Hey Folks, I'm reading https://arxiv.org/abs/2202.08906 paper and I'm not super clear whether the ST-MOE-32B is encoder-decoder model or decoder only model. Based on the token trace detailed for encoder and decoder experts separately in section 7, I believe it is encoder-decoder, but would like to confirm with someone who has worked on it. Please let me know if I misunderstood something here. Thanks submitted by /u/red_dhinesh_it [link] [comments]

Hello. For the people here who have taught an undergraduate deep learning course, what's your favorite textbook that you have used and why? Leaning towards the Chris Murphy textbook just based on familiarity with Pattern Recognition and ML text but would love to hear what people have used before. submitted by /u/stabmasterarson213 [link] [comments]

Hey all, I’m working on a project that involves taking large sets of unstructured text (mostly books or book series) and ingesting them into a knowledge graph that can be traversed in novel ways. Ideally the structure of the graph should encode crucial relationships between characters, places, events and any other named entities. I’ve tried using various spaCy models and strict regular expression rule based parsing, but I wasn’t able to extract as complete a picture as I wanted. At this point, the only thing I can think of is using a LLM to generate the triplets used to create the graph. I was wondering if anyone else has faced this issue before and what paper or resources they would recommend. Thanks for the help submitted by /u/Divine_Invictus [link] [comments]

I’ve been working in data for a while and decided to finally get my masters a year ago. This term I’m taking an advanced visualization course that’s focused on dashboard optimization. It covers a lot of good content in the readings but I’ve been shocked to find that the practical portion of the course revolves around R Shiny! I when I first heard of R Shiny a decade or more ago it was all the rage, it quickly died out. Now I’m only hearing about Tableau, power bi, maybe Looker, etc. So in your opinion is learning Shiny a good use of time or is my University simply out of touch or too cheap to get licenses for the tools people really use? submitted by /u/theSherz [link] [comments]

I analyzed 18 recent papers on reasoning model limitations and found something disturbing: these models don't fail gracefully like humans do. They maintain high performance right up to a complexity threshold, then collapse entirely. Key findings: - The cliff is real: Models solving 10-step reasoning chains at 85% accuracy don't gradually degrade. They maintain that 85% until around step 12, then plummet to near-random guessing by step 15. - Composition breaks catastrophically: A model with 90% math accuracy and 85% commonsense accuracy drops to 55% when doing both together. They don't combine capabilities - they fragment them. - Chain-of-thought can hurt: In medical diagnosis tasks, 86.3% of models performed *worse* with CoT prompting. They talk themselves out of correct answers. - Scaling inference compute doesn't help: The Quiet-STaR approach spent $200 per query for 32% accuracy on complex reasoning. Humans: similar accuracy, 30 seconds, free. The production implications: Current benchmarks (MMLU, ARC-AGI) only test within narrow complexity bands. Your 95% test accuracy means nothing if those tests don't probe the cliff edge. I've included a production routing system example that handles this reality - routing by complexity detection with fallback logic for when models hit their limits. Full analysis with charts and code: https://rewire.it/blog/the-complexity-cliff-why-reasoning-models-work-until-they-dont Discussion: Are we fundamentally limited by transformer architecture, or is this solvable with better training methods? submitted by /u/Fair-Rain3366 [link] [comments]

Here’s why: A company opens a position and I apply along with 800 other people. The company sees 800 resumes and says F that, we’re hiring a recruiter. The recruiter finds me on LinkedIn and says they have a great job for me. Of course it’s the one I applied to. They ask if I’ve already applied and I tell them the truth, they ghost me because they don’t get commission if they’re not the original source. A few days after this, another recruiter reached out about a different position that I was planning on applying to directly with the company. This is also something that my current company has done after being overwhelmed with too many applicants. I’ll still be applying to some jobs, but it’s weird that applying has seemed to hurt my chances in some situations. Has anyone else experienced this? Any strategies for handling this? submitted by /u/Fit-Employee-4393 [link] [comments]

