Give children and LLMs the exact same mystery-solving task — how does their reasoning differ? 🧒 A study that puts human and AI inference side by side, fairly. Title: Hypothesis Generation and Inductive Inference in Children and Language Models URL: 🧒 Overview This study has both children and LLM agents solve a task of inferring hidden causes under uncertainty, then carefully compares them. It examines how closely humans and AI align — and where they diverge — in generating hypotheses and reasoning inductively. ❓ Challenges Solved Humans, especially children, build mental models quickly from sparse cues. ・It was unclear whether the computational principles behind human reasoning under uncertainty also appear in LLMs placed under matched constraints ・There wasn't even a fair framework for putting children and AI side by side This work takes that question head-on. 💡 Methodology & Proposed Approach The researchers designed an inductive-inference "Box Task" for inferring hidden causes. ・Sequential environment interaction: discover latent causes by acting on the environment ・Modeled with Bayesian particle-based inference ・Systematic manipulation of evidence reliability and observability ・Measures both task completion and rule generalization Analysis uses two complementary frameworks: constraint satisfaction over hypotheses and program synthesis evaluation. 🌍 Use Cases / Experimental Results The similarities and differences between humans and AI came through sharply. ・Both groups discounted unreliable evidence and sought more information to partially resolve uncertainty ・Both showed a dissociation between task completion and causal generalization (solving a task doesn't guarantee generalizing the rule) ・LLM agents over-observe and over-comply with instructions relative to children ・Despite similar environmental adaptation, they had distinct information-seeking costs and inductive biases This offers insight into cognition and a guide to where LLM agents differ from humans by design. #CognitiveScience# #LLMAgents#

Wow, this tweet went very viral! I wanted share a possibly slightly improved version of the tweet in an "idea file". The idea of the idea file is that in this era of LLM agents, there is less of a point/need of sharing the specific code/app, you just share the idea, then the other person's agent customizes & builds it for your specific needs. So here's the idea in a gist format: You can give this to your agent and it can build you your own LLM wiki and guide you on how to use it etc. It's intentionally kept a little bit abstract/vague because there are so many directions to take this in. And ofc, people can adjust the idea or contribute their own in the Discussion which is cool.

🌐 The key to building strong AI agents may actually be designing the environments they operate in. This 63-page survey systematizes the view of "environment engineering." Title: Agentic Environment Engineering for Large Language Models: A Survey of Environment Modeling, Synthesis, Evaluation, and Application URL: 📝 Overview LLM agents don't act alone; they operate inside interactive environments. This survey organizes the research landscape through the lens of "environment engineering," the engineering design and construction of those environments themselves. ❓ Challenges Solved Until now, how to build environments was discussed only in fragments. Even though agent capability depends heavily on good environment design, there was no unified framework to organize it. 💡 Methodology & Proposed Approach It classifies environments along the development lifecycle in four pillars. ・Environment modeling: characterizing representative environments and assessing core capabilities ・Environment synthesis: two paradigms, symbolic and neural ・Environment evaluation: domain-specific assessment aligned with the synthesis paradigms ・Environment application: agent-environment co-evolution across four pathways, memory-centric, orchestration-centric, trajectory-centric, and exploration-centric 🎯 Use Cases It helps agent researchers locate their own work on a map and spot missing perspectives, and serves as a starting point when designing environment synthesis, evaluation, and self-evolution. 📊 Trends and Outlook ・It organizes evolution approaches into three families: neural-driven, difficulty-driven, and scaling-driven ・It analyzes across eight attributes and eight application domains ・It points to Environment-as-a-Service, multi-agent systems, and neural-symbolic integration as future directions #AIAgents# #LLM#