Tootfinder

How to tell a vibe coder of lying when they say they check their code.
People who will admit to using LLMs to write code will usually claim that they "carefully check" the output since we all know that LLM code has a lot of errors in it. This is insufficient to address several problems that LLMs cause, including labor issues, digital commons stress/pollution, license violation, and environmental issues, but at least it's they are checking their code carefully we shouldn't assume that it's any worse quality-wise than human-authored code, right?
Well, from principles alone we can expect it to be worse, since checking code the AI wrote is a much more boring task than writing code yourself, so anyone who has ever studied human-computer interaction even a little bit can predict people will quickly slack off, stating to trust the AI way too much, because it's less work. I'm a different domain, the journalist who published an entire "summer reading list" full of nonexistent titles is a great example of this. I'm sure he also intended to carefully check the AI output, but then got lazy. Clearly he did not have a good grasp of the likely failure modes of the tool he was using.
But for vibe coders, there's one easy tell we can look for, at least in some cases: coding in Python without type hints. To be clear, this doesn't apply to novice coders, who might not be aware that type hints are an option. But any serious Python software engineer, whether they used type hints before or not, would know that they're an option. And if you know they're an option, you also know they're an excellent tool for catching code defects, with a very low effort:reward ratio, especially if we assume an LLM generates them. Of the cases where adding types requires any thought at all, 95% of them offer chances to improve your code design and make it more robust. Knowing about but not using type hints in Python is a great sign that you don't care very much about code quality. That's totally fine in many cases: I've got a few demos or jam games in Python with no type hints, and it's okay that they're buggy. I was never going to debug them to a polished level anyways. But if we're talking about a vibe coder who claims that they're taking extra care to check for the (frequent) LLM-induced errors, that's not the situation.
Note that this shouldn't be read as an endorsement of vibe coding for demos or other rough-is-acceptable code: the other ethical issues I skipped past at the start still make it unethical to use in all but a few cases (for example, I have my students use it for a single assignment so they can see for themselves how it's not all it's cracked up to be, and even then they have an option to observe a pre-recorded prompt session instead).

FakeHunter: Multimodal Step-by-Step Reasoning for Explainable Video Forensics
Chen Chen, Runze Li, Zejun Zhang, Pukun Zhao, Fanqing Zhou, Longxiang Wang, Haojian Huang
https://arxiv.org/abs/2508.14581 …

FakeHunter: Multimodal Step-by-Step Reasoning for Explainable Video Forensics
FakeHunter is a multimodal deepfake detection framework that combines memory-guided retrieval, chain-of-thought (Observation-Thought-Action) reasoning, and tool-augmented verification to provide accurate and interpretable video forensics. FakeHunter encodes visual content using CLIP and audio using CLAP, generating joint audio-visual embeddings that retrieve semantically similar real exemplars from a FAISS-indexed memory bank for contextual grounding. Guided by the retrieved context, the system…

Critical Design Strategy: a Method for Heuristically Evaluating Visualisation Designs
Jonathan C. Roberts, Hanan Alnjar, Aron E. Owen, Panagiotis D. Ritsos
https://arxiv.org/abs/2508.05325

Critical Design Strategy: a Method for Heuristically Evaluating Visualisation Designs
We present the Critical Design Strategy (CDS) - a structured method designed to facilitate the examination of visualisation designs through reflection and critical thought. The CDS helps designers think critically and make informed improvements using heuristic evaluation. When developing a visual tool or pioneering a novel visualisation approach, identifying areas for enhancement can be challenging. Critical thinking is particularly crucial for visualisation designers and tool developers, espec…

Large language models can learn and generalize steganographic chain-of-thought under process supervision
Joey Skaf, Luis Ibanez-Lissen, Robert McCarthy, Connor Watts, Vasil Georgiv, Hannes Whittingham, Lorena Gonzalez-Manzano, David Lindner, Cameron Tice, Edward James Young, Puria Radmard
https://arxiv.org/abs/2506.01926

Large language models can learn and generalize steganographic chain-of-thought under process supervision
Chain-of-thought (CoT) reasoning not only enhances large language model performance but also provides critical insights into decision-making processes, marking it as a useful tool for monitoring model intent and planning. By proactively preventing models from acting on CoT indicating misaligned or harmful intent, CoT monitoring can be used to reduce risks associated with deploying models. However, developers may be incentivized to train away the appearance of harmful intent from CoT traces, by …

This https://arxiv.org/abs/2505.17115 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csMA_…

Swarm Intelligence Enhanced Reasoning: A Density-Driven Framework for LLM-Based Multi-Agent Optimization
Recently, many approaches, such as Chain-of-Thought (CoT) prompting and Multi-Agent Debate (MAD), have been proposed to further enrich Large Language Models' (LLMs) complex problem-solving capacities in reasoning scenarios. However, these methods may fail to solve complex problems due to the lack of ability to find optimal solutions. Swarm Intelligence has been serving as a powerful tool for finding optima in the field of traditional optimization problems. To this end, we propose integrating sw…