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Alexander Loth

@alexanderloth.bsky.social
71 followers 48 following 223 posts

Researcher exploring how generative AI reshapes disinformation & public trust. Building JudgeGPT · Author of books on data visualization & AI · iOS dev (Trackless Links, Mindful Coffee) 🌐 alexloth.com · 🔬 github.com/aloth · 📚 alexloth.com/books

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Alexander Loth @alexanderloth.bsky.social · 03/10/2026
New research suggests LLM math performance hides a deeper issue: the main bottleneck isn’t execution, it’s discovery. Once given the right mathematical primitives, models solve far more problems than expected. A useful shift in how we evaluate reasoning. arxiv.org/abs/2610.02191v1
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Alexander Loth @alexanderloth.bsky.social · 23/09/2026
Flash-dLLM speeds up diffusion LLM inference by tackling a hidden bottleneck: GPU memory I/O. With fused KV-cache kernels and self draft-and-verify decoding, it reaches 11× speedup on HumanEval over prior methods. arxiv.org/abs/2609.26796v1
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Alexander Loth @alexanderloth.bsky.social · 18/09/2026
New paper on AI coding agents: models failed to read all assigned files in 67.9% of runs, and when reviews were incomplete, they were misleading 80.4% of the time. The benchmark measures whether agents’ reports match what they actually did. arxiv.org/abs/2609.20812v1
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Alexander Loth @alexanderloth.bsky.social · 13/09/2026
RogueGPT is published in the Journal of Open Source Software. Open-source framework for controlled, reproducible news stimuli in misinformation research. 3,278 multilingual fragments, 10 models, 6 providers. doi.org/10.21105/joss.11219
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Alexander Loth @alexanderloth.bsky.social · 08/09/2026
CUA-Universe trains computer-use agents to combine GUI interaction with CLI execution over shared app state. The result: higher success rates with far fewer steps and tokens across OSWorld benchmarks. Hybrid orchestration may be the next step for practical agents. arxiv.org/abs/2609.05374v1
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Alexander Loth @alexanderloth.bsky.social · 03/09/2026
A new coding-focused LLM system surpassed the top human score on an IOI problem set under real contest constraints. The key insight: post-training + iterative test-time refinement mattered more than scale alone. arxiv.org/abs/2609.02849v1
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Alexander Loth @alexanderloth.bsky.social · 01/09/2026
CRED-1 tracks 2,674 domains with credibility scores: 2,001 unreliable, 233 fake, 199 mixed, 120 conspiracy, 108 satire. 32 domains rescored in August across four weekly releases. github.com/aloth/cred-1
CRED-1 domain credibility dataset banner: a shield with a checkmark next to the title CRED-1, Domain Credibility Dataset, with a network graph on a dark blue background.
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Alexander Loth @alexanderloth.bsky.social · 29/08/2026
CLAP trains video world models across humans and multiple robot embodiments, turning heterogeneous internet-scale videos into zero-shot physical simulators. Cross-embodiment learning may be a key step toward scalable robotic foundation models. arxiv.org/abs/2608.27406v1
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Alexander Loth @alexanderloth.bsky.social · 29/08/2026
Most fake-news research still relies on tiny, static datasets. RogueGPT is open-source and generates fully parameterized synthetic news across models, languages, and styles with provenance built in. What would you test or build with it? github.com/aloth/RogueGPT
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Alexander Loth @alexanderloth.bsky.social · 25/08/2026
CRED-1: 2,674 domains scored for credibility, 50 of them rescored this past month across 5 weekly releases. github.com/aloth/cred-1
CRED-1 domain credibility dataset banner: a shield with a checkmark, the title CRED-1, subtitle Domain Credibility Dataset, and a network graph on a dark background.
