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VibeThinker: 3B param model that beats Opus 4.5 on reasoning with novel SFT+GRPO
- c121618 3mo ago[flagged]
- deleted 3mo ago[deleted]
- sosojustdo 3mo ago[flagged]
- aero2146 3mo agoI tried generating the classic pelican svg, but it failed horribly just showing me a rectangle and a black circle...
- realitysballs 3mo agoThat’s all I needed to hear
- pylotlight 3mo agoAs in, you learnt that a useless test that no one should be using was tested here, that's what you meant right?
- fransje26 3mo agoright?
- physPop 3mo agoIts for reasoning not generating art?
- websap 3mo agoCan you explain this a bit more
- tyre 3mo agoImagine you want to make a smaller model that is really good at one thing, say, driving a car. You could remove the parameters that lead it to correctly answer, "What is the powerhouse of the cell?" or, "Who was the first president of the United States?" It would look really dumb if someone asked it that, but that's fine. You're trying to make a model that is optimized for efficiency for a specific task. As much as possible, you should prune uncorrelated things.
- pylotlight 3mo agoSVG generation is a useless test, what's there more to know?
- steve_adams_86 3mo agoWhat if you're reasoning about how to generate SVG correctly?
- Mtinie 3mo agoIn this case, I’d expect it should make a web search tool call to find the Python library best suited for SVG generation and manipulation, and then use what it learns there to execute the task you’ve asked it to do (either asking if you’d like to incorporate the library as a dependency or to roll its own implementation of a subset of the features if that was your preference), Assuming tool calling hasn’t been entirely stripped out of this model. (Edit) No tool calling, per this comment: https://news.ycombinator.com/item?id=48640189 https://news.ycombinator.com/item?id=48640189
- fwipsy 3mo agoI think this is predicted? Part of the story is how they were able to preserve core reasoning ability while cutting knowledge like "pelicans have wings." > these findings motivate the Parametric Compression-Coverage Hypothesis, which views verifiable reasoning as compressible into compact reasoning cores, while open-domain knowledge and general-purpose competence require broad parameter coverage over facts, concepts, and long-tail scenarios.
- pylotlight 3mo agoThe only real essential item here is tool calling capability is it not? So I assume they tested a strong read/write/edit tool consistency?
- btown 3mo agoI'm not seeing any mention of tools in the paper, much less a bias towards "curiosity" to use those tools when it encounters gaps in its knowledge. So perhaps this is a good proof-of-concept that single-pass code generation is viable with this small a model - but we're still a long way from a viable solution.
- nsingh2 3mo agoThis model doesn't support tool calling, was not part of its training. It's focused on Python (and I think C++) competitive programming and mathematics tasks, i.e. tasks with verifiable rewards. So if you have a task that fits that description, the size-to-capability ratio is good. These kinds of models might be more useful as tools to be used by larger orchestrator models, than being the orchestrators themselves.
- sheepscreek 3mo agoSo I think the takeaway here is, this is a super fast companion model to larger models, that reasons quickly. Perhaps this technique can be used to train a highly optimized reasoning "expert" in MoEs.
- kristopolous 3mo agotry it again but give a careful explanation of what a bicycle and a pelican is and how the pelican would sit atop the bicycle. Then give it a reference to the SVG tags you want it to use with documentation. Here's what I got https://9ol.es/tmp/pelican.png https://9ol.es/tmp/pelican.png with https://9ol.es/tmp/prompt_pelican.txt https://9ol.es/tmp/prompt_pelican.txt using prithivMLmods/VibeThinker-3B-GGUF:Q4_K_M
- noperator 3mo agoHaving some success while testing this model out as a replacement for GPT-5 nano in source code security review. Running on RTX 3090 (24 GB VRAM) via vLLM. It's not great on structured output (as noted in the model card) but I'm working around that in my harness.
- dummydummy1234 3mo agoCan't you just force it to do structured output via constrained generation?
- noperator 3mo agoYes, I did end up figuring out a clean way to allow normal reasoning inside <think> and then force JSON _after_ the closing </think>. Example here: https://gist.github.com/noperator/6c711ab19027ea8056442df839f2d7e6 https://gist.github.com/noperator/6c711ab19027ea8056442df839...
