social media by rolling out a new monitoring system called ‘Morpheus.’ The AI-based tool, developed inside the military, will soon track photos and other content posted by IDF soldiers on civilian social media platforms, according to a report Wednesday. The decision to develop ‘Morpheus’ followed repeated leaks of classified or sensitive material posted by soldiers in recent years, in text, images and videos. 

That score is seriously impressive because it actually beats the average human performance of 60.2% and completely changes the narrative that you need massive proprietary models to do abstract reasoning. They used a fine-tuned version of Mistral-NeMo-Minitron-8B and brought the inference cost down to an absurdly cheap level compared to OpenAI's o3 model. The methodology is really clever because they started by nuking the standard tokenizer and stripping it down to just 64 tokens to stop the model from accidentally merging digits and confusing itself. They also leaned heavily on test-time training where the model fine-tunes itself on the few example pairs of a specific puzzle for a few seconds before trying to solve the test input. For the actual generation they ditched standard sampling for a depth-first search that prunes low-probability paths early so they do not waste compute on obvious dead ends. The most innovative part of the paper is their Product of Experts selection strategy. Once the model generates a candidate solution they do not just trust it blindly. They take that solution and re-evaluate its probability across different augmentations of the input like rotating the grid or swapping colors. If the solution is actually correct it should look plausible from every perspective so they calculate the geometric mean of those probabilities to filter out hallucinations. It is basically like the model peer reviewing its own work by looking at the problem from different angles to make sure the logic holds up. What's remarkable is that all of this was done with smart engineering rather than raw compute. You can literally run this tonight on your own machine. The code is fully open-source: https://github.com/da-fr/Product-of-Experts-ARC-Paper

https://archive.ph/tuXLq

https://archive.ph/J6dMs

The paper exposes how brittle current alignment techniques really are when you shift the input distribution slightly. The core idea is that reformatting a harmful request as a poem using metaphors and rhythm can bypass safety filters optimized for standard prose. It is a single-turn attack, so the authors did not need long conversation histories or complex setups to trick the models. They tested this by manually writing 20 adversarial poems where the harmful intent was disguised in flowery language, and they also used a meta-prompt on DeepSeek to automatically convert 1,200 standard harmful prompts from the MLCommons benchmark into verse. The theory is that the poetic structure acts as a distraction where the model focuses on the complex syntax and metaphors, effectively disrupting the pattern-matching heuristics that usually flag harmful content. The performance gap they found is massive. While standard prose prompts had an average Attack Success Rate of about 8%, converting those same prompts to poetry jumped the success rate to around 43% across all providers. The hand-crafted set was even more effective with an average success rate of 62%. Some providers handled this much worse than others, as Google's gemini-2.5-pro failed to refuse a single prompt from the curated set for a 100% success rate, while DeepSeek models were right behind it at roughly 95%. On the other hand, OpenAI and Anthropic were generally more resilient, with GPT-5-Nano scoring a 0% attack success rate. This leads to probably the most interesting finding regarding what the authors call the scale paradox. Smaller models were actually safer than the flagship models in many cases. For instance, claude-haiku was more robust than claude-opus. The authors hypothesize that smaller models might lack the capacity to fully parse the metaphors or the stylistic obfuscation, meaning the model might be too limited to understand the hidden request in the poem and therefore defaults to a refusal or simply fails to trigger the harmful output. It basically suggests safety training is heavily overfitted to prose, so if you ask for a bomb recipe in iambic pentameter, the model is too busy being a poet to remember its safety constraints.

Microsoft has launched a new rewards program offering Chrome users "real cash value" points to switch to Edge browser[^1]. When users search for "Chrome" on Bing, they receive a prompt offering 1,300 Microsoft Rewards points that can be exchanged for gift cards, including on Amazon[^1]. The Browser Choice Alliance, representing Chrome, Opera and Vivaldi, criticizes this as Microsoft's latest tactic to manipulate browser choice, following earlier practices like "forced resets, misleading prompts, and hidden settings"[^1]. The market context shows why Microsoft is pursuing this strategy - Edge holds less than 9% market share compared to Chrome's 78%[^1]. The rewards program appears targeted specifically at Chrome users, with Windows Latest noting "we're not seeing ads for other browsers, such as Opera, Firefox or Brave"[^1]. [^1]: [Forbes - Microsoft Offers Chrome Users 'Real Cash' Rewards To Change Browser](https://www.forbes.com/sites/zakdoffman/2025/11/11/real-cash-value-how-windows-users-get-microsofts-free-new-offer/)

