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Domain > sliced-ai.com
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More information on this domain is in
AlienVault OTX
Is this malicious?
Yes
No
DNS Resolutions
Date
IP Address
2026-01-06
3.5.84.249
(
ClassC
)
2026-01-11
3.5.81.247
(
ClassC
)
2026-02-23
52.92.211.171
(
ClassC
)
Port 80
HTTP/1.1 200 OKx-amz-id-2: p+Bov0P+WhAA1Z6Ug+XAHD+oxTkNHzCulNfL0eHjrSwKYDFlbQa839F434ZoHlYAcwBo5Wvl2Jcx-amz-request-id: AX39611WTG3TQR9HDate: Mon, 23 Feb 2026 21:44:58 GMTLast-Modified: Thu, 05 Sep 2024 23:51:03 GMTETag: 9de15f288d1facfe60acfd400588540fContent-Type: text/htmlContent-Length: 4575Server: AmazonS3 !DOCTYPE html>html langen>head> meta charsetUTF-8> meta nameviewport contentwidthdevice-width, initial-scale1.0> title>Slice AI - LLM Research/title> !-- Link to external CSS --> link relstylesheet hrefstyle.css>/head>body> div classcenter-content> !-- 1. Company Name and Small Logo on the Right --> div classtitle-logo> h1>Slice AI LLC/h1> img srcimages/slicedbread.png altSliced Bread Logo classsmall-logo> /div> !-- 3. Company Description --> p>Slice AI focuses on enabling large language models (LLMs) to operate with increased autonomy. The objective is to facilitate the development of artificial general intelligence (AGI) by allowing LLMs to learn continuously with minimal supervision./p> p>Our systems allow LLMs to learn from new examples, fostering more independent decision-making./p> !-- Contact Information --> p>Contact: a hrefmailto:Charles@sliced-ai.com>Charles@sliced-ai.com/a>/p> !-- 4. Research Section --> h2>Research/h2> ul> li>a href#paper1>Memory Retention, Learning Rates, and Rare Memory Injection in LLMs/a>/li> li>a href#paper2>Expanding Embedding Spaces/a>/li> li>a href#paper3>Exploring Thousands of Inferences on a Single Prompt/a>/li> /ul> !-- 5. Discussions Section --> h2>Discussions/h2> ul> li>a hrefhttps://medium.com/@charles.curt/why-dont-our-heads-explode-thinking-about-stop-signs-and-my-thoughts-on-squirrels-fe5e997440e1 target_blank>Why Don’t Our Heads Explode Thinking About Stop Signs?/a>/li> li>a hrefhttps://medium.com/@charles.curt/are-humans-deterministic-and-should-llms-make-spelling-mistakes-c47debc5c13b target_blank>Are Humans Deterministic? Should LLMs Make Spelling Mistakes?/a>/li> li>a hrefhttps://medium.com/@charles.curt/strong-vs-weak-ai-agents-b9f7e2379ba9 target_blank>Strong vs. Weak AI Agents/a>/li> li>a hrefhttps://medium.com/@charles.curt/llm-meetings-a-study-on-multi-agent-coherency-cee91fd663b2 target_blank>LLM Meetings: A Study on Multi-Agent Coherency/a>/li> li>a hrefhttps://medium.com/@charles.curt/llm-agent-depth-coherence-study-incomplete-547a4dcd289a target_blank>LLM Agent Depth Coherence Study (Incomplete)/a>/li> li>a hrefhttps://medium.com/@charles.curt/compounding-reasoning-chains-2c5cb0df2ee9 target_blank>Compounding Reasoning Chains/a>/li> li>a hrefhttps://medium.com/@charles.curt/the-forever-ai-agent-438e480dc9fc target_blank>The Forever AI Agent/a>/li> /ul> !-- 6. Research Papers --> h3 idpaper1>Memory Retention, Learning Rates, and Rare Memory Injection in LLMs/h3> p>a hrefpapers/lr_nad_model_collapse.pdf download>Download Investigating Learning Rates and Memory Retention/a>/p> p classsummary>This research investigates how learning rates affect memory retention in LLMs, revealing significant variations depending on the learning rates used./p> img srcimages/lr_vs_correct.png altLearning Rate vs Correct Count classsmall-image> h3 idpaper2>Expanding Embedding Spaces/h3> p>a hrefpapers/ae_embedding_studies.pdf download>Download Expanding Embedding Spaces/a>/p> p classsummary>This study explores how expanding embedding spaces improves data retrieval in long-running tasks for LLMs, using autoencoders and progressive training./p> div classimage-row> img srcimages/embedding_AE.png altAutoencoder Embedding Space> img srcimages/embedding_raw.png altRaw Embedding Space> /div> !-- 7. New Paper --> h3 idpaper3>Exploring Thousands of Inferences on a Single Prompt/h3> p>a hrefpapers/thousands_inferences.pdf download>Download Exploring Thousands of Inferences on a Single Prompt/a>/p> p classsummary>This paper studies how hyperparameters like temperature, top p, sequence length, and token length affect output diversity across thousands of inferences. Despite subtle variations, outputs remain too similar to predict hyperparameters effectively./p> img srcimages/embedding_clusters.png altEmbedding Clusters classsmall-image> /div>/body>/html>
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