- nickalaso 3mo agoThanks for that, and credit to you for the idea, I quickly vibe coded (heh) a working minimal tool calling harness thats allows it to make multiple tool calls each turn. Currently its working pretty damn well all things considered: https://github.com/NickalasLight/VibeHarness.git https://github.com/NickalasLight/VibeHarness.git
- hypfer 3mo ago> but I'm working around that in my harness. How?
- uberex 3mo agoMaybe limiting logits to what is syntactically correct? E.g. {"hello" has to be followed by whitespace or colon. Any other logits get dropped.
- gslepak 3mo agoNote that these are Python-only results, the model will not do as well with other languages. I'm glad to see more domain-focused SLMs, we need more of them! A programming focused MoE should work well across many languages.
- nsingh2 3mo agoLots of confusion about what this model is actually focused on. It is a cheap specialist for closed-world, verifiable reasoning tasks like math, self-contained coding problems, and similar. "Closed-world" means the needed information is already in the context. It is not a tool-using agent that can discover missing context. "Verifiable" means answers are hard to generate but easy to check. So no open ended research, repo wide agent work, factual Q&A, or SVG generation. More of a compact reasoning module for bounded problems.
- nsingh2 3mo agoTo follow up on this, I had it solve a nasty ODE problem that I saw in the recent Mathematica 15 release post: Solve the following first-order ODE for f(x): ((-1 - 2*x)*f(x)*tan(1 + x - exp(-61 - 2*x)*f(x)/x) + exp(61 + 2*x)*x*(1 - x*tan(1 + x - exp(-61 - 2*x)*f(x)/x)) + x*tan(1 + x - exp(-61 - 2*x)*f(x)/x)*f'(x)) = 0 Find the general solution f(x). And surprisingly it found a valid solution! Extra impressive because it runs 25 tok/s on my measly RTX 2070 super. f(x) = x*exp(61 + 2*x)*(1 + x - arccos(C/x)) C is an arbitrary constant. Apparently Mathematica 14.3 couldn't solve this ODE.
- trick-or-treat 3mo agoHow do we know the solution isn't in the weights though?
- kame3d 3mo agoInteresting! I just tried the quantized Q4_K_M from [1] in my RTX 2070 Super, it ran at 110 tok/s with 1800 tok/s prefill, and found the same solution to your prompt. It generated valid LaTeX for the answer but its reasoning trace uses mostly compact ASCII math notation. Took 3min 22s to answer, spending 22k tokens almost all on thinking. [1] https://huggingface.co/prithivMLmods/VibeThinker-3B-GGUF https://huggingface.co/prithivMLmods/VibeThinker-3B-GGUF
- deftio 3mo agoThere is some base level of intelligence any model needs to be useful, even in narrow tasks. Could you teach a 5 year old to drive a car? A 10 year old? A 12 year old? To drive a car requires being able to read, to have judgement about ice or rainy conditions, to anticipate a child running after a ball. By the time a human in in their mid teens they have acquired the base knowledge... Small models need to have enough base knowledge to be able to be good enough -- even in a seemingly narrow regime. Where is that? Obviously they don't need all the obscure knowledge of a frontier model but there is some base level which is probably more than it would first seem.
- universa1 3mo agoA 10 year old definitely,and 5year old is close, but not unrealistic, To drive a car you don't need to be able to read... To drive a car on the road with other people is a whole other story :-)
- 3eb7988a1663 3mo agoI suspect plenty of five year olds can do a respectable job in Mario Kart, Gran Turismo, etc driving games. Gaming has too low of stakes to judge them on perfectly adhering to the rules of the road, but the ability is there.
- smokel 3mo agoBeing able to drive a car properly also depends on having the right exploration-exploitation balance. A three-year-old is likely to explore too much in a situation where mistakes can be dangerous. This requires not only knowledge, but also the control systems that develop with the prefrontal cortex. LLMs don't do much control yet.
