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Sajjad Pakdamansavoji

savoji
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masterset-ai's profile picture VIDRAFT-lab's profile picture murtadha9's profile picture
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https://sajjadpsavoji.github.io

AI & ML interests

VLM, VLA, LLM, Robotics, Computer Vision

Recent Activity

posted an update about 23 hours ago
Idol, a new Apache-2.0 image-to-3D model from @speridlabs, makes a 3D Gaussian splat from one image in 7.2 GB of peak VRAM. It is the #1 trending model on the Hub with 1,057 likes. It writes a standard 3DGS .ply with up to 1,048,576 Gaussians, including sides the picture never showed, and does background removal and framing itself. The catch: the only quality evidence is the authors' own benchmark, on a repo created 2026-10-28. 📊 The numbers • Authors' benchmark, 1 x H100: best in 6 of 8 columns vs TripoSplat, TRELLIS and LGM (SSIM 0.726, LPIPS 0.213, DINOv2 0.781, CLIP 0.885) • LGM wins PSNR (12.46 vs 12.25) and speed (1.1 s). Idol takes 6.8 s vs 7.1 s for TRELLIS and 9.9 s for TripoSplat • 262,144 Gaussians, 50 steps: 3.4 s on B300, 6.7 s on H100, 19.1 s on RTX 4090, 46.2 s on a 10 GB RTX 3080 🛠️ Try it • Install idol-sdk, accept the terms of the gated DINOv3 and FLUX.2-dev models, run hf auth login • python -m idol.sample --image photo.jpg --mode real • --num_gaussians runs from 32,768 (0.4 MB) to 1,048,576 (12.2 MB) ⚠️ Caveats • Test set and size are not named, and no independent evaluation exists yet • The card says "under 2 seconds", but the timing table shows 3.4 s at best • Single objects only, NVIDIA CUDA required, no failure cases shown https://huggingface.co/speridlabs/idol https://speridlabs.com/research/idol If you run it on your own messy photos, how do the unseen sides hold up? 📚 Read it elsewhere • The deep dive on Medium: https://medium.com/p/5f660ae6ae56 • The long read on Substack: https://sajjadpakdamansavoji.substack.com/p/speridlabs-idol-builds-3d-splats • The thread on X: https://x.com/saavoji/status/2119137739580858545 • Every version on sajjadpsavoji.github.io: https://sajjadpsavoji.github.io/blog#speridlabs-idol
posted an update 2 days ago
VFlash cuts per-step latency 5.1 to 5.2x on Jetson Thor and DGX Spark, and its one-pass drafter beats the autoregressive 4B model it drafts for on 3D robot trajectories. A planner does not have to write points, boxes and trajectories one coordinate token at a time. A primitive's type, dimensionality and length fix its layout before decoding, so a lightweight drafter fills the whole primitive in one forward pass. The decoder accepts it if it is structurally valid, with no token agreement with the target. Text keeps verified speculative decoding. 🔍 Why token matching fails • DFlash commits 5.5 tokens per iteration on subtask text but only 2.2 inside trajectory blocks, so it only about halves decoding time • A coordinate one bin off fails the check even when the geometric error is small 📊 The numbers • AgiBot waypoints within 5 cm: 50% to 73% (3D only) and 40% to 74% (2D+3D); ADE falls 18 to 61% across InternA1, AgiBot and MolmoBot • Under AR, decoding is 75 to 87% of a step on Thor; VFlash cuts it 12 to 42x. H100 gets 3.6x • MolmoSpaces success: 70.2% with VFlash plans, 62.6% baseline, 61.0% with AR plans ⚠️ Caveats • Latency is a pipeline measurement at batch size one, with a 4B target and 36 to 84 token primitives • Boxes need an AR fallback: without it, mean F1 drops about 20 points on Dense200 • The 25x dense point motion figure is preliminary, and accuracy there is mixed https://huggingface.co/papers/2611.08449 https://arxiv.org/abs/2611.08449 If your planner's cost is mostly coordinate decoding, would you swap token-level verification for a structure validator? 📚 Read it elsewhere • The deep dive on Medium: https://medium.com/p/26ed0ca0bb84 • The long read on Substack: https://sajjadpakdamansavoji.substack.com/p/vflash-drafts-whole-robot-plans-in • The thread on X: https://x.com/saavoji/status/2118773042503623062 • Every version on sajjadpsavoji.github.io: https://sajjadpsavoji.github.io/blog#vflash-drafts
posted an update 3 days ago
Cutting an agent's context window from 256K to 16K decodes 3.6x to 7x more tokens per second on identical H100s (GLM 5.2 3.6x, Kimi K2.6 5.6x, Qwen 3 Coder Next 7x), and in the paper's tests accuracy barely moved. "You don't need long context" (arXiv 2611.05306) argues that long-horizon agents mostly need memory management, not bigger windows. Frequent compaction into a compact state, plus selective reading of inputs with bash tools, kept accuracy close to full-history baselines while contexts stayed short. 📊 The numbers • Compaction over 6 benchmarks: Opus 4.5 -3.7 to +5.6 ppts, GPT 5.6 Sol -3.3 to +4.4 versus full history • One action per context: SWE-bench Verified 70.2 for Opus 4.5 (baseline 70.0) and 82.4 for GPT 5.6 Sol (baseline 84.4), median peak context 7K to 9K tokens • Long inputs stored as files and read selectively: Opus 4.5 -0.4 to +40.0 ppts, estimated cost savings 4.4x to 233.3x across both models 🛠️ Try it • Set a compaction threshold near 12K to 36K tokens • Add a tool that reads files or history on demand • Compare accuracy, total tokens and cost against full history ⚠️ Caveats • Speedups come from open models, accuracy results from closed ones (Opus 4.5, GPT 5.6 Sol) • Cost savings are estimates, the harness is bash-only, and the preprint has no independent replication yet https://huggingface.co/papers/2611.05306 https://arxiv.org/abs/2611.05306 What would your agent need to carry in its handoff state, and what could it safely fetch later? 📚 Read it elsewhere • The deep dive on Medium: https://medium.com/p/41f69cdbe3b3 • The long read on Substack: https://sajjadpakdamansavoji.substack.com/p/qwen-3-coder-next-decodes-7x-faster • The thread on X: https://x.com/saavoji/status/2118402118927438127 • Every version on sajjadpsavoji.github.io: https://sajjadpsavoji.github.io/blog#claude-opus-4
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