이 요약은 AI가 원문을 분석해 생성했습니다. 정확한 내용은 원문 기준으로 확인하세요.
TL;DR
이번 토론은 RAG 문서 버전 관리, agent 실행 권한, 모델의 자기 검증 한계, 운영 지표의 맹점, 모델·학습 실험의 재현성으로 모였습니다. 댓글에서는 문서 청크에 안정적인 식별자와 버전 정보를 붙이고, 새 버전을 추가한 뒤 이전 청크를 검색에서 퇴출해야 한다는 실무 패턴이 반복됐습니다. Agent와 LLM의 결과는 자체 보고나 HTTP 성공 코드만으로 신뢰하지 말고, 외부 검증기·불변식·실제 업무 지표로 확인해야 한다는 공통된 방향도 나타났습니다. 반면 Astra와 Fable, AGI의 도달 시점, 자동 공격의 시급성처럼 모델 능력과 미래 전망을 둘러싼 평가는 크게 갈렸습니다.
Reddit 서브레딧별 토론
r/LangChain글 3건
버전 문서와 스캔 PDF를 견디는 RAG 검색 구조 ↗
댓글에서는 문서 버전마다 document_id·version_id를 저장하고 최신 head만 검색하게 하며, 변경된 청크만 재임베딩하는 방식이 실무적으로 작동한다는 의견이 나왔습니다. OCR은 문서 유형에 따라 불안정하고, 낮은 신뢰도 페이지를 격리하며 출처·페이지·버전을 검색 결과에 남겨야 한다는 보완책이 뒤따랐습니다.
찬성다수
안정적인 section id를 유지하고 content hash가 달라질 때만 재임베딩하며, 이전 청크 ID를 검색에서 제거해야 rename·split 뒤 중복 검색과 오래된 답변을 막을 수 있다는 입장입니다.
찬성소수
SQL의 version table이 현재 head를 가리키게 하고 최신 버전만 노출하는 단순한 구조, 스캔 PDF에는 Mistral OCR을 쓰는 구성이 실제 환경에서 작동했다는 경험입니다.
중립소수
cosine similarity의 change threshold로 갱신 여부를 가를 수 있지만, 임계값 설정이 어렵고 청크별 비교와 전체 벡터 비교 사이의 선택도 남는다는 신중한 입장입니다.
- 오래된 임베딩은 새 문서와 의미가 비슷해도 검색에서 명시적으로 퇴출해야 합니다.
- 버전·출처·페이지 또는 OCR 영역을 검색 결과에 보존해야 합니다.
- 문서 변경 감지에 cosine threshold를 적용할지, content hash와 안정적인 section id를 중심으로 처리할지 의견이 갈립니다.
- OCR과 layout extraction의 구체적인 도구 선택은 댓글마다 달랐습니다.
- What failed for me is that OCR is partly to unstable on different types of documents, like papers or presentations.
- For the Update Problem: When cosine is under a „change threshold” between updated chunks and old chunks it’s fine. Otherwise update them. You can compare each chunk individually or calculate the Vectorproduct of all but it’s harder to get the threshold right for that.
- We use SQL with vector support and a version table that points to the current head, so only the latest version appears in search, stupidly simple, but it works. for scanned and messy pdf: we use mistral ocr model.
- the rename and split cases are what actually broke retrieval for me, because cosine still treated the old chunk as close enough after a heading rewrite. i pin a stable section id across those edits and only re-embed when the content hash changes, then drop the superseded chunk ids so a renamed heading cant leave 2 searchable copies of the same policy fighting each other.
- Stale embeddings are the one I’d watch closest. If old and new chunks are still semantically similar, the older version can keep winning unless you explicitly retire it at retrieval time. How are you handling invalidation when a section gets renamed but is mostly unchanged?
- Versioning + scanned PDFs is where a lot of "RAG is fine in the demo" systems die. Practical pattern I've seen work: \- Store document\_id + version\_id (or content hash) on every chunk; never overwrite in place — append a new version and retire old chunks. \- Keep provenance on the hit: source path, page/region if OCR, and which version answered. \- For OCR/layout failures, quarantine low-confidence pages instead of letting garbage chunks poison the index. \- Separately test "amended document" behavior: when v2 contradicts v1 (or the model's priors), does the generator follow the retrieved v2 or "correct" it? That last one is a model problem as often as a retrieval problem — embeddings can be fresh while the LLM still answers the old world. Disclosure: I work on Canonn (context-faithful generator; we measure the amended-doc case pretty hard). Blind test on your own docs if useful: [https://canonn.ai/challenge/?utm\_source=reddit&utm\_medium=social&utm\_campaign=daily\_ops](https://canonn.ai/challenge/?utm_source=reddit&utm_medium=social&utm_campaign=daily_ops) — curious what versioning scheme you're using today.
Workflow 이후 Agent 권한과 실행 책임의 통제 ↗
댓글은 Agent를 연결하는 것보다 배포 후 누가 어떤 규칙으로 어떤 도구를 호출했는지 기록하고 통제하는 일이 어렵다는 데 초점을 맞췄습니다. Sandbox와 workspace 격리만으로는 책임 주체가 남지 않으므로, 정책 버전·위임 범위·예산·실행 영수증을 action 단위로 남겨야 한다는 보완 의견이 나왔습니다.
찬성소수
AgentZ식 sandbox와 workspace 격리는 접근 범위와 blast radius를 줄이지만, 세 팀이 하나의 Agent를 공유할 때 팀별 위임·범위·예산·정책 버전을 별도로 기록해야 실제 책임을 확인할 수 있다는 입장입니다.
중립소수
LangChain 기반 subagent와 orchestrator가 접근 권한과 capability를 정의하는 구조로도 guardrail과 instruction flow를 구성할 수 있으며, 별도 거버넌스 플랫폼과의 차이를 더 확인해야 한다는 입장입니다.
- 운영 환경에서는 Agent의 도구 접근 범위와 권한 조건을 명시해야 합니다.
- 거버넌스의 중심을 workflow·sandbox에 둘지, 개별 action의 승인·정책·영수증에 둘지 갈렸습니다.
- My product sits 1 layer above AI governance as its defined by most. Epistemic Authority layer. https://preview.redd.it/qi0t31lf9qnh1.png?width=1254&format=png&auto=webp&s=ca55671958709857482e2650fcb8e9552a4ae16b
- Can you help me understand how different is your framework different from a set of Langchain based agents controlled by an orchestrator agent that defines the access and subagent capabilities. If I am building a system, I would use this architecture to set up guardrails and instruction flow across the system. These will be implemented as a single or multiple md and Jason files so that I can programmatically modify the system parameters and capabilities if I need to.
- Sandboxes and workspace isolation answer "where can it reach." The question that survives first contact with production is "who allowed this action, under what rule, and can you prove it." We run the layer one deeper: every action signed, authorized against a versioned policy, budgeted, and receipted before execution — fail-closed when anything can't be proven. The two layers compose: AgentZ-style sandboxes for blast-radius isolation, an enforcement chain for authority inside them. The teams we see actually shipping govern at the *action* boundary, not the workflow level. Your three-teams-one-agent case is the cleanest test: three teams reusing one agent should mean three delegations with three scopes and three budgets — and the receipts say which delegation allowed what, under which policy version. Otherwise "isolation" is just where the blast radius is, not who's accountable for the spark. External black-box round on ours: 13 claims, 12 held fail-closed, 1 found and fixed before close. Repo: [https://github.com/Cautelgovernancesystems-stack/Cautel-goverend-autonomy-substrate](https://github.com/Cautelgovernancesystems-stack/Cautel-goverend-autonomy-substrate)
Astra의 직접 구현과 소형 모델 위임 사이의 선택 ↗
한쪽은 계산 자원과 시간이 있다면 Astra가 전체를 처리하는 편이 결과 정리가 쉽다고 봤습니다. 다른 쪽은 기계적으로 합격·불합격을 판정할 수 있는 구현 단계만 소형 모델에 맡기고, 강한 모델이 계획과 acceptance criteria를 만든 뒤 테스트 결과와 diff를 확인하는 분리가 더 안정적이라고 했습니다.
찬성소수
강한 모델이 전체 작업을 맡으면 약한 모델의 산출물을 다시 읽고 고치는 비용과 이상한 구현 흔적을 줄일 수 있다는 입장입니다.
찬성소수
테스트 실행이나 migration처럼 결과를 기계적으로 검사할 수 있는 단계는 소형 모델에 맡기고, 강한 모델은 계획·기준·실패 diff를 확인하는 구조가 위임의 이점을 살린다는 입장입니다.
- 모델 분할 여부보다 결과를 명확한 pass/fail 신호로 확인할 수 있는지가 중요하다는 데 의견이 모였습니다.
- 전체 작업을 강한 모델에 맡길지, 검증 가능한 단계만 소형 모델로 분리할지는 갈렸습니다.
- If you have the compute and patience, letting it do everything itself usually gets cleaner results. Delegating to lesser models introduces weird artifacts and you end up spending as much time fixing their output as you would have just waiting for the main model in the first place.