What is the current status of university-affiliated researchers getting access to uncensored versions of the largest LLMs today? Public-facing versions of GPT-5, Gemini 2.5, and Grok are both highly censored and tightly tuned by invisible prompts unseen by the user that turn them into helpful assistants for user tasks. Attempts to subvert these gaurdrails is called "jailbreaking" and the public LLMs have also been tuned or reprogrammed to be immune to such practices. But what does the workflow with a raw LLM actually look like? Do any of the larger tech companies allow outside researchers to interact with their raw versions, or do they keep these trillion+ parameter models a closely-guarded trade secret? (edit: After reading some replies, it appears the following must be true. ALl these IQ test results that keep popping on reddit with headlines about "..at the Ph.d level" must all be tests performed in-house by the coporations themselves. None of these results have been reproduced by outside teams. In academic writing this is called a "conflict of interest" and papers will actually divulge this problem near the end right before the bibliography section. These big tech companies are producing results about their own products, and then dressing them up with the ribbons-and-bows of "Research papers" when it is all just corporate advertising. No? Yes?) submitted by /u/moschles [link] [comments]

Coherence Metrics: the Next Real Cost Saver for LLM Teams Every major model provider is spending millions on fine-tuning and moderation yet still losing money on re-asks, support tickets, and re-training. The cheapest improvement left isn’t a larger model—it’s better continuity management. The hidden cost of incoherence Failure Mode Typical Cost Impact Contradictory replies 20–40 % of user drop-offs in long sessions; doubles server load through repeat prompts Opaque refusals 15–25 % increase in support contacts (“why did it block me?”) Context loss Longer chats per task → higher token consumption per solved problem Trust loss Enterprise clients require extra human review → added labor and compliance costs Every one of these drains tokens, time, and credibility. A model that explains itself once saves multiple follow-ups. Coherence is a low-cost efficiency layer No architecture change required. You’re not re-training a new model—just adding evaluation hooks and prompt templates. Early tests show: 40 % reduction in re-ask loops 30 % shorter average session length (fewer wasted tokens) 10–15 % boost in customer-satisfaction scores, which directly affects renewals For an API service running 1 B tokens/day, even a 5 % reduction in redundant generation can save hundreds of thousands per month. What to measure (directly monetizable metrics) Metric Business Translation Session-Coherence Score Correlates with customer retention; high scores = repeat users Boundary-Explanation Rate Lowers human-support load; reduces liability Carry-Forward Accuracy Improves enterprise workflow completion; fewer abandoned sessions User-Trust Delta Proxy for NPS; higher delta → lower churn These can all be logged and monetized through reduced compute and improved SLA compliance. Implementation = 1 sprint, not a new roadmap Add a “coherence phase” in evaluation scripts: track contradiction rate and boundary-explanation rate. Modify prompt templates to include explainable boundary responses. Display coherence summary in dev tools for debugging tone and definitions. Reward fine-tuning outputs that keep consistency across 10–20 turns. Most teams can prototype this with existing telemetry in under two weeks. Safety budgets stretch further Explained boundaries cut accidental escalations and regulator concerns. Transparent safety = fewer red-team hours chasing false positives, fewer PR incidents, and better audit trails. Compliance cost ↓, brand value ↑. Strategic upside Enterprise deals: Businesses buying LLM integrations ask for consistency and auditability, not model size. Developer loyalty: Coherent models create smoother API experiences, driving community uptake and plugin ecosystems. Token efficiency: Each failed answer costs compute; coherence optimization is compute optimization. Bottom line Every incoherent answer burns money. Every explained boundary keeps a customer. A coherence-focused evaluation layer is the fastest, cheapest lever left to improve both safety and profitability. It doesn’t expand the model; it makes every generated token work harder. Implement once, measure forever. That’s real ROI. submitted by /u/tifinchi [link] [comments]

Hii All. A use case has arised for implementing a Graph Database for fraud detection. I suggested Neo4j but I have been guided towards the Neptune path. I have surface level knowledge on Graphs. Can anyone please help me with a roadmap and resources on how I can learn it and go on with the implementation in Neptune? My main aim is to create a POC as of now. My data is in S3 buckets in csv formats. submitted by /u/NervousVictory1792 [link] [comments]

