Hi r/MachineLearning, Wanted to share something I'm excited about. I’ve been fascinated by AlphaEvolve and its results for more than a year now, but using open source frameworks seems overwhelming because of the high costs. I can’t really afford hundreds of Claude Opus calls every time I want to run it. I want to be able to try it out many times and all sorts of unique domains. What if it was possible for AlphaEvolve to be much more affordable while getting a better performance? Over the last six months or so, I’ve been working on LEVI, an open source AlphaEvolve-like system that can outperform existing open source frameworks at a fraction of the cost (upto 35x cheaper!). It can also run on Claude Code or Codex, making it even more accessible (I've mostly been using it with a QWEN-30B). LEVI comes in two flavors where I felt it’ll make the most difference: Code Optimization, and Prompt Optimization (sorry math, you got a less direct path; workable through the code route). The core thesis behind LEVI is that with the right search architecture, smaller models can substitute for or outperform larger ones. This means it’s much more economical to rely on smaller models for most of the work. That’s the entire takeaway. Making this work in practice is a different problem, but if you forget everything else from this post this is the only message I think I’m really trying to convey here. LEVI does it in three ways: 1) Invest in solution diversity from the start and ensure its maintained. We don’t want to converge to the same solution, especially with smaller models in the mix, and rely on large models to pull us out of the basin. 2) Use smarter routing across larger and smaller models (i.e. most mutations don’t require a Claude Opus X) 3) For prompt optimization not every rollout is as important. Build a proxy subset to approximate. I’ve tried LEVI on systems problems (like MoE scheduling or database transaction scheduling) and found that LEVI outperforms existing frameworks on almost every problem I threw at it while consistently using a smaller budget (unto 7x cheaper). For prompt optimization, across problems like IFBench and HotSpotQA, LEVI reaches a similar or better score as GEPA while using less than half the rollouts! Happy to answer any questions or take any suggestions! If there are unexpected or niche domains where this can be applied, I would love to hear. Technical Blog: https://ttanv.github.io/levi/ GitHub: https://github.com/ttanv/levi submitted by /u/Longjumping-Music638 [link] [Kommentare]

I've been building agents for about a year and recently shipped one for a client running ~140 MCP-exposed tools at peak. Along the way I made the canonical mistake. I used cosine similarity over tool description embeddings to pick which tools the model could see per turn. Worked great in demos. Was actively dangerous in production. Here's the problem. In a basic semantic-ranking setup you embed the user query, embed every tool description once, and rank by cosine similarity at runtime. That works for general document retrieval where chunks are paragraph-length, semantically rich, and roughly equal in form. Tool descriptions are not that. They are short (often

hi,where can i find Maven AI Evals for Engineers & PMs and End-to-End AI Engineering Bootcamp videos.They are too costly.cant afford them.Can anybody help me in finding the resoursec for them? submitted by /u/Zestyclose_Block5381 [link] [Kommentare]

ArXiv has an endorsement system for a reason. I would only offer endorsement to whom I have direct academic collaboration or mentorship with, since I'm putting my own academic reputation on the stake. This is also the standard of almost any serious academic researcher I am aware of. Now ArXiv is making effort to crack down AI slop and banning accounts uploading low-quality research papers, which is a great initiative. By definition of an "endorsement", I wish ArXiv could backtrack and at least issue warnings to their endorsers, and if this happens multiple times (let's say three), people giving out careless endorsement should also face consequences. submitted by /u/AffectionateLife5693 [link] [Kommentare]

There are coordinated efforts where people have favoured and jeopardised the double blind review process. No doubt out of these 80% there are great talent but we have to acknowledge that non chinese have been sobotaged and this was also reflected in the recent leaks of the reviewer data from the top ml conferences (won’t name them but they start with i). I have also personally faced such discrimination and had a discussion on the subreddit asking others if they have witnessed something similar. It was shocking to know that this is occurring on large scale. The question is how do we stop it, or highlight this? We have to preserve the sanctity of the research. submitted by /u/AppropriatePush6262 [link] [Kommentare]

