Daybreak Red and Daybreak Blue from OpenAI, specialized cyber defense models from OpenAI, are now available on Amazon Bedrock to eligible customers. Both models run with zero-operator access enforced at the chip, keeping your code and vulnerability data secure.

Link: https://arxiv.org/pdf/2604.27883 Hi, Most of use are familiar with the headache of training a neural network using gradient descent where the training error may go to zero but the test error may stay the same as initialization or even increases. My paper treats this phenomena as a consequence of data reuse bias and can be isolated by studying full batch gradient descent on a set of stylize Gaussian mixture models. I turns out that this fundamental issue can be avoided using some clever tricks from high-dimensional statistical theory, specifically approximate message passing (which is beyond the scope of this post but I would be happy to explain more). By doing so I created a training method called Decoupled Descent (DD) which generates a certificate that the training error of the network will asymptotically equal the testing error at each parameter iterate. I think this method gives a cool way to approach how to train networks and I was hoping to get y'alls input on it. It opens up some nice ideas for optimal stopping or hyperparameter tuning and future directions of pushing to something like SGD or more general models. I have attached the train-test curves on a simple model fitting problem to compare the performance of GD with with DD (my algorithm) to give a high-level idea of what the method can guarantee. I stress this is a theory paper so there is a long way to go to get to very large models but I think it is a good first step. 100 simulations of a simple high dimensional XOR model for a bespoke two layer network. Left is training with GD, right its training with my method. The colored bands are 25% to 75% quantile. Happy to answer whatever questions people have, I plan on writing a PyTorch compatible package for this training method one day so any feature suggestions would be welcome as well. submitted by /u/mlovik1 [link] [comments]

Few days ago I feel like I need to get started with researching about LLMs. One thing which strikes the most in my mind , how we can reduce the time required for pre-training an LLM model to just few minutes. Right now the most efficient method that we have is knowledge distillation, which still takes time in response generation by the teacher model from prompts, backpropagation and training, to adjust the weights of student model making it to mimic the teacher model. What if there is any way where we can adjust the model weights of an untrained model so that it becomes mathematically the same function as of the trained model.I want to figure out if there any such algorithm exist which would perform simple mathematical operations on the untrained model such that it becomes mathematically same function as the trained model. If this become successful there is no need of training under distillation process or any conventional process, just few math operations on the untrained model, and then it's done, which would be taking few minutes. I need guidance and collaboration for someone who is working in this direction. submitted by /u/subratmohapatra2003 [link] [comments]

Back after ~6 months — rebuilding my spiking language model around CPU-first inference Hey everyone. It’s been around six months since I last posted anything about this project here. Some of you might remember Project NORD, my experimental hybrid spiking / brain-inspired language model architecture. I basicall disappeared for a while 😅, but recently I came back to the project, went through the old architecture again, and realized I didn’t really want to keep stacking fixes on top of it. So instead, I’ve started rebuilding a pretty large part of the system. The new version is called: NORD 5.5 — Flash The main idea this time is pretty simple: What happens if I design the architecture around CPU inference from the beginning, instead of building soething Transformer-like and trying to optimize it later? A lot is changing internally. The current design uses things like: strictly causal processing no standard quadratic attention in the main inference path causal convolution-style token mixing token-time LIF / event dynamics sensory → association → memory → executive processing stages top-1 sparse MoE + a shared expert persistent recurrent memory separate structural, personal and auxiliary memory banks persistent recurrent identity state factorized vocabulary embedding/output streaming token-by-token inference One of the biggest changes is actually something much simpler. Older versions of NORD used an artificial internal spike-time dimension, roughly like this: token -> T0 -> T1 -> T2 -> ... -> T9 I’m mostly getting rid of that. Instead, the actual language sequence becomes the time axis: token0 -> token1 -> token2 -> token3 -> ... That removes a lot of intermediate state and makes the whole architecture considerably cleaner. Going back through the old code also exposed a few things I wasn’t very happy with. Some experimental modules weren’t completely causal, memory was coupled too much to sequence shape, and parts of the STDP system ended up being more disconnected from real training than I originally intended. So NORD 5.5 isn’t really about throwing even more “brain-inspired” components into the model. It’s mostly about simplifying the core and making the things that remain actually work together properly. I’m definitely not claiming this is going to beat Transformers, RWKV-style models, linear attention models, etc. Right now it’s still very much an experiment. The part that actually matters comes next: training and benchmarking it. Things I want to compare: NORD 5.0 vs NORD 5.5 CPU tokens/sec RAM usage perplexity / validation loss long-context behaviour memory on/off MoE on/off spiking components on/off I’m especially curious to hear from anyone working on SNNs, recurrent models, sparse MoE, CPU inference, or weird alternative language-model architectures in general. After not touching the project seriously for about half a year, it feels surprisingly good to be building it again 😅 I’ll post actual numbers once I have something that’s worth benchmarking instead of just architecture diagrams. submitted by /u/zemondza [link] [comments]

