kanaria007 PRO
kanaria007
AI & ML interests
None yet
Recent Activity
posted
an
update
about 13 hours ago
✅ New Article: *Designing Goal-Native Algorithms* (v0.1)
Title:
🎯 Designing Goal-Native Algorithms: From Heuristics to GCS
🔗 https://huggingface.co/blog/kanaria007/designing-goal-native-algorithms
---
Summary:
Most systems still run on “Inputs → model/heuristic → single score → action”.
But real deployments have multiple goals plus non-negotiable constraints (safety, ethics, legal).
This article is a design cookbook for migrating to goal-native control: make the goal surface explicit as a **GCS vector**, enforce **hard constraints first**, then trade off soft objectives inside the safe set.
> The primary object is a GCS vector + constraint status — not a naked scalar score.
---
Why It Matters:
• Stops safety/fairness from becoming silently tradable via “mystery weights”
• Makes trade-offs auditable: “why this action now?” can be reconstructed via Effect Ledger logging
• Gives a repeatable build flow: goals → constraints → action space → GCS estimator → chooser
• Shows how to ship safely: shadow mode → thresholds → canary, with SI metrics (CAS/SCover/EAI/RIR)
---
What’s Inside:
• A recommended GCS convention (higher=better, scales documented, weights only for soft goals)
• Chooser patterns: lexicographic tiers, Pareto frontier, context-weighted tie-breaks
• Practical patterns: rule-based+GCS wrapper, safe bandits, planning/scheduling, RL with guardrails
• Migration path from legacy heuristics + common anti-patterns (single-scalar collapse, no ledger, no PLB/RML)
• Performance tips: pruning, caching, hybrid estimators, parallel evaluation
---
📖 Structured Intelligence Engineering Series
Formal contracts live in SI-Core / GCS specs and the eval packs; this is the *how-to-design / how-to-migrate* layer.
replied to
their
post
1 day ago
✅ New Article: Designing Semantic Memory (v0.1)
Title:
🧠 Designing Semantic Memory: SIM/SIS Patterns for Real Systems
🔗 https://huggingface.co/blog/kanaria007/designing-semantic-memory
---
Summary:
Semantic Compression is about *what meaning to keep*.
This article is about *where that meaning lives*—and how to keep it *queryable, explainable, and governable* using two layers:
* *SIM*: operational semantic memory (low-latency, recent, jump-loop-adjacent)
* *SIS*: archival/analytic semantic store (long retention, heavy queries, audits)
Core idea: store “meaning” as *typed semantic units* with scope, provenance, goal tags, retention, and *backing_refs* (URI/hash/ledger anchors) so you can answer *“why did we do X?”* without turning memory into a blob.
---
Why It Matters:
• Prevents “semantic junk drawer” memory: *units become contracts*, not vibes
• Makes audits and incidents tractable: *reconstruct semantic context* (L3-grade)
• Preserves reversibility/accountability with *backing_refs*, even under redaction
• Adds semantic health checks: *SCover_sem / SInt / LAR_sem* (memory that stays reliable)
---
What’s Inside:
• Minimal *semantic_unit* schema you can run on relational/doc/graph backends
• Query/index playbook: ops (L1/L2) vs evidence/audit (L3)
• Domain patterns (CityOS / OSS supply chain / learning-support)
• Migration path: sidecar writer → low-risk reads → SI-Core integration
• Failure modes & anti-patterns: missing backing_refs, over-eager redaction, SIM-as-cache, etc.
---
📖 Structured Intelligence Engineering Series
Formal contracts live in the spec/eval packs; this is the *how-to-model / how-to-operate* layer for semantic memory that can survive real audits and real failures.
updated
a dataset
1 day ago
kanaria007/agi-structural-intelligence-protocols
Organizations
None yet