📢 It’s here. The Atlas Leaderboard is now live — your new source of truth for LLM evaluation. Benchmark top models like ChatGPT, Claude & Gemini with real-world data, live updates, and powerful insights. 👉 #AI #LLM #Benchmarking #AtlasLeaderboard

🚨 The best models all pass Big Bench Hard — but at what cost?.Speed, accuracy, and trade-offs collide in our latest eval. ⚡ Who's sharp and fast? Who's just slow?. 📊 See full results → #AI #LLM #benchmarking @layerlens_ai. 🎙️ We go live in 30 mins to

🚨 AI folks—this one's for you. Frontier models score sky-high on standard benchmarks. but still stumble on basic reasoning. Why does this happen? What do the evals reveal? And how can we build trust in real-world AI performance?. Join us tomorrow for:.“Reasoning Evals & What

We're unpacking this (and more) in our next webinar:.🧪 "Reasoning Evals & What We Can Learn from Them".🗓️ July 8.🎟️ Register here: Let’s talk about where frontier models really struggle

The takeaway?.🔍 Reasoning ≠ memorization.💡 Explaining steps ≠ solving correctly.🧩 Models still fumble core logic patterns. If they miss on boolean order, what happens in high-stakes reasoning?.

On Big Bench Hard (BBH):.📊 85.7% accuracy.🟢 Readable, clean responses. But. ⚠️ Failed deeply nested logic.⚠️ Misread 'and', 'or', 'not' operator chains. Ex: “True or not False and True and False is. ” → model said True. It’s False.

ERNIE nailed:.✅ MATH-500.✅ AGIEval Chinese.✅ AI2 Reasoning Challenge. But tanked on:.❌ Humanity’s Last Exam (3.7%).❌ SimpleQA (36.9%).It’s high variance. Specialization ≠ generalization?

ERNIE 4.5 300B A47B just dropped on Atlas 🧠. Built by @Baidu_Inc, this MoE model dominates some benchmarks. but struggles with logic and nuance. We ran 10+ evaluations. What did we learn? 👇.🔗

Want to go deeper?. We’re hosting a webinar on July 10 to walk through key findings, model comparisons, and what it means for devs, researchers, and teams deploying LLMs. 🗓️ Register here:.

📢 Sneak peek before our full Q2 report drops. What happened when the model release cycle slowed down? We got to see what stuck. From Claude’s reasoning push to China’s open-source rise, here’s what to expect in our upcoming Q2 frontier model report:.

A must-read from @mahedmousavi et al. just dropped on arXiv: It confirms what many in the evaluation space already suspect: High benchmark scores ≠ robust reasoning. Using top LLMs (GPT-4, Claude, LLaMA 3.1), the authors audit 3 popular reasoning.

Want to dive deeper?. 🎙️ Join us for our upcoming webinar:. “Reasoning Evals and What We Can Learn from Them”. 📅 July 8 | 🕑 6PM CET | 👤 Hosted by @ArchChaudhury . Sign up here → #AIevals #LLMreasoning #Webinar.

Companies deploying AI can’t afford surface-level scores. You need:.– Transparent evals.– Edge-case coverage.– Traceable metrics.– Human + domain-informed testing. That’s where LayerLens comes in. Explore Atlas → #aiinfrastructure #LLMops #MLOps.

Most benchmarks are:.✅ Narrow.✅ Optimized to death.✅ Easy to game. What we don’t test for enough:.❌ Ambiguity.❌ Stress.❌ Reasoning chains.❌ Model uncertainty. 👉 Real-world AI needs real-world benchmarks.

Today’s LLMs can ace MMLU, ARC, and GSM8K. and still hallucinate, fumble reasoning, or break in production. The problem?. We’ve built a system that rewards benchmarks, not reliability. Accuracy isn't enough. We need nuance. #AIbenchmarking #LLMfailures.

🚨 AI models are getting better—but real-world failures are getting worse. What’s going on?. We’re in the middle of a benchmarking crisis, and nobody wants to talk about it. Here’s what you need to know. 🧵👇. #AI #LLM #MachineLearning.

⚠️ The opportunity?. Build context-aware UX that complements Gemma’s strengths, not stretch it into use-cases like auditing or multi-hop QA (see chart 👇). For more granular evals like this:.🔗 #AIbenchmarking #Gemma3n #LayerLens.

Where it shines:. ✨ Mobile agents.🗣️ Light on-device assistants.🧪 Science education tools. Its strong evals in simple reasoning make it a fit for low-latency, structured use-cases where efficiency > complexity. #LLMs #EdgeComputing #AI4Education.

🔍 So how does Gemma 3n 4B actually perform?. It crushes basic science benchmarks like AI2 Reasoning – Easy (93.5% accuracy). But stumbles on multi-step math & subtle inference (10% on AIME 2024). ➡️ It’s great at facts. Struggles with abstraction. #MLperf #AIbenchmarking

🧠 What’s Google’s new Gemma 3n 4B all about?.It’s a 4B parameter model optimized for mobile and low-resource devices, combining a compact size with flexible 32K context, PLE caching, and MatFormer architecture. Designed for real-world, privacy-focused use. →.