This story was originally published on HackerNoon at: https://hackernoon.com/the-fatal-math-error-killing-every-ai-architecture-including-the-new-ones.
AI's Fatal Flaw: Why JEPA, LLMs & Transformers Can't Escape the Flatland, until Toroidal Math
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This story was written by: @josecrespophd. Learn more about this writer by checking @josecrespophd's about page, and for more stories, please visit hackernoon.com.

The “predict-the-next-word” LLM era is over. The new killer on the stage isn’t language, it’s world modeling. An AI that understands reality like a conceptual puzzle. “Stochastically parroted” was cute for 2023;now it is becoming a fossil.

This story was originally published on HackerNoon at: https://hackernoon.com/aioz-ai-the-people-powered-ai-stack-on-aioz-network.
AIOZ AI is the intelligence layer of the AIOZ Network, connecting a global community through a peer-to-peer compute economy. Learn more here!
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AIOZ AI is the intelligence layer of the AIOZ Network. Built for developers, researchers, and creators, it transforms how people build, share, and use artificial intelligence. It is powered by the Decentralized Physical Infrastructure Network.

This story was originally published on HackerNoon at: https://hackernoon.com/zenos-paradox-and-the-problem-of-ai-tokenization.
Token prediction forces LLMs to drift. This piece shows why, what Zeno can teach us about it, and how fidelity-based auditing could finally keep models grounded
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This story was written by: @aborschel. Learn more about this writer by checking @aborschel's about page, and for more stories, please visit hackernoon.com.

Zeno Effect is a structural flaw baked into how autoregressive models predict tokens: one step at a time, based only on the immediate past. It looks like coherence, but it’s often just momentum without memory.

This story was originally published on HackerNoon at: https://hackernoon.com/exploring-and-explaining-the-new-frontiers-of-advanced-prompt-injection.
This article explores four advanced attack patterns, backed by the latest research, that have virtually no overlap with the “ignore previous instructions”
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This story was written by: @mattleads. Learn more about this writer by checking @mattleads's about page, and for more stories, please visit hackernoon.com.

Security community has become obsessed with prompt injection. But a new, far more insidious class of attacks has emerged. This “Prompt Injection 2.0” is a systemic threat that targets the entire AI ecosystem.

This story was originally published on HackerNoon at: https://hackernoon.com/evaluating-visual-adapters-mivpg-performance-on-single-and-multi-image-inputs.
Details MIVPG experiments across single- and multi-image scenarios. Model uses frozen LLM and Visual Encoder, updating only the MIVPG for efficiency.
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This story was written by: @instancing. Learn more about this writer by checking @instancing's about page, and for more stories, please visit hackernoon.com.

Details MIVPG experiments across single- and multi-image scenarios. Model uses frozen LLM and Visual Encoder, updating only the MIVPG for efficiency.

This story was originally published on HackerNoon at: https://hackernoon.com/mivpg-and-instance-correlation-enhanced-multi-instance-learning.
MIVPG uses a Correlated Self-Attention (CSA) module to unveil instance correlation, fulfilling all MIL properties while outperforming Q-Former.
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This story was written by: @instancing. Learn more about this writer by checking @instancing's about page, and for more stories, please visit hackernoon.com.

MIVPG uses a Correlated Self-Attention (CSA) module to unveil instance correlation, fulfilling all MIL properties while outperforming Q-Former. CSA improves aggregation and reduces time complexity.

This story was originally published on HackerNoon at: https://hackernoon.com/mil-perspective-analyzing-q-former-as-a-multi-head-mechanism.
Proves Q-Former is a Multi-Head MIL module due to permutation invariance in its cross-attention.
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This story was written by: @instancing. Learn more about this writer by checking @instancing's about page, and for more stories, please visit hackernoon.com.

Proves Q-Former is a Multi-Head MIL module due to permutation invariance in its cross-attention. Notes its limitation: it assumes i.i.d. instances, overlooking crucial instance correlation.

This story was originally published on HackerNoon at: https://hackernoon.com/inside-darpaverse-the-us-militarys-next-big-leap-in-predictive-warfare-technology.
DARPA’s new “DARPAVERSE” aims to simulate and predict human behavior to optimize military operations — echoing Eris, goddess of discord.
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This story was written by: @thesociable. Learn more about this writer by checking @thesociable's about page, and for more stories, please visit hackernoon.com.

DARPA is putting out a “program competition” to create a ‘DARPAVERSE’ platform to model and simulate scenarios for optimizing military operations. The idea is to keep improving upon modeling systems to arrive at the best results with minimal downsides. The platform is intended to work under 24 hours.