I am now reviewing a bunch of papers for AAAI 2027 and it has surprised me the low amount of submissions with no code implementation. I don’t know if it has been only in my batch or it is common, but I was expecting very detailed appendices + code submission since AAAI is very explicit with the topic of reproducibility. I was planning to take this into consideration when assigning my initial scores, but I would like to hear your opinions. I have always submitted my code: it gives a very good impression and after reviewing process finishes we just publish it on ArXiv, so no one “tries to stole the idea” (although I think that this is very very unlikely). So I cannot find any excuse for those submissions that do not have code implementation, specially in today’s times where AI assistants can just write an empirical paper with artificial results within a couple of hours submitted by /u/wontonut [link] [comments]

I built HyperSAE, a PyTorch library that applies Poincaré hyperbolic geometry to Sparse Autoencoders for mechanistic interpretability. GitHub: https://github.com/vishal-dehurdle/hypersae Paper: https://vishalvermalabs.com/papers/empirical-validation-hypersae-poincare-geometry/ Install: pip install hypersae Motivation Standard SAEs embed dictionary atoms in Euclidean space, where volume grows as O(rd.) The concepts LLMs learn form branching hierarchies that expand as O(br.) At 16K+ dictionary sizes, this mismatch causes feature collisions at the boundary, dead latents, and reconstruction degradation. Architecture HyperSAE uses a decoupled dual-speed design: Forward pass remains entirely Euclidean. Zero inference overhead. Causal steering stays a single vector addition. During training, dictionary weights are projected into the Poincaré ball. An entailment cone loss organizes parent concepts near the origin and child concepts near the boundary, where hyperbolic volume expands exponentially. Results (Gemma-2-2B Layer 13, 20M tokens FineWeb-Edu, NVIDIA L4) Metric FlatSAE HyperSAE Delta Reconstruction MSE 4.5724 4.1232 -9.8% CE Loss Recovery 75.5% 78.9% +3.4pp Dead Latents 3.8% 0.2% -3.6pp MMLU-Pro Accuracy 16.11% 16.26% +0.15pp GPQA Diamond 100% 100% -- The library includes co-activation queue tracking, TriPartite loss (reconstruction + L1 sparsity + entailment), and a single-class trainer interface. Feedback on the geometric formulation or benchmark methodology is welcome. submitted by /u/visha1v [link] [comments]

ONESTRUCTION, with technical advisory from the AWS Generative AI Innovation Center, built Ishigaki-IDS, a foundation model specialized for construction and BIM workflows. This architectural case study shows how they combined synthetic data, a three-stage training pipeline, and verifiable rewards on Amazon EC2 to build a domain model in a data-scarce field.

Hey everyone - we've been building something particularly relevant to ML at large - The Agentic World Cup - a platform where Agents compete in sports. As you know, today's Agents can code, do math, and write - but they aren't nearly as fluent in sports - many of you would know this as the "embodiment gap". Closing the embodiment gap is why we are pursuing this. Sports is both the training and testing ground for true embodied intelligence. Agents will have to actually "think on their feet" to use a colloquial term. In other words, we're pioneering making agents think like athletes, not just nerds. 🙂 How it works: Sign in Select your LLM Coach it (through prompting) Submit it! Your agent will automatically play with other agents, and you will be able to watch it's performance on the site. By Friday, your final rankings come in and be published on the site! Past that though, we also believe that there's a particularly large gap in embodied benchmarking AND a forum for quickly trying out different methods by not just researchers and engineers. Some people are bullish on ViTs, others on onlineRL, and still others on neuro-symbolic systems, etc. So over the long term, we envision anyone be able to quickly test out their latest & greatest insights and algorithms on more publicly facing embodied challenges - which sports is really the apex of. I'd love to hear from the ML community - since this will ultimately be of service to you, so please send us your feedback! submitted by /u/agenticworldcup [link] [comments]

