submitted by /u/rhiever [link] [comments]

LangChain just wrapped Day 1 of Interrupt 2026 and announced a few things worth knowing about: SmithDB — A purpose-built distributed database for agent observability. The problem they're solving: agent traces are getting too large and complex for general-purpose databases. SmithDB is built with Rust, Apache DataFusion, and Vortex, designed specifically for multimodal content and long-span tracing. They're reporting P50 latency of 92ms for loading trace trees and 400ms for full-text search, with up to 12x speedup over previous LangSmith performance. Architecture is object storage + small Postgres metadata store + stateless services, so it scales elastically and can be self-hosted. Context Hub — A centralized system for managing agent context (AGENTS.md files, skills, policies, memory) in LangSmith. The interesting part is they're working with MongoDB, Pinecone, Elastic, and Redis on an open standard for agent memory — covering episodic, semantic, and procedural memory with versioning and portability across frameworks. Deep Agents v0.6 — New release includes ContextHubBackend integration, an installable code interpreter that gives agents a programmable workspace inside the agent loop (distinct from sandboxes — this is for composing tools and managing state within the reasoning process), and you can scope specific file paths to different backends. The conference also has production case studies from Toyota, Coinbase, Lyft, LinkedIn, Bridgewater Associates, and others on deploying agents at enterprise scale. Andrew Ng keynoted alongside Harrison Chase. submitted by /u/Equal_Winter3150 [link] [comments]

I had a meeting today that basically gave me an existential crisis. I spent most of the morning cleaning a mess of a dataset and building out what I thought was a pretty slick visualisation on consumer behaviour. I go into the meeting, present the findings, and instead of receiving questions about methodology as I expected, my manager asked me how to show him the actual strategy, which i never thought was part of my role in the first place. Actually, I would prefer no questions at all lol. Anyway, I am doing the technical work behind the scenes and it seems that it’s kind of invisible for everyone else. In fact, I am getting more requests on giving my input on strategy and consumer psychology lately, so I started doing some research. It’s actually interesting how everything changes, but also quite overwhelming because I really do not like the storytelling part. Usually, I do my bit, present it, and I’m out lol. What I wanted to share with you here is that while this situation is definitely not in my advantage, I started to do some digging and found some really interesting perspectives on this and what expectations organisations have now with the massive implementation of AI everywhere. I use AI daily and it makes my work sooooo much easier, but using AI is not enough anymore apparently. Here it is: https://www.qualtrics.com/articles/strategy-research/market-research-trends/ The main idea here is that technical skills are the baseline, not the real value added to the organisation...??? Does anyone else feel like the goalposts are moving? I’m genuinely wondering if I should stop grinding LeetCode and start reading business strategy books just to stay relevant. Would love to hear if your roles are actually changing or if I'm just overthinking one bad meeting. submitted by /u/prattman333 [link] [comments]

I keep hearing some version of this: “A paper that got accepted years ago wouldn’t stand a chance today.” Honestly, for a lot of ML subfields, this doesn’t sound crazy anymore. A paper that once looked solid can now look under-evaluated, under-ablated, weak on baselines, or just too obvious. So maybe the real claim is: A mediocre accepted ML paper from years ago would probably get rejected today. Do people agree? Has the bar actually gone up, or has the field just become more crowded and more competitive? submitted by /u/Hope999991 [link] [comments]

https://preview.redd.it/p9cd2zmfy01h1.png?width=2000&format=png&auto=webp&s=a8e99bac438c2505d97ed3716983aa731da855f8 Sharing a new paper from the GPP and PokeAgent teams. Gemini Plays Pokémon (GPP) was the first AI system to complete Pokémon Blue, Yellow Legacy on hard mode, and Crystal without losing a battle. How? Early signs of iterative harness development. In the Blue era a human watched the stream and edited the harness. By Yellow Legacy and Crystal, the model itself was performing most of the editing through general meta-tools (define_agent, run_code, notepad edits). Our new paper, Continual Harness: Online Adaptation for Self-Improving Foundation Agents, formalizes the loop and automates the refining role end to end. We then carry the same loop into training, enabling model-harness co-learning. The takeaways: 1. Iterative harness refinement closes most of the gap to a hand-engineered version. 2. Long-horizon agency requires self-refinement, and self-refinement requires a useful model. 3. The future of agents is model-harness co-learning. Paper (arXiv). https://arxiv.org/abs/2605.09998 Article (Substack). https://sethkarten.substack.com/p/gemini-plays-pokemon-discovered-something Project page (video demos). https://sethkarten.ai/continual-harness submitted by /u/PokeAgentChallenge [link] [comments]

