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What superpower does Kimi-K2.5 bring to the table?
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Leco Li PRO
imnotkitty
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reacted to SeaWolf-AI's post with 🔥 about 4 hours ago
🌍 World Model Bench — does your world model actually think?
FID measures realism. FVD measures smoothness. But neither tells you whether the model understood the scene.
We just released WM Bench — the first benchmark for cognitive intelligence in world models. The core question: when a beast charges from 3 meters away, does the model know to sprint — not walk? Does it respond differently to a human vs an animal? Does it remember the left corridor was blocked two steps ago?
Those are cognitive questions. No existing benchmark asks them. So we built one.
3 Pillars · 10 Categories · 100 Scenarios · 1,000-point scale
- 👁 P1 Perception (25%) — Can it read the scene?
- 🧠 P2 Cognition (45%) — Does it predict threats, escalate emotions, utilize memory?
- 🔥 P3 Embodiment (30%) — Does the body respond with the right motion?
All evaluation is via simple JSON I/O — no 3D engine, no special hardware. Any model with an API can participate.
We also built PROMETHEUS as a live reference implementation — runs in your browser on a T4, no install needed. Combines FloodDiffusion motion generation with a LLM cognitive brain (Perceive → Predict → Decide → Act). Scored 726/1000 (Grade B) on Track C — the only directly verified model so far. Submissions from other teams very welcome.
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🗂 Dataset → https://huggingface.co/datasets/FINAL-Bench/World-Model
🌍 Demo → https://huggingface.co/spaces/FINAL-Bench/World-Model
🏆 Leaderboard → https://huggingface.co/spaces/FINAL-Bench/worldmodel-bench
📝 Article → https://huggingface.co/blog/FINAL-Bench/world-model
Part of the FINAL Bench Family — alongside FINAL Bench (Feb 2026). Feedback on rubrics and missing models always welcome! liked a model about 4 hours ago
Jackrong/Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled posted an update 14 days ago
🚀 GLM-5-Turbo just dropped: The "OpenClaw Native" Model
Z.ai just shipped a 744B MoE beast that's 2-3x faster than GLM-5, with 200K context + 128K max output.
What's different:
👉 Tool call stability (no more random failures mid-chain)
👉 Complex instruction decomposition (breaks down messy prompts)
👉 Time-aware execution (understands scheduled/persistent tasks)
👉 High-throughput long-chain efficiency (doesn't choke on 50-step workflows)
📊 ZClawBench: Leads mainstream models in OpenClaw scenarios
💰 Trade-off: +20% price vs GLM-5
Anyone trying it yet? 👀
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Uncensor any LLM with abliteration
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