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Alexander Loth @alexanderloth.bsky.social · 24/08/2026
CLEAR tackles a core LLM alignment problem: improving safety without hurting utility. By dynamically routing a safety adapter based on hidden states, it cuts HarmBench ASR from 32.3% to 0.5% while preserving downstream performance. arxiv.org/abs/2608.21278v1
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Alexander Loth @alexanderloth.bsky.social · 19/08/2026
“Chain-of-Experience” shows LLMs can improve during inference by learning from iterative feedback. Across GPT-5, Gemini-2.5 Pro, and Claude-4.5 Sonnet, this boosts performance by 5.6% while cutting API cost by 19%. arxiv.org/abs/2608.18027v1
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Alexander Loth @alexanderloth.bsky.social · 18/08/2026
CRED-1 now tracks 2,674 domains with credibility scores: 2,001 unreliable, 233 fake, 120 conspiracy, 108 satire. 19 domains rescored in August. github.com/aloth/cred-1
CRED-1 domain credibility dataset banner: a glowing shield with a checkmark beside the title CRED-1, Domain Credibility Dataset, on a dark background with a network graph motif.
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Alexander Loth @alexanderloth.bsky.social · 14/08/2026
New result: VC classes are adversarially robustly learnable with sample complexity linear in the VC dimension - an exponential improvement over prior bounds. The key ingredient? Classic bagging combined with robust ERM. arxiv.org/abs/2608.13514v1
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Alexander Loth @alexanderloth.bsky.social · 13/08/2026
Most “AI detection” tools ask you to trust a black box while fighting misinformation. I open-sourced an iPhone app that verifies C2PA signatures, edit history, EXIF data, and AI markers directly on-device. Should tools that verify truth be fully auditable? arxiv.org/abs/2602.03423
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Alexander Loth @alexanderloth.bsky.social · 11/08/2026
CRED-1 tracks 2,674 domains with credibility scores. 46 rescored across the last 5 weekly releases. TypeScript library, CLI, and MCP server so agents can check a source before trusting it. github.com/aloth/cred-1
CRED-1 domain credibility dataset banner: a shield with a checkmark next to the title CRED-1, Domain Credibility Dataset, on a dark background with a network graph.
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Alexander Loth @alexanderloth.bsky.social · 09/08/2026
Programmatic tool calling may outperform JSON tool calling for AI agents. Across 14 models on BFCL v4, typed Python-based tool use matched or beat JSON in most cases, stayed stronger under parallel tasks, and degraded less with long context. arxiv.org/abs/2608.06370v1
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Alexander Loth @alexanderloth.bsky.social · 07/08/2026
2,438 human judgments later, AI-generated news is fooling more people than most expect. JudgeGPT lets you test yourself against real + AI-written news used in our “Industrialized Deception” research pipeline. What’s the one cue that makes you trust a news story? github.com/aloth/JudgeGPT
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Alexander Loth @alexanderloth.bsky.social · 04/08/2026
Diffusion models may be learning from the structure of pseudorandom number generators themselves. This paper shows that different finite-precision random streams can measurably change training loss and image quality - even with matched statistics. arxiv.org/abs/2608.02575v1
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Alexander Loth @alexanderloth.bsky.social · 04/08/2026
CRED-1 now tracks 2,674 domains for mis/disinformation credibility: 2,001 unreliable, 233 fake, 120 conspiracy, 108 satire. Updated weekly, open license. Open source TypeScript library, CLI, and MCP server so AI agents can check domain trust in real time 🛡️ github.com/aloth/cred-1
CRED-1 domain credibility dataset banner showing tracked domain count and credibility tiers.