I trained a set of deep learning (transformer-based) chess models to play like humans (inspired by MAIA and Grandmaster Chess Without Search). There's a separate model for each 100-point rating bucket from ~800 to 2500+. I started with training a mid-strength model from scratch on a 8xH100 cluster, then fine-tuned models for the other rating ranges on my local 5090 GPU. The total training size was nearly a year of Lichess data, about 1B total games. Each rating range actually has 3 models: A move model, a thinking time model, and a white win / draw / black win model. Despite being quite small (only 9MM parameters!) the move models achieve better accuracy than MAIA-2 and are approximately on par with MAIA-3 (see here for MAIA-2 comparison). AFAIK this is the only attempt to train on thinking times in chess, so I don't have a benchmark to compare against for that. Likely because of the network size, at high ratings the models aren't quite as good as they could be. They see short tactical motifs but can't do deep calculation - probably a bigger model would help here. The move and win models take into account player ratings and clock times. For instance, under extreme time pressure a much stronger player has a lower win prob even if their opponent is weaker. The models blunder more under time pressure as well. The data pipeline is C++ via nanobind, then training with Pytorch. Getting this right was actually the thing I spent the most time on. Pre-shuffling the dataset and then being able to read the shuffled dataset sequentially at training time kept the GPU utilization high. Without this it spent a huge percentage of time on I/O while the GPU sat idle. Happy to answer questions about the rating-conditioning, the clock model, or the data pipeline. Code (including training code and model weights) is at https://github.com/thomasj02/1e4_ai/. A demo is at https://1e4.ai/ but all the frontend code is also in the repo if you want to self-host. submitted by /u/hazard02 [link] [comments]

We've been building Scenema Audio as part of our video production platform at scenema.ai, and we're releasing the model weights and inference code. The core idea: emotional performance and voice identity are independent. You describe how the speech should be performed (rage, grief, excitement, a child's wonder), and optionally provide reference audio for voice identity. The reference provides the "who." The prompt provides the "how." Any voice can perform any emotion, even if that voice has never been recorded in that emotional state. Limitations (and why we still use it) This is a diffusion model, not a traditional TTS pipeline. Common issues include repetition and gibberish on some seeds. Different seeds give different results, and you will not get a perfect output with 0% error rate. This model is meant for a post-editing workflow: generate, pick the best take, trim if needed. Same way you'd work with any generative model. That said, we keep coming back to Scenema Audio over even Gemini 3.1 Flash TTS, which is already more controllable than most TTS systems out there. The reason is simple: the output just sounds more natural and less robotic. There's a quality to diffusion-generated speech that autoregressive TTS doesn't quite match, especially for emotional delivery. Audio-first video generation As this video points out, generating audio first and then using it to drive video generation is a powerful workflow. That's actually how we've used Scenema Audio in some cases. Generate the voice performance, then feed it into an A2V pipeline (LTX 2.3, Wan 2.6, Seedance 2.0, etc.) to generate video that matches the speech. Here's an example of that workflow in action. On distillation and speed A few people have asked this. Our bottleneck is not denoising steps. The diffusion pass is a small fraction of total generation time. The real costs are elsewhere in the pipeline. We're already at 8 steps (down from 50 in the base model), and that's the sweet spot where quality holds. Prompting matters This model is sensitive to prompting, the same way LTX 2.3 is for video. A generic voice description gives you generic output. A specific, theatrical description with action tags gives you a performance. There's also a pace parameter that controls how much time the model gets per word. Takes some experimentation to find what works for your use case, but once you do, you can generate hours of audio with minimal quality loss. Complex words and proper nouns benefit from phonetic spelling. Unlike traditional TTS, it doesn't have a phoneme-to-audio pipeline or a pronunciation dictionary. If it garbles "Tchaikovsky," you would spell it "Chai-koff-skee" or whatever makes sense to you. Docker REST API with automatic VRAM management We ship this as a Docker container with a REST API. Same setup we use in production on scenema.ai. The service auto-detects your GPU and picks the right configuration: VRAM Audio Model Gemma Notes 16 GB INT8 (4.9 GB) CPU streaming Needs 32 GB system RAM 24 GB INT8 (4.9 GB) NF4 on GPU Default config 48 GB bf16 (9.8 GB) bf16 on GPU Best quality We went with Docker because that's how we serve it. No dependency hell, no conda environments. Pull, set your HF token for Gemma access, then docker compose up. ComfyUI Native ComfyUI node support is planned. We're hoping to release it in the coming weeks, unless someone from the community beats us to it. In the meantime, the REST API is straightforward to call from a custom node since it's just a local HTTP service. Links All demos + article: scenema.ai/audio Model weights: huggingface.co/ScenemaAI/scenema-audio Code + setup: github.com/ScenemaAI/scenema-audio YouTube demo: youtu.be/VnEQ_ImOaAc This is fully open source. The model weights derive from the LTX-2 Community License but all inference and pipeline code is MIT. submitted by /u/a__side_of_fries [link] [comments]

