Unlocking the Potential of Image Editing via Concept Scaling and Dense Supervision
Paper โข 2608.16812 โข Published โข 47
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Computer Vision
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Imagine Before You Predict: Interleaved Latent Visual Reasoning for Video Event Prediction
RIVER: A Real-Time Interaction Benchmark for Video LLMs
CuiLong7ย
submitted a
paper to Daily Papers 1 day ago
prithivMLmodsย
posted an update 8 days ago
Post
5063
The Qwen3.8 27B demo for object grounding is now available on Hugging Face Spaces.
It features three tasks: Object Detection (Bounding Boxes), Point Localization (Keypoints), and Spatial Guidance (Path Mapping).
Try it now: prithivMLmods/Qwen3.8-27B-Object-Detection
It features three tasks: Object Detection (Bounding Boxes), Point Localization (Keypoints), and Spatial Guidance (Path Mapping).
Try it now: prithivMLmods/Qwen3.8-27B-Object-Detection
Post
2000
Anthropic gave me six months of Claude Max 20x through the Claude for Open Source program, granted based on my Hugging Face work. Thank you 
Anthropic for supporting open source.
So far I've been pointing it at Markdown Minimap, an Obsidian plugin that adds a scrollable IDE-style minimap to your notes. This week I've been clearing a backlog of user-reported issues on it, with Claude often handling them end to end.
https://github.com/Nymbo/Markdown-Minimap โ issues and PRs welcome.
So far I've been pointing it at Markdown Minimap, an Obsidian plugin that adds a scrollable IDE-style minimap to your notes. This week I've been clearing a backlog of user-reported issues on it, with Claude often handling them end to end.
https://github.com/Nymbo/Markdown-Minimap โ issues and PRs welcome.
Andy1621ย
authored a
paper 23 days ago
prithivMLmodsย
posted an update 27 days ago
Post
5518
Made a demo for Text/Image-to-3D Video and Image-to-3D Video asset generation using TRELLIS.2. It is paired with Z-Image-Turbo to accelerate the input image preprocessing pipeline, streamlining the Image-to-3D workflow. The generated GLB (GL Transmission Format) files are converted into MP4 (MPEG-4) videos, making them easy to preview and share. Try it now on Hugging Face Spaces.๐ค
โ Image-to-3D-Video-Asset-Generator: prithivMLmods/Image-to-3D-Video-Asset-Generator
โ collection: https://huggingface.co/collections/prithivMLmods/multimodal-implementations
โ github: https://github.com/PRITHIVSAKTHIUR/Image-to-3D-Video-Asset-Generator
โคท To learn more, visit the app page or the respective model pages.
โ Image-to-3D-Video-Asset-Generator: prithivMLmods/Image-to-3D-Video-Asset-Generator
โ collection: https://huggingface.co/collections/prithivMLmods/multimodal-implementations
โ github: https://github.com/PRITHIVSAKTHIUR/Image-to-3D-Video-Asset-Generator
โคท To learn more, visit the app page or the respective model pages.
Post
5764
Introducing Inflect-v2, two exceptionally small, open-weight English TTS models at just 3.9M and 9.3M parameters. Both generate speech multiple times faster than real-time on CPU. Despite their size, Inflect-v2 delivers quality that is competitive with much larger lightweight TTS systems, including KittenTTS, Piper, and Supertonic-3.
CPU, CUDA, PyTorch, and ONNX are supported. Apache 2.0.
See it for yourselves:
owensong/Inflect-Micro-v2
owensong/Inflect-Nano-v2
Try the Demos:
Nymbo/Inflect-TTS (unlimited CPU usage)
owensong/Inflect-v2 (ultra-fast ZeroGPU usage)
CPU, CUDA, PyTorch, and ONNX are supported. Apache 2.0.
See it for yourselves:
owensong/Inflect-Micro-v2
owensong/Inflect-Nano-v2
Try the Demos:
Nymbo/Inflect-TTS (unlimited CPU usage)
owensong/Inflect-v2 (ultra-fast ZeroGPU usage)
- 6 replies
Euraykaย
authored a
paper about 1 month ago
wzk1015ย
submitted a
paper to Daily Papers about 1 month ago
ganlinyangย
authored a
paper 2 months ago
shepnerdย
updated a
collection 2 months ago
ganlinyangย
submitted a
paper to Daily Papers 2 months ago
Post
4760
We trained an open-source Mythos like cybersecurity LLM for the Build Small Hackathon meet OpenMythos
Trained in two stages: SFT on ~1.84K filtered ArXiv cs.CR papers + real CVE data, then RLVR using paired with past vulnerabilities GitHub repos with a verifier model checking outputs against ground truth.
Trained on: H100s from Modal
The RLVR stage made the biggest difference responses got more precise and less prone to confusing similar vulnerability classes.