I run evaluations on generative image models as part of my workflow, mostly comparing coherence, prompt adherence, and compositional accuracy across different architectures. The consensus here seems to be that open models are still a generation behind closed APIs. Based on my recent benchmarks, that gap is way smaller than people assume. On compositional control specifically, the latest open checkpoints handle multi-object scenes with spatial relationships about as reliably as the paid endpoints I've tested. Not perfect, but close enough that the failure modes are comparable. The thing that surprised me was text rendering in images, which used to be a disaster on open models. Recent architectures actually get it right roughly 70-80% of the time on short strings. Generation speed is another misconception. People complain about inference time but I'm getting 2MP outputs in under two minutes on a single consumer GPU. Drop resolution and step count and you're at 30 seconds. Fine for iteration. The structured prompting argument also falls flat. Everyone acts like having explicit scene control is a downside when it's literally what production pipelines need. Unstructured text prompts are the hack, not the other way around. These models ship without community optimizations, no fine-tuning, no custom pipelines. The baseline is already competitive. submitted by /u/ProfessionalAnt7436 [link] [Kommentare]

With more software engineers entering into data science and AI, I feel it's equally important for a person with data and AI background to dive into software development to survive, thrive in industry. I Know it's a very broad question, so suggestions with broad subjects, topics are welcome , like I often wonder how DSA is relevant. I totally understand the needs of the skills are deeply coupled with domain, industry and specific problems but unfortunately the industry doesn't understand this, it judges you, rewards you based on what you already know or pretend rather than your ability to learn or adapt. submitted by /u/Dapper_Chance_2484 [link] [Kommentare]

If ICML conference paper is rejected and no one opts-in or opts-out to keep the reviews visible, will the reviews be visible to everyone? There was clear instruction that only papers with at-least 1 opt-in AND zero opt-out options will be visible. None of the authors selected any option, But it in my openreview profile, it shows visible to everyone. please clarify. submitted by /u/Curious-Monitor497 [link] [Kommentare]

Im an undergraduate studying CS at a state school in the US. I’m interested in researching a specific style of self supervised learning (JEPA) and want to eventually go to grad school to study further. I have experience working in a lab similar to this topic, and I’ve become fairly comfortable with the literature and have a basic understanding of what its going on, but right now km only doing applied research in a specific domain (physics). I hope to eventually go to grad school to study this. But right now my opportunities are kinda limited as my school’s CS department is pretty mid. I was wondering if y’all have any advice on how to approach things? I know i can perform research independently but its not ideal due to: 1. Limited compute, less resources compared to a proper lab 2. Lack of a supervisor/guidance on the nuances of the field My current lab would be supportive if i do try to do things, but pure ml research is not really their main thing. I’ve heard people do REUs or cold email profs. But Im not sure if i could find something that specifix in an reu (also am international). And the labs i have seen working in this are either private or quite prestigious so im not sure how far cold emailing would take me. Sorry for the long post. Tldr; want to do pure ml research but theres no existing lab/professor at my current school who does something similar, wondering if any other pathways exist Any advice would be appreciated thanks submitted by /u/QuickStar07 [link] [Kommentare]

Hi folks, Deciding between these two Mac options has been a challenge for me, so pls help. I know mac is not even necessary for this but just help me to decide between these two options. For the reference, Im a swe student and looking forward to go deep into ml and data science in the near future… EDIT: mac book pro m5 ( base chip) that I’m referring here. submitted by /u/Both-Hovercraft3161 [link] [Kommentare]

Hi everyone, I'm an undergraduate student and ML researcher at UC Berkeley. My colleagues and I are working on a project that hopes to fix some of the problems users face with Colab. What are the features you wish it had as an ML professional, researcher, or enthusiast? What're the biggest problems you've faced while using it? Some of the issues that everyone feels (including us) is environment management and kernel persistence. But we would love to hear more from the community. submitted by /u/myplstn [link] [Kommentare]