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Alexander Loth @alexanderloth.bsky.social · 03/08/2026
The obvious architecture was the wrong one. A server the extension queries on every page load is trivial. It also means a privacy tool ships your browsing history somewhere. So the dataset went on the device instead. alexloth.com/cred-1-research-datase…
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Alexander Loth @alexanderloth.bsky.social · 31/07/2026
Mac & i hat Trackless Links getestet (Heft 4/2026): „Praktisch, um digitale Spuren zu verwischen und lästige Einschränkungen von Websites zu umgehen." www.heise.de/select/mac-and-i/2026/…
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Alexander Loth @alexanderloth.bsky.social · 30/07/2026
Open retrieval research gets a major boost: DenseOn and LateOn achieve state-of-the-art BEIR results for their size using fully open data and training pipelines. The surprise: late-interaction models generalize better to unseen languages and scripts. arxiv.org/abs/2607.27178v1
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Alexander Loth @alexanderloth.bsky.social · 28/07/2026
📊 CRED-1 Monthly Update — July 2026 2,674 domains tracked for credibility. 18 rescored this month, +1 new (epochtimes.de). 74.8% unreliable, 8.7% fake, 7.4% mixed, 4.5% conspiracy, 4% satire, 0.5% reliable. Open dataset, CC-BY-4.0: github.com/aloth/cred-1
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Alexander Loth @alexanderloth.bsky.social · 23/07/2026
Most “AI detectors” ask you to trust a black box while fighting misinformation. So I open-sourced an iPhone tool that verifies C2PA signatures, edit history, EXIF, and AI markers directly on-device. Should authenticity tools be fully auditable? apps.apple.com/us/app/origin-lens/i…
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Alexander Loth @alexanderloth.bsky.social · 21/07/2026
📊 CRED-1 monthly update: 2,674 domains tracked (+1 this month). New addition: epochtimes.de (mixed, 0.49). No rescoring needed - dataset stable. Open credibility data for news domains, CC-BY-4.0. github.com/aloth/cred-1
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Alexander Loth @alexanderloth.bsky.social · 15/07/2026
New research identifies a “seriality gap” in video diffusion models: performance collapses on long chains of dependent physical events, and more denoising steps don’t fix it. The issue may be structural, not just scaling-related. arxiv.org/abs/2607.13031v1
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Alexander Loth @alexanderloth.bsky.social · 14/07/2026
📊 CRED-1 Monthly Update (Jul 2026) • 2,674 domains tracked • 33 domains rescored this month • 1 new addition: epochtimes.de • Now on Homebrew & GitHub Sponsors Open dataset for web credibility scoring — check any news source instantly. github.com/aloth/cred-1
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Alexander Loth @alexanderloth.bsky.social · 10/07/2026
A diffusion model can have near-perfect score-matching error and still produce numerically unstable samples. This paper shows weak convergence may hold while every Wasserstein distance diverges - and proposes a simple projection fix for compact supports. arxiv.org/abs/2607.08757v1
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Alexander Loth @alexanderloth.bsky.social · 09/07/2026
We still “verify” viral images by zooming into pixels and reading comments. Origin Lens checks cryptographic C2PA signatures, edit history, EXIF, and AI markers directly on your iPhone instead. Would you trust human intuition or signed provenance more? github.com/aloth/origin-lens
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Alexander Loth @alexanderloth.bsky.social · 07/07/2026
📊 CRED-1 June update: 2,674 domains tracked (+1 new: epochtimes.de), 5 weekly rescoring cycles, and a new MCP server for AI agent integration. CLI: npm i -g @aloth/cred1 github.com/aloth/cred-1
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Alexander Loth @alexanderloth.bsky.social · 05/07/2026
PAW proposes a new paradigm: compile natural-language specs into small reusable neural programs instead of prompting large LLMs per request. A 0.6B local interpreter matches Qwen3-32B-level results at ~1/50th the memory footprint. arxiv.org/abs/2607.02512v1
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Alexander Loth @alexanderloth.bsky.social · 03/07/2026
One of the weirdest findings from JudgeGPT: people were often more confident when they were completely wrong about AI-written news. This figure helped us see how “authenticity” and “human-written” get confused in practice. What makes a story feel real to you? arxiv.org/abs/2601.22871
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Alexander Loth @alexanderloth.bsky.social · 02/07/2026
A fake war photo spread across millions of feeds before journalists proved it was AI-generated. This app checks C2PA credentials, edit history, EXIF data, and AI markers directly on your iPhone in seconds. Should every viral image come with proof of origin? arxiv.org/abs/2602.03423
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Alexander Loth @alexanderloth.bsky.social · 30/06/2026
📊 CRED-1 June Update • 2,673 domains tracked • 12 domains rescored • NEW: MCP server for AI agents • NEW: npm CLI @aloth/cred1 Check any source: npm i -g @aloth/cred1 github.com/aloth/cred-1 #MediaLiteracy #Misinformation #OpenData