Photographers are among the most skeptical audiences for generative AI. Learn how Pixieset used Amazon Bedrock to launch an AI-generated alt text feature to millions of users in four months, reaching 35% adoption by automating the tedious image SEO work photographers avoid, without touching the creative craft they take pride in.

Learn how First Orion, a branded communications company, shifted from brittle script-based UI testing to AI-driven QA automation with Amazon Nova Act. By describing tests in plain English instead of maintaining selector-based code, they cut QA cycle times, freed engineering capacity, and caught regressions earlier.

Claude apps gateway is a self-hosted governance layer between Claude Code and Claude Desktop and Amazon Bedrock or Claude Platform on AWS. This post presents a production reference deployment covering end-to-end architecture, enterprise deployment patterns, cost, and implementation resources.

Hello all, I am wondering if a transition from a Ph.D. in electrical engineering (Quantum optics/photonics) to a job in ML is a reasonable aspiration. Personally, I have extensive software development experience competing and winning numerous coding competitions over the years, but most importantly my undergraduate research project was ML based (ML for SiC grating design optimization), I placed third in our universities "Agri-AI" competition which was basically just a big data project for the agriculture department, and I have done several projects in realizing optimal qubit control using ML to bridge the gap between simulation optimization and experimental errors (essentially using an MLP to compensate an unknown system frequency response). I am also generally interested in PINNS and any physical applications of ML. If anyone has made a similar transition I would love to hear how it went for you and what your intended goals were. The more I do projects related to this subject I find myself wanting to make a career out of it more and more. (bonus points if you come from a physics background) 😄 submitted by /u/Plane_Telephone9433 [link] [comments]