Everything is open:
๐ค Demo โ build-small-hackathon/OpenMythos
๐ง Model โ build-small-hackathon/OpenMythos
๐ฆ CVE Dataset โ build-small-hackathon/CVE_Vulnerailities_Detailed
๐ ArXiv Dataset โ himanshu17HF/ArvixImport-Filtered-Final
Try it out and let us know where it breaks ๐
Trained in two stages: SFT on ~1.84K filtered ArXiv cs.CR papers + real CVE data, then RLVR using paired with past vulnerabilities GitHub repos with a verifier model checking outputs against ground truth.
Trained on: H100s from Modal
The RLVR stage made the biggest difference responses got more precise and less prone to confusing similar vulnerability classes.
Everything is open:
๐ค Demo โ build-small-hackathon/OpenMythos
๐ง Model โ build-small-hackathon/OpenMythos
๐ฆ CVE Dataset โ build-small-hackathon/CVE_Vulnerailities_Detailed
๐ ArXiv Dataset โ himanshu17HF/ArvixImport-Filtered-Final
Try it out and let us know where it breaks ๐
- 2 replies
Post
384
Shipped v0.1.2 of vtx โ a minimalist coding agent for the terminal.
Most agentic CLIs ship 10k+ token system prompts. Vtx is ~2,200. Less prompt overhead means more room for your code in the model's context window.
Vtx is a from-scratch Python implementation of the design philosophy behind pi-mono โ same principles, pure Python, no transpiled runtime.
What ships out of the box:
โ Textual TUI + headless CLI (vtx -p "fix the failing test")
โ 49 LLM provider gateways, all declared in a single provider.yaml
โ 5 core tools (read / edit / write / bash / find) plus web search and fetch
โ Session tree with compaction, handoff, and resume
โ AGENTS.md / CLAUDE.md auto-discovery
โ Skills system โ drop SKILL.md files in .agents/skills/ and they become slash commands
โ Two OAuth flows (GitHub Copilot device flow, OpenAI Codex PKCE)
โ Two-mode permissions: prompt (default) or auto, with a safe-command allowlist
This release adds a proper extension system. Register new LLM-callable tools, intercept tool calls, hook lifecycle events, and add slash commands from a single register(api) function in a Python file under ~/.vtx/agent/extensions/. Extensions can override built-in tools by name and chain handler logic across subscribers.
Apache 2.0. uv tool install vtx-coding-agent and you're running.
GitHub: https://github.com/OEvortex/vtx-coding-agent
PyPI: https://pypi.org/project/vtx-coding-agent
Built in the open. Feedback, extensions, and PRs welcome.
Most agentic CLIs ship 10k+ token system prompts. Vtx is ~2,200. Less prompt overhead means more room for your code in the model's context window.
Vtx is a from-scratch Python implementation of the design philosophy behind pi-mono โ same principles, pure Python, no transpiled runtime.
What ships out of the box:
โ Textual TUI + headless CLI (vtx -p "fix the failing test")
โ 49 LLM provider gateways, all declared in a single provider.yaml
โ 5 core tools (read / edit / write / bash / find) plus web search and fetch
โ Session tree with compaction, handoff, and resume
โ AGENTS.md / CLAUDE.md auto-discovery
โ Skills system โ drop SKILL.md files in .agents/skills/ and they become slash commands
โ Two OAuth flows (GitHub Copilot device flow, OpenAI Codex PKCE)
โ Two-mode permissions: prompt (default) or auto, with a safe-command allowlist
This release adds a proper extension system. Register new LLM-callable tools, intercept tool calls, hook lifecycle events, and add slash commands from a single register(api) function in a Python file under ~/.vtx/agent/extensions/. Extensions can override built-in tools by name and chain handler logic across subscribers.
Apache 2.0. uv tool install vtx-coding-agent and you're running.
GitHub: https://github.com/OEvortex/vtx-coding-agent
PyPI: https://pypi.org/project/vtx-coding-agent
Built in the open. Feedback, extensions, and PRs welcome.
prithivMLmodsย
posted an update 2 months ago
Post
7956
Wan2.2-I2V-Fast with highly upscaled sequential frame sampling is now available as a Spaces demo, built using Wan2.2-I2V and FLUX.2-Klein. Try the demo using the links below.๐
โ wan2.2-i2v-fast : prithivMLmods/Wan2.2-Fast
โ github: https://github.com/prithivsakthiur/wan2.2-i2v-fast
โ collection: https://huggingface.co/collections/prithivMLmods/image-generation-apps-collection
โคท To learn more, visit the app page or the respective model pages.
โ wan2.2-i2v-fast : prithivMLmods/Wan2.2-Fast
โ github: https://github.com/prithivsakthiur/wan2.2-i2v-fast
โ collection: https://huggingface.co/collections/prithivMLmods/image-generation-apps-collection
โคท To learn more, visit the app page or the respective model pages.
qishisurenย
submitted a
paper to Daily Papers 2 months ago
yanziangย
authored a
paper 3 months ago
Euraykaย
authored a
paper 3 months ago
linghan199ย
authored a
paper 3 months ago