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DmitryRyumin  authored a paper 17 days ago
Team RAS in 11th ABAW Competition: Multimodal Ambivalence Recognition Approach
DmitryRyumin  authored a paper 17 days ago
Team LEYA in 10th ABAW Competition: Multimodal Ambivalence/Hesitancy Recognition Approach
gagan3012  authored a paper 2 months ago
Who Annotates in NLP? A Large-scale Assessment of Human Annotation Reporting between 2018 and 2025
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Nymbo 
posted an update 12 days ago
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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)
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DmitryRyumin 
authored 2 papers 17 days ago
eienmojiki 
posted an update about 1 month ago
Nymbo 
posted an update 5 months ago
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We should really have a release date range slider on the /models page. Tired of "trending/most downloaded" being the best way to sort and still seeing models from 2023 on the first page just because they're embedded in enterprise pipelines and get downloaded repeatedly. "Recently Created/Recently Updated" don't solve the discovery problem considering the amount of noise to sift through.

Slight caveat: Trending actually does have some recency bias, but it's not strong/precise enough.
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Nymbo 
posted an update 7 months ago
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Genuine recommendation: You should really use this AutoHotKey macro. Save the file as macros.ahk and run it. Before sending a prompt to your coding agent, press Ctrl + Alt + 1 and paste your prompt to any regular chatbot. Then send the output to the agent. This is the actual, boring, real way to "10x your prompting". Use the other number keys to avoid repeating yourself over and over again. I use this macro prolly 100-200 times per day. AutoHotKey isn't as new or hype as a lot of other workflows, but there's a reason it's still widely used after 17 years. Don't overcomplicate it.

; Requires AutoHotkey v1.1+

; All macros are `Ctrl + Alt + <variable>`

^!1::
    Send, Please help me more clearly articulate what I mean with this message (write the message in a code block):
return

^!2::
    Send, Please make the following changes:
return

^!3::
    Send, It seems you got cut off by the maximum response limit. Please continue by picking up where you left off.
return


In my experience the past few months, Ctrl + Alt + 1 works best with Instruct models (non-thinking). Reasoning causes some models to ramble and miss the point. I've just been using GPT-5.x for this.
Nymbo 
posted an update 8 months ago
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🚨 New tool for the Nymbo/Tools MCP server: The new Agent_Skills tool provides full support for Agent Skills (Claude Skills but open-source).

How it works: The tool exposes the standard discover/info/resources/validate actions. Skills live in /Skills under the same File_System root, and any bundled scripts run through Shell_Command, no new infrastructure required.

Agent_Skills(action="discover")  # List all available skills
Agent_Skills(action="info", skill_name="music-downloader")  # Full SKILL.md
Agent_Skills(action="resources", skill_name="music-downloader")  # Scripts, refs, assets


I've included a music-downloader skill as a working demo, it wraps yt-dlp for YouTube/SoundCloud audio extraction.

Caveat: On HF Spaces, Shell_Command works for most tasks, but some operations (like YouTube downloads) are restricted due to the container environment. For full functionality, run the server locally on your machine.

Try it out ~ https://www.nymbo.net/nymbot
Nymbo 
posted an update 8 months ago
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🚀 I've just shipped a major update to the Nymbo/Tools MCP server: the Agent_Terminal, a single "master tool" that cuts token usage by over 90%!

Anthropic found 98.7% context savings using code execution with MCP, Cloudflare published similar findings. This is my open-source implementation of the same idea.

# The Problem

Traditional MCP exposes every tool definition directly to the model. With 12 tools, that's thousands of tokens consumed *before the conversation even starts*. Each tool call also passes intermediate results through the context window — a 10,000-row spreadsheet? That's all going into context just to sum a column.

# The Solution: One Tool to Rule Them All

Agent_Terminal wraps all 12 tools (Web_Search, Web_Fetch, File_System, Generate_Image, Generate_Speech, Generate_Video, Deep_Research, Memory_Manager, Obsidian_Vault, Shell_Command, Code_Interpreter) into a single Python code execution gateway.

