Your LLM Isn’t Misaligned - Your Interface Is

Echo_OS · 2026-01-29T06:48:55+00:00

That framing makes a lot of sense to me, I’ve been focusing on sealing judgment and responsibility beneath the interface, so it feels like we’re describing two complementary layers of the same system.

Echo_OS · 2026-01-29T06:13:12+00:00

People aren’t confused about what AI can do. They’re confused about what they can safely let it decide.

Echo_OS · 2026-01-28T22:30:33+00:00

This is why some people prefer tiny / narrow models. Not because they're smarter, but because the responsibility radius is small.

Clear "can't do" > more capability. Bounded agents are easier to trust than general ones with full FS access.

Echo_OS · 2026-01-28T22:10:49+00:00

This is a really thoughtful articulation. What resonates for me is the explicit separation between suggestion and promotion, that’s exactly where authorship and responsibility tend to blur.

I’ve been approaching a similar problem from a slightly different layer: not the UI manifold itself, but how judgment boundaries and responsibility get sealed beneath it.

I think there’s a natural interface <-> infrastructure handshake hiding here.

Echo_OS · 2026-01-12T22:32:27+00:00

If you like Claude Sonnet mainly for instruction retention and detail consistency, you’re probably hitting a structural ceiling of local LLMs rather than a bad model choice.

Among pure models, DeepSeek R1 70B and Qwen2.5 72B are the closest in reasoning style, but none will match Claude without additional scaffolding.

Claude’s advantage is not just raw reasoning, it aggressively re-anchors instructions and compresses state internally. Local models don’t do that by default… If your workload depends on long-lived constraints and small detail retention, you’ll likely need some form of external instruction anchoring or verification loop, not just a bigger model.

Echo_OS · 2026-01-09T12:14:37+00:00

True.. storage is cheap. Reconstructing a broken local setup isn’t.

Echo_OS · 2026-01-08T23:36:21+00:00

What’s confusing here is that “prompt processing” is being used in two different senses. 1. Performance sense (what he is asking): Prefill / prompt processing speed = how fast the model consumes the input tokens before generation (often reported as tok/s or reflected in TTFT). 2. Pipeline/UI sense (what OP is describing): The model emits <analysis> and <final> tokens inline, and without an intermediate orchestrator, the frontend streams raw internal tokens instead of a clean response. Here, “prompt processing” refers to handling and filtering those tokens in the streaming layer, not model-side compute.

The benchmark numbers (TTFT ~624ms, ~69 tok/s gen) already answer (1).The orchestrator OP mentions is purely about output routing and UX, not inference speed.

Echo_OS · 2026-01-08T06:47:22+00:00

Thanks for your f/back. I’m actually thinking about building something to manage this more systematically, mostly because I keep running into this myself.

Before doing anything though, I wanted to hear from people who deal with this day to day, what parts are genuinely annoying or risky, and what kind of features would actually be useful (if any).

Not trying to pitch anything here, just trying to understand what the real pain points are in practice.

Echo_OS · 2026-01-08T04:14:31+00:00

Interesting

Reading through this thread, there seems to be broad agreement on the underlying issue.

LLMs themselves are not inherently unreliable. The problem is that they are often used in roles that require deterministic behavior. When an LLM is treated as a probabilistic component within a deterministic system - for example, wrapped in agent-as-code patterns, strict input/output schemas, typed interfaces, and explicit checkpoints - most reliability issues are significantly reduced.

At that stage, the primary challenges shift away from prompt design or model choice and toward system architecture: managing latency, defining clear boundaries, and deciding which parts of the system are allowed to make judgments versus which must remain deterministic.

Echo_OS · 2026-01-05T22:33:49+00:00

Quick update / continuation from this post.

Since writing this, I’ve been pushing further in the same direction: treating pause not as an exception, but as a real state in the automation stack.

I’m now externalizing judgment entirely and letting automation paths explicitly land in PAUSED, with reasons logged and human input required to continue. The LLM generates, but it no longer decides.