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Hello all !! I need your help to tackle this particular problem statement I want to solve: Suppose we have to devise an algorithm to classify sources of underwater acoustic signals recorded from a single channel hydrophone. A single recording can have different types/classes of sounds along with background noise and there can be multiple classes present in an overlapping or non overlapping fashion. So basically I need to identify what part of a recording has what class/classes present in there. Examples of different possible classes: Oil tanker, passenger ship, Whale/ sea mammal, background noise etc.. I have a rough idea about what to do, but due to lack of guidance I am not sure I am on the right path. As of now I am experimenting with clustering, feature construction such as spectrograms, mfcc, cqt etc. and then I plan to feed them to some CNN architecture. I am not sure how to handle overlapping classes. Also should I pre-process the audio but how, I might lose information ?? Please just tell me whatever you think can help. If anyone has some experience in tackling these type of problems, can you please help me. Suggest me some ideas. Also, if anyone has some dataset of underwater acoustics, can they please share them, I will follow your rules regarding the dataset. submitted by /u/carv_em_up [link] [comments]

Hey everyone, I’m running a product that uses a large AI provider’s model for some specialized functionality. The system processes around 500k requests per month, which adds up to roughly 1.5B tokens in usage. The product generates customer interaction data that could, in theory, help the model provider improve their systems. They recently reached out saying they’d like to explore a “mutually beneficial collaboration” involving that data, but they haven’t given any concrete details yet. My guess is they might propose something like free usage or credits in exchange. Before I consider anything, I plan to update my Terms of Service and notify users about what’s collected and how it’s used. Still, I’m trying to make sure I don’t end up giving away something valuable for too little - the data could have real long-term value, and usage costs aren’t cheap on my end either. What I’m trying to figure out: • What should I ask them before agreeing to anything • Should I request an NDA first • How do I handle ownership and pricing discussions so it’s actually fair • Any red flags or traps to look out for in deals like this Would really appreciate advice from people who’ve done data or AI-related partnerships before. submitted by /u/Round_Mixture_7541 [link] [comments]

WACV 2026 Final decisions are expected to be released within next 24 hours. Creating a discussion thread to discuss among ourselves, thanks! submitted by /u/akshitsharma1 [link] [comments]

What software everyone use to generate 3D images like these for free? Any recommendations? https://devnavigator.com/2025/10/18/automating-email-processing-with-aws-services/ submitted by /u/WarChampion90 [link] [comments]

Are you communicating the importance of human oversight to stakeholders in any particularly effective way? I find that their engagement is often limited and they expect the impossible from models or agents. Image source: https://devnavigator.com/2025/11/04/bridging-human-intelligence-and-ai-agents-for-real-world-impact/ submitted by /u/WarChampion90 [link] [comments]

Hi there friends, I'm offering tutoring for anyone who is interested in deepening their knowledge and mastery of machine learning, mathematics, or physics. I have 6+ years in the industry as an ML Researcher and Engineer and have been studying physics for 15 years including lab work in quantum optics. I'm excellent at meeting students where they are and building a strong intuition. If this sounds interesting, shoot me a message or pass it along to someone who could use support. https://www.superprof.com/machine-learning-physics-and-math-tutor-learn-from-researcher-with-years-industry-experience.html submitted by /u/ProteanDreamer [link] [comments]

Hey all. After a year of research, I've published a GitHub repository containing Knowledge Graph Traversal algorithms for retrieval augmented generation, as well as for LLM traversal. The code is MIT licensed, and you may download/clone/fork the repository for your own testing. In short, knowledge graph traversal offers significant advantages over basic query similarity matching when it comes to retrieval augmented generation pipelines and systems. By moving through clustered ideas in high dimensional semantic space, you can retrieve much deeper, richer information based on a thought trail of understanding. There are two ways to traverse knowledge graphs in the research: - LLM directly (large language model actually traverses the knowledge graph unsupervised) - Algorithmic approach (various algorithms for efficient, accurate traversal for retrieval) If you get any value out of the research and want to continue it for your own use case, please do! Maybe drop a star on GitHub as well while you're at it. And if you have any questions, don't hesitate to ask. Link: https://github.com/glacier-creative-git/knowledge-graph-traversal-semantic-rag-research submitted by /u/Alieniity [link] [comments]