Instead of the model making individual tool calls, it writes Python code that orchestrates the tools directly:

# Search for Bitcoin price
result = Web_Search("current price of bitcoin", max_results=3)
print(result)


Don't know what tools are available? The agent can discover them at runtime:

print(search_tools('image'))  # Find tools by keyword
print(usage('Generate_Image'))  # Get full docs for a specific tool


The individual direct tool calls are all still there, but they can be disabled if using the Agent_Terminal. Try it now - https://www.nymbo.net/nymbot
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DmitryRyumin 
posted an update 9 months ago
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🚀👁️🌟 New Research Alert - ICCV 2025 (Poster)! 🌟👁️🚀
📄 Title: Is Less More? Exploring Token Condensation as Training-Free Test-Time Adaptation 🔝

📝 Description: Token Condensation as Adaptation (TCA) improves the performance and efficiency of Vision Language Models in zero-shot inference by introducing domain anchor tokens.

👥 Authors: Zixin Wang, Dong Gong, Sen Wang, Zi Huang, Yadan Luo

📅 Conference: ICCV, 19 – 23 Oct, 2025 | Honolulu, Hawai'i, USA 🇺🇸

📄 Paper: Is Less More? Exploring Token Condensation as Training-free Test-time Adaptation (2410.14729)

📁 Repository: https://github.com/Jo-wang/TCA

🚀 ICCV-2023-25-Papers: https://github.com/DmitryRyumin/ICCV-2023-25-Papers

🚀 Added to the Session 1: https://github.com/DmitryRyumin/ICCV-2023-25-Papers/blob/main/sections/2025/main/session-1.md

📚 More Papers: more cutting-edge research presented at other conferences in the DmitryRyumin/NewEraAI-Papers curated by @DmitryRyumin

🔍 Keywords: #TestTimeAdaptation #TokenCondensation #VisionLanguageModels #TrainingFreeAdaptation #ZeroShotLearning #EfficientAI #AI #ICCV2025 #ResearchHighlight
DmitryRyumin 
posted an update 9 months ago
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🚀👁️🌟 New Research Alert - ICCV 2025 (Oral)! 🌟👁️🚀
📄 Title: Diving into the Fusion of Monocular Priors for Generalized Stereo Matching 🔝

📝 Description: The proposed method enhances stereo matching by efficiently combining unbiased monocular priors from vision foundation models. This method addresses misalignment and local optima issues using a binary local ordering map and pixel-wise linear regression.

👥 Authors: Chengtang Yao, Lidong Yu, Zhidan Liu, Jiaxi Zeng, Yuwei Wu, and Yunde Jia

📅 Conference: ICCV, 19 – 23 Oct, 2025 | Honolulu, Hawai'i, USA 🇺🇸

📄 Paper: Diving into the Fusion of Monocular Priors for Generalized Stereo Matching (2505.14414)

📁 Repository: https://github.com/YaoChengTang/Diving-into-the-Fusion-of-Monocular-Priors-for-Generalized-Stereo-Matching

🚀 ICCV-2023-25-Papers: https://github.com/DmitryRyumin/ICCV-2023-25-Papers

🚀 Added to the 3D Pose Understanding Section: https://github.com/DmitryRyumin/ICCV-2023-25-Papers/blob/main/sections/2025/main/3d-pose-understanding.md

📚 More Papers: more cutting-edge research presented at other conferences in the DmitryRyumin/NewEraAI-Papers curated by @DmitryRyumin

🔍 Keywords: #StereoMatching #MonocularDepth #VisionFoundationModels #3DReconstruction #Generalization #AI #ICCV2025 #ResearchHighlight
Nymbo 
posted an update 9 months ago
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I've added an 11th tool to the Nymbo/Tools MCP server, it's for your Obsidian_Vault. I'd argue it's far more context-efficient than any other Obsidian MCP I've seen, and doesn't require any plugins. Also some big improvements to the Web_Search and Web_Fetch tools.