- ygjb 3mo ago> Could you teach a 5 year old to drive a car? A 10 year old? A 12 year old? To drive a car requires being able to read, to have judgement about ice or rainy conditions, to anticipate a child running after a ball. By the time a human in in their mid teens they have acquired the base knowledge... I would be interested to see a formal study of this. I say this not out of anything other than a observation that I think the only real blockers are a) judgement, and b) physical reflexes/strength. As a kid I was certainly aware of ice,snow, and rain, because I road my bike year round and had low confidence in my own ability to control my bike on snowy or wet terrain, especially during season changes. That translated into learning to drive in northern Canada in the winter and applying those lessons to driving. In an environment devoid of consequences, I have seen kids operate driving simulations (both real simulations, and video games) with a degree of precision that is shocking, including seeing several 9-11 year olds play the simulations and games with a much higher degree of confidence than adult drivers. Children have an awareness that the simulations are consequence free, unless given other motivation. Adults that are consistent drivers have muscle memory and preconceived expectations that govern the decisions they make when playing the game. I am curious about the level of training and exposure required for children to overcome their lack of awareness of the hard limits and consequences of driving and driver error, versus the amount of training and exposure required for expert drivers that are novice gamers to stop applying their learned experience to consequence free simulations.
- SwellJoe 3mo agoIt's terrible at hunting security bugs (I expected it to be, but I wanted to be sure). I added it to a benchmark I made with a corpus of some Mythos-discovered bugs, and it found zero. The smallest pretty successful models remain Qwen 3.6 and Gemma 4 (but I haven't tested the very small variants of those yet). https://swelljoe.com/post/will-it-mythos/ https://swelljoe.com/post/will-it-mythos/
- nsingh2 3mo agoThe lack of tool use will hinder it a lot I think, since bug hunting requires collecting context across a code base and stitching it together. It might be good in a more narrow sense, i.e "is there a bug in this block of code" and not considering how it interacts with the rest of the code base. That's also more aligned to its leetcode style training data, the code under test is fully in the context window. It might be interesting to have a bigger tool use model go through the effort of collecting the context, and feeding it into this kind of model for analysis only. It becomes more of a thinking tool, instead of the orchestrator.
- secretslol 3mo agoAm I right in thinking this is a tiny model which has been trained well to reason, and that's it? Makes me think of a smart person who doesn't know anything about a given topic, but with the right tools will go and research the heck out of it. I really like the sound of this... why have models train on learning anything when you can just train them how to learn and let them get on with it from something as small as a Pi Zero and an internet connection.
- numlock86 3mo agoThis has been my dream ever since. Instead of encoding "all the knowledge" into those parameters, how about just making a model that has the same size, but all (or rather most) it does is reasoning? Just give it the ability to browse the net (e.g. language specifications, documentation and best practices) and just have it do its thing. Why does my coding agent need to know the population of New York, know a cheese cake recipe or the general lifespan of an ostrich? Just give it the bare minimum knowledge to think and reason about, and let it figure out the rest. Sadly that's not how LLMs work, since all they do is "token prediction". At least the models we have to today ...
- tomaskafka 3mo agoEducation had this sad 15 year period where it thought “competences” are all you need. Turns out that without the world knowledge to have a base of facts, it is not.
- riponcm 3mo ago[dead]
- NotSuspicious 3mo agoThe interesting thing about models this small is they should be able to be put on a single Taalas chip (the HC1 already runs a Llama 3.1 8B model). We're already at the point where half-decent reasoning could be run on an ASIC (and at mind-boggling speeds).
- pants2 3mo agoYeah, if they can fit an 8B model that's really good at improving the output by thinking, running at 16K tok/s on Taalas would be mind-blowing.
- le-mark 3mo agoGiven this and the quality of open models, it makes no sense to me that there’s a future for Anthropic et all?
- james_marks 3mo agoPackaging a capability into a consumable form will still be business. It's like web hosting; all the open source tools are there and free, and yet website tools, hosts, etc flourish.
- WhiteDawn 3mo agoIt’s true, but hosting prices are still within spitting distance of rolling it yourself. SOTA providers are expecting some level of margin. Companies everywhere have a tight eye on their AI bills right now. The motivation is there if the models get good enough, even if it’s more painful.
- CamperBob2 3mo agoExactly, it's like web hosting. And I don't see a lot of web hosts with twelve-digit valuations.
- anuramat 3mo agoit's not like anthropic will stop improving their models
- zkmon 3mo agoDoes python coding depend on political facts of the world? It might appear not, but actually, the process of reasoning is not an isolated act. The right and wrong way of doing things is codified in social evolution that absorbed all facets of life. Why should you optimize a piece of code for performance? Why performance is needed? What is a bug? What features and UI themes would be more intuitive for humans? There is a butterfly effect. Everything affects everything to some extent.
