6/6) Call to Action: Current LLMs still leave substantial headroom on IndustryBench (top score is 2.083/3). Industrial LLM evaluation must move beyond aggregate accuracy and prioritize source-grounded, safety-aware diagnosis.
1/6) Excited to share our latest work from the Multimodal and Industrial AI team at Alibaba: IndustryBench! 🚀⚙️
In industrial procurement, an LLM's answer is only useful if it survives strict standards checks. Partial correctness can mask safety-critical contradictions.
Check out the full paper for deep dives into capability dimensions and model comparisons! Feedback and PRs are highly welcome. 👇
Data: https://t.co/8ZflFcHw5W
Code: https://t.co/iTRMcJQhDr
Paper: https://t.co/reTXgWdDrf
#Alibaba #Gemini #Qwen #GPT #Claude #Kimi #GLM #Mimimax
5/6) The Multilingual Blindspot: We released 2,049 items with aligned renderings in EN, RU, VI, and ZH.
Across 17 models, "Standards & Terminology" is the most persistent capability weakness. This weakness survives across all four language translations—proving this is a structural knowledge gap, not just a translation artifact.
Legally it acts as a liability shield, but strictly speaking it's a UX innovation. It bridges the gap and brings the tech closer to the average person.
Openclaw hype is kinda wild to me.
It’s literally just a wrapper to Claude code with more risk.
The fact that it can run autonomously and call apis is the same thing Claude code can do…
Not sure what I’m missing
❓ Do diffusion-LLMs truly generalize in agentic tasks?
We reveal systematic failure modes in causal reasoning & tool use, and introduce DiffuAgent for comprehensive evaluation ⚡
📄 https://t.co/lDI7YhXP8l
🔗 https://t.co/0KZhAYAD5L
#AgenticAI#LLM#EmbodiedAI#ToolUse