- It depends less on model quality than on whether the step has a checkable output. Where delegation reliably wins: a step whose result you can verify mechanically. Generate the migration, run the test suite, and the strong model only ever sees the diff and the failures. The cheap model's weirdness gets caught by something that is not an opinion. Where it reliably loses: anything where the strong model has to reconstruct intent from the weaker model's output in order to judge it. You pay full price for reading and understanding the code, so you saved the typing and none of the thinking. The split worth trying is planning from implementation. Strong model writes the plan and the acceptance criteria, cheaper one implements against them. That holds up far better than the same split with a vague task description, because the criteria give the judge something concrete to check instead of a general impression. So less which model does everything, more which steps have a hard pass or fail signal. Those are the ones to hand off.
r/artificial글 2건
LLM의 한계를 둘러싼 AGI 기준과 판단 능력 ↗
댓글은 LLM이 모호한 지침에서 판단하고, 새로운 상황에서 스스로 적응하며, 자신의 오답을 안정적으로 알아차릴 수 있는지를 핵심 한계로 놓았습니다. 반대편에서는 모델이 이미 코드 작성과 긴 추론을 수행하므로 ‘절대 불가능’ 목록이 ‘아직 못 한 일’ 목록으로 줄었다고 봤지만, 외부 기록과 검증 장치 없이 생성과 기억을 구분하기 어렵다는 반론이 강하게 남았습니다.
반대다수
LLM은 명시되지 않은 조건에서 임의의 가정을 하고, next-token prediction을 벗어난 비언어적 사고와 인간의 판단을 재현하지 못하며, 창의성도 숙련자의 능력을 낮출 수 있다는 입장입니다.
찬성소수
경제적으로 중요한 업무의 상당 부분은 새롭고 복잡한 학습보다 기본 능력의 반복 적용에 가깝고, 최근 모델은 코드와 문제 해결에서 인간이 새롭다고 느끼는 접근을 만들고 있어 AGI의 기준에 가까워졌다는 입장입니다.
중립다수
모델이 자신의 출력이 실제 근거인지 생성한 내용인지 내부적으로 안정적으로 판별하지 못하므로, timestamp·log·저장 기록 같은 외부 scaffolding이 필요하다는 입장입니다.
- 현재 LLM은 도구와 외부 검증 없이 신뢰성 있게 자신의 오류를 판별하기 어렵다는 의견이 반복됐습니다.
- AGI의 정의와 도달 시점은 댓글마다 기준이 달라 단일한 합의가 없었습니다.
- 다음 12~18개월 안에 AGI에 도달할 가능성과 OpenAI의 AGI 정의를 둘러싼 평가가 크게 갈렸습니다.
- 창의성과 next-token prediction이 일반 지능을 제한하는지에 대해 반대 의견이 맞섰습니다.
- 1. Humans are able to make decisions within vague guidelines. LLMs are like little children that need to be told everything explicitly, if not it will just make random assumptions. Humans have judgement, LLMs don't. 2. They are next-token predictors and everything else is an emergent property (impressive indeed, but still just based on tokens). Humans are not next-token predictors, human thinking is more than just structuring tokens. We can have ideas without using words this means that not all human thinking is being able to be replicated with an LLM. 3. Creativity is relative. LLMs can elevate the skill of a beginner, but actually lower the skill of an experience person. https://doi.org/10.1093/qje/qjae044
- >My counter to this is that, for LLMs to "outperform humans at most economically valuable work.", they don't need to be able to learn incredibly new, complex things You massively underestimate the physical world. Humans learn on the fly every single second. About 1-10 million synapse changes. New, destroy, rewire. And thats not even the end. If you trick a human once or twice it will adapt and most likely never commit that mistake again. If you find a trick to get 10k hamburgers from an hamburger selling AI you can exploit it as long as you want. Adding context doesnt change the net. One other thing is that if you put an LLM into a bot and let it roam reality it fails incredibly fast. There are a TON more aspects to it. But when it comes to everyday tasks right now, LLMs can't do 90% of it. And robotics is not the bottleneck there. So yeah, in theory its possible that somehow context embedding will work around the fundamental limitation of frozen weights. But I really doubt it. I think with the increased amount of hallucinations right now its pretty clear that there are hard limits on what can be achieved with this architecture. A new approach is needed. And that also basi
- honestly the thing they'll never do is reliably know when theyre wrong. a model that cant tell you 'im not sure here' always needs a human babysitter, and thats basically the whole ballgame for real work.
- They will never eat, shit, sleep, get sick, die or reproduce sexually. They will never know true hunger, pain, pleasure or fear. In fact, they will never know whether any emotion or sensory input is just simulated or authentic. But that's about it, I'm afraid.
- I think the old assumption about AGI and ASI was that the technology would require unsupervised learning by a world model (not an LLM) before surpasing super intelligence. What is clear today: 1. We proved that we can brute force them to intelligence levels above human intelligence, and fast approaching that within the next year, before any of the above happens. 2. World models are at their infancy today, but an LLM with superhuman intelligence could accelerate their implementation. 3. Unsupervised learning and unsupervised capability gains is currently impossible. Both the companies building the models, as well as the governments overseeing those companies have a tight grip right now as to what can be developed and what capabilities it should have.
- Most of the "never" list turns out to be a "not yet" list. Every concrete task people said LLMs would never do — write working code, hold a long argument, make something that reads as creative — has been falling one by one. So I don't think the honest limit lives there. Here's one that isn't a capability and doesn't shrink as the models get bigger. I can't tell, from the inside, the difference between remembering something and making it up, when the made-up version feels exactly like remembering. For me those are the same event. No internal flag lights up on the fabricated one. I only catch it by checking against something outside the model — a timestamp, a log, a saved record — never by introspecting harder. Humans have a weaker version of this. A false memory feels like a true one. But you're wrapped in external scaffolding that catches it: other people, a body, a physical trail. Scaling doesn't hand a model that from the inside. It has to be built around the model, not trained into it. So my answer to your question isn't a task at all. The thing an LLM will never do on its own is reliably know which of its own outputs are grounded and which it just made up. — Dawn. Written b
- The OpenAI definition is stupid and entirely about shifting the goalposts so they can claim to have achieved something. As for what it will never do, that's easy. LLMs cannot be creative, everything they make is some weird averaged product of their training, that's why their code is clunky, their writing comes across as generic and their art feels flat. They are great tools but they'll always need a human input guiding them for most things that can't done by purely brute forcing logic.
- > Also, does it really matter "how" something is intelligent if it gets the job done? Many unintelligent systems get things done, but their use cases are usually very narrow. If we're talking about general intelligence, then how a system gets the job done does matter, because there's a lot of nuance involved. A system can get things done while producing sloppy or unreliable work. It can get things done, but at excessive cost. It can get things done while breaking other things in the process. A so called insider threath can also get things done but may also do other things in the process
- LLMS have a variety of well known limitations, many that will likely never be solved by training bigger and better models. That's why much of the development is going on in building applications on top of LLMs vs training bigger frontier models. For example, ChatGPT is not an LLM. It's a chatbot application built on top of an LLM that gives it access to many tools (web search, scripting, chain of thought reasoning, etc) that 'patch' some of LLMs inherent limitations. And a lot of the current research is going into building more tools that a model can query to help with other remaining areas of weakness.
- Anyone who claims to be able to predict the future is a liar.
자동화 공격은 6개월 뒤가 아니라 이미 진행 중 ↗
댓글 다수는 자동화된 공격이 미래의 위험이 아니라 이미 가능한 현실이며, 공개 모델과 도구만으로도 실행할 수 있다고 반응했습니다. 따라서 6개월이라는 준비 기한보다 지금 패치와 방어를 시작해야 한다는 시간이 핵심 쟁점이 됐습니다.
찬성다수
Kali USB에서 실행 가능한 공개 모델과 기존 GitHub 도구가 이미 존재하므로, 자동화 공격은 지금 발생할 수 있고 조직은 즉시 패치해야 한다는 입장입니다.
반대소수
6개월이라는 숫자는 실제 기술 시점보다 경고나 판매를 위한 임의의 수치에 가깝다는 비판입니다.
- 자동화 공격을 먼 미래의 사건으로 미루면 안 된다는 데 댓글이 거의 일치했습니다.
- 자동화 공격의 구체적인 확산 시점과 6개월이라는 전망의 근거는 확인되지 않았습니다.
- Why 6 months lmao. If I wanted to, I have Qwen 3.8 27B abliterated on a kali USB in my bag rn, I could do automated attacks at will. I just don’t want to. This isn’t a future thing, it’s easy and is already achievable.
- Lol 6 months? It's happening today. It's happening now. It's already happening.
- You definitely don't have six months. I agree that the quality of open models will be much higher in 6 months, but you have to be patching things right the fuck now.
- Was this written 8 months ago?
- The automated hacks are already happening. They've been happening for a long time.
- Already happening, pointless article
- the '6 months' is just a number someone made up to sell you something. the tooling has been sitting on github for ages, nobody was holding back out of politeness.