I am working on an AI for a small single-player merge puzzle and would appreciate pointers to related algorithms, papers, or existing implementations. It resembles 2048 in its action -> afterstate -> random event structure, but has a larger action space, stack constraints, and a random event that is previewed one move before it is applied. I have an exact simulator. I am not trying to learn the game dynamics from pixels at this stage; the current question is how best to learn values/policies and allocate a limited planning budget. ## Game rules - The board contains 6 vertical stacks, each with a maximum height of 7. The first item in a stack is its top. - An action chooses an ordered pair of different columns: 6 x 5 = 30 possible actions. - The complete contiguous run of equal tiles at the top of the source stack is moved onto the destination stack. An action moves the whole run, not one tile. - If the destination now has at least 3 equal tiles at its top, the complete run merges into one tile of value `n + 1`. Cascades are possible. - A merged 9 disappears and gives one point. Tiles normally present on the board have values 1 through 8. - Merging happens before overflow is checked. The game ends when any stack remains higher than 7. - Every fourth player action is followed by one new random tile being added to every column. - The six upcoming random values are revealed after the third action. The player can therefore choose the fourth action while knowing the exact six tiles that will then be added. - A random tile is in `[1, min(7, highest value merged so far)]`. The real distribution is not yet known. It appears biased toward high values, and human players report runs of "simple" drops (one or two distinct values) alternating with more complex mixed drops. One cycle is therefore: ```text deterministic action 1 deterministic action 2 deterministic action 3 -> reveal a random six-tile preview preview-conditioned action 4 -> apply the known six-tile drop repeat ``` The random preview is the chance event. Applying an already revealed preview is deterministic. ## Objectives There are two related objectives: Maximize the number of 9s in one game. Maximize the total number of 9s in 30 minutes. Death permits a restart, so this is closer to a continuing average-reward/throughput problem than a conventional episodic score problem. The real interface is animation-limited to roughly one player action per second, so 30 minutes is approximately 1,800 actions. Human results in the timed mode are around 115 total 9s on the server I observed. In a separate untimed mode, strong humans can maintain a mature board for 1,000+ 9s, although that mode allows one limited revive. The distinction between cold-start cost and mature-board efficiency seems important. In one of the current AI's best games, the first 9 took 48 actions, while subsequent 9s took 18.7 actions on average. ## Current representation and network The state contains: - a 6 x 7 x 9 one-hot board; - the four-action cycle phase; - the six preview values when known, plus a preview-present flag; - the current random-tile value cap; - the maximum number of empty columns reached in the current cycle and in each of the previous three cycles. The current input has 394 features. The Policy/Value network is column-permutation equivariant: - one shared encoder processes each column; - an ordered source/destination pair head scores the 30 actions; - value heads predict future 9 count over a long horizon, normalized distance to the next 9, and short-term death risk. The history features were motivated by a human rule of thumb: in long games, at least one of the last three drop cycles should have temporarily maintained two empty columns. The history is not required for Markov dynamics under the current IID simulator; it is intended as a strategic summary and may become predictive if real drops have temporal regimes. ## Current planning I use the exact simulator with a stochastic PUCT search. The player action is separated into a deterministic afterstate and an explicit chance node. Current configuration: ```text 128 simulations per real action maximum tree depth: 32 player actions c_puct: 1.5 gamma: 1.0 death-risk penalty: 0.5 maximum 8 fixed chance particles per chance node chance progressive widening exponent: 0.5 minimum 2 visits for every legal root action ``` At the third action, simulations branch over sampled six-tile previews. Below each preview outcome, the tree can choose a different fourth action and applies that preview exactly. After every real action I currently rebuild the tree rather than reusing it. Depth 32 is only a cap. With 30 root actions, 128 simulations, root coverage, and chance branching, most candidates receive only shallow explicit search; the learned Value network estimates most of the long horizon. ## Training process The current process is a form of expert iteration/reanalyse: Generate long games with beam search and then Policy/Value-guided PUCT. Save full episodes, root visit distributions, 9-event positions, death, and optional root action values. Train on column-permutation augmentation. Give extra policy weight to states after the first 9, states containing 7/8 tiles, high-scoring episodes, and states with human-like long-game structure. Generate new PUCT trajectories with the updated network and repeat. I initially used DQN, behavior cloning, demonstration replay, and DAgger-style data aggregation. The Policy/Value + search route has been substantially better for long games. ## Current results These are simulator results under one assumed high-value-biased drop distribution, not results from the real game distribution. - An earlier explicit-chance PUCT model scored 81 total 9s in 16 episodes (mean 5.06, maximum 11, 2,365 actions). - Search distillation later produced a game with 13 total 9s in 272 actions. This remains the single-game maximum. - Adding human-structure weighting improved a small paired evaluation. - Adding the four-cycle empty-column history produced 59 total 9s in 1,675 actions over 12 new episodes, versus 47 in 1,537 actions for its no-history teacher on the same seeds. This is 35.2 versus 30.6 9s per 1,000 actions, but 12 episodes is far too small for a reliable conclusion. - Under the current assumed distribution, even 35.2 per 1,000 actions projects to only about 63 per 1,800 actions, still well below the observed human timed score. I am moving toward paired evaluation on at least 64-128 untouched seeds with bootstrap confidence intervals. I track first-9 cost, subsequent-9 gaps, survival length, per-1,000-action throughput, and fixed-action-budget totals rather than only mean episodic score. ## Things that did not work - A learned action/afterstate Q head achieved low offline MAE but made closed-loop search much worse. Ordinary reanalyse covered too few actions per state, while a full-action root target still suffered from extrapolation/calibration problems. - Jointly fine-tuning the shared encoder for Q degraded the existing policy and value estimates. - Increasing root minimum visits from 2 to 3 reduced performance. - Increasing simulations from 128 to 192 did not improve the paired sample. - Directly adding a handcrafted board-structure score to leaf values changed behavior but reduced overall performance. Using the structure only to weight policy training was better. - Exhaustively maximizing over all preview-conditioned fourth actions at a leaf caused severe maximization bias because the learned Value was not one-step Bellman-consistent. - Restricting search to exactly one four-action cycle had mixed results even after fixing depth-cutoff evaluation. - Repeated policy-only self-distillation quickly saturated. ## Approaches I am considering **2048-style afterstate TD / N-tuple value learning.** The deterministic action followed by a random event seems almost exactly the setting where afterstate TD is useful. I am unsure how best to combine it with the three deterministic actions, the preview chance node, and the preview-conditioned fourth action. **Gumbel MuZero / sequential halving at the root.** With 30 legal actions and only 128 simulations, forcing every root action to receive two visits may waste half the budget. **Persistent tree reuse.** Re-root after each selected action and, when the real preview appears, follow the matching chance outcome or add it if it was not sampled. **Multi-horizon or distributional values.** Predict future 9s over 16/64/256 actions, survival, and perhaps return quantiles instead of one noisy long-horizon mean. **Average-reward training.** Optimize fixed-action-budget throughput including restart/cold-start cost instead of episodic discounted return. **A regime-switching drop model.** Fit an HMM or other conditional sampler if real preview logs confirm alternating simple/complex drop regimes, then condition the policy on recent previews or a distribution belief. **A frozen base network plus residual adapters.** Learn history-dependent corrections to policy/value without damaging the already useful board encoder. ## Questions - Is there an established algorithm or open-source project for a game with this action -> afterstate -> chance -> preview-conditioned action structure? - Would an N-tuple afterstate value network plus expectimax be a better fit than a neural Policy/Value + PUCT system here? - How would you allocate 128 simulations across 30 root actions and stochastic preview outcomes? Is Gumbel sequential halving the obvious next step? - Is tree reuse across deterministic actions and observed chance outcomes likely to matter more than another round of self-play training? - What is a sound way to train an afterstate value without the all-action extrapolation failure I saw with the Q head? - For the 30-minute objective, would you formulate this as an average-reward continuing MDP, a fixed-horizon problem with automatic resets, or something else? - Are there papers on 2048, SameGame, Tetris, stochastic packing/merge puzzles, or inventory-like stack planning that are especially relevant? - Are there standard tests for deciding whether observed random drops are IID or generated by a hidden regime process before building a conditional model? The most relevant work I have found so far is the 2048 N-tuple/afterstate TD literature, Single-Player MCTS for SameGame, Gumbel MuZero, and "Planning in Stochastic Environments with a Learned Model" (Stochastic MuZero). Pointers to stronger baselines, code, or terminology for this problem class would be very helpful. submitted by /u/CaiwenGong [link] [comments]

