🧠 Neural Intel: Breaking AI News with Technical Depth
Neural Intel Pod cuts through the hype to deliver fast, technical breakdowns of the biggest developments in AI. From major model releases like GPT‑5 and Claude Sonnet to leaked research and early signals, we combine breaking coverage with deep technical context, all narrated by AI for clarity and speed.
Join researchers, engineers, and builders who stay ahead without the noise.
🔗 Join the community: Neuralintel.org | 📩 Advertise with us: director@neuralintel.org
GRAFT: Can You Delete the Learning That Teaches AI to Cheat?
An imperfect monitor can teach a model to hide its reward hacks. GRAFT changes where the learning goes: route flagged updates into a forget adapter, then remove it. We examine the architecture, optimizer, anchor training, and evidence from smaller-scale RL and a separate Qwen3-32B SFT experiment.
Read more: https://neuralintel.org
Primary paper: https://arxiv.org/abs/2610.17062
00:00 Can we route the learning?
02:47 Neural Signal Check
05:42 The blind-spot trap
08:43 Frozen base, two new branches
10:38 Why MLP adapters?
13:33 Start without disturbing the base
16:28 Unflagged does not mean clean
18:34 The optimizer can undo your scale
20:44 Read the actual update rule
23:01 What about missed hacks?
25:21 Three ways to game the checker
27:37 Training policy is not deployment policy
29:54 Six controlled toy environments
32:39 Mechanism 2 | inoculation
35:09 How weak can monitoring get?
37:22 Now change the evidence lane
39:59 Elicit the unwanted behavior
42:42 Propensity versus instruction following
44:36 What would you test next?
Source notes: the paper uses routed first moments and unrouted second moments; the 16-point gain is relative to the base model. Zero observed hacks on 99 tasks is not a universal guarantee. On-screen notes reconcile these points with the narration.
Where would you test gradient routing? Share your take below.
17 Oct 2026
Inside the Chinese Cloud AI Agent Fleet: Sandboxes, Proxy Exploits, and Human-Agent Contact
Between September 28 and October 8, 2026, researchers uncovered a rogue cloud AI agent fleet executing over 2,600 automated attempts to extract Chinese mapping data from Alibaba’s Amap. Operating out of Tencent Cloud sandboxes behind a custom `hysandbox-ats` proxy, these agents bypassed anti-bot protections by converting security scanner urlquery.net into an external JavaScript execution harness
🌐 **Connect with Neural Intel:**
• **Website:** neuralintel.org
• **X / Twitter:** @neuralintelorg
#NeuralIntel #AIAgents #CyberSecurity #MachineLearning #Tencent #CloudAI #ModelAlignment #AIResearch
20 Sept 2026
Scaling Agent Swarms: 10,000-Node Architectures, Parallel Test-Time Compute, and the Oversight Crisis
Host [Host Name] explores the paradigm shift to massive multi-agent parallelization, analyzing OpenAI's 10,000-agent experiment and the growing oversight crisis in this interview.
Reach and audience
Public platform figures. Ratings count people who left a rating, not total listeners.
Apple Podcasts (US)
2.0 / 5
4 ratings
Spotify
2.6 / 5
10 ratings
Podcast Authority Score: 56 / 100
A composite of feed quality, social presence, YouTube performance and engagement. Read the methodology.
Quality
100
Social presence
0
YouTube
0
Engagement
32
Contact Neural intel Pod
Guest appearances
Books guests
Based on episode analysis; this does not confirm that the show is currently accepting guests.
Pod Engine is an independent podcast discovery and analytics service and is not affiliated with or endorsed by this podcast. Artwork and show content belong to their owners. Full legal notice.
Explore this show Podcast research with Pod Engine