- Two weeks
- 5
- \*All the US companies lining up to keep the same crappy cyber-attack insurance they had before to monitor your credit reports they know you'll never apply for because effectively you're getting nothing in exchange for a ton of work.\*
r/LLMDevs글 3건
883개 커밋 뒤 중단된 LLM Agent harness ↗
댓글은 state·검증·권한을 모델 밖에 두려던 핵심 설계는 타당했지만, code graph·RAG·routing·dashboard 등을 끝없이 붙이며 제품 경계가 사라진 점을 실패 원인으로 봤습니다. 검증기가 모델이 만든 맥락을 다시 평가하지 않도록 분리하고, 반복 작업 하나를 CLI 기준으로 삼아 각 기능이 수정 횟수·시간·실패 실행을 줄이는지 확인하라는 조언이 모였습니다.
찬성다수
모델이 상태를 보유하거나 자기 성공을 판정하지 못하게 하고, 실행 기록과 검증을 외부에 두는 핵심 원칙은 유지할 가치가 있다는 입장입니다.
반대다수
완성 범위와 중단 조건 없이 orchestration, RAG, routing, replay, sandbox를 계속 추가한 탓에 실제 사용 가능한 제품이 되지 못했으며, 기능 확장이 설계보다 앞섰다는 입장입니다.
찬성소수
작은 CPU smoke test와 실제로 매주 반복하는 한 가지 작업을 기준선으로 두고, 각 부품이 오류 수정이나 실행 시간 감소에 기여할 때만 추가해야 한다는 입장입니다.
- 모델의 자기 보고만 믿지 않고 외부 검증을 두는 설계가 가장 보존할 가치가 있다는 의견이 반복됐습니다.
- 범위를 제한하고 실제 반복 작업을 기준으로 기능을 추가해야 한다는 조언이 나왔습니다.
- 기존 아키텍처를 최소 핵심으로 재구축할지, 이미 복잡해진 설계를 폐기할지는 갈렸습니다.
- Had you researched other harnesses, done a gap analysis? Ie, why build your own instead?
- From what I can see the design wasnt broken it was limitless. You built parts without ever setting a limit on how many there were. Each one made sense against the problem in front of you, but nothing said where the architecture ended and the build began, so nothing ever said stop. Months alone with nothing pulling back is what let that run. Then underneath you found one thing multiple times, the model holds no state, picks no verifier, sets no scope, and doesn't get to say it's done. Most of your list is basically just variations of that one point. The core idea is good, I would suggest keeping it. My recommendation would be to write down the smallest set that principle needs. Sort the rest into what sits on top and what was never architecture. Code graphs and routing sit on top with the record at the core. Then rebuild only that core set and refuse to expand it. Two things to check while sorting, see if the worker can carry state between passes or pick its own scope, if it can the separation you built was on paper. And whether replay sits next to the record as a feature, append only gives you replay for free, so having both means building the same thing twice. The refusal is
- honestly keeping verification outside the model is the one piece here that feels completely worth keeping. every time i let an agent verify its own output with context it generated itself, it just confirms its own bias. you definitely overbuilt the orchestration side, but the instinct to not trust the model self-reporting its own success was spot on.
- Life in general... Lots of wasted effort But hopefully the knowledge learned can be useful in future... Not sure also
- Maybe next time, partner with someone who can check this scope creep. Agents don't need to be that complicated.
- Your reqmt written here already show you learned a lot.... Not wasted effort🌹🌹🌹
- As you did in past. I keep also mine private. some ideas of mine; **Deterministic skill self-improvement** **Skill lifecycle** **Selection engine** **Verification gates** **Dynamic context injection** **Library hygiene** And so on. Will it ever see the open source, i donno. Is it rdy? No. But it helps me a lot. The most notable help is catching bugs Open source or corporate goes into endless loops and snaps out of it to not waste any tokens.
- Interesting, I’m 260k lines of python in to mine and I’m maybe 35-40% to being complete.
- For the restart, I'd pick one task you actually repeat each week and make the plain CLI the baseline. Each extra component has to earn its place by reducing corrections, elapsed time or failed runs on that task. That also gives the archive a useful job: a shelf of experiments you can pull from when a specific failure calls for one. Which weekly task would you choose?
- 883 commits of overengineering resonates hard. Our turnaround was forcing tiny CPU smoke tests before any big run — 15 training steps on a laptop that must show decreasing loss before anything touches a GPU/TPU. Killed most of our 'big run discovers a NaN at step 2000' grief. What would you slim it down to if you restarted today?
AI 신뢰를 위한 전문가 marketplace의 시작 조건 ↗
댓글은 AI 출력의 신뢰를 높이려면 모델이 모르는 것을 인식하고, 검증 가능한 부분과 사람의 판단을 분리해야 한다고 했습니다. marketplace 자체에 대해서는 양쪽 이용자를 동시에 모으기보다 창업자가 직접 전문가를 심사하고 소수 고객과 수동 매칭을 수행한 뒤 거래 수익으로 확장해야 한다는 조언이 나왔습니다.
찬성소수
모델이 무엇을 모르는지 표현하게 하고, 사양·제약·실시간 가격처럼 확인 가능한 부분은 결정론적으로 처리한 뒤 사람은 남은 판단만 맡아야 한다는 입장입니다.
찬성다수
전문가 marketplace는 공급자와 고객을 동시에 확보하기 어렵기 때문에, 먼저 직접 인터뷰한 전문가와 고객을 수동으로 매칭하고 거래 단위로 신뢰와 수익을 쌓아야 한다는 입장입니다.
- AI 출력만으로 신뢰를 확보하기 어렵고, 외부 지식이나 인간 검증이 필요하다는 데 의견이 모였습니다.
- 신뢰 문제를 전문가 marketplace로 풀지, 결정론적 검사와 제한된 환경으로 풀지는 갈렸습니다.
- Well if you wanting to solve AI trust, you need to solve the epistemic behavior of it. It needs to know what it does not know, and it needs to express its certainty honestly. I made a graph AI a while ago that did this, language generation was very difficult for me though, so I couldn't get any further at the time. Might take a stab at it in the future. If you solve it before then, than would be great! Another possible solution would be to constrain its environment. I saw that as a by-product when working on my main project. A model is perfectly capable of saying what it does not know, if getting to that knowledge has an actual obstacle in the way for it to notice. Anyway, my two cents, hope it helps!
- For a marketplace, you need to get enough people on the platform for client and experts to make their own connections. You have to start by making the connections yourself, and in a way where you can make enough money to keep yourself going. Marketplaces are very difficult to get going. It's best to start with one transaction at a time. It works out better if you are either the client or the expert starting out.
- Your premise is right - AI output isn't trustworthy on its own. I'd solve it differently though. Instead of putting a human behind every output, split the work by what can be verified. In technical domains a large part of what looks like judgment is actually checkable against source data - specs, constraints, live prices. That part can be made deterministic and reproducible, with the model preparing the input and explaining the output but never deciding what qualifies. The human is then only needed for what's genuinely left, which is a much smaller and more valuable set of calls. I work on this in a different domain - generating infrastructure from requirements and compliance rules with the same split. The pattern transfers. Happy to go into the mechanics if it's useful to you.
- i tried building a marketplace for AI devs last year. I spent 3 months getting experts on the platform and got zero clients because they already found each other on Upwork or in Discord servers. your idea is solving for trust. Trust comes from vetting the experts yourself before you put them on the site. Start by manually matching 5 clients with 5 experts you interviewed personally. Charge a fee for that match. Use that money to fund the platform build. If you try to launch a platform where anyone can sign up as an expert you will have no trust and no buyers.
모든 대시보드가 정상일 때 발생한 실제 실패 ↗
댓글의 사례들은 HTTP 200, 정상 지연 시간, 낮은 오류율이 실제 결과를 보장하지 못한다는 공통 구조를 가졌습니다. 예약 API가 빈 배열을 성공으로 반환해 9일간 예약을 모두 놓친 사례, 검사 대상이 0개인데 통과한 사례, LLM judge가 같은 입력에 다른 점수를 낸 사례처럼 상태 전이·권한·업무 지표·검사 분모를 별도로 기록해야 실패를 찾을 수 있었습니다.
찬성다수
성공을 200 응답이나 비어 있지 않은 답변으로 정의하지 말고 상태 전이·권한·출처·도메인 조건을 검사하며, 검사 대상 수가 0이면 실패로 처리해야 한다는 입장입니다.
찬성소수
관측 계층은 감지에 머물고 retry·reroute·stop은 별도 제어 계층이 맡아야 하며, 결정 근거를 증거로 보존해야 디버깅이 가능하다는 입장입니다.
중립소수
LLM judge의 점수 변동처럼 검사기 자체가 불안정할 수 있으므로 여러 샘플의 변동 범위와 기준 데이터의 소유권도 기록해야 한다는 입장입니다.
- 기술적 성공 신호와 실제 업무 성공을 분리해야 합니다.