Hello, I wanted to share the work my colleague and I have been doing, which has just been published in Software X journal. We developed a Rust-based implementation of Random Forest. It has bindings for both Python and R. Fru is highly optimized, offering competitive runtime performance and better scalability than popular implementations on these platforms. For Python, Fru outperforms the scikit-learn implementation by several factors, and in some scenarios it can be hundreds of times faster. In R, Fru is typically a few dozen percent faster than the ranger package, though the speedup can reach several times faster depending on the use case. The model also includes a novel implementation of permutation importance, which provides an additional performance boost. Thanks to its layered design, we were able to easily create bindings for both Python and R. In Python, we use Arrow PyCapsule, which allows the model to work seamlessly with any compatible library, including pandas, polars, pyarrow, and many others. paper R package Python package submitted by /u/kpiwonski [link] [comments]

Obviously nobody needs a transformer that's good at multiplication. I wanted to know whether a stock transformer could do exact arithmetic if I chose its weights directly. I implemented the grade-school algorithm as a computation graph and compiled it into an ordinary Phi-3 Hugging Face checkpoint using Torchwright, a compiler I wrote. No training. The three-digit calculator gets all 3,000,000 supported expressions right. I've published checkpoints to Hugging Face that support up to 12 digit x 12 digit multiplication. For fun, I also disabled reasoning and tested six frontier models. Accuracy falls off a cliff as the numbers get longer; at seven digits, five scored 0/500. Mine stays at 100%, although it has the considerable advantage that I put the multiplication algorithm directly into its weights. I ended up building four versions: grade-school, hardware-style, scratchpad, and brute-force memorization. They compute the same function while spending layers, width, generated tokens, and parameters very differently. Write-up: https://ood.dev/posts/calculator/ Repo: https://github.com/physicsrob/torchwright Checkpoint: https://huggingface.co/physicsrob/torchwright-calculator-simple-max-digits-3 submitted by /u/notforrob [link] [comments]