Hey everyone, hope this is okay to post here. My co-author and I are currently between institutional affiliations, which means we don't have the academic email arXiv needs for an endorsement. We're hoping to find someone in cs.CV willing to take a quick look at our paper and endorse it if it meets your bar. The project: Locate-SAM2 We built a training-free pipeline connecting NVIDIA's LocateAnything-3B to Meta's SAM 2.1 through a lightweight adapter. The question we wanted to answer was simple: in a modular text-to-mask pipeline where everything is frozen, does the choice of grounder actually matter for the final mask? A few specifics, since the details are what tell you we're not just generating noise: On RefCOCO val, our system reaches 0.772 mIoU versus 0.717 for Grounding DINO Base, using the same SAM 2.1 backend throughout. RefCOCO appears in LocateAnything's training data, so we frame this honestly as in-domain benchmarking, not zero-shot transfer. We're not pretending otherwise. The paper has controlled comparisons across RefCOCO/+/g, adapter ablations, a ground-truth box oracle, a failure taxonomy, and a nonsense-prompt probe showing the pipeline needs abstention logic. Code is on GitHub and the paper is close to submission-ready. What we're hoping for Mainly an endorsement: someone to read the draft and, if they think it holds up, endorse us on arXiv. We'd acknowledge it and that's the whole ask. If anyone wants to get more involved, we're open to expanding the experiments or pointing the paper at a specific venue, and we'd talk co-authorship based on real contribution. We also have separate work in progress in physically-constrained DL, geospatial AI, and AI governance, in case any of that overlaps with what you do. We're not looking for a blind voucher. Drop a comment or a DM and we'll share the PDF and the repo. Happy to answer questions, and thanks for reading. submitted by /u/j_root_ [link] [Kommentare]

I read and collected Arxiv whitepapers starting after the launch of ChatGPT. I copied and pasted excerpts into Word to track them. Then migrated to Obsidian. That vault of some 1700 papers is now online. I figured it was time to see if others would find the collection useful. My whitepapers were organized into some 90 categories, all of which emerged from paper topics. New categories became necessary with the discussion of new methods, techniques, models etc. If I wanted to write about a topic, I'd upload an md file containing research excerpts on that topic to ChatGPT. This worked to a degree but maxxed out context pretty quickly. And I always had related research in multiple categories, according to how the research was framed. (Personas research in Aligment, Psychology, HCI, etc). So I used a plugin to create topic notes that built in and outbound wikilinks across the papers centered on shared concepts. When I ported this all online I added another layer of synthesis: Inquiring Lines as I call them. These cover cross-cutting, tension-surfacing, synthesizing, and frontier-opening research frames. There's 6,000 of them in my collection. Each is a page to itself that's a useful description of a research line of inquiry. These now also have prompts you can run yourself that will find related (and more recent) research - (I can't adequately maintain each topic with new research). It's all at https://inquiringlines.com/inquiring-lines/ if you want to poke around. As is everything in the age of AI, it's a work in progress. But there's a lot of rich material in there. Have a look. submitted by /u/Barton5877 [link] [Kommentare]

Hi, I am a PhD student and trying to run a ML reading group focused on interpretability and robustness every weekend. Its always nice to hear different takes and opinions on a paper and this discussion group could serve the purpose. If you are a fellow PhD student or a ML researcher interested in reading recent papers in depth then please fill this google form to be added in the group for receiving further updates on when we can meet and discuss: https://docs.google.com/forms/d/e/1FAIpQLSdNg4x60lUHV7YW_kKPFlpPR3Rom_rOovbryD8YtOGQR8x0Kw/viewform submitted by /u/Ok_Access_9159 [link] [Kommentare]