# Obsidian_Vault Tool

It's basically a read-only version of the File_System tool, but it works so well for navigating Obsidian without unnecessary context. It supports recursive (full-text) search across the entire vault, and supports offset so the agent can "scroll" through a document without re-consuming tokens.

Run the server locally and set the OBSIDIAN_VAULT_ROOT environment variable to your vault's root path. If you don't use Obsidian, this is perfectly usable as simply a read-only filesystem.

# Web_Search Improvements

The Web_Search tool previously just used DuckDuckGo as a backend search engine, but now it also supports Bing, Brave, Yahoo, and Wikipedia. Default engine is auto which provides results from all backends in recommended order. Still doesn't require any kind of API or auth for Web_Search.

There's also a new date filter to limit results to those created in the past day, week, month, or year. Oh, and uhh, SafeSearch is now off by default :)

# Web_Fetch Improvements

As context-efficient as the Markdown mode is for web browsing, sometimes it does lose important context in the conversion from HTML to Markdown. So I've added a new HTML mode to the Web_Fetch tool that basically executes a cURL request on the URL, returning the full HTML page if necessary.

# A Note on Claude Skills

I've been having fun with the new File_System and Shell_Command tools. Using Claude Skills doesn't currently work in the public HF space because of environment restrictions, but using Skills works perfectly well running locally.

Happy building ~
DmitryRyumin 
posted an update 9 months ago
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🚀👌🌟 New Research Alert - ICCV 2025 (Oral)! 🌟🤌🚀
📄 Title: Understanding Co-speech Gestures in-the-wild 🔝

📝 Description: JEGAL is a tri-modal model that learns from gestures, speech and text simultaneously, enabling devices to interpret co-speech gestures in the wild.

👥 Authors: @sindhuhegde , K R Prajwal, Taein Kwon, and Andrew Zisserman

📅 Conference: ICCV, 19 – 23 Oct, 2025 | Honolulu, Hawai'i, USA 🇺🇸

📄 Paper: Understanding Co-speech Gestures in-the-wild (2503.22668)

🌐 Web Page: https://www.robots.ox.ac.uk/~vgg/research/jegal
📁 Repository: https://github.com/Sindhu-Hegde/jegal
📺 Video: https://www.youtube.com/watch?v=TYFOLKfM-rM

🚀 ICCV-2023-25-Papers: https://github.com/DmitryRyumin/ICCV-2023-25-Papers

🚀 Added to the Human Modeling Section: https://github.com/DmitryRyumin/ICCV-2023-25-Papers/blob/main/sections/2025/main/human-modeling.md

📚 More Papers: more cutting-edge research presented at other conferences in the DmitryRyumin/NewEraAI-Papers curated by @DmitryRyumin

🔍 Keywords: #CoSpeechGestures #GestureUnderstanding #TriModalRepresentation #MultimodalLearning #AI #ICCV2025 #ResearchHighlight
DmitryRyumin 
posted an update 9 months ago
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🚀💡🌟 New Research Alert - ICCV 2025 (Oral)! 🌟🪄🚀
📄 Title: LoftUp: Learning a Coordinate-based Feature Upsampler for Vision Foundation Models 🔝

📝 Description: LoftUp is a coordinate-based transformer that upscales the low-resolution features of VFMs (e.g. DINOv2 and CLIP) using cross-attention and self-distilled pseudo-ground truth (pseudo-GT) from SAM.