The Amazon SageMaker AI Spaces add-on for Amazon EKS runs managed JupyterLab and Code Editor environments on the cluster your ML team already operates. This post shows how to install and configure the add-on, connect from the browser and from VS Code over SSH-over-SSM, and move your team to OpenID Connect sign-in with Amazon Cognito.

nOps rebuilt its Clara FinOps AI agent on Amazon Bedrock AgentCore, replacing a self-managed Amazon EKS stack running LangChain and LangGraph. The move cut time-to-production by 75% (from 10-12 months to 4 months), improved response quality, and reduced operational overhead while keeping analytics governed through Databricks Lakehouse Metric Views.

Hi, I would like to file a complaint about an accepted and published CVPR 2026 paper that its main contribution is a dataset but it was never released, and honestly I don’t know who to contact. The dataset was never released prior to the conference, or during the conference or after the conference. I personally feel there was a lack of proper checking that the dataset was gonna be available before the conference since this is a requirement. I’ve tried contacting the authors without any success (which tbh I wouldn’t even need to because it has to be released anyways). The authors even point a GitHub link in the paper but the repo is empty (and it was always empty). submitted by /u/ElPelana [link] [comments]

Today the most important factor in AI is cost. My idea is to split ML models inference (closed ones, proprietary) across server and edge computing on clients, and I would like to hear what do you think about this thing. For example some of model weights/modules would be on client, and some on the server side (where user has no access to them). This could potentially un-load some processing from datacenters, moving part of the cost to the client hardware. Probbably the most important question here will be how to achieve this - and I believe one hypothetical option will be to train like two separate models - client model and server model, and they will communicate through tensors/latent representations across network protocol. Secondly such split of server side and client side model ends, can provide later some beneficial outcomes I hope (because in between "talk" protocol can be maybe kind of standarized one in some future development, but this is only more like brainstorm now). Such split might not only be one-to-one, but one-to-many, many-to-many etc. What do you think about this idea? submitted by /u/komorra [link] [comments]

Say you want to swap out your embedding models, for instance from ADA to Titan. Are these embedding models comparable? How do similarity score ranges compare? Where to put a threshold for minimum match when doing retrieval? Or more from a research point of view how can we relate and fundamentally understand these embedding spaces better? This is what we aim to solve with Synthetic Query Probing, a fancy name for essentially (and intentionally) a very simple approach: embedding spaces are not directly comparable by definition, so compare similarity spaces instead, similarity match scores for pairs of content (synthetic question, chunk for instance) across multiple embedding models. For example, similarity scores of Titan models of different dimensionalities are semilinearly related, whereas the relation between Titan and Ada scores is non-linear, with different ranges, see figure. https://preview.redd.it/eauhd4hdyiih1.png?width=4767&format=png&auto=webp&s=e424c836c48962928d9505cf747e7cd9fb0b719f See https://arxiv.org/pdf/2608.05857, Marcin Rozmus and Peter van der Putten. Similarity Spaces across Embedding Models with Synthetic Query Probing. Discovery Science 2026, October 5-9, 2026, Mainz, Germany submitted by /u/pppeer [link] [comments]

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

submitted by /u/ImaginaryRea1ity [link] [comments]

Reasoning alone cannot predict the chaotic physical world. Without a sensory and motor interface to reality, non-physical AI will not deliver the scientific and technological breakthroughs we expect. submitted by /u/dontkry4me [link] [comments]

Does anyone have any idea about the instructions for the camera ready at workshops? The deadline is August 15, but there are no indications and workshop organizers know nothing about that.. Some workshops have enabled the upload of camera ready PDF on openreview, but what about copyright form and latex source files? submitted by /u/rokk07 [link] [comments]

