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Hi everyone, I missed the ICML conference tickets because I was waiting for some travel funding confirmation and now they are sold out. Do you know any other ways I could still purchase one? There seems to be no waiting list… or if you know anyone who needs to cancel theirs, please let me know 🙏🏻 submitted by /u/TopPerformance1255 [link] [留言]
It seems that there are two ways to build voice AI: Half-duplex: strict turn-taking. You speak, the other side waits until you’re done, one direction of speech at a time. ← This is how almost every voice assistant works today. Full-duplex: two channels, both sides can talk at any time - no more waiting for your “turn”. ← This is the way humans actually talk. In fact, there are three crucial things half-duplex voice models can't really do: Overlap - talking and listening at the same time without falling apart Backchannels - the "mhms," "rights," and "yeahs" you drop in while the other person is still going Barge-in - getting interrupted mid-sentence and recovering gracefully These three features are a big reason why voice agents still feel “robotic” to this day. But what exactly is the spectrum from half-duplex to full-duplex? Is a Moshi-style architecture the only way to approach full-duplex natural voice conversations? What are ways half-duplex systems could imitate full-duplex? Would love to hear others' thoughts on this. submitted by /u/Chilly5 [link] [留言]
This article was originally published on davidohnstad.net . I cross-post here to reach the Dev.to community. { " @context ": " https://schema.org ", " @graph ": [ { "@type": "Person", " @id ": " https://davidohnstad.com/#author ", "name": "David Ohnstad", "url": " https://davidohnstad.com ", "sameAs": [ " https://www.linkedin.com/in/davidohnstad/ ", " https://orcid.org/0009-0007-9023-7456 ", " https://davidohnstad5.mystrikingly.com/ ", " https://github.com/davidohnstad40-netizen ", " https://hashnode.com/@davidohnstad ", " https://davidohnstad.com ", " https://davidohnstad.net ", " https://davidohnstad.info ", " https://david-ohnstad.com ", " https://davidohnstadminnesota.com " ], "jobTitle": "Senior Data Product Manager", "worksFor": { "@type": "Organization", "name": "Veeam Software", "url": " https://www.veeam.com " }, "alumniOf": { "@type": "CollegeOrUniversity", "name": "College of St. Scholastica" }, "address": { "@type": "PostalAddress", "addressLocality": "Duluth", "addressRegion": "MN", "addressCountry": "US" }, "description": "Senior Data Product Manager at Veeam Software, MS and MBA from the College of St. Scholastica, based in Duluth, Minnesota. Specializes in data architecture, AI/ML integrations, and SaaS platform development." }, { "@type": "Article", " @id ": " https://davidohnstad.net/why-enterprise-ai-projects-fail-platform-first#article ", "headline": "Why Enterprise AI Projects Fail: Platform-First Thinking", "description": "David Ohnstad reveals why enterprise AI initiatives fail despite massive investment. Learn the platform-first trap and how successful teams build differently.", "url": " https://davidohnstad.net/why-enterprise-ai-projects-fail-platform-first ", "datePublished": "2026-05-29T14:06:46Z", "dateModified": "2026-05-29T14:06:46Z", "author": { "@type": "Person", " @id ": " https://davidohnstad.com/#author " }, "publisher": { "@type": "Organization", "name": "David Ohnstad", "url": " https://davidohnstad.net ", "logo": { "@type": "Ima
Hey everyone, hope this is ok to post here. I built a free EU AI Act risk assessment tool and would love some feedback from people who actually know this space. You fill out a 10-question form describing your AI system, it classifies your EU AI Act risk tier, and emails you a PDF report with your applicable Articles and priority actions. Takes about 2 minutes, no account required. https://assessment.aiella.com Eventually I want to build a monitoring SDK that works like a Python library and automatically documents compliance of the technically measurable requirements at inference time. Looking for design partners for that down the road. Genuine feedback welcome, especially from anyone who has been through a real EU AI Act compliance process. Happy to answer questions about the classification methodology or the AWS architecture behind it. submitted by /u/aiandi [link] [留言]