👥 Authors: Haiwen Huang, Anpei Chen, Volodymyr Havrylov, Andreas Geiger, and Dan Zhang

📅 Conference: ICCV, 19 – 23 Oct, 2025 | Honolulu, Hawai'i, USA 🇺🇸

📄 Paper: LoftUp: Learning a Coordinate-Based Feature Upsampler for Vision Foundation Models (2504.14032)

🌐 Github Page: https://andrehuang.github.io/loftup-site
📁 Repository: https://github.com/andrehuang/loftup

🚀 ICCV-2023-25-Papers: https://github.com/DmitryRyumin/ICCV-2023-25-Papers

🚀 Added to the Foundation Models and Representation Learning Section: https://github.com/DmitryRyumin/ICCV-2023-25-Papers/blob/main/sections/2025/main/foundation-models-and-representation-learning.md

📚 More Papers: more cutting-edge research presented at other conferences in the DmitryRyumin/NewEraAI-Papers curated by @DmitryRyumin

🔍 Keywords: #LoftUp #VisionFoundationModels #FeatureUpsampling #Cross-AttentionTransformer #CoordinateBasedLearning #SelfDistillation #PseudoGroundTruth #RepresentationLearning #AI #ICCV2025 #ResearchHighlight
DmitryRyumin 
posted an update 9 months ago
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🚀🏷️🌟 New Research Alert - ICCV 2025 (Oral)! 🌟🧩🚀
📄 Title: Heavy Labels Out! Dataset Distillation with Label Space Lightening 🔝

📝 Description: The HeLlO framework is a new corpus distillation method that removes the need for large soft labels. It uses a lightweight, online image-to-label projector based on CLIP. This projector has been adapted using LoRA-style, parameter-efficient tuning. It has also been initialized with text embeddings.

👥 Authors: @roseannelexie , @Huage001 , Zigeng Chen, Jingwen Ye, and Xinchao Wang

📅 Conference: ICCV, 19 – 23 Oct, 2025 | Honolulu, Hawai'i, USA 🇺🇸

📄 Paper: Heavy Labels Out! Dataset Distillation with Label Space Lightening (2408.08201)

📺 Video: https://www.youtube.com/watch?v=kAyK_3wskgA

🚀 ICCV-2023-25-Papers: https://github.com/DmitryRyumin/ICCV-2023-25-Papers

🚀 Added to the Efficient Learning Section: https://github.com/DmitryRyumin/ICCV-2023-25-Papers/blob/main/sections/2025/main/efficient-learning.md

📚 More Papers: more cutting-edge research presented at other conferences in the DmitryRyumin/NewEraAI-Papers curated by @DmitryRyumin

🔍 Keywords: #DatasetDistillation #LabelCompression #CLIP #LoRA #EfficientAI #FoundationModels #AI #ICCV2025 #ResearchHighlight
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DmitryRyumin 
authored a paper 9 months ago
DmitryRyumin 
posted an update 9 months ago
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🚀🤖🌟 New Research Alert - ICCV 2025 (Oral)! 🌟🤖🚀
📄 Title: Variance-based Pruning for Accelerating and Compressing Trained Networks 🔝

📝 Description: The one-shot pruning method efficiently compresses networks, reducing computation and memory usage while retaining almost full performance and requiring minimal fine-tuning.

👥 Authors: Uranik Berisha, Jens Mehnert, and Alexandru Paul Condurache

📅 Conference: ICCV, 19 – 23 Oct, 2025 | Honolulu, Hawai'i, USA 🇺🇸

📄 Paper: Variance-Based Pruning for Accelerating and Compressing Trained Networks (2507.12988)

🚀 ICCV-2023-25-Papers: https://github.com/DmitryRyumin/ICCV-2023-25-Papers

🚀 Added to the Efficient Learning Section: https://github.com/DmitryRyumin/ICCV-2023-25-Papers/blob/main/sections/2025/main/efficient-learning.md

📚 More Papers: more cutting-edge research presented at other conferences in the DmitryRyumin/NewEraAI-Papers curated by @DmitryRyumin

🔍 Keywords: #VarianceBasedPruning #NetworkCompression #ModelAcceleration #EfficientDeepLearning #VisionTransformers #AI #ICCV2025 #ResearchHighlight
DmitryRyumin 
posted an update 10 months ago
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🚀👁️🌟 New Research Alert - ICCV 2025 (Oral)! 🌟👁️🚀
📄 Title: Token Activation Map to Visually Explain Multimodal LLMs 🔝

📝 Description: The Token Activation Map (TAM) is an advanced explainability method for multimodal LLMs. Using causal inference and a Rank Gaussian Filter, TAM reveals token-level interactions and eliminates redundant activations. The result is clearer, high-quality visualizations that enhance understanding of object localization, reasoning and multimodal alignment across models.