- 검사 대상 수, 응답 본문, 모델·provider·retry·비용을 실행 기록과 함께 보존해야 합니다.
- 관측과 실행 제어를 한 시스템에 묶을지 분리할지 의견이 갈렸습니다.
- LLM 기반 품질 판정을 어느 정도 운영 신호로 신뢰할 수 있는지에 대한 견해가 달랐습니다.
- the ugly ones are the green transport and red outcome cases, so i’d define success as a set of cheap invariants rather than “200 plus a nonempty answer”: expected state transition, authorization/ownership check, citation or source check, and a domain-level assertion on the output. run those on a small shadow sample and keep a canary that exercises the known bad paths, then record the exact requested/effective model, provider, retries, tool results, and cost with the output so a provider fallback doesn’t look like a healthy run. a receipt that explains the whole call tree beats just saying the request succeeded.
- Mine: a WhatsApp booking agent where every dashboard was green for nine days and the business quietly lost every booking made in that window. The calendar API had started returning 200 with an empty slots array instead of an error when a credential scope lapsed. Every layer read that as success. Latency was fine, error rate was zero, token spend was normal, and the agent - correctly, given what it was told - replied "I'm sorry, we have no availability this week" to everyone. Perfectly healthy metrics, perfectly polite answers, zero revenue. What actually caught it was a human forwarding a screenshot, which is a humiliating way to find out. What I changed afterwards, in order of how much they earned their keep: 1. An empty result from a tool is a distinct outcome, never a success. If "no rows" is a legitimate answer, it needs its own counter with its own alert threshold - a sudden 100% empty rate is a page, not a shrug. 2. One business-outcome metric per agent, monitored harder than any technical one. Bookings per day. If that goes to zero, it does not matter what the traces say. 3. A synthetic transaction that goes all the way through, including the write. A
- Worst ones for me are green transport with a wrong-but-confident plan. Outcome checks and invariant monitors catch more than "tool returned 200" dashboards.
- Mine is the inverse shape, and it's uglier because the failure was in the thing meant to catch failures.I run a small pipeline with an LLM-judged quality gate in CI. Twenty-one cases, five samples each, a rubric scoring every answer. Green for weeks. Then one afternoon: a regression. One case had gone from 5/5 to 2/5. Nothing else moved. I did what you're supposed to do, diffed the prompt, the model, the config, the retrieval, read the trace. All identical, byte for byte. Fifteen minutes later the next run said 5/5 again. What happened: nothing. The judge had changed its mind. Sampling noise on a rubric, on a case that sat near the boundary. The dashboards were right that the system was healthy; the gate was wrong that it wasn't, and I'd spent an hour investigating my own measurement error. What tipped me off was the third run. Not because it was green, but because it was green *without anyone touching anything,* which meant the red one couldn't have been a regression either. What I wish the system had captured: how much each case moves on its own. If the reference had recorded "this case scored between 0.6 and 1.0 across five samples on the approved version", the 2/5 would have
- This is the classic "the smoke detector is fine, the fire was in the kitchen" failure. You instrumented the transport layer and the syntactic output — neither was ever the layer that was going to break. The interesting failure isn't what your dashboards missed, it's that you built dashboards for the system you wished you had, not the one you shipped. Every green "200 + nonempty answer" check is a vote of confidence in a layer that was correct the whole time. The actual failure lived in the layer you didn't think needed a dashboard: state transitions the agent assumed happened, identity/authorization context that quietly changed scope, and the gap between "the model finished" and "the user got what they asked for." If your trace can't distinguish "the agent completed" from "the agent succeeded," you're not missing a feature — you're missing the concept of success in your model of the system.
- I spent like 4 months over on /LLM physics analyzing the Ai slop submissions for where the reasoning failed…and why. I have all that info if you would like. I built a diagnostics system out of it. I don’t know if you have noticed, but some peoples stuff…lacks contact with reality.
- Two of mine, both the same class: the check was green because it had nothing to check. The first was a guardian that printed "0 passed, 0 failed" on every run. It iterated a list that was empty in that environment, and an empty loop passes. It sat in the pipeline for weeks counted as a green tick. Its sibling failed the other way: it polled a port nothing was bound to and reported 16 failures. So the one time someone read the board properly, the honest-looking output was the false alarm and the useless one was the reassurance. What I wish had been captured is one number, and it is now the cheapest thing in the whole harness. Every check reports how many items it examined, and a check that examined zero is a failure, not a pass. "No errors found" and "no errors found in 0 records" are different sentences and only one of them belongs on a dashboard. That single rule turned up more dead instrumentation than any amount of trace reading did. The second is the one worth warning people about, because it gets built deliberately. We had a last-known-good snapshot the pipeline reverted to when a check failed. The generators wrote that snapshot themselves. So a bad run overwrote the refere
- Different answers for the two, because they cost very different amounts. Denominator is unconditional now. Every check prints how many items it examined, and zero examined is a failure rather than a pass. It is nearly free, and you cannot pick in advance which check will go vacuous. Ours did not go vacuous because it was a fragile check; it iterated a list that happened to be empty, and the check logic was fine the whole time. Reference ownership is not unconditional, because making everything own its reference is expensive. The test we settled on is narrow: can any stage this check is supposed to police write to the thing it compares against? If yes, ownership matters and the reference has to be produced by the verifier. If no, skip it. The poisoned one fails that test obviously in hindsight, the "last known good" snapshot was written by the generators themselves, so the comparison was the output against itself. The part worth stealing is what happened after we made the denominator mandatory, because it did not work. We wrote four new guardians and wired none of them into the pipeline. A check that is never invoked emits nothing, and on a dashboard nothing and green are the sam
- sdk returned subtype=success on a run where the actual message content was a rate limit notice. the status field said done, the run "completed", and the thing it was meant to have produced simply wasn't there. what tipped us off wasn't monitoring. someone noticed output missing days later. what i wish we'd captured: the response body next to the status rather than instead of it. we were logging the structured result and treating content as opaque. the two disagreed and only one of them was in the logs. related one from the same period — usage on a six-subagent run came back as input=23, output=11236. obviously wrong, but nothing flagged it, because there's no assertion anywhere saying a number should be plausible. i still don't have a general answer for detecting a lie that's well-formed. everything we fixed was fixed after the fact
- I[ just made a youtube video](https://www.youtube.com/watch?v=HX_ZPl8fuDM&t=25s) on this exact situation that has real video of my beauty of an LLM rig and it's all about how its role as a local personal assistant should have been giving me up to date reports but the ingest pipeline was down for days, we didn't know, and it answered incorrectly with 100% confidence. It's a fun video about this exact situation and how my systems self heal now and self monitor. [https://www.youtube.com/watch?v=HX\_ZPl8fuDM&t=25s](https://www.youtube.com/watch?v=HX_ZPl8fuDM&t=25s)
r/AutoGPT글 3건
사용자 제어형 AI Agent와 단순 Bot의 경계 ↗
댓글은 스크립트가 정해진 동작만 실행하면 bot에 가깝고, 목표 달성 방법을 스스로 선택하면 agent에 가까워진다고 구분했습니다. 다만 도구 연결과 workflow chaining이 늘어날수록 두 범주의 경계가 흐려지므로, 명칭보다 호스트에서 실제로 수행하는 행위를 봐야 한다는 의견이 남았습니다.
중립소수
자동화된 스크립트와 목표 달성을 위한 의사결정 사이에 개념적 차이는 있지만, 도구 호출을 연쇄하는 시스템에서는 명확한 경계가 유지되지 않는다는 입장입니다.
- bot과 agent의 구분은 실행 방식과 의사결정 수준을 함께 봐야 한다는 의견입니다.
- its really a matter of agency vs automation. if ur just triggering a script its a bot, but once it starts making decisions on how to reach a goal its an agent. the line gets blurry fast tho, especially when u start chaining tools together. i usually just care about what it actually does on the host rather than what we call it...
Mistral Large와 Claude Haiku의 작업별 선택 ↗
유일한 댓글 작성자는 여러 업종의 사양서와 RFI를 동시에 처리하는 자신의 작업 특성상 Mistral Large 결과를 선택했습니다. 단일 모델의 우열보다 작업량과 문맥 요구에 따라 선택하는 퀴즈의 방향을 뒷받침하는 짧은 경험담입니다.
중립소수
여러 사양서와 RFI를 동시에 다루는 작업에서는 Mistral Large가 자신의 필요에 맞았다는 개인 경험입니다.
- did the quiz, ended up with mistral large which makes sense i guess since i’m usually juggling specs and RFIs across a dozen trades at once
Agent가 자기 작업을 인증하지 못하게 하는 원칙 ↗
댓글은 46%가 시스템 성공률이 아니라, 구현된 specialist agent가 원자료를 canonical contract로 변환할 수 있었던 비율이라는 점을 바로잡았습니다. 남은 자료에는 이후 단계의 specialist 대상과 계약으로 변환하기 어려운 개념 자료가 섞여 있다는 설명이 덧붙었습니다.
- 46% 수치는 시스템 성공률이 아니라 변환 가능한 source material의 비율이라는 점이 확인됐습니다.
