John6666/forum2
Updated
•
1.18k
•
20
John Smith PRO
John6666
AI & ML interests
None yet
Recent Activity
reacted
to
kanaria007's
post
with 👀
1 minute ago
✅ New Article: *Goal-Native Algorithms for Structured Intelligence*
Title:
🧭 Goal-Native Algorithms for Structured Intelligence
🔗 https://huggingface.co/blog/kanaria007/goal-native-algorithms
---
Summary:
SI-Core gives you something most AI stacks don’t have: structurally constrained, reversible, auditable, ethics-traced intelligence.
But once you *have* a safe, reversible core, the next question is:
> “Given this core, *how do we actually choose good actions?*”
This article introduces *goal-native algorithms*: ways of deciding *where to move* inside an SI-conformant core — using explicit goal objects, *Goal Contribution Score (GCS)*, semantic compression as an optimization problem, and multi-goal schedulers.
---
Why It Matters:
* Moves beyond “safe shells” to *goal-aware decision-making*.
* Makes optimization *auditable*: every jump can be tied to named goals and logged contributions.
* Bridges *RL rewards, Pareto optimization, and constraints* with SI-Core’s [OBS]/[ETH]/[MEM]/[ID] stack.
* Gives AGI/agent teams a concrete roadmap for building planners *inside* a structured, reversible OS.
---
What’s Inside:
* Goals as *first-class structured objects* (owners, metrics, horizons, constraints, priorities).
* *GCS*: a signed, normalized measure of how much a step helped or hurt each goal — including multi-goal vectors and lexicographic policies.
* *Semantic compression as a goal problem*: deciding what *not* to look at based on impact on future GCS, not just bandwidth.
* GCS-aware *optimizers & schedulers* for risk-bounded action selection under compute/time budgets.
---
📖 Structured Intelligence Engineering Series
This piece sits on top of the SI-Core, SI-NOS, and SIC whitepaper set, focusing on *how agents decide*, not just how they stay safe. It’s a design guide for anyone building structured, goal-aligned AI on top of reversible, auditable cores.
reacted
to
rajkumarrawal's
post
with 👍
1 minute ago
September(2025) LLM Safety & Reliability Benchmarks Report By AI Parivartan Research Lab (AIPRL-LIR)
Monthly LLM's Intelligence Reports for AI Decision Makers :
Our "aiprl-llm-intelligence-report" repo to establishes (AIPRL-LIR) framework for Large Language Model overall evaluation and analysis through systematic monthly intelligence reports. Unlike typical AI research papers or commercial reports. It provides structured insights into AI model performance, benchmarking methodologies, Multi-hosting provider analysis, industry trends ...
( all in one monthly report ) Leading Models & Companies, 23 Benchmarks in 6 Categories, Global Hosting Providers, & Research Highlights
Here’s what you’ll find inside this month’s intelligence report:-
Leading Models & Companies :
23 Benchmarks in 6 Categories :
With a special focus on Safety & Reliability performance across diverse tasks.
Global Hosting Providers :
Research Highlights :
Comparative insights, evaluation methodologies, and industry trends for AI decision makers.
Disclaimer:
This comprehensive Safety & Reliability analysis represents the current state of large language model capabilities as of September 2025. All performance metrics are based on standardized evaluations and may vary based on specific implementation details, hardware configurations, and testing methodologies. Users are advised to consult original research papers and official documentation for detailed technical insights and application guidelines. Individual model performance may differ in real-world scenarios and should be validated accordingly. If there are any discrepancies or updates beyond this report, please refer to the respective model providers for the most current information.
Repository link is in comments below :
https://huggingface.co/blog/rajkumarrawal/september-2025-aiprl-lir-safety-reliability
https://huggingface.co/AiParivartanResearchLab
upvoted
an
article
1 minute ago
Transformers v5: Simple model definitions powering the AI ecosystem
Organizations