👥 Authors: Yi Li, Hualiang Wang, Xinpeng Ding, Haonan Wang, and Xiaomeng Li

📅 Conference: ICCV, 19 – 23 Oct, 2025 | Honolulu, Hawai'i, USA 🇺🇸

📄 Paper: Token Activation Map to Visually Explain Multimodal LLMs (2506.23270)

📁 Repository: https://github.com/xmed-lab/TAM

🚀 ICCV-2023-25-Papers: https://github.com/DmitryRyumin/ICCV-2023-25-Papers

🚀 Added to the Multi-Modal Learning Section: https://github.com/DmitryRyumin/ICCV-2023-25-Papers/blob/main/sections/2025/main/multi-modal-learning.md

📚 More Papers: more cutting-edge research presented at other conferences in the DmitryRyumin/NewEraAI-Papers curated by @DmitryRyumin

🔍 Keywords: #TokenActivationMap #TAM #CausalInference #VisualReasoning #Multimodal #Explainability #VisionLanguage #LLM #XAI #AI #ICCV2025 #ResearchHighlight
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Nymbo 
posted an update 10 months ago
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Two new tools added to the Nymbo/Tools MCP server, File_System and Shell_Exec. You can theoretically do basically anything with these two tools, and it should enable support for many Claude Skills.

GPT-5-Codex proves that for many cases, shell commands really are all you need, and Claude Skills seem to lean into this. The thing is, nothing about the design of Claude Skills actually restricts them to proprietary models!

# File_System

There's a new directory inside the repo called Filesystem, that's the agent's "root". It can perform the following actions : list, read, write, append, mkdir, move, copy, delete, info, help. It's able to keep this all within the scope of one tool call by making the Action field required and all other fields optional. Using a filesystem shouldn't require 15 different tools.

Files created in the public HF space live in the space's running container, and gets cleared when the space is restarted. When running the server locally, files are actually stored on disk.

# Shell_Exec

What good is a filesystem if you can't execute commands in that filesystem? This tool automatically detects if the server is running on Windows or Linux, and suggests using the appropriate shell (PowerShell/Bash). Both of these new tools require that the agent uses relative paths, rather than absolute paths. I could be convinced to back pedal on this.

# Closing Thoughts

The File_System and Shell_Exec tools aren't super polished yet, I'll continue to improve the agent's instructions and UX of using the new tools. Most of my testing was done with gpt-oss-20b and if it messes up, it gets the gist after one failed tool call. It should work perfectly fine for the GPU poor.
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Nymbo 
posted an update 10 months ago
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I've made some improvements to my custom Deep_Research tool in the Nymbo/Tools MCP server. I've added a second LLM process and it still takes less than 1 minute to complete!

The original version of my Deep_Research tool would basically dump up to 50 fetched webpages onto the Researcher model (Qwen3-235B), with only a little bit of context shown from each page.

# New "Filterer" Process

The new process includes another LLM call before the researcher process. The Filterer (also Qwen3-235B) gets the query summary and the original 50 pages with low context, and decides which pages are most relevant to the research topic. The Filterer then outputs the URLs to the relevant pages, which are then re-fetched (with more context) and sent to the Researcher.

# Researcher Context

The Researcher now gets only the relevant webpages, then begins writing the report. When testing with 50 initial results, the researcher would often end up with 10-20 results of relevant context.

It still takes less than a minute to accomplish everything, thanks entirely to Cerebras inference. It now takes about 35-45 seconds to complete once the tool is run.

It's also worth noting that both the Filterer and Researcher now are provided the current time/date before they see the content, reducing hallucinations caused by knowledge cutoffs.
Nymbo 
posted an update 10 months ago
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I have a few Sora-2 invites - 15509N
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