We recently hit a classic gradient boosting trap with our pricing engine (Flyback), and I wanted to share the ablation data. We run LightGBM quantile regression to forecast secondary market watch prices. We engineered a variant-conditioned Bayesian target encoder to isolate within-reference pricing dynamics. LightGBM absolutely loved it. It ranked #1 in feature importance at q90 by a wide margin, with gains several times the next-highest feature, across all our multi seed runs. But when we ran a strict 4-seed × 3-variant ablation on the hold-out set, the results inverted. Test MAPE regressed by +0.28pp and the between-variant delta was 7x the within-variant standard deviation. The encoder was finding effective splits that completely failed to generalize because the signal it was learning was driven by irreducible label variance: unobserved factors like condition nuance, seller behavior, and timing that no feature can capture. I wrote a full post breaking down the architecture, the ablation methodology, and the mechanism behind the divergence. Happy to discuss LightGBM split mechanics, target encoding leakage, or the ablation setup. Full post and ablation results: https://flyback.ai/engineering/target-encoding-divergence submitted by /u/Nj-yeti [link] [留言]
Financial aid results for ICML are out and unfortunately I wasn't selected. I was wondering, does this mean I wasn't selected for Volunteering as well? Or should I expect a separate email? submitted by /u/RussB3ar [link] [留言]
Hello, I have a task to fine-tune small LLMs on annotated conversational data. The dataset contains not only the final answers, but also reasoning traces and tool-calling decisions (i.e., when the model should think and when it should call a tool). I am wondering what the best training approach would be and why. My current dataset is stored in a chat format similar to this: ```text system user assistant_think assistant_tool assistant_answer user assistant_think assistant_tool assistant_answer ... ``` My current idea is to split each conversation into multiple training samples. For example, if a conversation contains two user turns, I would create two samples: Sample 1 text system user assistant_think assistant_tool assistant_answer Sample 2 ```text system user assistant_think assistant_tool assistant_answer user assistant_think assistant_tool assistant_answer ``` In other words, each sample contains all previous conversation history up to the assistant response being trained. For training, the loss would be computed only on the assistant-generated tokens: text assistant_think assistant_tool assistant_answer while the system and user messages would be masked out from the loss. Is this approach correct, or is there a better way to structure the training data for reasoning and tool-calling behavior? My second question is about reinforcement learning. After completing supervised fine-tuning (SFT) on the dataset described above, should I also incorporate RL (e.g., PPO, GRPO, DPO, or another approach) to further train the model on when a tool should or should not be called? If so: What advantages would RL provide over SFT alone for tool use and reasoning? How would you design the reward function? Under what circumstances is RL actually necessary, and when is SFT sufficient? I would appreciate any practical advice, papers, blog posts, or open-source examples related to training reasoning and tool-calling models. ``` submitted by /u/zdeneklapes [link] [留言]
Scroll through social media today, and you'll likely come across AI-generated images everywhere. From anime-style portraits and fantasy landscapes to hyper-realistic photographs of places that don't even exist, AI image generators have quickly become one of the most fascinating applications of artificial intelligence. What makes this technology so impressive is its accessibility. A few years ago, creating professional-quality artwork required design skills, expensive software, and hours of effort. Today, anyone can generate stunning visuals simply by typing a few words. But what actually happens behind the scenes when you enter a prompt and click "Generate"? Turning Ideas into Images At a basic level, AI image generators convert text into visuals. When a user enters a prompt such as: "A futuristic Mumbai skyline at sunset with flying cars" the AI doesn't search for an existing image online. Instead, it creates a completely new image based on patterns it learned during training. These models are trained using millions of image-text pairs, allowing them to understand concepts such as objects, colors, lighting, artistic styles, and even relationships between different elements within a scene. As a result, the AI can interpret the user's description and transform it into a visual representation. Starting with Random Noise One of the most interesting aspects of modern AI image generation is that the process usually begins with random noise. Imagine the static pattern seen on an old television screen. Initially, the AI starts with something similarly meaningless. It then gradually removes the noise while adding details that match the prompt. This process is known as a diffusion model , and it is the foundation of many modern AI image generators. To understand the idea, consider the following simple Python example: import random prompt = " A futuristic Mumbai skyline at sunset " noise_level = random . randint ( 1 , 100 ) print ( f " Prompt: { prompt } " ) print ( f " Start