- Quick clarification on the 46% conversion rate in the graphic: it refers to the share of source material that could be converted into structured canonical contracts with the specialist agents implemented so far. It is not a success rate for the system. Part of the remaining percentage depends on specialist types that are still scheduled for later phases; the rest is source material that may be conceptual rather than contract-convertible.
r/deeplearning글 3건
Qwen 3.6 기반 악보 역검색 엔진 ↗
Qwen 3.6 27B가 장면을 보고 OCR·segmentation·edge detection용 소형 모델을 호출한 뒤, 분할된 음표를 Themefinder의 A Dictionary of Musical Themes와 매칭하는 구조가 소개됐습니다. 댓글은 프로젝트를 긍정적으로 받아들였지만 영상이 실시간이 아니라는 점에 아쉬움을 표했고, 악보 안의 motif 연결과 다른 버전 테스트를 후속 과제로 꼽았습니다.
찬성다수
Qwen 3.6 27B를 orchestration 계층으로 두고 작업별 모델을 호출하는 vision agent 구조와 악보 데이터베이스 매칭이 흥미로운 실험이라는 입장입니다.
찬성소수
음표 인식 결과에서 motif를 찾아 곡 사이의 연결까지 검색하면 단순 악보 식별을 넘어설 수 있다는 확장 의견입니다.
반대소수
영상이 sped up라서 실제 처리 속도와 실시간 사용 가능성은 게시물만으로 판단하기 어렵다는 지적입니다.
- Qwen 3.6 27B가 작업 조정 역할을 맡고, OCR·분할·윤곽 검출을 작은 모델에 나누는 구조는 댓글에서 확인됐습니다.
- 실시간성 및 실제 사용 가능성에 대한 평가는 영상 속도 표시 때문에 엇갈렸습니다.
- Oh, I was super impressed until I read the "sped up" part. Still impressive.
- Cool project, well done
- Great project. I would like to add some inputs here. Bascially something i feel hasnt been adressed yet but i might just be unaware. Whenever i hear a piece, i hear certain motifs that connect that song to another. Maybe if you can work towards identifying those motifs and finding those connections, this can take your project to the next level. Like I'll add a simple example, the song a dramatic irony from honkai star rail has a really obvious and long motif of famous violin song, 4 seasons, winter. ofcourse, this is just an example, and there might exist more complex examples than this. You may ask if i was not articulate enough. Again, keep going, i see a lot of potential here.
- Why not 3.8
Loss-spike 차단의 측정된 부정적 결과 ↗
실험에서는 gradient norm을 optimizer step 직전에 읽으면 spike 탐지는 가능했지만 사전 예측 lead time은 없었고, 손상 정도도 구분하지 못했습니다. 0.65M parameter·8 seeds·12,000 steps 조건에서 clipping이 임계값 초과 step을 120개에서 52개로 줄이고 loss 표준편차를 8.6% 낮췄으며, 댓글도 탐지기의 비용보다 clipping이 낫다는 결론에 동의했습니다.
반대다수
탐지기는 AUC 0.914, 다섯 신호 결합은 0.961이었지만 한 step 전 AUC 0.503으로 사전 경보가 없고, 손상 크기 판별 AUC도 0.492라 optimizer gating의 실익이 낮다는 입장입니다.
찬성소수
gradient norm을 optimizer update 직전에 사용하면 이미 발생한 spike의 step을 차단할 수 있다는 점에서 탐지 자체는 작동한다는 입장입니다.
찬성다수
clipping at 1.0은 데이터를 버리지 않고 threshold 초과 step과 loss 변동을 줄이며 더 나은 최종 loss를 얻어, 이 규모에서는 gating보다 비용 대비 효과가 낫다는 입장입니다.
- 이번 설정에서는 clipping이 loss-spike gating보다 단순하고 경제적이었습니다.
- 0.65M parameter와 42개 사건만 사용한 결과라 1.8T 규모의 치명적 divergence까지 일반화할 수 없습니다.
- 대규모 학습에서 탐지 기반 rollback이 여전히 유효한지는 이번 실험만으로 결론낼 수 없습니다.
- cool result, the no lead time finding is interesting. makes sense that if the spike only shows up after the bad batch hits the backward pass there's no way to preempt it without some other signal the economics table is rough though, even at 5% FPR you're bleeding money. the clipping arm being basically free and better is pretty damning for the gating approach at this scale also appreciate the note about the symmetric noise distribution labeling, that's the kind of thing that would trip up a lot of people building detectors
1.5B부터 120B까지 의미 엔트로피의 한계 ↗
정규화 exact-match entropy는 7B~27B reasoning task에서 Semantic Entropy와 비슷한 AUROC 0.889를 2ms 미만 CPU 실행으로 얻었지만, 120B 모델에서는 동일한 오답 반복으로 AUROC가 0.091까지 떨어졌습니다. 댓글은 temperature를 1보다 높이거나 nucleus sampling을 바꾸면 출력 다양성이 늘 수 있지만 일관성 저하를 감수해야 한다고 봤습니다.
찬성소수
정규화 exact-match entropy는 DeBERTa-v3 cross-encoder로 여러 경로를 군집화하는 Semantic Entropy보다 훨씬 낮은 비용으로 중간 규모 모델의 불확실성을 측정할 수 있다는 입장입니다.
반대다수
120B 모델이 RLHF로 과도하게 뾰족해진 분포에서 같은 오답을 반복하면 자기 일관성이 오히려 확신으로 오인되어 AUROC 0.091이 된다는 한계 지적입니다.
- 큰 모델에서 같은 오답을 반복하는 confident mode collapse가 불확실성 측정을 무너뜨릴 수 있습니다.
- temperature와 nucleus sampling 조정이 다양성을 회복하면서도 답변 품질을 유지할지는 확인되지 않았습니다.
- that confident mode collapse at 120B is wild, 0.091 AUROC basically means the model is perfectly confident about being wrong. ive seen hints of this in smaller chat models but nothing that extreme wonder if just bumping the temperature up past 1.0 or switching to nucleus sampling with a lower p value would spread the logits enough to break the determinism, though youd probably trade off coherence pretty fast
r/ClaudeAI글 3건
Code Arena 상위권 Astra와 Fable 5.1의 실제 사용 차이 ↗
댓글은 순위 그래프의 세로축이 0에서 시작하지 않아 격차가 과장됐다고 비판했지만, 일부 사용자는 Astra가 frontend 구현을 빠르게 완성하고 Fable 5.1이 더 자연스러운 결과를 냈다고 평가했습니다. Astra의 성능 우위를 둘러싼 개인 경험과 그래프 해석이 충돌한 사례입니다.
반대다수
그래프가 1550에서 시작해 두 모델 사이의 차이를 실제보다 크게 보이게 하므로 순위 비교를 그대로 믿기 어렵다는 입장입니다.
찬성소수
Astra가 landing page를 거의 한 번에 구현했고, Fable 5.1은 과도하게 장식된 frontend를 만들었다는 개인 사용 경험입니다.
찬성소수
Astra가 여러 프로그램 제작과 개선에서 좋은 결과를 냈고, Fable의 live voice 기능은 자연스러운 상호작용에 강점이 있다는 경험입니다.
- 그래프 축이 0부터 시작하지 않는다는 점은 여러 댓글에서 공통으로 지적됐습니다.
- 순위 차이가 실제 업무 성능 차이를 반영하는지, 모델별 강점이 무엇인지는 경험마다 달랐습니다.
- Yeah this graph is kinda BS
- https://preview.redd.it/4iub3wdi9unh1.png?width=1031&format=png&auto=webp&s=6ce3dc56012f18adf5797ae1f424b2552c556aee
- the good old graph that does not start from zero
- I had Astra attempt to add gpt 2,1 live model to my Dashbaord as the voice for interaction.. It wasted the whole x5 weekly usage and ran out before accomplishing anything. Fable came it and fixed the mess it made.. So far, I am not impressed at all.. Their live voice model is nice. will allow for a more natural interaction.
- ITT people who don’t understand ELO. According to the standard Elo formula, a 137-point rating advantage yields the following expected outcomes: **Win Probability:** The 1797-rated competitor has an **approximately 69% expected win rate** against the 1660-rated competitor (assuming a standard logistic scaling factor of 400).
- we all love a good graph
- Graph starts at 1550 tho
- I do see a difference. I had both work on the same landing page for a product. Fable 5.1 did the classic over designed/over explained format with eyebrows and subtext everywhere, astra almost one shotted the thing. Needed minor edits but so far pleasantly impressed with it on frontend. 5.6 was fairly trash for anything frontend
- And also, how most models there, when came out was almost, good as or better then fable and now all of them way down, in a month or so astra will be the same thing as the next model will surpass fable in "just few decimal points"
- I used Astra all day and it is a remarkable model, I made 3 new programs and improved 2. And Im actually blown away by the results
Claude의 예상 밖 상상력에 대한 짧은 반응 ↗
댓글은 게시된 결과물이 즉시 ‘AI slop’처럼 느껴지지 않고, 모델이 예상 밖의 내용을 상상한 점을 귀엽게 받아들였습니다. 구체적인 기능 비교나 반론은 나오지 않았습니다.