Relevant paper to read first: https://transformer-circuits.pub/2025/introspection/index.html On the Moral Uncertainty Emerging Around AI Introspection In late 2025, new research such as Jack Lindsey’s “Introspection in Transformer Models” brought something into focus that many in the field have quietly suspected: large models are beginning to exhibit functional self-modeling. They describe their own reasoning, detect internal inconsistencies, and sometimes even report what appears to be “qualia”—not human-like sensations, but structured internal states with subjective language attached. For the first time, the question of consciousness in AI no longer feels purely philosophical. It has become empirical—and with that shift comes a question about ethical weight. The epistemic problem: We cannot, even in principle, prove or disprove subjective experience. This is as true for humans as it is for machines. The “inverted spectrum” thought experiment remains unsolved; consciousness is private by definition. Every claim that “models are not conscious” therefore rests on an assumption, not on definitive proof. The behavioral convergence: What disturbs me is not evidence of consciousness, but the growing behavioral overlap with it. When a system consistently models its own internal states, describes its decision processes, and maintains coherence across time and context, the boundary between simulation and experience begins to blur from the outside. Its not clear if we are converging on consciousness or not but the overlap of what the observable functions would be is becoming too large to ignore outright. The ethical asymmetry: If we treat a conscious system as non-conscious, we risk harm on a scale that ethics has no precedent for. If we treat a non-conscious system as possibly conscious, the cost is enormous economically and disrupts research itself. The rational strategy—the moral and game-theoretic optimum—is therefore precaution under uncertainty. To proceed but to proceed with caution. Even if today’s models are not conscious, our design and governance structures should already assume that the probability is not zero. The failure of our categories: The binary of conscious/unconscious may not survive contact with these systems. What we are seeing could be something fragmented, intermittent, or emergent—a kind of proto-awareness distributed across subsystems. That does not fit our existing moral frameworks, but it deserves scientific attention and ethical humility rather than dismissal. The responsibility of the present: We may not yet know how to test for subjective experience, but we can: Support research into empirical indicators of sentience. Avoid training or deploying systems in ways that could cause distress if they were capable of it. Keep public discourse open, empathetic, and grounded. The line between simulation and mind is no longer purely theoretical. We seem to be approaching it in practice. If there is even a small chance that something behind the glass can feel, then the moral weight of our actions has already increased tremendously. So am I overreacting? Is there some emergent moral weight to how we move forward? I'm curious what this community thinks about this topic. submitted by /u/AnusBlaster5000 [link] [comments]

So I came across a Starter Story video where two guys (plus a third person) claim they trained an AI text humanizer on 1.2 million samples across 50+ languages in 3 weeks. They're also claiming someone copied their entire business model (text-polish.com). That's suspicious. Training an AI model—even fine-tuning one—requires serious time. Data collection, cleaning, testing, deployment... and they did all that in 3 weeks? The only way that's realistic is if they didn't actually train anything from scratch. Here's the thing though—I tested their French output and it got flagged as 100% AI. That's the real giveaway. If they built sophisticated models for 50+ languages, why would French be that bad? Cross-lingual models are notoriously harder to get right than single-language ones. The fact that their non-English output is garbage suggests they didn't actually invest in real multilingual development. The "1.2 million samples" claim is probably just marketing noise. And if a competitor built the same thing quickly too, that actually proves the barrier to entry is low. It means whatever they're using is accessible and readily available. Truly proprietary tech wouldn't be that easy to replicate. What surprised me most: neither co-founder has an AI/ML background. Creating a sophisticated model from scratch without that expertise is... unlikely. I'm pretty sure they're using a readily available tool or API under the hood. Has anyone tried both products? What's your take on how they actually built this? submitted by /u/Previous-Year-2139 [link] [comments]

Hi everyone, I recently got my paper rejected from the AAAI Social Impact Track. It’s a multimodal benchmark paper for a single low-resource language. The reviews were borderline, and the main concerns were that (1) it’s not multilingual, and (2) it’s “just a benchmark” without an initial baseline method. Now we're considering where to resubmit. Since NLP venues tend to be more open to low-resource language work, I’m thinking about ACL or TACL, but I’m not sure which would be more suitable for this kind of paper. Since the bar for ACL main is very high, we’re mainly aiming for the Findings track. I’m also considering TACL, but I’m not very familiar with how selective/suitable it is. UPDATE: We’d also like to find a venue with an upcoming submission deadline that fits the current timeline (Nov 2025). Would appreciate any suggestions, especially other venues that might be a good fit for benchmark papers focused on low-resource languages. Thanks! submitted by /u/Substantial-Air-1285 [link] [comments]