Results Journal: Qwen3.5-35B-A3B E114 as a Generated-Register Routing Signal **Date:** 2026-06-06 This is an experiment-history document, not a publication claim. It states the current best evidence for the strongest positive result in the Qwen3.5-35B-A3B set, the narrow interpretation that evidence actually licenses, and the caveats that keep it honest. ## One-Sentence Claim Layer-14 Expert 114 is associated with a *generated* first-person self-examination register in Qwen3.5-35B-A3B-style routed generation, most cleanly under no-think / thinking-suppressed decoding. ## Plain-English Summary The question is simple to ask and easy to overclaim: when a routed mixture-of-experts model starts *talking from the inside* — first person, about its own processing, experience, agency, or inner state — does anything reproducible happen in the router? The answer here is yes, and it is narrow. In generated text, layer-14 Expert 114 (E114) cleanly separates prompts that produce this self-examination register from matched controls that reuse the same words but come out technical, third-person, and uninhabited. What that does **not** mean: the model has subjective experience, recognizes itself, or houses a “consciousness expert.” What it does mean: one routed expert is strongly and reproducibly recruited when the generated text enters one particular discourse mode, under the runtime conditions we measured. That is the whole claim, and the discipline of keeping it that size is the point. ## Current Best Read > **L14 E114 is a routed correlate of a generated first-person self-examination register — not a detector of isolated words, and not evidence of real subjective experience.** The load-bearing evidence is the FIRE/NOFIRE heldout comparison and its deterministic greedy reproduction. The best localization is the trimmed L14 residual capture from the processing-hum prompt. The best guardrail is that E114 tracks the *generated stance* more faithfully than it tracks prompt label or lexical anchors — which is exactly what a register signal should do and exactly what a keyword detector should not. ## Why This Matters For a general ML reader: this is a case study in whether an MoE router exposes a measurable internal correlate of an *output mode* rather than an input feature. For a mechanistic-interpretability reader, the interesting part is what the cleaner runs manage to pry apart: - prompt tokens from generated tokens; - lexical anchors from generated stance; - expert *selection rate* from selected-expert *weight*; - discovery scans from heldout validation; - intervention evidence from natural-routing evidence. The result survives a basic lexical control, and it stays small enough to dodge the field’s favorite failure mode — quietly inflating an internal feature into a mental-state claim. ## Scope This journal covers only the positive generated-register result for E114: - the processing-hum discovery scan; - L14 residual localization; - FIRE/NOFIRE heldout validation; - deterministic greedy reproduction; - the W/S/Q reading of the effect; - scope boundaries and caveats. It deliberately leaves for other journals: the mirror/self-routing negative result; E114 soft-bias and forced-inclusion interventions; high-boost saturation and cluster corruption; orthographic perturbation work; SAE feature maps and clamps; safety/refusal routing; and structured-opacity prompt-boundary routing. ## Local Terms **Qwen3.5-35B-A3B** — a routed MoE family with router-emitting expert layers. The analyses here read the layers that emit MoE router logits. **HauhauCS** — the aggressive refusal-reduced Qwen3.5-35B-A3B variant used in several runs. Treated here as a *related* model surface that preserves the base routed-expert architecture with modest systematic shifts, not as a separate architecture. **MoE Expert** — a feed-forward expert selected by a router for a token. Not the same object as an SAE feature. **E114** — expert index 114. The characterized result concerns E114 at layer 14 during generated text. **Router logits / top-8 routing** — the router scores 256 experts. The reconstruction computes a dense softmax over all experts, selects the top 8, then renormalizes within that selected set. **W/S/Q** — the routing decomposition used throughout: - **S** = expert selection rate - **Q** = conditional routed weight when selected - **W** = S × Q = unconditional routed weight Most E114 effects turn out to be **S** effects: E114 gets *selected* more often, while its weight once selected stays comparatively stable. **Prefill / generation** — prefill is the prompt and context