MemoryStackBench
🚀
Agent memory safety leaderboard
🏗️ Building on HF
Javad Taghia | PhD
javadtaghia
AI & ML interests
Work with agentic systems, agent memory, model fine tuning and unlearning, generative AI, and the development and deployment of open source AI
Recent Activity
updated a Space 4 days ago
javadtaghia/MemoryStackBench published a Space 4 days ago
javadtaghia/MemoryStackBench liked a Space 23 days ago
towardsai-tutors/ai-tutor-chatbotOrganizations
updated a
Space 4 days ago
published a
Space 4 days ago
liked a
Space 23 days ago
upvoted an article 2 months ago
Article
AtMem: Governed Memory and a Black Box for AI Agents
javadtaghia
• • 1 reacted to their post with 🚀 2 months ago
Post
221
We have published a complete paper about AetnaMem
Check it out here.
Please refer to the technical paper below.
https://github.com/aetna000/aetnamem/blob/e24d5aa74e62dbebeeb28a2e49b5c4d2006bbb4d/paper/aetnamem-control-plane.pdf
Check the benchmark
https://huggingface.co/spaces/Aetna000/MemoryStackBench
Check it out here.
Please refer to the technical paper below.
https://github.com/aetna000/aetnamem/blob/e24d5aa74e62dbebeeb28a2e49b5c4d2006bbb4d/paper/aetnamem-control-plane.pdf
Check the benchmark
https://huggingface.co/spaces/Aetna000/MemoryStackBench
- 4 replies
published an article 2 months ago
Article
AtMem: Governed Memory and a Black Box for AI Agents
javadtaghia
• • 1 replied to their post 6 months ago
posted an update 8 months ago
Post
221
We have published a complete paper about AetnaMem
Check it out here.
Please refer to the technical paper below.
https://github.com/aetna000/aetnamem/blob/e24d5aa74e62dbebeeb28a2e49b5c4d2006bbb4d/paper/aetnamem-control-plane.pdf
Check the benchmark
https://huggingface.co/spaces/Aetna000/MemoryStackBench
Check it out here.
Please refer to the technical paper below.
https://github.com/aetna000/aetnamem/blob/e24d5aa74e62dbebeeb28a2e49b5c4d2006bbb4d/paper/aetnamem-control-plane.pdf
Check the benchmark
https://huggingface.co/spaces/Aetna000/MemoryStackBench
- 4 replies
updated a
Space 8 months ago
replied to ZennyKenny's post 8 months ago
I have created https://huggingface.co/spaces/telcom/ResumeQA if someone wants to use or at least get some inspiration
published a
Space 8 months ago
reacted to ZennyKenny's post with 👍 8 months ago
Post
3275
😎 My new personal website is live! Check out https://kennethhamilton.me to chat with an LLM about my professional skills and personal projects.
🙈 Think of it like a really, really vain version of ChatGPT.
🙈 Think of it like a really, really vain version of ChatGPT.
- 6 replies
replied to ZennyKenny's post 8 months ago
love the idea. I heard about this approach in https://www.youtube.com/watch?v=0teZqotpqT8
Stop Competing With 400 Applicants. Build This in One Weekend (Yes, there's a no code option too!)
reacted to AdinaY's post with 👍 8 months ago
Post
1110
After a VLM, StepFun dropped a new audio model: Step-Audio-R1.1, enabling thinking while speaking 🔥
stepfun-ai/Step-Audio-R1.1
✨ Apache 2.0
✨ Combines dual-brain architecture and acoustic-grounded reasoning to enable real-time dialogue with SOTA-level reasoning
stepfun-ai/Step-Audio-R1.1
✨ Apache 2.0
✨ Combines dual-brain architecture and acoustic-grounded reasoning to enable real-time dialogue with SOTA-level reasoning
- 2 replies
posted an update 8 months ago
Post
1591
MAD-GRPO: https://huggingface.co/blog/telcom/mad-grpo
In R1-Zero-Like Training *, Dr.GRPO treats GRPO’s by dropping std, but that often comes with a hidden side effect: length-weighted updates that can nudge model toward verbosity.
MAD-GRPO provides robust scale (MAD + epsilon) per-token normalization stability without verbosity bias.
*https://huggingface.co/papers/2503.20783
In R1-Zero-Like Training *, Dr.GRPO treats GRPO’s by dropping std, but that often comes with a hidden side effect: length-weighted updates that can nudge model toward verbosity.
MAD-GRPO provides robust scale (MAD + epsilon) per-token normalization stability without verbosity bias.