I have a paper that has been "on-hold" for about 2 weeks now. I understand that it might take a little longer now because of inundation of AI generated low-effort papers but my papers have gone from "on-hold" to "submitted" within a couple of days in the past. Wondering if anyone else is facing the same issue. submitted by /u/Megixist [link] [comments]

Has anyone got into EEML Summer School in Montenegro? I did and please feel free to DM to manage stay or other plans after the summer school. I see that it's tricky to get there and find a stay. submitted by /u/ade17_in [link] [comments]

I am working on a benchmark and need to manage several interlocking components: datasets and metadata, diverse ML tasks (varying inputs and outputs), and baseline experiments covering models, training, and evaluations. Any pointers to projects that handle these through clean/minimal data structures like Dataclasses or Pydantic. Specifically, I want to see how others manage: Dataset Information: Representing dataset cards, metadata, and split definitions as first-class objects. Task Schemas: Defining ML tasks with specific input and output types to ensure consistency across different models. Experiment Composition: Structures that link a model and training configuration to a specific evaluation and prediction set. If you have seen repositories that maintain these abstractions with minimal boilerplate and high type safety, please share them. I am interested in internal code organization rather than external tools like W&B or MLflow. Definitely aware of cookie-cutter data-science, looking for for datastructures. submitted by /u/LetsTacoooo [link] [comments]

Van Rooij, Guest, de Haan, Adolfi, Kolokolova, and Rich claimed to have proven that AGI via ML is impossible in Computational Brain & Behavior in 2024. The basic idea was to try to reduce a known NP-hard problem to the problem of learning a human-level classifier from data. The purported result, called "Ingenia Theorem" by the authors, made some noise on the internet, including here. My paper showing that the proof is irreparably broken is now also out in CBB (ungated preprint here). The basic issue is that "human-level classifier" is not mathematically defined, which the authors solve by ... never defining it. They have a construct that corresponds to "distribution of human situation-behaviour tuples" when they introduce the problem, but the construct then gets swapped out for "for all polytime-sampleable distributions" when it comes time to doing the formal proof. This means that the paper, if you find-and-replace human situation-behavior tuples for ImageNet inputs/labels, also proves that learning to classify ImageNet is intractable. Blogpost discussion similar attempts from Penrose to Chomsky here. submitted by /u/mike_uoftdcs [link] [comments]