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Alexander Loth @alexanderloth.bsky.social · 27/06/2026
Most fake news studies can’t reproduce their own stimuli. An open-source pipeline generates synthetic news with strict controls across models, languages, and styles, logging every parameter so anyone can extend it. What would you add first? arxiv.org/abs/2601.22871
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Alexander Loth @alexanderloth.bsky.social · 26/06/2026
We’re entering a world where reading the news no longer tells you who wrote it. This platform measures how well humans can spot AI-written news and how that gap quietly erodes trust. If people can’t tell, what happens to truth when machines write the headlines? judgegpt.streamlit.app
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Alexander Loth @alexanderloth.bsky.social · 25/06/2026
Models can learn a rule, generalize it - and later forget it mid-pretraining, with no loss signal. This “natural ungrokking” shows rule survival is decided by corpus support frequency, and forgetting is asymmetric and hard to reverse. arxiv.org/abs/2606.26050v1
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Alexander Loth @alexanderloth.bsky.social · 25/06/2026
Remember the viral photo of an explosion near the Pentagon that briefly moved markets? Origin Lens could’ve flagged it fast by showing no valid C2PA credentials and signs of AI manipulation on-device. Would you trust an image before checking its provenance? arxiv.org/abs/2602.03423
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Alexander Loth @alexanderloth.bsky.social · 23/06/2026
📊 CRED-1 June 2026 Update 2,673 domains tracked • 2 weekly refreshes • Now on npm! 🆕 Published @aloth/cred1 (CLI + library) 🆕 MCP server with 5 AI credibility tools 🆕 OIDC trusted publishing ⭐ 12 GitHub stars | ACM WebSci 2026 → github.com/aloth/cred-1
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Alexander Loth @alexanderloth.bsky.social · 20/06/2026
LiveCodeBench reshaped code evaluation - but mostly for Python. Multi-LCB extends it to 12 languages and finds clear Python overfitting and big multilingual gaps across 24 LLMs. A tougher, more realistic test for code models. arxiv.org/abs/2606.20517v1
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Alexander Loth @alexanderloth.bsky.social · 20/06/2026
Different LLMs don’t just write differently, they erode trust at different rates. RogueGPT let us generate the same news across models and compare how “truth-default” breaks in humans. Which model would you trust if you didn’t know the source? github.com/aloth/RogueGPT
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Alexander Loth @alexanderloth.bsky.social · 19/06/2026
Reading the news is quietly turning into a trust test. This platform measures how well people can spot AI-written vs real news, at scale. If we can’t tell the difference, what happens to trust in an AI-shaped society? github.com/aloth/JudgeGPT
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Alexander Loth @alexanderloth.bsky.social · 16/06/2026
📊 CRED-1 June Update 19 domains rescored across 3 weekly runs. 2,673 domains tracked. New this month: npm package @aloth/cred1 (CLI + library + MCP server) now live! 🔗 github.com/aloth/cred-1 📦 npmjs.com/package/@aloth/cred1 #FactChecking #Misinformation #OpenData
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Alexander Loth @alexanderloth.bsky.social · 15/06/2026
VLMs have “gaze heads”: a small set of attention heads that track the image region being described. Redirect them, and you redirect the description - no retraining. A clean example of mechanistic insight enabling control. arxiv.org/abs/2606.14703v1
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Alexander Loth @alexanderloth.bsky.social · 13/06/2026
🚀 Just submitted RogueGPT to JOSS for peer review! A Python framework for controlled generation of AI news stimuli across 15+ LLMs, 6 languages & multiple journalistic styles. 🔬 2,300+ fragments 📦 CLI + Streamlit + MCP server github.com/aloth/RogueGPT
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Alexander Loth @alexanderloth.bsky.social · 13/06/2026
Fake news research breaks when one model sets the tone. A single pipeline runs GPT, Llama, and Mistral side by side, generating controlled news with identical prompts. If models disagree this much, which one do you trust? arxiv.org/abs/2601.22871
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Alexander Loth @alexanderloth.bsky.social · 13/06/2026
Just shipped @aloth/cred1 on npm — check any news source's credibility from your terminal: npx @aloth/cred1 check infowars.com 2,672 domains, weekly updates, open source. Powers Trackless Links. github.com/aloth/cred-1
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Alexander Loth @alexanderloth.bsky.social · 11/06/2026
That viral photo everyone shared last year? It had no proof of origin. An on-device check could’ve flagged no C2PA signature, broken edit history, and likely AI or reuse. Would you have shared it if your phone showed that first? github.com/aloth/origin-lens
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Alexander Loth @alexanderloth.bsky.social · 10/06/2026
Most steering methods act on features that detect behavior after it appears. This paper shows that predicting future behavior from intermediate reasoning is far more effective - and enables steering with almost no quality loss. arxiv.org/abs/2606.11172v1
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