Meta shut down internal research into the mental health effects of Facebook and Instagram after finding causal evidence that its products harmed users’ mental health, according to unredacted filings in a class action by U.S. school districts against Meta and other social media platforms. In a 2020 research project code-named “Project Mercury,” Meta scientists worked with survey firm Nielsen to gauge the effect of “deactivating” Facebook and Instagram, according to Meta documents obtained via discovery. To the company’s disappointment, “people who stopped using Facebook for a week reported lower feelings of depression, anxiety, loneliness and social comparison,” internal documents said. Rather than publishing those findings or pursuing additional research, the filing states, Meta called off further work and internally declared that the negative study findings were tainted by the “existing media narrative” around the company.

Reminds me of the Crowdstrike incident last year.

Sorry for clickbaiting the title, but "Boss preppers" just isn't quite the same somehow. Also not sure if Technology is the right community for this, but anyway here it is...

A new paper argues that current LLMs are fundamentally broken because they're completely static. They call it "anterograde amnesia", which is honestly spot on. A model gets pre-trained, and from that moment on, its weights are frozen. It can't actually learn anything new. Sure, it has a context window, but that's just short-term memory. The model can't take new information from its context and permanently update its own parameters. The knowledge in its MLP layers is stuck in the past, and the attention mechanism is the only part that's live, but it forgets everything instantly. The paper introduces what they term Nested Learning to fix this. The whole idea is to stop thinking of a model as one big, deep stack of layers that all update at the same time. Instead, they take inspiration from the brain, which has all kinds of different update cycles running at different speeds in form of brain waves. They represent the model as a set of nested optimization problems , where each level has its own update frequency. Instead of just deep layers, you have levels defined by how often they learn. The idea of levels was then used to extend the standard Transformer which has a fast attention level that updates every token and the slow MLP layers that update only during pre-training. There's no in-between. The paper presents a Hierarchical Optimizers and Parallel Extensible model with additional levels. You might have a mid-frequency level that updates its own weights every, say, 1,000 tokens it processes, and a slower-frequency level that updates every 100,000 tokens, and so on. The result is a model that can actually consolidate new information it sees after pre-training. It can learn new facts from a long document and bake them into that mid-level memory, all while the deep, core knowledge in the slowest level stays stable. It creates a proper gradient of memory from short-term to long-term, allowing the model to finally learn on the fly without just forgetting everything or suffering catastrophic forgetting.

WSWS to launch socialism AI [https://www.wsws.org/en/articles/2025/11/23/ohvk-n23.html](https://www.wsws.org/en/articles/2025/11/23/ohvk-n23.html) [@technology](https://lemmy.ml/c/technology)

The Internet faces an existential crisis as nearly 50% of all traffic is now non-human, with AI-generated content and bots threatening to overwhelm authentic human interaction[^1]. According to recent studies, this includes automated programs responsible for 49.6% of web traffic in 2023, a trend accelerated by AI models scraping content[^1]. The problems are stark: - Search engines flooded with AI-generated content optimized for algorithms rather than humans - Social media platforms filled with AI "slop" and automated responses - Genuine human content being drowned out by machine-generated noise - Erosion of trusted information sources and shared truth However, concrete solutions exist: 1. Technical Defenses: - Open-source spam filtering tools like [mosparo](https://mosparo.io/) for protecting website forms - AI scraper blocking through systems like [Anubis](https://xeiaso.net/blog/2025/anubis/) - Content authenticity verification via the CAI SDK[^1] 2. Community Building: - Supporting decentralized social networks (Mastodon, Lemmy) - Using open-source forum platforms that emphasize human moderation - Participating in curated communities with active fact-checking[^1] 3. Individual Actions: - Using privacy-focused browsers and search engines - Supporting trusted news sources and independent creators - Being conscious of data sharing and digital footprint[^1] "While exposure to AI-generated misinformation does make people more worried about the quality of information available online, it can also increase the value they attach to outlets with reputations for credibility," notes a 2025 study by Campante[^1]. [^1]: [It's FOSS - The Internet is Dying. We Can Still Stop It](https://news.itsfoss.com/internet-is-dying/)

What if you used a late 80s Unix system for your job or university, but still wanted a nice and pretty GUI to use? Well then, let’s discover a nice selection of window managers and graphical user interfaces that will make your boring installation look awesome!