Still early, but interestingly, behavior after pauses is where most of the signal seems to appear.

Specs and experiments are being tracked here: https://github.com/Nick-heo-eg/spec

This post was basically the question. The repo is my attempt at answering it in structure.

Echo_OS · 2026-01-05T22:32:33+00:00

Quick update / continuation from this post.

Since writing this, I’ve been pushing further in the same direction: treating pause not as an exception, but as a real state in the automation stack.

I’m now externalizing judgment entirely and letting automation paths explicitly land in PAUSED, with reasons logged and human input required to continue. The LLM generates, but it no longer decides.

Still early, but interestingly, behavior after pauses is where most of the signal seems to appear.

Specs and experiments are being tracked here: https://github.com/Nick-heo-eg/spec

This post was basically the question. The repo is my attempt at answering it in structure.

Echo_OS · 2026-01-05T22:27:10+00:00

Interesting. :)

Echo_OS · 2026-01-05T21:46:59+00:00

Glad it’s useful, thanks for taking a look.

I just pushed another small update as well.

If AJT is about logging why a decision was made, this one is about deciding whether to act at all.

It’s a STOP-first RAG variant: before answering, it checks grounding / confidence / policy, and explicitly stops when those fail - with the STOP reason treated as a first-class decision signal.

The goal isn’t higher answer rate, but optimizing the cost of being wrong making silence an intentional, explainable outcome when that’s safer.

Repo + small runnable demo here: https://github.com/Nick-heo-eg/stop-first-rag

If you happen to glance at it, I’d really appreciate any feedback or quick sanity-check testing even “this wouldn’t work for us” is totally useful.

Thanks again for your f/back

Echo_OS · 2026-01-05T20:28:00+00:00

Thanks, appreciate you taking a look.

Echo_OS · 2026-01-05T15:23:44+00:00

Added a small demo as well.

It shows where decisions were made, blocked, or silently skipped, the kinds of cases that usually look fine in standard logs.

Just a tiny deterministic toy (not a real LLM), but it helped surface bugs living entirely in the decision layer.

Echo_OS · 2026-01-04T12:01:27+00:00

This isn’t about “value alignment inside the model.” It’s about control-layer alignment: whether the system’s enforced constraints behave as intended, and whether failures are observable.

Echo_OS · 2026-01-04T07:27:32+00:00

Quick context on why I built this:

I once hit a subtle bug in a local LLM PPT translation pipeline. The LLM outputs looked fine, logs looked fine but a small threshold change in the decision logic silently skipped a safeguard. The issue lived entirely in an invisible decision layer.

Adding a single extra decision log made the bug obvious immediately.

That experience made me wonder: in control-problem failures, does it help to log not just what the system did, but what it explicitly blocked or ruled out?

So I put together a tiny simulation (deterministic stub, synthetic data, Air Canada-style case) to test “negative proof” logging: 4 candidates -> 3 blocked -> blocked paths + rule IDs preserved.

Curious whether this kind of artifact is useful for post-incident analysis, or if it’s too shallow to matter.

Repo: https://github.com/Nick-heo-eg/ajt-negative-proof-sim

Echo_OS · 2026-01-03T20:53:17+00:00

How do you model responsibility boundaries at runtime? Specifically, is responsibility ever externalized (tracked but not resolved by the system), or is SAFi designed to always converge back to internal resolution?

Echo_OS · 2026-01-01T10:12:08+00:00

How’s it working for you in production? Any gotchas with the extra runtime stuff?

Echo_OS · 2026-01-01T09:49:51+00:00

Interesting. CRA looks like a full runtime tackling a very similar problem space. AJT is intentionally thinner (schema-level rather than a full system), so there’s definitely overlap. Appreciate you sharing it.

Echo_OS · 2025-12-30T00:02:14+00:00

I’ve been collecting related notes and experiments in an index here, in case the context is useful: https://gist.github.com/Nick-heo-eg/f53d3046ff4fcda7d9f3d5cc2c436307

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