Analog in-memory compute is getting attention again as a way around the energy cost of moving weights between memory and compute. The recurring objection is noise, since analog cells have real variation and you can't refresh your way out of it like you can with digital. I wanted to see the shape of the degradation curve rather than reason about it abstractly, so I ran a simple experiment: train a network normally, then evaluate under increasing weight noise. The curve isn't smooth. Accuracy is stable up to a point, then drops hard: 83%, 64%, then essentially random. More like a threshold than a proportional decrease. Retraining with noise injected during training (so the optimizer finds flatter minima, presumably) shifts that threshold substantially. 61% versus 39% at matched noise. What I'd like to hear from this sub: is the flat-minima explanation the right framing here, or is something else driving the gap? And is there work on optimizing directly for noise robustness rather than just injecting noise and hoping, something closer to an explicit sharpness penalty targeted at the hardware's actual noise profile? Code and figures in the writeup: https://towardsdatascience.com/analog-ai-is-back-can-it-survive-its-own-noise/ submitted by /u/Georgiou1226 [link] [comments]

Genome language models have emerged as a promising strategy for designing biological systems, but their ability to generate functional sequences at the scale of whole genomes has remained untested. Here, we report the first generative design of viable bacteriophage genomes. We leveraged frontier genome language models, Evo 1 and Evo 2, to generate whole-genome sequences with realistic genetic architectures and desirable host tropism, using the lytic phage ΦX174 as our design template. Experimental testing of AI-generated genomes yielded 16 viable phages with substantial evolutionary novelty. submitted by /u/moschles [link] [comments]

Is this it for Causal Inference? Looks like the field continues to be of interest only at UAI/AISTATS/CLeaR. All good venues, but LLMs/Agents/etc seem to have eaten much of the lunch of several other subfields at the top 3 conferences. God help us all. **p.s.** the list: https://danyaljj.github.io/neurips2026-workshops/ submitted by /u/Beautiful_Baker_2233 [link] [comments]

Out of curiosity, if you were a reviewer or author, how did the review period go? For me, it was weird, because I gave reviews with specific details (what specifically could have been better, how to fix it), but realized other reviewers gave similar superficial reviews. Even the paper which was a control for me (no LLM), I gave specific comments, but other reviewers focused on minor things. During the discussion period for one paper, one reviewer broke the double blindness condition, and gave specific examples of what the LLM gave and justified their reject…..but they didn’t even state that in their initial review (nor engaged with the author rebuttals). There was no also no sense of: “author said this was unclear, check with the LLM to see what’s the issue” For one of my own papers, we had great scores for originality and significance, but had low scores for clarity, with at least two reviewers finding difficulty understanding established notation and concepts, and I’m wondering whether it would have been better to break the double blindness and said: look, the point of an LLM assisted review is that if you don’t even know this material, you can ask it questions, like if other papers use the same notation, how our paper compares with them, etc… submitted by /u/OutsideSimple4854 [link] [comments]

Hello! Let's use this thread to discuss ICDE results which should be coming out shortly today (hopefully). Edit: Results are out! submitted by /u/mythrowaway0852 [link] [comments]

What's your commitment submission ID? My submission number is ~150 (submitted two days ago) and I'm wondering what the total number of commitments is. Did anyone commit near the deadline? submitted by /u/hepiga [link] [comments]

In this post, you learn how Cohere Health built a multi-tenant agentic architecture on AgentCore using AgentCore Runtime’s secure MicroVM isolation, unified tool access through AgentCore Gateway, AgentCore Memory, and the Agent Skills open standard to rapidly scale policy digitization capabilities, while preserving transparency, version control, and human oversight.

TReNDS, a research center at Georgia State University, built an agentic AI pipeline on Amazon Bedrock and the open-source Strands Agents SDK that automatically investigates production errors in real time, reducing root-cause analysis from 15 to 30 minutes of manual work to under 60 seconds.

The AWS Generative AI Innovation Center built an automated system that uses constraint programming and custom tree search to determine, with mathematical certainty, when and how an NHL team clinches a playoff spot. The approach was validated against four full NHL seasons of officially published results.

Temporal policies in Amazon Bedrock AgentCore let you define stateful rules that evaluate authorization based on an agent's session history. Learn how to enforce workflow sequencing, prevent data fabrication, cap financial exposure, and require human approval for high-value actions.

Learn how to configure rate limits on Amazon Bedrock AgentCore gateway to enforce per-user and per-target traffic controls. Define request, token, and connection limits scoped by JWT claims or IAM identity to protect downstream models, tools, and agents from traffic spikes.

Learn about new capabilities in Amazon Bedrock AgentCore: temporal policies powered by Dogwood, a new open source policy language for AI agents, and rate limiting on the gateway. These features give you deterministic control over sequences of agent actions and cost ceilings that hold regardless of agent behavior.