- spacebacon 3mo ago[dead]
- CamperBob2 3mo agoTrue, but this model provides something of a lower bound on just how much world knowledge is really needed for unrelated reasoning tasks. That lower bound appears to be quite low indeed. Lower than I thought it would turn out to be. This thing is just bonkers.
- anonyfox 3mo agoWake me up when it does OCaml fine.
- jkwang 3mo ago[flagged]
- scotty79 3mo agoIf you could pair it somehow with a model that can code and describe code this could be a very powerful combo.
- brainless 3mo agoI recently came across this model and I would love to try it with my coding agent soon. I really like the idea of small models that can reason but do not have too much knowledge. Also, no emphasis on tool calls. I think the agent should do the heavy lifting and reach half way. I use really small models, like Qwen 3.5 0.8B to 9B - no tool calling, no MCP, no skills, nothing. No multi-turn chat even. Models are given very specific tasks using a vast number of system prompts and all the response handling is done in the agent(s). https://github.com/brainless/nocodo https://github.com/brainless/nocodo
- SubiculumCode 3mo agoMaybe no tool calling, but seems it could be really good at deciding which tool to use and when?
- brainless 3mo agoThat is a good point. I do think these models would be good in the decision making. The large models are trained to use tool calling. Perhaps the small models can generate the text that would express their decision but not generate good JSON to reply with correct syntax. I do not know but this is my hunch.
- maxignol 3mo ago3B param on par with opus 4.5 sounds interesting. Will read the full article before making my mind
- lisa_luoyf 3mo ago[flagged]
- t_e_s_t 3mo ago[flagged]
- t_e_s_t 3mo ago[flagged]
- t_e_s_t 3mo ago[flagged]
- rbbydotdev 3mo agoLooks like we are seeing small but mighty model breakthroughs, outpacing the pure capital firepower of SOTA providers. I love rooting for the little guy, but is it too soon to call it? To play devils advocate, could it just be the benchmarks are not efficient enough to capture success of real developer workflows?
- bakies 3mo agoI've just started using qwen3.6:35b a couple days ago running on my framework desktop and rather impressed. It runs really well and reminds me of probably the first Claude model I used. It's the first local model that's actually working for me in a coding agent I've tried. Very exciting!
- smcleod 3mo agoTry 27b, it's significantly smarter than 35b-a3b (although it is slower, it's not so bad with MTP).
- ignoramous 3mo agoAt least according to gertlabs, Qwen3.6 27B outperforms every SoTA (closed) model at Kotlin: https://archive.vn/RYBCL https://archive.vn/RYBCL / https://gertlabs.com/rankings?mode=agentic_coding&language=kotlin https://gertlabs.com/rankings?mode=agentic_coding&language=k...
- iosjunkie 3mo agoInteresting. I wonder if there is opportunity to train a set of small model variants to excel at a certain stacks. Eg Qwen3.6-27B for Node + React or Qwen3.6-27B for Rust + TUI
- mft_ 3mo agoThis is always how I've imagined small/consumer-hardware models going in time. If I only ever code in Python, give me a model that does just that (plus some general CS, algorithms, structure, etc.) and does it super-fast and well. Make it small enough that if I need a Python back end and an HTML front end, another specific model can load alongside and collaborate on the front end. Or give me a pure shopping model that has a general understanding of products and product categories, and then will playwright/scrape/API into shopping sites to compare options and find me what I want. Etc.
- uberex 3mo agoWhat is the idiots guude to run this one local now?
- yousif_123123 3mo agoUse LM Studio.
- uberex 3mo agohow do I get these weights in particular?
- Landing7610 3mo agoomlx makes it quite easy
- CamperBob2 3mo agoAsk an existing LLM harness like Claude Code. "Install this and run the demo program" or "Set this up with llama-server" or "Give me an Open WebUI page for this model" will work.
- uberex 3mo ago[dead]
- androiddrew 3mo agoI have been thinking about how to use this. Since it doesn’t support tool calling I have been considering a dual model deployment, where a small tool calling llm drives the majority of the user experience, and vibe thinker is tapped for reasoning by the other llm. So who has suggestions on small models with excellent tool calling capabilities?
- reddec 3mo agogranite 4
- j-bos 3mo agoMaybe bonsai 8b would make the duo, if you do try it, pls post here as I'm a bit curious too.
- smallerize 3mo agoGemma 4 E4B and Qwen 3 4B are pretty good, but fine-tuning makes them really good. There are tradeoffs at this size, so you'll have to find (or make) a finetune that does what you need.