I built a routing-based approach to lightweight real-time multilingual ASR as part of my research at Gladia. The core problem was how multilingual models that accurately handle mid-conversation language switches are often too big for most local hardware and have poor accuracy. So rather than relying on one massive multilingual model, the system routes audio between smaller, specialized monolingual models (~100M parameters each). Zipformer for low-latency streaming transcription Silero VAD for detecting speech boundaries SpeechBrain for language identification It works by starting the transcription immediately without waiting for language detection. A coordinator buffers audio, monitors language confidence, and when a switch is detected above a threshold, it rolls back to the last speech boundary and re-transcribes with the correct model. Users may briefly see incorrect text, but it self-corrects quickly. Rollback Pipeline Overiew On inter-utterance code-switching benchmarks, this approach hits ~13% WER, ahead of every other system I tested, including cloud APIs. Intra-utterance switching (mid-sentence Spanglish, etc.) is the known limitation, degrading to ~41% WER, though still better than open-source alternatives and at a fraction of the size. Open-source repo with instructions and the detailed benchmark results. https://github.com/gladiaio/realtime-multilingual-asr-router Let me know what you think. Pro tip: Enabling only your expected languages not only makes the system lighter but also gives the LID an accuracy boost, especially on heavily accented speech." submitted by /u/JeanMichelRanu [link] [留言]
This is a follow-up to SynaptoRoute: A Study in Local Semantic Routing . If you haven't read it, the short version is: SynaptoRoute is a zero-token semantic routing engine that classifies user queries into intents using local embeddings instead of LLM API calls. SynaptoRoute v0.3.0: Matching Semantic Router While Scaling to 50,000 Routes What Changed Since v0.2.0 When I published the first post, SynaptoRoute had just shipped dynamic batching and O(1) hot-reload. The throughput numbers were promising, but the accuracy story was incomplete. I had internal benchmarks but no comparison against a widely adopted baseline under identical, reproducible conditions. That gap is now closed. v0.3.0 is live on PyPI: pip install synaptoroute == 0.3.0 The Benchmarking Journey Getting to these numbers took multiple benchmark revisions. Early synthetic datasets produced catastrophic accuracy collapse and initially suggested that both SynaptoRoute and Semantic Router were performing poorly. After deeper investigation, the root cause turned out to be flaws in the dataset generation pipeline rather than limitations of the routing engines themselves. Several rounds of validation, failure analysis, threshold tuning, adversarial testing, and external benchmarking followed. All final results presented in this article come from independent public datasets with strict train/test separation, eliminating dataset leakage and benchmark inflation. That process was valuable because it forced the project to validate assumptions against real-world data instead of relying on synthetic benchmarks. The Benchmark That Actually Matters I evaluated SynaptoRoute against Semantic Router on two standard NLU datasets. Same embedding model ( BAAI/bge-small-en-v1.5 ). Same hardware. Same evaluation script. Same train/test splits loaded from HuggingFace. CLINC150 150 intents spanning 10 domains, plus an out-of-domain class. This is the standard stress test for intent routers. Metric SynaptoRoute Semantic Router
Please post your questions here instead of creating a new thread. Encourage others who create new posts for questions to post here instead! Thread will stay alive until next one so keep posting after the date in the title. Thanks to everyone for answering questions in the previous thread! submitted by /u/AutoModerator [link] [留言]
MLE-Bench scores have jumped from 30% to 80% over the last two years. But how much of that is real algorithmic progress vs. better base models + problem definition shifts + overfitting? Turns out: not much. Once you control for the same step budget and models, and then test on a different set of tasks, the two-year-old AIDE algorithm matches modern agent/evolutionary search systems. Figure from FML-Bench, a new automated ML research benchmark, which unifies the code editing agent, step definition, and val/test split, and tries to benchmark the algorithmic efficiency (search/memory) of the agents. paper link: https://arxiv.org/pdf/2605.17373 test improvement and pairwise win-rate submitted by /u/Educational_Strain_3 [link] [留言]
