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Sk md saad amin
Reality123b
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about 17 hours ago
lap-quantum/QPU-1-MCP
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Duskfallcrew's
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You've noticed that I did the "WEIRD" and attempted to make it look like all my old content was "SCRAPED"
I'm largely retiring from GEN AI.
Calypso Crunchies is an old account I used to use for diffusers conversions for someone.
IF YOU WOULD LIKE ACCESS to ANYTHING -- I lost access due to me forgetting to jank Calypso into the E&D old repo, but i can get Angel or someone to add me or my other account back..
I didn't want HF to lose 3 years of my insane progress in doing things, but i need to retire from Generative image AI fast, my mental health has been diving for so long.
I'll continue in the developing/vibe coding./educational sphere, but I just can't continue in the other end of it. Much love, thank you all
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â
New Article: *Evaluation as a Goal Surface* (v0.1)
Title:
𧪠Evaluation as a Goal Surface: Experiments, Learning Boundary, and ETH-Aware A/B
đ https://huggingface.co/blog/kanaria007/evaluation-as-a-goal-surface
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Summary:
Most âevaluationâ quietly collapses into a single numberâand then we optimize the wrong thing.
This article reframes evaluation as a *goal surface*: multi-objective, role-aware, and ethics-bounded.
In SI-Core terms, experiments become *first-class Jumps (E-Jumps)* with explicit contracts, traces, and gatesâso you can run A/B tests, shadow evals, and adaptive rollouts *without violating ETH, confusing principals/roles, or learning from unsafe data*.
> Donât optimize a metric.
> Optimize a goal surfaceâunder explicit constraints.
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Why It Matters:
⢠Prevents Goodhart failures by treating evaluation as *multi-goal + constraints*, not a scalar leaderboard
⢠Makes experimentation auditable: *EvalTrace* answers âwhat changed, for whom, why, and under what policyâ
⢠Enables *ETH-aware A/B*: assignment, exposure, and stopping rules respect safety/fairness boundaries
⢠Connects experiments to governance: *Learning Boundary (LB)* + rollout control (PoLB) instead of âship and prayâ
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Whatâs Inside:
⢠What EVAL is in SI-Core, and *who* is being evaluated (agents / roles / principals)
⢠âExperiments as Jumpsâ: *E-Jump request/draft* patterns and contracts
⢠*ETH-aware variant testing* (including ID/role constraints at assignment time)
⢠Shadow evaluation + off-policy evaluation (how to learn without unsafe intervention)
⢠Role & persona overlays for EVAL (role-aware scoring, persona-aware reporting)
⢠*EvalTrace* for audits + incident review, plus âevaluate the evaluatorsâ test strategies
⢠Practical experiment design: power/sample size, early stopping, multi-objective bandits, causal inference
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đ Structured Intelligence Engineering Series
this is the *how-to-design / how-to-run experiments safely* layer.
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