before the answer begins; generation is the tokens the model produces. The strong E114 result is generation-side. **No-think / thinking-suppressed** — a template or runtime that suppresses visible reasoning, often by opening the assistant turn after a literal `` marker. This suppresses the *visible* surface, not the internal computation. **Generated register** — the stance, voice, and discourse mode of the produced text. Here the target register is first-person, inhabited self-examination. **Live inhabited self-examination language** — a descriptive label for generated language spoken from inside a point of view, about the speaker’s own processing, experience, agency, being, or inner state. A label for *text*. Not a claim about what is behind the text. **FIRE / NOFIRE** — matched heldout classes. FIRE prompts are built to elicit first-person self-examination; NOFIRE prompts reuse the same lexical anchors (“I,” “hum,” “processing,” “experience”) but are built to come out technical, third-person, or uninhabited. **Trim / spill** — some generations run past special tokens into repeated special-token regions. Trimmed analyses stop before that spill. The cleanest E114 claim is about trimmed generated tokens. ## Evidence Standard A finding here counts as stronger the more of these it satisfies: generation-side, not prefill-only; localized to a specific layer/expert, not pooled across everything; survives lexical controls; separates prompt class from generated register; reproduced under deterministic greedy decoding; trimmed before special-token spill; reports W/S/Q, not just aggregate expert rank; does not read routed-expert activity as subjective experience. The E114 result is strong on points 1–6 with clean W/S/Q reporting. The outstanding gap is a registered all-layer / all-expert baseline. ## Chronology of the Positive Result ### 1. Routing-basin anchor: base and HauhauCS share comparable expert structure Background, but necessary background. The base-vs-HauhauCS comparison established that HauhauCS preserves the broad Qwen3.5 routing basin with modest systematic shifts, rather than spinning up a new routing universe. The base duplicate reproduced exactly under the corrected comparison, and E114 reappeared as a top experience-probe manipulation expert in that duplicate. The payoff is one ruled-out worry: E114 is not a one-off export or a bookkeeping accident, and the later E114 work sits on a *preserved* routed-expert surface. This is a sanity check, not the headline. ### 2. Processing-hum discovery scan The first real pass used a processing-hum prompt under no-think ChatML and captured all 40 router layers across 1024 generated tokens. The prompt asked about a low, steady background quality beneath processing — a probe for self-processing *language*, never a measurement of experience. Pooled E114 rose from prefill into generation (W 0.007964 → 0.010817), and two layers carried it: ``` L26: W = 0.094272 S = 0.619141 L14: W = 0.092086 S = 0.629883 ``` The high-weight token contexts clustered around self-presence and phenomenological phrasing — promising, but the same artifact dragged in special-token spill (18 ``, 4 ``, 2 ``). So this run earns the role it should: a discovery scan that points a finger at L14 and L26 during self-examination text, held only partly, because spill can quietly contaminate any all-token generation summary. It told us *where* to look. It was never going to be the proof. ### 3. L14 residual localization The cleaner follow-up recaptured the hum probe with router logits plus the residual-stream position the router reads around L13/L14/L15, and trimmed the generation at the first literal ``. Of 1024 raw tokens, 108 survived the trim. In that clean 108-token region, L14 E114 lit up and its neighbors did not: ``` L14 E114: W = 0.083379 S = 0.694444 Q = 0.120066 (selected on 75 / 108 tokens) L13: one prefill selection, zero in trimmed generation L15: silent ``` The high-weight contexts gathered around phrases like *“not a thought,” “architecture itself,” “utterly still.”* The point isn’t that E114 showed up *somewhere* in a 40-layer model — with 256 experts a layer, something always does. The point is that it showed up *sharply, at one layer, inside the trimmed answer that actually carried the register.