Built my own SVM classifier from scratch in Rust. It uses SMO optimization, have linear and rbf kernel, uses grid search to tune the hyperparameters. I tested it on two datasets one using Linear dataset and other using RBF, these were the results: Dataset Kernel Accuracy Recall F1 Banknote Auth Linear 96% 94% 95% Breast Cancer RBF 93% 100% 92% https://preview.redd.it/uw26u1uo0w0h1.jpg?width=720&format=pjpg&auto=webp&s=1784e1d7d310a26fa67efc63fa5191f45433a695 https://preview.redd.it/o0ahkq7p0w0h1.jpg?width=720&format=pjpg&auto=webp&s=dcb1053c34931d11b82831c6ad8cd4755ebc5816 The plot.rs file, used for plotting only was written using AI as I could not wrap my head around plotters crate, apart from that everything was by my own. Repo Link: Github Repo Happy to get some feedback! submitted by /u/Yeet132416 [link] [comments]

Hi guys! I've spent 1y trying to predict company growth from the full text of their 10-k filings. It completely failed. But I've had a lot of fun playing with encoder transformers and making them good at numbers (bypassing the tokenizer/prediction head for numbers). I've MLM-trained a modified ModernBERT for this and it works really well. The model is available on HF: https://huggingface.co/edereynal/financial_bert Then, I've made this MLM-trained model into a nice sequence embedder. I've experimented with JEPA, but it failed. The auto-encoder setup worked much better. But I encountered a strange frequency bias, where the decoder only cared about high-frequency information, and I had to mitigate it by adding a Contrastive Loss term. I also investigated the tendency of transformers to have a low effective-dimensionality output space (compared to its input embedding space). So, here's the technical blog post, that reads a bit like "how to waste 1,000 hours and $400 trying to solve an unsolvable real-world problem, but having a lot of fun along the way": https://www.eloidereynal.com/p/i-spent-1-year-trying-to-predict submitted by /u/Academic_Sleep1118 [link] [comments]

Wanted to share our latest paper on an alternative building block for Vision Transformers. Illustration of our model's accuracy and dense features Traditional ViTs utilize dense (N2) self-attention, which can become pretty costly at higher resolutions. In this work, we propose an alternative backbone with a core-periphery block-sparse attention structure that scales as (2NC + C2) for C core tokens. We further train this using nested dropout, which enables test-time elastic adjustments to the inference cost. The whole model can achieve very competitive dense & classification accuracy compared with DINOv3, and is stable across resolutions (256 all the way to 1024). Interestingly, the core-dense attention patterns exhibit strong emergent behavior. At early layers of the network the attention maps are isotropic (spherical), but become increasingly semantically aligned deeper into the network. Visual Elastic Core Attention paper abstract While adjusting the number of core tokens, if you decrease the number of cores, the attention patterns become more diffuse & cover a spatially larger region. If you increase the number of core tokens, the attention patterns become smaller & more concentrated. Paper: https://arxiv.org/abs/2605.12491 Project with the code (still in progress): https://github.com/alansong1322/VECA Happy to answer any questions about our research. submitted by /u/44seconds [link] [comments]

Large language models (LLMs) are trained for downstream tasks by updating their parameters (e.g., via RL). However, updating parameters forces them to absorb task-specific information, which can result in catastrophic forgetting and loss of plasticity. In contrast, in-context learning with fixed LLM parameters can cheaply and rapidly adapt to task-specific requirements (e.g., prompt optimization), but cannot by itself typically match the performance gains available through updating LLM parameters. There is no good reason for restricting learning to being in-context or in-weights. Moreover, humans also likely learn at different time scales (e.g., System 1 vs 2). To this end, we introduce a fast-slow learning framework for LLMs, with model parameters as "slow" weights and optimized context as "fast" weights. These fast "weights" can learn from textual feedback to absorb the task-specific information, while allowing slow weights to stay closer to the base model and persist general reasoning behaviors. Fast-Slow Training (FST) is up to 3x more sample-efficient than only slow learning (RL) across reasoning tasks, while consistently reaching a higher performance asymptote. Moreover, FST-trained models remain closer to the base LLM (up to 70% less KL divergence), resulting in less catastrophic forgetting than RL-training. This reduced drift also preserves plasticity: after training on one task, FST trained models adapt more effectively to a subsequent task than parameter-only trained models. In continual learning scenarios, where task domains change on the fly, FST continues to acquire each new task while parameter-only RL stalls. https://arxiv.org/abs/2605.12484v1 submitted by /u/LakshyAAAgrawal [link] [comments]

submitted by /u/rhazn [link] [comments]