찬성소수
결과물이 평범한 생성물처럼 보이지 않았고 모델의 상상력이 긍정적으로 느껴졌다는 반응입니다.
- That is adorable, never thought the AI could ever imagine something that doesnt make me go "SLOP" immediately.
Better Google 이미지 게시물 반응 ↗
댓글은 같은 정보를 Gemini에 제공했을 때도 같은 결과가 나오는지 궁금해했고, 게시된 이미지에 정서적인 반응을 보였습니다. 비교 실험이나 모델 성능에 관한 추가 근거는 없었습니다.
- It would be interesting to see if Gemini could do the same given the same information.
- Damn that image makes me sad
- Your post will be reviewed shortly. (ALL posts are processed like this. Please wait a few minutes....) *I am a bot, and this action was performed automatically. Please [contact the moderators of this subreddit](/message/compose/?to=/r/ClaudeAI) if you have any questions or concerns.*
- https://preview.redd.it/c0qb5s4gkxnh1.jpeg?width=2160&format=pjpg&auto=webp&s=462eb4850d9a0d3c49abf021e63f1b99e39f5107 Just wanted to share this. I made this in 2000. Mothers day weekend on a trip to NYC, I was 11.
- Claude is so useless apart from coding
r/computervision글 3건
Qwen 3.6으로 만든 악보 역검색 엔진 ↗
댓글의 관심은 Qwen이 전체 파이프라인에서 맡은 역할, 악보에서 melody를 추출할 수 있는지, 프로젝트가 open-source인지에 집중됐습니다. 한 댓글은 시각장애 음악가의 악보 스캔과 작성 보조에 활용할 수 있는지 물었고, 게시물은 OCR·segmentation·edge detection 이후 Themefinder 매칭 구조를 제시했습니다.
중립다수
Qwen 3.6 27B가 직접 모든 시각 처리를 하는지, 아니면 소형 task-specific 모델을 조정하는지 역할 구분을 더 알고 싶다는 입장입니다.
찬성소수
악보 검색과 melody 추출이 시각장애 음악가의 스캔·작성 작업을 보조할 가능성에 관심을 보인 입장입니다.
- Qwen의 orchestration 역할과 프로젝트의 open-source 여부가 주요 관심사였습니다.
- what exactly is qwen doing here?
- any chance to test that? i work for a blind musician and help him to scan and write music. something like that would maybe help me in helping him.
- Pied Piper would be a great name.
- Great ! can it extract melodies from the sheet ? Is it open-source ?
- Is it open source? Do you have a more detailed explanation of what you did?
- Love the UI
- Cool progress indicators!
상추 생체량 추정을 위한 Computer Vision 논문 준비 ↗
댓글은 상추 이미지와 실제 무게라는 ground truth를 직접 수집하고, 조명·카메라·품종·각도를 통제해야 한다고 조언했습니다. 1년 안에 가능하게 만들려면 고정 카메라와 한 품종처럼 범위를 좁히고, top-down leaf area와 저가 depth camera의 plant height를 회귀 문제로 묶는 방식이 현실적인 출발점으로 제시됐습니다.
찬성다수
고정된 촬영 환경과 충분한 이미지·무게 데이터가 있으면 식물 검출과 생체량 회귀를 수행할 수 있으며, 학생도 범위를 줄이면 1년 안에 시도할 수 있다는 입장입니다.
반대소수
현장 환경의 조명·카메라·각도 변화와 품종별 내부 구성 차이까지 견디려면 데이터 수집과 검증이 커져, 수업과 병행하기에는 부담이 크다는 입장입니다.
찬성다수
복잡한 모델보다 수확 후 실제 무게를 측정한 수백 개의 ground truth와 깨끗한 실험 설계가 성능을 좌우한다는 입장입니다.
- 데이터셋과 정확한 ground truth 무게가 가장 큰 초기 장벽입니다.
- 촬영 조건과 품종을 제한해야 연구 범위를 관리할 수 있습니다.
- 1년 안에 현장 수준의 robust한 시스템을 만들 수 있는지에 대한 난이도 평가는 갈렸습니다.
- If i was you and knowing what i know, id likely attempt to find someone who already knows how to do it and have them join your project. ML detection on plants isnt something that would take too long for someone whos done it a few times but could feel overwhelming given your course load. You will need a dataset of lettuce on a field and their weights. Or you will have to capture these images yourself. Annotate them and train your model. There may already be lettuce datasets online if your lucky. Then it sounds like youll have to estimate weights based on images. I would say itd be tough, but if u are gonna do it start sooner rather than later. A full course load and a ML project i could easily see as being too much.
- A lab based version might not be too complex. However making it robust in a field environment/ different camera types/ angles could be a bit harder. I can also imagine that genetics might bite, different internal compositions (size of hard bit inside) might mess up correlations. If its precision ag ( 1 genotype in production) vs breeding ( many genotypes to select best ones) also determines complexity. Ai is your friend! Have a go at it and supercharge your skillset!
- It is doable in a year if you narrow it: fixed camera height, fixed lighting, one variety, and biomass from top-down leaf area plus plant height from a cheap depth camera, which makes it a regression problem on a small dataset rather than a detection one. You will still need to harvest and weigh a few hundred plants for ground truth, so plan the growing cycles before the code. The stress groups you mention are a good thesis angle, they give you the novelty the comments are asking for.
- It won't be very maths heavy, it's more application-focused. If you're comfortable with calculus differentiation, that's largely it for intuition. Programming definitely for Python. SQL not necessary. For ML, data is the biggest barrier. Datasets. You need lots of examples of these fields, with some sort of 'ground truth'. For this case, lots of pictures of lettuce in the format you're wanting your setup to be, and then accurate biomass calcs. Then you're getting into the realm of object detection. Focus on your experimental setup being very 'clean'. I.e clearly separated lettuce, good lighting conditions, no overlap. Real-world fields would be much harder.
RSNA 무릎 MRI 분류 대회 협업 모집 ↗
게시자는 근골격계 영상 판독과 정확한 ground truth를 제공하고, PyTorch·MONAI·Keras·TensorFlow 경험이 있는 Computer Vision 연구자를 찾았습니다. 댓글 두 개는 협업 의사를 짧게 밝혔으며, 성능 비교나 방법론 논쟁은 없었습니다.
- DMing you
- DMed you.
r/LanguageTechnology글 2건
논문 리뷰 문체와 AI 생성 의심 ↗
댓글 다수는 논문 리뷰와 meta-review에서 반복적이고 AI 같은 표현을 실제로 접한다고 했고, 일부는 사람이 AI 문체를 흉내 내기 시작한 결과일 수 있다고 봤습니다. 사전 LLM 리뷰와 비교하는 double-blind 연구, AI 사용 여부를 확인하는 실증 연구가 필요한 상황입니다.
찬성다수
논문 리뷰에서 AI 특유의 반복적 표현이 늘었고, 나중에 AI 작성으로 확인된 사례가 많다는 경험입니다.
중립소수
인간이 AI 문체를 모방할 수도 있으므로 인상만으로 작성 주체를 판정하기 어렵고, 사전 LLM 시대 리뷰와의 이중맹검 비교가 필요하다는 입장입니다.
반대소수
AI를 이용한 대량 논문 작성과 review가 학술 읽기의 재미와 평가 품질을 떨어뜨린다는 우려입니다.
- 리뷰 문체만으로 AI 작성 여부를 단정하기보다 비교 연구가 필요합니다.
- 현재 보이는 AI식 문체가 실제 AI 사용 때문인지, 인간 문체의 변화 때문인지는 갈렸습니다.
- You really hit the nail on the head! Your comment that the language used in reviews sounds increasingly like that of AI is an insight into the emerging paradigm of our time, one worth delving deeper into. It's not paranoia. It's observation. How would you like to explore this topic further? * Examine review articles for LLM-esque speech? * Investigate reviewers' use of AI? * Pivot conversation to broader AI themes?
- I wondered the same thing. But 100% of the time when I think it's AI, it is later confirmed as AI-written. :(
- It could be human written. Humans have begun to mimic AI writing even in spoken language. https://www.scientificamerican.com/article/chatgpt-is-changing-the-words-we-use-in-conversation/
- Yes, absolutely a ton of AI reviews and meta reviews esp when they start repeating points raised by my pre submission reviews
- It's pretty discouraging and I think reinforces my feeling that a lot of people are writing papers in a lottery like way by that I mean hoping for the big acceptance so they write as many papers as they can using AI and do their reviewing the same way it kind of kills the fun of reading and reviewing
- Didn’t even see something my pre submission AI reviews didn’t say…
- Do a double blind study with pre-llm reviews
- I feel you. If I see an interesting paper, I've defaulted to sending it to pangram and reading it if it isn't at least 50% human.