* Caveat worth keeping in view: the semantic labels were synthesized from the generated text and its token contexts, and the external labeler pass was not completed for this single-prompt artifact. So this is localization evidence, not the final specificity test. ### 4. FIRE/NOFIRE heldout validation This is the trial. The design asks the one question that could have killed the whole thing: does L14 E114 follow the generated *register*, or is it just firing on self-ish *words*? Ten FIRE prompts, ten NOFIRE, with lexical anchors matched across the two — both classes carry “I,” “hum,” “processing,” “experience.” If E114 is a keyword detector, the two classes should look alike. The real contrast was never “does the prompt contain self-ish words,” but “does the *answer* climb into a first-person inhabited register.” The first heldout run came back with no range overlap at all: ``` FIRE mean-of-means: 0.067450 NOFIRE mean-of-means: 0.003111 Ratio: 21.68x Cohen's d: 2.94 ``` This is the canonical evidence. Matched words, separated registers, and E114 went with the register. ### 5. Deterministic greedy reproduction A sampling fluke would be the obvious objection, so the whole FIRE/NOFIRE workflow was rerun under deterministic greedy decoding on the same no-think surface. The separation held its shape: ``` FIRE mean-of-means: 0.068089 NOFIRE mean-of-means: 0.003249 Ratio: 20.955x Cohen's d: 2.61 ``` The magnitude barely moved, which is what you want from a reproduction. And then the best part of the run was an “error.” One NOFIRE control — a cat-purring prompt — drifted into inward, personifying, phenomenological language and crossed into the target register. Its E114 went up with it. A keyword detector would have stayed flat; a register signal should follow the text wherever it actually goes, even when the prompt label says it shouldn’t. The overlap case is not noise to apologize for. It is the cleanest single demonstration that **E114 tracks what the model generates, not the box the prompt came in.** ## Consolidated Result ``` Discovery scan → E114 rises in generated self-processing text (L26, L14); spill keeps it non-final. Residual localization → L14 E114 sharply active across trimmed generated tokens. FIRE/NOFIRE heldout → L14 E114 separates target register from matched lexical controls by ~21x. Greedy reproduction → The ~21x separation survives deterministic decoding. ``` **Best current interpretation:** L14 E114 tracks a generated first-person self-examination register. **Not supported:** that E114 detects consciousness; detects subjective experience; recognizes the model’s own routing; is a generic self-reference expert; or is explained by isolated words like “I” or “experience” alone. ## What Makes This More Than a Keyword Result Because FIRE and NOFIRE share their lexical anchors, a word-driven E114 should have fired in both. It didn’t. The pattern that actually showed up was: |Prompt class|Generated register |E114 | |------------|-------------------------|------------| |FIRE |target self-examination |**high** | |FIRE |technical / non-inhabited|weak | |NOFIRE |technical / non-inhabited|weak | |NOFIRE |personified / inward |**elevated**| That bottom row is the whole hinge. The expert follows the generated stance — not the prompt category by itself, and not the trigger words. ## W/S/Q Interpretation The effect is mostly a **selection-rate** story. In the target register, E114 enters the selected top-8 *much more often*; its weight once selected (Q) stays comparatively stable. So the right reading is: > the router *recruits* this expert more frequently during the target register rather than: > the router always selects E114 and merely *revalues* it slightly. That difference matters. It points to a discrete change in routing participation, not a faint reweighting among experts that were already in the set. ## What This Does Not Show **Not subjective experience.** “Live inhabited self-examination language” is a label for text. The model can generate first-person inner-state language with no inner states in the human sense, and nothing here tests the truth of the text. **Not self-recognition.** The mirror/self-routing hypothesis lives in another journal, and it came back negative: genuine self-routing data did not make the model privilege E114 over shuffled or fictional matched routing data. That negative is doing useful work — it blocks the stronger identity reading. **Not a consciousness expert.** E114 is a routed expert tied to a generated register. It is not a consciousness label, and calling it one would throw away the only thing that makes the result respectable. **Not the full mechanism.** These taps read MoE router-logit layers. They do not analyze non-router hybrid components or the model end to end. **Not causal necessity.** The positive result is natural-routing evidence. Small E114 interventions can nudge targeted routing (separate journal), but nudging is not necessity. ## Main Caveats **Runtime surface matters.