Didn't make it to New York for the Knowledge Graph Conference this year, but caught some talks virtually and managed to download all the decks. Sharing them below because some of what was shown is worth knowing about. Majority of the presentations described live production systems. Enterprises showing up with real engineers delivering real compliance requirements. That's not usual for most ai eventss. Most talks are proofs of concept with a "coming soon to prod" slide at the end. For eg - Bloomberg showed a formal dependency model for ontology governance. AbbVie walked through ARCH, their internal KG for drug and disease-area intelligence, connected to a scoring engine, a researcher dashboard, and an LLM companion for plain-language queries. The KG is the source of truth. The LLM is the interface. Even Morgan Stanley showed continuous SHACL drift detection on risk reporting data - automated weekly checks that alert when the semantic layer deviates from what's governed. Crux: knowledge graphs are being actively used as infrastructure, not a retrieval layer on top of vectors. The graph is doing reasoning work, not lookup work. We've been skeptical of the "only using vector dbs" framing for a while. These production systems are the clearest evidence I've seen of where that breaks down - and what the alternative actually looks like when it's running. Link to the all the decks in the comment. All decks here: https://drive.google.com/drive/folders/1Csdv4hZePrBMJGggsisPXYBueTRCK1kV?usp=sharing submitted by /u/Ok_Gas7672 [link] [comments]

Hi everyone, I'm looking for some honest input from people who've been down this road or know the landscape well. My background: B.Com in Finance & Accounting from Delhi University (2019) During Covid somewhat made my way into machine learning by doing self study at home. Currently a Senior ML Engineer at a large financial data/tech company in Bengaluru Day-to-day work spans around NLP/LLM systems, real-time ML pipelines, distributed data infra, and AWS. What I'm trying to do: I want to seriously deepen my foundations in applied mathematics for ML — think probability, linear algebra, optimization, statistical learning theory, the actual mathematical machinery behind modern ML rather than just the engineering side. I've been doing ML professionally for a few years now and I keep hitting the ceiling where deeper math intuition would make me significantly better at my job (and at research-leaning problems). My constraints: Can't leave my job. I need a fully online / part-time / WILP-style program. Based in India, so an Indian program is ideal (IISc, IIT online degrees, CMI, ISI, BITS, etc, i know getting into top tiers college is very very hard for someone whose background isn't in engineering but still if there's any way they accept non-techincal degree holders, I would like to know more about how one can enrol for such programes) Open to foreign universities too if the program is genuinely online and the time zones work out What I'd love input on: Programs you'd actually recommend (and ones to avoid) for applied math / mathematical ML at the master's level, fully online If anyone has done IIT/IISc online degrees coming from non-technical background in math/stats/ML while working full-time, how was the experience and workload? Not looking for career change advice happy in my role. Just trying to build deeper foundations the right way. Any pointers appreciated. submitted by /u/Lamba_ghoda [link] [comments]

Hello everyone, Presenting at a top-tier conference for the first time and having a very hard time coming up with an appropriate design for my poster. Everything I do seems basic and banal. My paper is more theory-oriented, and apart from putting math formulas in bold in the middle, I am not sure what the best way is to design the poster. Even the sizing choice is complicated as ICML gives 3 different recommendations to pick from, and somehow from my computer, I can’t see how the PowerPoint slide will look like printed on those dimensions. And Printing a poster is nearly $100 CAD, so there’s no room for trial and error. So If anyone has any tips on how to do it properly, I have been using PowerPoint, but perhaps I should go to Canvas? Or Does anyone have another software to recommend? submitted by /u/DazzlingPin3965 [link] [comments]

Hi all, I made my own minimal implementation of JEPA algorithms. Making things minimal and removing all the things needed for scaling the algorithm always helped me understand the essence. So I stripped everything but the algorithm parts. What's left is 160-200 lines of code that distills the essence of the mathematics. It is very easy to compare with the math in the paper and the code and how it can be implemented in PyTorch. I added [algo]_tutorial.md files to help with understanding. https://github.com/keon/jepa submitted by /u/kwk236 [link] [comments]