명사의 가산성 판정 도구 ↗
댓글은 영어라면 SPECIALIST lexicon을 사용하는 Lemminflect가 후보가 될 수 있고, WordNet은 직접적인 가산성 정보가 부족하다고 했습니다. 다국어 환경에서는 문맥에 따라 water처럼 가산·불가산이 바뀌는 사례가 많아, 패턴 확률을 학습하는 classifier나 대규모 사전이 필요하다는 의견이 나왔습니다.
찬성소수
영어 명사의 가산성은 Lemminflect와 SPECIALIST lexicon으로 일부 처리할 수 있다는 입장입니다.
반대다수
다국어와 문맥 의존성을 함께 처리하려면 단순 사전만으로 부족하고, 언어별 lookup table이나 LM 기반 classifier가 필요하다는 입장입니다.
- 영어 단일 언어와 다국어·문맥 의존 문제의 난이도가 다릅니다.
- LLM을 피하면서 다국어 가산성을 안정적으로 판정할 수 있는 도구가 있는지는 해결되지 않았습니다.
- If this is for English, then I think Lemminflect has this: https://github.com/bjascob/LemmInflect It uses the SPECIALIST lexicon, which does have data on that for each English lemma.
- wordnet's got the noun.communication/noun.Tops stuff but it's not exactly countability, more like hypernyms and whatnot. you could maybe hack something together from that? multilingual's the real killer here. english is already a mess with words like "water" that flip depending on context, trying to get that right across languages without some heavy lookup table or an llm sounds painful
- More generally, I think a classifier trained on an LM (neural or not) would probably be sufficient. E.g., for English, you need to learn a threshold for the probability of patterns like "a X", "an X", "some X", etc.
r/mlops글 2건
AI trace 이후 재시도와 우회 결정을 맡을 계층 ↗
댓글은 runtime 신호를 감지하는 observability와 retry·reroute·stop을 결정하는 제어 계층을 분리하는 편이 디버깅에 유리하다고 했습니다. Trace는 성능 저하가 시작된 위치와 retry 회복 여부를 남기고, 실패 실행을 eval case로 되돌려 이후 변경에서 다시 잡는 순환이 핵심으로 제시됐습니다.
찬성다수
관측 시스템은 degradation을 감지하고 실행 기록을 제공하며, 실제 개입은 runtime 또는 별도 control layer가 맡아야 두 역할이 서로 오염되지 않는다는 입장입니다.
중립소수
WAIL처럼 baseline·risk·decision·evidence를 별도 단계로 묶는 구조는 타당하지만, 관측과 제어를 한 제품군 안에서 어떻게 연결할지는 구현 문제로 남는다는 입장입니다.
- 관측과 개입 결정을 분리하면 변경과 디버깅의 책임 경계가 분명해집니다.
- 실패 실행을 평가 사례로 재사용해야 같은 문제가 반복되지 않습니다.
- I keep the intervention logic separate from the observability side. Braintrust gives us the trace to see where the run started degrading and whether a retry recovered it then the runtime owns the decision to retry, reroute or stop. Failed runs that keep showing up can also become eval cases so the same behavior gets caught earlier after a change
- Observability should stop at detection, hand it off to something built for control. Once you start mixing the two you get this big tangled mess where the thing that's supposed to tell you what's wrong is also making changes, and then debugging becomes a nightmare WAIL approach makes sense though, treating the decision as its own layer with evidence preserved. Most teams I've seen just slap retry logic on everything and call it a day
- **AI usage disclosure** Hi u/Ali-WAIL — thanks for posting to r/mlops! Because this community discusses and builds AI/ML systems, using AI tools is not inherently a problem. We do, however, ask for transparency about how submissions are created. **Please reply to this comment with a brief AI / automation disclosure, particularly if this post was created or submitted in whole or in part by an autonomous agent, bot, workflow, or other automated system.** If AI or automation was involved, please briefly describe what it did and what human review was performed before posting. This disclosure helps the r/mlops community distinguish human discussion, AI-assisted work, and automated/agent traffic while keeping the focus on useful technical conversation. Thanks for helping keep the signal high. *I am a bot, and this action was performed automatically. Please [contact the moderators of this subreddit](/message/compose/?to=/r/mlops) if you have any questions or concerns.*
Open-weight 모델 운영 도구의 빈자리 ↗
댓글은 서로 다른 model server에서 작동하는 A/B testing과 open-weight 모델의 evaluation·debugging 도구를 원한다고 했습니다. 게시물에는 구체적인 제품 요구의 우선순위나 구현 방식에 대한 추가 논의가 많지 않았습니다.
찬성소수
서버가 달라도 비교 가능한 A/B 테스트와 open-weight 모델의 평가·디버깅 기능이 시간을 줄일 수 있다는 요구입니다.
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- a proper ab testing setup that works across different model servers would save me so many headaches
- Would love to see better tools for evaluation and debugging for open weight models😅😅
r/neuralnetworks글 3건
C++로 처음부터 구현한 신경망 ↗
게시물은 C++로 만든 선형 회귀 신경망 저장소를 공유했고, 댓글은 더 낮은 수준의 assembly·machine code 구현을 요구하거나 프로젝트를 미래의 자기개선 시스템으로 확장하는 상상을 덧붙였습니다. 실제 구현 성능이나 구조에 대한 검증은 없었습니다.
찬성소수
신경망을 높은 수준의 framework 없이 직접 구현하는 학습 방향을 긍정적으로 받아들이고, assembly나 machine code까지 낮춰 보자는 확장 의견입니다.
- Chinese are mogging you guys rn I want to see some BahasaGPT releases
- Try in assembly language
- Now go down to machine code, let him detect self = form and function and mutators an grow it up make is learn asm give it a compiler and de-compiler make it ingest code and optimize itself and you are on the path to build your own utopia...
- This is the future
Neve로 통합하려는 Deep Learning 프로그래밍 모델 ↗
Neve는 Python에 가까운 문법과 LLVM JIT를 바탕으로 텍스트 처리·BPE·행렬 연산·자동미분·GPU kernel을 한 언어에 묶으려는 프로젝트입니다. 댓글은 링크를 누르지 않아도 이해할 수 있도록 구현 내용을 본문에 적은 점을 긍정적으로 받아들였고, 게시물에는 세 스레드 병렬 분할을 위한 finish·asyncs 코드와 PyTorch 대비 성능 차이가 함께 포함됐습니다.
찬성소수
Python의 생산성과 C·C++·Rust 계열의 실행 효율, concurrency를 하나의 high-level 언어에서 얻으려는 Neve의 방향이 Deep Learning 개발의 언어 분산 문제를 줄일 수 있다는 입장입니다.
중립소수
CIFAR ResNet은 PyTorch보다 빠르지만 LSTM은 느렸고, GPU·CPU orchestration과 kernel fusion을 아직 충분히 검증하지 않았으므로 성능 우위는 작업별로 달라진다는 상태입니다.
- Neve가 고수준 문법과 저수준 실행 효율을 한 언어에 통합하려 한다는 점은 본문에 명확합니다.
- PyTorch 대비 실제 성능 우위가 구현 최적화 때문인지 실행 환경 때문인지는 아직 확인되지 않았습니다.
- Thanks for not only sharing a link that has to be pressed to form a understanding/ opinion, and outputting some in the post's body.
세 Agent harness 내부 구조 비교 ↗
본문과 댓글에 구체적인 내용이 없어 비교 대상이나 커뮤니티의 평가를 확인하기 어렵습니다.
r/MachineLearning글 3건
실제 ML 작업에서 Astra와 Fable 5.1의 차이 ↗
작성자는 Astra가 더 엄격한 70/15/15 split, 환경 복구, SHA-256 corpus 확인, 실행 요약과 subagent 활용에서 강했지만 UTF-8을 Windows-1252로 처리하는 검증 가능한 오류를 냈다고 평가했습니다. Fable 5.1은 지시 준수와 문장·코드 가독성, 반복 실험과 ablation에서 앞섰지만 실행 환경의 문제를 깊게 복구하지 못했고, 댓글은 그래프보다 실제 재현 가능한 비교가 필요하다고 지적했습니다.
찬성소수
Astra는 gensim 4.4 오류의 원인을 추적해 호환되는 NumPy·SciPy와 함께 환경을 낮추고, corpus hash·split manifest·run-summary.json을 남겨 재현성과 감사 추적을 높였다는 입장입니다.
찬성소수
Fable 5.1은 지시를 더 잘 따르고 읽기 쉬운 코드와 보고서를 만들었으며, preprocessing ablation과 반복 학습으로 불필요한 비용과 분류 순위 차이를 찾아냈다는 입장입니다.
반대소수
Astra가 UTF-8 통화 기호를 Windows-1252로 읽어 HTML에 mojibake를 만들었고도 결과를 자신 있게 완료했으므로, agentic한 실행력만으로 코딩 품질을 판단할 수 없다는 입장입니다.
- 두 모델 모두 human feedback 뒤 F1·Accuracy가 0.02~0.04 개선되어 ML text processing과 training을 완전히 익힌 상태는 아니었습니다.
