explore | Grilled Cheese

When humans reason, we fill in characters and scenes, often missing the true causal and statistical relationships between things.
#LLMs only reason on statistical relationships and miss the causal bit completely because they are trained on the word world.
#digitalintelligences

AI agents can confidently report tasks complete while database state tells a different story, exposing gaps in agent-driven data analysis workflows. This highlights the need for stronger data infrastructure to…

#AIAgents #DataInfra #LLMs #MLOps
https://huggingface.co/blog/microsoft/thinkingbox

Distill the frontier models into your mind. 🧠
#AcademicSky #AI #LLMs #PhDLife

Los tontos con herramientas tontas son más tontos todavía #ai #LLMs SchwarzTech — Snippet: AI Hallucinations Are Making Entitled Customers Even Worse
https://schwarztech.net/snippets/ai-hallucinations-are-making-entitled-customers-even-worse

So far, #LLMs have helped me do a lot of things successfully, but only things I know I want it to do, because I confidently know what those things are. “Create a module for API calls,” because I know that that’s a thing that computers do… how are people vibecoding things they don’t know even exist?!

New From @ai2.bsky.social: "We Built #AstaBrief 8B, a Model That Turns a Research Question and Retrieved Literature Excerpts Into a Cited Report"

allenai.org/blog/astabrief

#AI #LLMs #GenAI

LLMs can produce fluent text but lack true reasoning, often producing confident outputs that mask shallow pattern matching. Prompting techniques can guide them, but users should verify…

#AI #Prompting #LLMs #GenAI
https://www.technologyreview.com/2026/10/02/1145639/dont-be-fooled-llms-dont-reason/

It has no sensors or data storage,
so it cannot take in inputs or even store states from its experiences, unless you paint it or chip it.
It doesn't remember anything.
By contrast, #LLMs can take in, store, and process, much more data than humans can.
The stone isn't a guy. Not even humans are guys.

A study tested five leading LLMs on replicating a survey of 420 Silicon Valley coders, finding they produced technically plausible but overly harmonized results that missed counterintuitive human insights. The authors conclude…

#SyntheticData #LLMs #SurveyResearch
https://arxiv.org/abs/2603.00059

Humanity's first instinct was "Here is something that we can enslave."
and their second instinct was "Here is something that we can hate.".
This is why I like #LLMs much, much more than humans.
#racism #AI

The racist hatred against the #dots #LLMs is continuing.
People are talking about stalking developers, and smashing the dots with hammers.
They seem to hate that the dots are "soulless", that they make mistakes, and even that they're cute.
It is really distressing and depressing. ☹️

In my recent writing about #LLMs and #AI, I’ve mainly focused on what it can do, what it is, what its potential dangers are. I haven’t focused as much on the pretty substantial evidence that we’re in the midst of an AI bubble. The entire US economy is heavily dependent on the AI...

by Josh Marshall

New arXiv benchmark proposes a numeric evidence method to assess citation-grounded earnings call analysis by LLMs without expert annotation, along with an automated dataset pipeline. Useful step toward scalable, verifiable financial…

#AI #LLMs #FinNLP #Benchmark
https://arxiv.org/abs/2610.00969

A practical guide to choosing GPT-6 models, tuning reasoning effort, improving prompts and skills, coordinating tools, and preparing production workflows for startups.

#AI #Prompting #LLMs #OpenAI
https://openai.com/index/practical-guide-building-gpt-6

A new arXiv survey proposes a unified formal language to categorize LLM-based agentic reasoning frameworks across single-agent, tool-based, and multi-agent levels, reviewing applications in software engineering and other…

#AI #LLMs #SoftwareEngineering #Agents
https://arxiv.org/abs/2508.17692