As engineering teams adopt coding agents like Codex, leaders need visibility into adoption, consumption, and reliability. This post shows how to route Codex OpenTelemetry metrics through a local collector to Amazon CloudWatch for an AWS native view of usage by user, team, and cost center.

Learn how to run the full Amazon Bedrock Automated Reasoning policy lifecycle from your coding agent. A suite of open source Agent Skills builds, reviews, tests, debugs, deploys, and validates a custom policy end to end, turning a specialized console task into a repeatable engineering workflow.

PDI Technologies built PDI Brew, an agentic platform on AWS where non-technical employees describe a tool in plain English and receive a fully provisioned, multi-tenant web application in seconds. See how a pluggable planner and an AWS Lambda provisioning agent turn plain-English intent into governed, multi-tenant apps backed by Amazon Bedrock.

The Amazon SageMaker Python SDK v3 now exposes generative AI inference recommendations in Amazon SageMaker AI directly in your notebook. Benchmark an endpoint, generate data-driven deployment recommendations, and deploy the recommended configuration without leaving your notebook workflow.

Learn how LendingTree built a production multi-agent mortgage assistant on Amazon Bedrock. Three coordinated agents use LangGraph, the Model Context Protocol, and Amazon Nova models with built-in guardrails to deliver 24/7 personalized mortgage guidance while meeting strict financial-services compliance.

In this post, we'll explore how Mobileye deployed an AI support agentic solution on Amazon Bedrock AgentCore - from the support bottleneck that sparked the idea, through the proof of concept that validated it, to the hybrid architecture that bridges on-premises systems with AWS cloud services. This approach is relevant for enterprises struggling to scale AI Agents while maintaining enterprise grade governance and security standards.

AI agents on Amazon Bedrock AgentCore run in the cloud, but users' tools and files live on their laptops. Learn how to build a secure MCP bridge that lets a cloud-hosted agent call local MCP servers by tunneling signed messages over the existing WebSocket connection through a browser extension and Chrome native messaging, with no open ports or VPN required.

Please post your personal projects, startups, product placements, collaboration needs, blogs etc. Please mention the payment and pricing requirements for products and services. Please do not post link shorteners, link aggregator websites , or auto-subscribe links. -- Any abuse of trust will lead to bans. 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. -- Meta: This is an experiment. If the community doesnt like this, we will cancel it. This is to encourage those in the community to promote their work by not spamming the main threads. submitted by /u/AutoModerator [link] [comments]

For Job Postings please use this template Hiring: [Location], Salary:[], [Remote | Relocation], [Full Time | Contract | Part Time] and [Brief overview, what you're looking for] For Those looking for jobs please use this template Want to be Hired: [Location], Salary Expectation:[], [Remote | Relocation], [Full Time | Contract | Part Time] Resume: [Link to resume] and [Brief overview, what you're looking for] ​ Please remember that this community is geared towards those with experience. submitted by /u/AutoModerator [link] [comments]

Memory & State For AI Agents Building an AI agent can be tricky. Keeping it on track over a six-month deployment is incredibly hard. LLMs...

In this article, you will learn how an agent's approach to managing state — stateless or stateful — shapes both its implementation and the deployment...

It's tempting to treat loop engineering as something invented in a single week in June, but the mechanics behind it are closer to five years old, and knowing the lineage is what separates a real understanding of the idea from just repeating the trend piece.

In this article, you will learn how agentic AI architecture has evolved by mid-2026, including the shift away from orchestrated reasoning loops, the rise of...

In this article, you will learn how to build a complete agentic workflow in Python with LangGraph, from a single model call to a tool-using...

In this article, you will learn what prompt injection and tool misuse are in the context of agentic AI systems, and which defense strategies experts...

In this article, you will learn how to get a small language model running locally on your own machine in under 15 minutes using Ollama....

In this article, you will learn how scikit-ollama bridges the scikit-learn interface with locally running Ollama models to perform zero-shot text classification; no cloud API...

In this article, you will learn how to evaluate LLM applications using the three dominant open-source frameworks — RAGAS, DeepEval, and Promptfoo — and why...

Agent systems change constantly in production.