- scotty79 3mo agoQwen3.6-35B-A3B is pretty amazing. I'm using it with 96k context on 24GB VRAM through ollama.
- unfirehose 3mo agothis is a good model. I benchmark reasoned answers to qwen 3.6 27b (no think)+ bash and it held up.
- virajk_31 3mo agoSLM when trained for single use case often beats the LLM. That's both the advantage and limitation.
- sorenjan 3mo agoHow would you best utilize a model like this for coding? I take it it's not meant for vibe coding a full app, and the reasoning probably makes it unsuitable for autocomplete. Would you use it to implement specific functions? I looked at one of the coding benchmarks used, Live Code Bench, and it seems to be problem descriptions with sample input and output, and then a solution with a single function or class. Seems like a really good model to use in an IDE when you still want control over the code structure then.
- aswegs8 3mo agoNot sure if it's suited for that. If you read the article it's stated that it is basically a research project to see how far they can push it with small models.
- deleted 3mo ago[deleted]
- iamgopal 3mo agoTwo model, one is optimised for system, reasoning etc, second is optimised for specific language ( rust or go ? ) , both small enough to run on local computer, will it work ?
- cold_harbor 3mo agoGRPO skips the value network that makes PPO expensive — it scores candidates relative to each other within a group. that's what makes verifiable-reward training practical at 3B scale
- mdp2021 3mo agoWhy was this downvoted.
- 4gotunameagain 3mo agoWhat are the implications of local SOTA inference, given the insane datacenter "investing" ? It surely cannot be justified only for training at this scale, and since models nowadays are improved more and more by fine tuning than re-training from scratch. Will a viable local model crash the US economy ? More importantly, are the LLM companies aware, and are they deliberately buying out all the RAM and GPUs in order to prolong the inevitable ? Probably not, but I wouldn't be surprised if that is the case.
- diseasedyak 3mo ago[dead]
- jpcompartir 3mo agoThe absolute worst name for a model I've seen
- yousif_123123 3mo agoI really hope that in a couple of years I can have a laptop that runs a reasonably good coding agent locally, that I can run fast and do most of my programming with, without running my laptop hot. I could keep open code and use other models when needed, but really for most of my work, I'm already breaking it down so that I can review code changes eventually, and I just need something reasonably decent and fast and unlimited. I think its coming.
- alkonaut 3mo agoI hope so too. But I fear that it will feel inadequate if we know there is always a $20 online model that is an order of magnitude better. I don't think there will be a "good enough" local model so long as frontier models look so much better.
- vadansky 3mo agoSeems like most people have settled on Opus 4.6 as the breaking point (me as well). Once I can spend 10k to run Opus 4.6 at home, I'm done.
- yogthos 3mo agoI'm very optimistic here as well. And it's also worth noting that tooling is improving along with the models. I really think we have to treat models and tools as a package. The models is your engine, but you need a chassis to run it. I find what makes frontier models actually work well isn't just the capability of the model, but how well the harness is tuned to its expectations. I wrote a about this in a bit more detail here. https://yogthos.net/posts/2026-06-08-dirge-code.html https://yogthos.net/posts/2026-06-08-dirge-code.html
- cheekygeeky 3mo ago[flagged]
- kmchandy 3mo agoThe paper makes a clear claim: "it provides an important and concrete proof: on well-constrained, verifiable reasoning tasks, first-tier performance is no longer the exclusive domain of ultra-large models" And that's exciting.
- achrono 3mo agoBeats Opus 4.5 on reasoning you say? Prompt: If A goes to B who then goes to C, can A send something to C? Response: We need to interpret best. The phrase "If A goes to B who then goes to C, can A send something to C?" could be a puzzle about the concept of sending something (like passing a ball) and the relationships. Scenario: A gives something to B, and B passes it on to C. Question: Can A also give the same thing to C? Answer: Only if A can obtain a second copy (e.g., the thing was duplicated). Otherwise, after handing it to B, A no longer holds it and cannot “send” it unless a copy exists. [Lots of other unnecessary commentary and "scenarios" that make even lesser sense]
- erdevs 3mo agoI am a human and I don't know how to interpret this prompt.
- mdp2021 3mo ago> If A goes to B who then goes to C, can A send something to C? "If John travels to the location of Mary, and later Mary reaches Paul, is John enabled to have an item delivered to Paul" Or what did you mean.