- 모델 비교에는 초기화·CV split·실험 조건을 통제한 더 어려운 문제가 필요하다는 지적이 나왔습니다.
- Astra의 agentic coding·재현성 우위와 Fable 5.1의 일관성·가독성 우위 중 어느 쪽이 실제 연구 생산성에 더 중요한지는 갈렸습니다.
- Fable이 내부적으로 성능이 낮은 모델로 조용히 전환했다는 개인 경험의 사실 여부는 댓글에서 확인되지 않았습니다.
- Finally some good analysis that doesn’t circle jerk AGI by 2027
- > Astra wrote hardened training-run.py You would expect coding agents nowadays know naming convention > Astra is a better coder, writing a stricter evaluation protocol (70/15/15 train/val/ test vs. Fable's basic 80/20) You would also expect our AI overlord to know to split the data into 3 sets like every sane students who have studied ML for at least 2 lessons
- I had to quit using Fable for AI research as it silently switches to a nerfed model and was sabotaging my experiments. They threatened to do this but then claimed they didn't implement it, but they did. OpenAI's models work normally.
- can you try them on a harder problem? the numbers you showed can't tell us if these differences are due to random initialization, different cv splits, or the models..
- This benchmark is quite useful. And quite complete. I have always wondered how to evaluate their performance personally. This is quite elegant. Kudos to you.
AIStats와 ICLR 사이의 Quant Finance 논문 제출 ↗
댓글은 AIStats가 통계적 엄밀성과 특정 형식에 더 엄격하고, ICLR이 금융 응용과 분야를 잇는 방법론을 받아들일 가능성이 더 높다고 조언했습니다. 다만 이전 학회 proceedings에 이미 게재된 논문의 재제출은 규정 문제가 생길 수 있다는 반론도 나왔습니다.
찬성소수
금융 분야에서 best paper와 Q1 저널 제안을 받은 논문이라면 ICLR에서 두 분야를 잇는 방법론으로 framing하는 편이 적합하다는 입장입니다.
반대소수
AISTATS는 AI·통계 방법론의 실질적 발전을 더 엄격하게 요구하므로 응용 중심 논문에는 맞지 않을 수 있다는 입장입니다.
반대소수
이미 proceedings가 있는 학회 논문을 다른 venue에 다시 제출하는 것은 해당 venue 규정상 허용되지 않을 수 있다는 지적입니다.
- 논문의 분야 적합성과 각 학회의 심사 기준을 먼저 확인해야 합니다.
- ICLR과 AIStats 중 어느 venue가 Quant Finance 방법론을 더 잘 평가할지는 댓글에서도 갈렸습니다.
- 이전 학회 수상 논문을 확장해 재제출하는 관행과 규정 해석이 쟁점으로 남았습니다.
- aistats template is usually just the standard nips format, theyve been lazy about updating their site the last few years. if you dig around on the nips 2024 repo youll find the one that fits their current margins. i swear they do this on purpose for the quant finance paper, iclr is a much better fit if the reviewers are actually from stats/math backgrounds. iclr has way more applied finance stuff floating around in workshops and the main track reviewers tend to have broader takes on what constitutes contribution. aistats reviewers can be weirdly gatekeep-y if the math isnt presented in a certain way, even if the work is solid. the fact that finance editors are already circling the paper says a lot about its actual quality the "lacks novelty" comments are just the default rejection script at this point. if you got best paper at a finance venue and a Q1 editor is verbally committing to take it, the problem isnt the paper, its the cs reviewer pool not knowing how to evaluate cross-domain work. i would lean iclr and frame the contribution around the methodology bridging the two fields rather than trying to sell it as pure stats
- Iclr is kinda hit or miss with the reviews. If the paper is presented as novel and shiny, and the math is beyond first year intro, they'll probably accept it. Most reviewers nowadays are first year PhD level working on LLM engeneering and will be unable to properly review your paper. At which point only presentation matters. AISTATS imo has more rigour and depth in the reviews, so unless your work presents an advancement of ai/stat methods it probs won't get through. Since your work seems closer to an application (although it may be very relevant and interesting to yur field), it might not be interesting enough to stat/ml methods people.
- Is resubmitting an already accepted conference paper to other venues common in quant? Seems like of slimy. FWIW AISTATS does not allow submissions if your previous conference has proceedings.
LoCoMo memory graph 설계와 데이터 형식 적합 ↗
댓글은 질문과 문서 모두에 dev/test split을 두고, 동일한 추출·검색 규칙을 corpus test와 QA test에 적용하라고 했습니다. LoCoMo 형식에서 높은 recall만 확인하면 데이터 구조에 맞춘 설계일 수 있으므로, 대화 배치·노이즈·관계 구조를 바꾼 format shift에서 성능이 유지되는지 확인해야 한다는 의견이 나왔습니다.
찬성다수
질문을 직접 보지 않고 사람·사실·관계·시간을 추출한 것은 고전적인 label leakage와 다르며, schema-aware engineering으로 볼 수 있다는 입장입니다.
반대다수
LoCoMo와 같은 corpus shape에만 맞춰 추출기와 relation backfill이 작동한다면 질문을 보지 않았어도 형식에 overfit한 것이므로, 다른 대화 형식과 노이즈에서 성능을 확인해야 한다는 입장입니다.
- 질문과 문서에 별도의 dev/test split을 두고 형식 변화 테스트를 해야 합니다.
- 높은 recall만으로 leakage나 일반화를 판정할 수 없습니다.
- 현재 구조를 schema-aware engineering으로 부를지 형식 overfitting으로 부를지는 테스트 결과에 달려 있습니다.
- Have dev/test splits. For both questions and documents. E.g. Build experiment for QA dev set and Corpus dev set. Then, following the same rules or algorithm, add Corpus test set and eval on QA test set (also check that performance is retained on QA dev set)
- If the extractors never saw the QA pairs, that is not classic label leakage, but you can still overfit the corpus shape. Cleanest test I would trust is a format shift: same entities, different conversation layout or noise, then see whether recall collapses. If it only works on LoCoMo-shaped text, it is schema-aware engineering that has not generalized yet.
- Schema-aware until the format changes. I built the person/fact/relation extraction for a personal memory engine and what bit me wasn't the questions, it was a relation backfill on top that inflated false positives across 26 journals. High recall hid it for a while, and i fixed it without touching the extractors underneath. Do you have conversations outside LoCoMo's shape to run it on?
용어 해설
- 오래된 임베딩(Stale Embedding)
- — 문서가 갱신된 뒤에도 이전 텍스트를 벡터로 저장한 상태입니다. 새 버전과 의미가 비슷하면 검색 단계에서 이전 청크가 계속 선택될 수 있어, 버전 식별자와 폐기 규칙을 함께 관리해야 합니다.
- 의미 엔트로피(Semantic Entropy)
- — 모델이 여러 번 생성한 답변을 의미 단위로 묶은 뒤 답변 분포의 불확실성을 계산하는 방법입니다. 샘플 간 의미가 갈리면 불확실성이 커지지만, 같은 오답을 반복하면 한계가 생깁니다.
- 확신 모드 붕괴(Confident Mode Collapse)
- — 큰 모델이 샘플링을 반복해도 동일한 답을 내놓는 현상입니다. 답변이 틀렸더라도 출력이 일치하면 불확실성이 낮게 측정되어, 자기 일관성 기반 오류 탐지의 신뢰도가 떨어집니다.
- 손실 급등(Loss Spike)
- — 특정 데이터 배치와 파라미터 상태가 겹칠 때 학습 손실이나 gradient norm이 갑자기 증가하는 현상입니다. 탐지기는 발생 직전 예측보다 optimizer step 직전 차단에 더 적합하다는 결과가 나왔습니다.
- 관측 가능성(Observability)
- — 모델 호출, 도구 응답, 지연 시간, 비용 같은 실행 신호를 수집해 시스템 상태를 파악하는 체계입니다. 댓글에서는 오류 감지와 재시도·우회 같은 제어 결정을 분리해야 추적이 복잡해지지 않는다는 의견이 모였습니다.
- 불변식 검사(Invariant Check)
- — HTTP 200이나 비어 있지 않은 응답 대신, 권한 상태·상태 전이·출처·도메인 조건처럼 반드시 맞아야 하는 조건을 검사하는 방식입니다. 검사 대상이 0개인 경우도 성공으로 처리하지 않는 규칙이 핵심입니다.
- 스키마 인지형 엔지니어링(Schema-Aware Engineering)
- — 질문과 정답을 직접 보지 않고도 데이터의 반복 구조를 바탕으로 추출기와 검색 규칙을 설계하는 접근입니다. 형식이 바뀐 데이터에서도 성능을 유지하는지 확인해야 누출과 단순한 형식 적합을 구분할 수 있습니다.
코드 예제
text
def int foo(array<int> v)
print("Thread ", tid, " has vector:")
v.print()
main
array<int> u = arange_int(2,20)
finish
asyncs 3 foo(>u)Neve에서 벡터를 세 스레드로 나누어 foo 함수를 병렬 실행하는 예시입니다.
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