Hi everyone, If you have purchased (at least one) GPU(s) for ML/DL studies and research: How is your experience and is it worth it? What do you use it for and how is the ROI? I have a MacBook Pro with M4 from some years ago, while MPS is useful in many occasions, it's no substitute for a NVDA GPU with CUDA support. So recently I am considering getting a 5060 Ti 16GB , but a GPU cannot run itself, so I then also need to buy other parts (e.g., CPU, RAM, SSD, motherboard, and so on...), which has been getting more expensive lately, especially the RAM. Since I'm still in job-seeking mode, I will mostly use it for learning DL, RL, and LLM-related things and local experiments (e.g., Stanford CS336), or low-level ones like GPU kernel programming and so on. Do you think a local physical GPU would help, or in my case a cloud service like Modal would suffice? Many thanks! submitted by /u/hedgehog0 [link] [留言]
An LLM-written kernel benchmarked 38% faster on a microbench. Here is what kernel-level validation showed it actually did at runtime. TL;DR Multi-agent LLMs are now writing CUDA kernels (RightNow AI’s AutoKernel, Meta’s KernelEvolve, a multi-agent system claiming 38% speedup on Blackwell). Source-level benchmarks measure clean throughput on a single isolated kernel. They do not measure SM occupancy under co-scheduling, DRAM bandwidth saturation, dispatcher off-CPU during a real serving workload, or NCCL wait correlation with sibling kernels. Kernel-level validation closes that gap: an eBPF trace of the same kernel running under the same workload as production answers all four questions in one capture. The kernel-writing wave Three pieces of work in April surfaced the same pattern: agents generate CUDA kernels, then quote a single throughput number against a baseline. RightNow AI’s AutoKernel (announced Apr 6) – LLM agents iteratively rewrite CUDA kernels for a target metric, claiming substantial speedups on selected microbenchmarks. Meta’s KernelEvolve – similar shape: agents propose kernel variants, rank by throughput, keep the best. Multi-agent system on Blackwell (Apr 29 reports) – claims a 38% speedup on a public kernel benchmark using a coordinated agent setup. All three are real research, all three produce real kernels, and all three report numbers that come from microbenchmarks. The microbench setup is exactly what you want for the optimization loop. It is not what you get in production. What microbenchmarks do not see Run an LLM-generated kernel under nvprof or nsight-compute on an otherwise-idle GPU and the throughput number is real. Put the same kernel in front of a vLLM serving workload and four properties change immediately: SM occupancy under co-scheduling. The kernel that achieves 95% SM occupancy in isolation will achieve 40-50% with three other kernels sharing the same SMs. The optimizer never sees this regime. DRAM bandwidth saturation. A kernel tha
A bit of background about myself: I have been accepted to RWTH Aachen's Computer Science program starting this fall, and one of the things that I am genuinly excited about is exploring the intersection of astrophysics and machine learning. The tricky part is that RWTH's CS department doesn't have a research group focused directly on this intersection. The two closest things I have found are the Quantum Information Systems group (I plan to reach out to the them once I am on campus to understand a bit more about them) and the Learning on Graphs group which does foundational GNN research. The second one got me thinking: graph neural networks feel like they could be well-suited to astrophysicla data, things like galaxy formation, cosmic web structure or particle interaction data all seem graph-like (or am I being waaaay too optimistic here?) So my questions for people who know this space better than I do: Are GNN's already being used in astrophysics research? What other ML subfields would you point someone toward if they are interested in this intersection? I know I could have applied to a more well-suited university for my needs, but RWTH Aachen was my top choice because I am a math nerd and I really like their way of teaching. So do help a brother out. Thanks in advance!!!! submitted by /u/pandemic_179 [link] [留言]
Modern search engines do more than match keywords. When you search for "cozy mystery set in Paris" and get results for "atmospheric detective novel in France" that's vector search at work: documents and queries are converted into lists of numbers, called embeddings, and the search engine finds the documents whose numbers are closest to the query's. Manticore Search supports this natively. Under the hood, it uses a data structure called HNSW: a graph that connects nearby vectors, so it can find nearest neighbors quickly without scanning every document. That makes vector search fast enough to run on millions of documents in milliseconds. But HNSW has an inefficiency. Early in the traversal, almost every distance computation finds a better candidate than the ones already in the result set. As the search goes on, those improvements become rarer, but the algorithm keeps traversing the graph until it exhausts its exploration budget. By that point, the