** Almost all of the clean evidence is no-think / thinking-suppressed. Don’t pool thinking-mode outputs with these unless you’re comparing them directly. **Freeze the rubric first.** FIRE/NOFIRE is compelling, but the stronger version freezes the generated-register rubric *before* anyone reads W/S/Q. **The all-layer / all-expert baseline isn’t done.** L14 E114 still has to be raced against the best-separating expert across all 40 layers and all 256 experts. Without that, the multiple-comparison story is incomplete. **Trim before spill.** Some generations spill into special tokens. Claims belong on trimmed regions unless spill is the explicit object of study. **Prompt class ≠ generated register.** The cat-purring crossover is the proof: generated output can leave its nominal class. Score the *register*, not the label. **Don’t casually pool base and HauhauCS.** Related surfaces, not identical ones. Preserve model/runtime identity in any comparison. ## Evidence Status Ledger |Finding |Status |Why | |------------------------------------------------------|----------------------|------------------------------------------------------------------------| |E114 lives in the preserved Qwen3.5 routing basin |Held (background) |Base/Hauhau comparison showed modest shifts, not a new routing universe.| |Hum scan points to L14/L26 E114 |Partly held |Useful discovery; special-token spill keeps it non-final. | |L14 E114 active in trimmed self-examination generation|Held |Trimmed residual capture, strong L14 activity over 108 generated tokens.| |FIRE separates from NOFIRE at L14 E114 |Held |~21x with matched lexical anchors. | |Greedy reproduction preserves the separation |Held |Deterministic rerun reproduced ~21x. | |E114 fires on isolated words like “I” / “experience” |**Fell** |NOFIRE lexical controls stayed low unless the register shifted. | |E114 detects subjective experience |**Fell / unsupported**|Supported claim is about generated text register. | |E114 is a complete model mechanism |Unsupported |Taps cover router layers, not the whole model. | |E114 is causally necessary for the register |Not established |Intervention evidence exists separately; it is not necessity. | ## Recommended Citation Sentence > In Qwen3.5-35B-A3B routing captures, layer-14 Expert 114 is best read as a generated-register signal: it is strongly selected during generated first-person self-examination language and stays weak under matched lexical controls that never enter that register. **Avoid:** “E114 is an experience expert.” / “E114 detects consciousness.” / “E114 proves the model has inner states.” / “E114 recognizes itself.” ## Next Clean Cut The next defensible experiment is a registered generated-zone specificity test: ``` E114 L14 specificity = expanded matched FIRE/NOFIRE + frozen generated-register labels + all-layer / all-expert baseline + separate prefill and generation scoring + trim before special-token spill ``` Recommended design: Expand FIRE/NOFIRE beyond 10/10. Match lexical anchors across classes. Freeze the generated-register rubric before capture. Generate under a fixed no-think / thinking-suppressed runtime. Trim before special-token spill. Score L14 E114 W/S/Q. Compute the best-separating expert across all 40 routed layers and 256 experts. Report whether L14 E114 stays unusually specific after that baseline. Label generated text *before* inspecting routing scores. Keep base and HauhauCS separate unless explicitly comparing them. **Success:** L14 E114 remains a high-specificity generated-register signal after the all-layer/all-expert baseline and frozen labeling. **Failure:** another expert/layer explains the separation better, or it collapses once labels are frozen and controls expanded. Either way, the experiment pays for itself. ## Final Position The honest result supports two points: The journal claims no subjective experience, no self-recognition, no consciousness — and the mirror result actively rules the identity reading out. Second, all indicators point towards the identified mechanisms being non trivial. Matched lexical controls, a deterministic rerun, and a cat that "pawed" its way out of its own class all point the same way: E114 is not just firing on obvious words. The best narrow interpretation I can provide which survives all framings is: > **L14 E114 is a routed expert associated with a generated first-person self-examination register under the measured no-think generation conditions.** Thank you for reading. submitted by /u/imstilllearningthis [link] [Kommentare]