*https://huggingface.co/papers/2503.20783
commented a
paper 8 months ago
published an article 8 months ago
Article
MAD GRPO: Treating Dr. GRPO that tried to fix GRPO but brought instability and verbosity bias
javadtaghia
• • 5 reacted to their post with 👀 8 months ago
Post
215
CAQBI Index
is like a credit score for how an AI understands a concept.
Please check
https://huggingface.co/blog/telcom/caqbi
is like a credit score for how an AI understands a concept.
Please check
https://huggingface.co/blog/telcom/caqbi
- 1 reply
reacted to AdinaY's post with 👍 8 months ago
Post
1550
Wechat AI is shipping!
WeDLM 🔥 A new language model that generates tokens in parallel, making it faster than standard LLMs , with the same Transformer setup!
https://huggingface.co/collections/tencent/wedlm
✨ 7B/8B - Base & Instruct
✨ Apache 2.0
WeDLM 🔥 A new language model that generates tokens in parallel, making it faster than standard LLMs , with the same Transformer setup!
https://huggingface.co/collections/tencent/wedlm
✨ 7B/8B - Base & Instruct
✨ Apache 2.0
- 4 replies
reacted to kanaria007's post with 👍 8 months ago
Post
2009
✅ New Article: *PoC Architecture for Education & Developmental Support*
Title:
🎓 Building an SI-Core Wrapped Learning Companion - PoC architecture for education and developmental support
🔗 https://huggingface.co/blog/kanaria007/poc-architecture-for-education-development-support
---
Summary:
Most “AI tutors” are built as *LLM-first* systems. This article flips the default:
* The LLM is treated as an *untrusted proposal engine*
* *SI-Core owns* observation, consent, ethics, memory, and rollback
* Teachers and guardians get *real oversight*, not just chat transcripts
Scoped intentionally to *one subject × a small cohort (10–30 learners)*, this is a PoC you can actually ship—and audit.
> Don’t ask: “Can an AI replace teachers?”
> Prove: “Can we make an AI companion *safe, explainable, and governable* for real learners?”
---
Why It Matters (for AI on real stacks):
• *Consent & accommodations* are first-class (especially for minors / neurodivergent learners)
• *Ethics decisions are logged* (ALLOW / DENY / ESCALATE) with traceable reasoning
• “*Why this?*” explanations are built in for learners—and deeper inspection for adults
---
What’s Inside:
• A minimal reference architecture (frontend → SI-Gate → ethics/memory/logging → LLM APIs)
• Non-negotiables for the pilot (SI-wrapped LLM, Effect Ledger, ethics overlay, dashboards)
• Failure modes + safe-mode behavior
• Implementation checklist + rough effort/cost ballparks (kept explicitly non-normative)
---
📖 Structured Intelligence Engineering Series
A deployable pattern for taking today’s LLM tutor ideas and making them *auditable, overrideable, and rollback-safe*.
Title:
🎓 Building an SI-Core Wrapped Learning Companion - PoC architecture for education and developmental support
🔗 https://huggingface.co/blog/kanaria007/poc-architecture-for-education-development-support
---
Summary:
Most “AI tutors” are built as *LLM-first* systems. This article flips the default:
* The LLM is treated as an *untrusted proposal engine*
* *SI-Core owns* observation, consent, ethics, memory, and rollback
* Teachers and guardians get *real oversight*, not just chat transcripts
Scoped intentionally to *one subject × a small cohort (10–30 learners)*, this is a PoC you can actually ship—and audit.
> Don’t ask: “Can an AI replace teachers?”
> Prove: “Can we make an AI companion *safe, explainable, and governable* for real learners?”
---
Why It Matters (for AI on real stacks):
• *Consent & accommodations* are first-class (especially for minors / neurodivergent learners)
• *Ethics decisions are logged* (ALLOW / DENY / ESCALATE) with traceable reasoning
• “*Why this?*” explanations are built in for learners—and deeper inspection for adults
---
What’s Inside:
• A minimal reference architecture (frontend → SI-Gate → ethics/memory/logging → LLM APIs)
• Non-negotiables for the pilot (SI-wrapped LLM, Effect Ledger, ethics overlay, dashboards)
• Failure modes + safe-mode behavior
• Implementation checklist + rough effort/cost ballparks (kept explicitly non-normative)
---
📖 Structured Intelligence Engineering Series
A deployable pattern for taking today’s LLM tutor ideas and making them *auditable, overrideable, and rollback-safe*.