result set has often already converged, and the remaining work does little or nothing to improve it. Early termination fixes this by detecting that point and stopping early. The effect becomes more noticeable as k grows, where k is the number of nearest neighbors the query asks Manticore to return. Returning more neighbors requires more graph exploration, and much of that extra work happens after the result set has already stabilized. That also makes early termination more valuable, because it has more unnecessary work to cut. This gets more pronounced with vector quantization . Quantization compresses stored vectors to save memory, which slightly lowers search precision. To recover it, Manticore uses oversampling : it fetches 3x more candidates than requested, then rescores them using the original full-precision vectors. With the default 3x oversampling, HNSW explores many more candidates per query. Large k values often come from this kind of candidate expansion: an application may ask the vector index for hundreds or thous
Hey everyone, We’re building Antitech , a security layer for AI agents and LLM-powered workflows. We’re opening a small number of free early-access assessments for teams/builders working on AI agents. If you give us access to an endpoint of a Dockerized / sandboxed environment of your agent, we’ll test it against common and emerging AI-agent attack vectors, including: Prompt injection Indirect prompt injection Tool abuse Data leakage / exfiltration Fake authority / malicious context Unsafe agent behavior Weak guardrails and policy bypasses In return, you get a free vulnerability report showing what we found, how serious it is, and practical recommendations to harden your agent. This is completely free. No catch. We’re doing this because we want to work closely with real AI-agent builders while shaping the product. Early participants will also get: A big discount once the final product is ready Insider updates while we build Early access to new features The option to become a design partner Priority access to future assessments What we need from you: An endpoint of a sandboxed/Docker environment Permission to test within agreed boundaries A short feedback call after the report We won’t publicly disclose anything without your permission. If you’re building AI agents and want to know how they can be attacked before someone else finds out the hard way, DM me or comment below. submitted by /u/TheAchraf99 [link] [留言]
How to make LLMs deterministic, in plain English. The version I share with founders and product teams before they make decisions worth real money. You use AI tools every day. But can you explain what happens when you hit send? Most people cannot. And that gap is costing them. Bad prompts. Broken products. Decisions made on the wrong assumptions. The Hard Truth Every LLM explainer out there is written for researchers or so basic it tells you nothing useful. Neither helps you build better products or work with AI more effectively. This is the version I share with senior leaders, founders, and product teams before they make decisions worth real money. 1. It Is Not a Search Engine. It Is Not a Database. It Is a Prediction Machine. When you type a prompt and hit send, the LLM is not finding an answer from somewhere. It is predicting the most likely words to follow your input. Based on patterns it learned from billions of documents. That is the whole process. Wrong: "The AI knows the answer." Right: "The AI predicts the most likely answer based on what it has seen." This changes everything about how you use it. When an AI gives you a wrong answer confidently, it is not broken. It is doing exactly what it was built to do. Predict. Not verify. 2. The Autocomplete Comparison (And Why It Only Gets You Halfway) You have probably heard the phrase "autocomplete on steroids." It is not wrong. But it misses something important. Your phone autocomplete learned from your messages. An LLM learned from most of the written internet. Books. Research papers. Code. Billions of examples. At that scale, the patterns start to look a lot like real thinking. Not because the model understands in the way you do. Because it has seen so much that it can predict what a good answer looks like. When I was building AstroNayak I fed Vedic astrology principles into the system prompt. The LLM produced interpretations that genuinely surprised me. It did not know Vedic astrology. It had seen enough of it t
Without revealing too much information, what were the circumstances? submitted by /u/XTXinverseXTY [link] [留言]
Hey everyone, I'm trying to get back into the loop on world models. The last time I followed SSL closely, the buzz was all about Barlow Twins and DINO, but now everything just looks like scaled-up video generation from big industry labs. What is the actual academic research community stressing over right now? submitted by /u/nat-abhishek [link] [留言]