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Pasquale Minervini

@neuralnoise.com

Researcher in ML/NLP at the University of Edinburgh (faculty at Informatics and EdinburghNLP), Co-Founder/CTO at www.miniml.ai, ELLIS (@ELLIS.eu) Scholar, Generative AI Lab (GAIL, https://gail.ed.ac.uk/) Fellow -- www.neuralnoise.com, he/they

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Ethan Mollick@emollick.bsky.social · 3w ago

I still believe that everyone is too fixated on the state of play in AI right now (which labs are ahead, how to manage costs, etc.) and not focused enough on the continued steepness of the capability curve for AI At higher capabilities (like the ones expected in the near term), a lot changes fast.

Mark Riedl@markriedl.bsky.social · 12mo ago

@togelius.bsky.social has thoughts on Genie 3 and games togelius.blogspot.com/2025/08/geni... Fairly close to my own, though I didn't get the preview the tech. Walking around a generated image-to-image world is not the same as playing a game. There are no game objectives.

Genie 3 and the future of neural game engines

Google DeepMind just announced Genie 3 , their new promptable world model, which is another term for neural game engine. This is a big neura...

togelius.blogspot.com

Scott McGrath@smcgrath.phd · last yr.

Anthropic research identifies “inverse scaling in test-time compute,” where longer reasoning degrades AI performance. On certain tasks, models become more distracted by irrelevant data or overfit to spurious correlations. #MLSky

Anthropic researchers discover the weird AI problem: Why thinking longer makes models dumber

Anthropic research reveals AI models perform worse with extended reasoning time, challenging industry assumptions about test-time compute scaling in enterprise deployments.

venturebeat.com

Sohee Yang@soheeyang.bsky.social · last yr.

🚨 New Paper 🚨 How effectively do reasoning models reevaluate their thought? We find that: - Models excel at identifying unhelpful thoughts but struggle to recover from them - Smaller models can be more robust - Self-reevaluation ability is far from true meta-cognitive awareness 1/N 🧵

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mr. TIM@timkellogg.me · last yr.

Inverse scaling of reasoning models a research collab demonstrated that there are certain types of tasks where all top reasoning models do WORSE the longer they think things like getting distracted by irrelevant info, spurious correlations, etc. www.arxiv.org/abs/2507.14417

Three panels at the top describe task types with example prompts: 1. Simple Counting Tasks with Distractors (Misleading Math & Python): • Prompts mention an apple and an orange, with added irrelevant or confusing information (e.g., probabilistic riddle, Python code) before asking the straightforward question: “Calculate how many fruits you have.” 2. Regression Tasks with Spurious Features (Grades Regression): • Given XML-style records about a student, the model must predict grades from features like sleep hours, social hours, and stress level. The challenge lies in identifying relevant vs. spurious attributes. 3. Deduction Tasks with Constraint Tracking (Zebra Puzzles): • Complex logical reasoning puzzle with multiple interrelated clues. Example: “What position is the person who likes salmon at?” Constraints involve foods, names, and relationships like “to the left of.” Bottom row contains 3 line plots comparing model performance across tasks: • Misleading Math (Left Plot): • Accuracy drops sharply for some models as reasoning tokens increase. Claude Sonnet 4 maintains high performance. o3 and DeepSeek R1 hold relatively stable accuracy; Qwen3 32B and QwQ 32B drop more. • Grades Regression (Middle Plot): • Shows negative RMSE (higher is better). Claude models remain strong across token counts; o3 also performs well. Qwen3 and QwQ struggle, with DeepSeek R1 performing modestly. • Zebra Puzzles (Right Plot): • Accuracy vs. average reasoning tokens. o3 and Claude Sonnet 4 maintain highest performance. Other models (e.g., DeepSeek R1, Qwen3 32B, QwQ 32B) show performance degradation or plateaus. Error bars reflect variability. Each plot uses colored lines with markers to indicate different model names.
Naomi Saphra@nsaphra.bsky.social · last yr.

Reasoning is about variable binding. It’s not about information retrieval. If a model cannot do variable binding, it is not good at grounded reasoning, and there’s evidence accruing that large scale can make LLMs worse at in-context grounded reasoning. 🧵