- nolist_policy 3mo ago> Multi-level Quality Control. > [...] > LLM-based Query Quality Filtering. We utilize capable LLMs to assess query quality, filtering out samples with incomplete descriptions, unreasonable conditions, invalid logic, or an inability to effectively assess target knowledge points.
- rapatel0 3mo agoRan the same query and there is a ton of stuff, but it looks like it's reasoning through the ambiguity of the sentence. It still gets the right answer. Moreover, if we consider the FLOPs expended to get to the answer, and compare that to opus, I think it's still a net win. My hunch is that Opus scale models probably have shortcuts encoded into the model that handle these ambiguities cases, wheres this model has learned a program to reason through the edge case (crystalized vs fluid intelligence). Remembering that probablity (frontier) vs calculating it on the fly (vibethink)
- nolist_policy 3mo agoNotable: VibeThinker-3B is developed through a staged post-training pipeline built upon Qwen2.5-Coder-3B base, a compact 3B foundation model. Qwen2.5 is ancient by LLM standards.
- diimdeep 3mo agoBF16 with no QAT quants == half backed bread
- viduus 3mo ago[flagged]
- andai 3mo agoI tried actually talking to it. It reminded me of GPT-2.
- CamperBob2 3mo agoIt's not supposed to be a chat model. It is crazy good at math.
- andai 3mo agoYeah, I thought so too. It's supposedly good at coding, too. But, if its English responses are insane, why should I trust it to understand my programming-related instructions? Well, only one way to find out! I mean, maybe it only says insane things and goes in circles if you ask non-coding questions, but it doesn't exactly inspire confidence.
- makethembroke 3mo agoI don't get this beating opus, It just hardcoded the tasks for bench , It does even respond normally A alot randomness in it Please don't hype
- delis-thumbs-7e 3mo agoI gave this a run on llama.cpp locally. My GPU is Ge1080, so I needed quantized version for even such a small model and… This. Is. Amazing. I am flabbergasted. I am not into the whole GenAI thing and I have very little need for anything agentic, but Python, C++ and Maths is exactly what I mostly used these for, so this might actually become my main work horse. This is so cool. I even used it for stuff it is not built for, asking complex qustion on history (Battle of Tours 732) and literature (Joyce’s Portrait of Artist) and it was surprisingly good, even though it started to hallucinate names and details (such as claiming Joyce’s father was a priest). For 3B I expected it to mainly spout complete nonsense.
- tracerbulletx 3mo agoMan just need something like this with tool calling.
- mvitorino 3mo agoReally enjoying seeing these really capable SMLs. Note that on HF they state: "This model was not trained on tool-calling or agent-based programming data. We therefore do not recommend using it for tasks that involve function calling, API orchestration, or autonomous coding agents." - https://huggingface.co/WeiboAI/VibeThinker-3B https://huggingface.co/WeiboAI/VibeThinker-3B So we can't just hook it up to a coding harness like pi.dev or something.
- troglodytetrain 3mo agoSounds like something that could be pretty useful as a 'validation' subagent. Provide it the details/context related to a larger LLM's run or turn in a harness and have it act as a gatekeeper. At this size and speed it looks like it could be economical to have it run every turn or even every tool call and inform the main agent about the result and success/failure.
- nickalaso 3mo agoSo I went ahead and quickly vibecoded a working harness with a barebones tool interface and some constraints on output (credit to noperator for the idea). github: https://github.com/NickalasLight/VibeHarness.git https://github.com/NickalasLight/VibeHarness.git Its meant for a Windows machine using ollama but I'm sure anyone who wants to mess with it can point claude code at it to convert it for your own operating system and requirements. After install you can ask it to do something with "vibe 'create me a poem about cheese in cheese.txt'" its workspace is by default the directory the cli was located in when you called it.
- darkoob12 3mo agoI still cannot trust evaluations and benchmarks. How can you prove that the test datasets are truly unseen examples? I think the only way to prove that these models are truly as good as they claim is to wait and see if they are getting adopted in practice.
- mdp2021 3mo ago> the only way to prove that these models are truly as good as they claim It would be actually to progress towards the solution of the "black box" problem, the goal of "transparency". You have to implement a reasoner (etc.), you conceive the best architecture for it - then implement and test it.