Your curated collection of saved posts and media
"Create a simple 3D animation from this video." An LLM agent builds a scene and uses our FlowHMR for mocap. The agent then handles retargeting, motion fixes, camera setup and rendering. Finally, MiniMax-H3 turns the 3D render into photorealistic video. https://t.co/KMHZNWTSHW https://t.co/plKShpGsLQ
We trained Gemini via RL to conduct psychiatric intake with simulated patients. In a double-blind study with 32 psychiatrists playing the role of patients, we show that multi-turn RL in simulation improves Gemini’s performance on structured text-based psychiatric intake. 🧵 https://t.co/V9A8ANnWIy
GPT 6.1 Terra is a strong frontier model baseline for document parsing. ✅ It spikes at table parsing, coming in 5th on our overall leaderboard, ahead of 6 Sol and behind Opus 5.5 / Astra ✅ It is decent at content faithfulness and reading order ✅ It struggles on layout, charts, and formatting Coming in at ~1c per page, it's not a bad option for table-heavy pages! You can use these alongside specialized OCR solutions (like LlamaParse) that will give more consistent performance across a variety of dimensions. Check it out on ParseBench: https://t.co/PWczfhp0OX
An update on our evaluations of AI agents conducting open-ended AI research. Most evaluations of agents conducting AI research focus on narrow, verifiable tasks. But AI research is often open ended. In July we tested the ability of frontier AI agents to conduct open-ended AI research, using Claude Opus 4.8 and GPT-5.6 Sol. We gave agents research questions from two unpublished papers and thousands of dollars of API credits and compute. The authors of the original papers reviewed the AI-generated papers, following a NeurIPS-style template, and rejected both papers. Since then, OpenAI has released GPT-6 Astra and Anthropic has released Fable 5.1. Can these new models conduct open-ended research with a $10,000 budget? In follow-up experiments on two papers (one with Fable 5.1 and another with GPT-6 Astra), we observed meaningful improvements to the agents' capabilities at experimental design and resource management. But we also saw continued limitations in creative problem solving, judgment, calibration, and communication. As a result, the expert reviewers still unambiguously rejected the agents' papers. We plan to continue running these evaluations with better models, better scaffolds, and higher budgets. If you are interested in long-running agent evaluations, reach out!
🎥 A long video. One coherent 3D scene. Meet HiP3R: piecing together local 3D reconstructions to keep the scene consistent across thousands of frames. Watch the demo and explore the details 👇 Project: https://t.co/BzGoFauLfq Paper: https://t.co/ttWFBtkbCc Code: https://t.co/33tRzAGuTp
We’re open-sourcing Drex-Max v1 The most capable Multimodal Decision Model ! And yes: it runs 100% locally on Apple Silicon (M4 & M5) Enjoy Building with Nace! Cloud Inference: https://t.co/fm3GXfjaqx Local Inference: https://t.co/9nwcBn7c2D https://t.co/Wgu8P4zByR
This is really cool work on our synthetic hospital benchmark. For patient diagnosis, this shows that small models + SFT + the right tools > GPT-6 Luna. Interesting is that the tools help small models, but not big models. So gaps in capability can be replaced by tools
spent a few days trying to train a tiny model to diagnose patients on the Synthetic Hospital environment out of CMU. RL didn't help much, but some SFT and a simple search tool got qwen3-4b to perform as well as a ~1T param model this is because the agent is more than just the m
MemT-1.0: Memory-native foundation models MemTensor introduces a new architecture where persistent memory is part of model computation, enabling 1.86x inference speedup and 66x storage reduction at 128K history. https://t.co/kU21sHGSPE
S4VY is an incredible advancement in the field of real-time 4D joint segmentation and depth prediction! We’ll be releasing the code and checkpoints soon, so stay tuned for more updates.
1/9 Introducing S4VY: Segment Anything, now in 4D 🧵 From any set of RGB observations (a video, a few scattered frames, or an unordered photo collection), S4VY segments every object in 4D, each with one consistent identity across space and time. https://t.co/2kO0wmHnj4
Open weights models fall further behind in v5. The leader, Kimi K3.1 Pro (weights not yet released), scores 46, 12 points behind the leader, compared with a 3-point gap in v4.3. Of models with released weights, DeepSeek V4.1 Flash 1017 (max) and GLM-5.3 (max) score highest at 32, and Claude Haiku 5.5 (max) outscores all of them. The gap is widest on HLE-Diamond and Terminal-Bench-Science, where Kimi K3.1 Pro scores 37% and 34%, compared with 68% for Claude Opus 5.5 (max) and 63% for GPT-6 Astra (max).
