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 understanding. 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]

(DISCLAIMER: I accidentally deleted the last post on this subreddit my apologies if this is your second time seeing it) Last year I made a post about my steam recommender The last one was great and served its purpose of showing many people new games, But this new version is much more functional! I love making recommendation systems that tell the user WHY they got the recommendation. During a steam sale event, I always find myself trying to look for new video games to play. If I wanted to find a new game I would try to whittle it down by using steam tags, but the steam tag system is very broad "action". could apply to many many games. That got me thinking, what aspects do I like about my favorite games? Well I like Persona 4 because of the city vibes and jazz fusion, Spore because of the unique character creation and whimsical theme. Balatro for its unique deck building synergies. What if I could capture unique tags that identify a game that aren't just "action" and put them into vectors to show the (focus) of a game For example I could break persona 4 into something like Game play Focus vector: Day cycle 20% Dungeon crawling 20% Social sim 20% Tags: Music: jazz fusion Vibe: Small rural town I find that this system makes searching for games more "fun" now I can see why I like balatro. I like it because of the card synergies not so much for its rogue-like nature. I also find that this helps find new underrated games, and beats the trap that Collaborative Filtering algorithms that get into where it "feels" like you get recommended the same things. find your next favorite game! : https://nextsteamgame.com/ pull a PR!: https://github.com/BakedSoups/NextSteamGame ( I actually made some git issues myself for problems I can't fix) if anyone has any criticism I would love to hear it! this is probably my favorite passion project. I made this during final season, Since the database takes around 1 day to build, there were some inevitable rate limiting errors that I go into. So I am sure there are many bugs. if you come across any and are willing to share that would be Amazing. Hope this website helps people find new games! Also I have a advance mode for people that don't mind messing with sliders and weird data terms. submitted by /u/Expensive-Ad8916 [link] [comments]

In this post, we demonstrate how Amazon FinTech teams are using Amazon Bedrock and other AWS services to build a scalable AI application to transform how regulatory inquiries are handled. Each team using this solution creates and maintains its own dedicated knowledge base, populated with that team's specific documents and reference materials.

In this post, we'll show you how our multi-document discovery feature solves this problem. It serves as an automated pre-processing step, analyzing unknown documents, clustering them by type, and generating schemas ready for the IDP Accelerator. You'll learn how the new capability uses visual embeddings for automatic clustering and agents for schema generation. We'll also walk you through running the solution on your own document collections.

In this post, we show you how to set up FLOPs tracking during LLM fine-tuning using the open source Fine-Tuning FLOPs Meter toolkit on Amazon SageMaker AI. You learn how to determine your compliance status with a single configuration flag and generate audit-ready documentation.

TabPFN-3 was released today, the next iteration of the tabular foundation model, originally published in Nature. Quick recap for anyone new to TabPFN: TabPFN predicts on tabular data in a single forward pass - no training, no hyperparameter search, no tuning. Built on TabPFN-2.5 (Nov 2025) and TabPFNv2 (Nature, Jan 2025), which together crossed 3M downloads and 200+ published applications. What's new: Scale: 1M rows on a single H100 (10x larger than 2.5).A reduced KV cache (~8GB per million rows per estimator) and row-chunked inference make this practical on a single GPU Speed: 10x-1000x faster inference than previous versions. 120x on SHAP via KV caching Thinking Mode (API only): test-time compute pushes predictions further via one-time extra fitting at inference. Beats every non-TabPFN method on TabArena by over 200 Elo, including 4-hour-tuned AutoGluon 1.5 extreme. Gap more than doubles to 420 Elo on the larger-data slice. Accuracy: it has a 93% win rate over classical ML on TabArena Many-class: native non-parametric retrieval decoder supporting up to 160 classes Calibrated quantile regression: bar-distribution regression head produces calibrated quantile predictions in a single forward pass Lifts adjacent tasks: time-series, interpretability, and new SOTA on relational benchmarks. 3 deployment paths: API, enterprise licensing, and open-source weights (permissive for research and academic evaluation) You can try it here or read the model report here. Happy to answer questions in the comments. submitted by /u/rsesrsfh [link] [comments]

I have analyzed some decoder transformer models using Lyapunov spectral analysis and found that the ratio of the MLP and attention spectral norms strongly indicates whether a model will eventually collapse to rank-1 or not by the final layers. I found that the spectral ratio is best kept around 0.5–2 for keeping the model stable till the final layers. Paper/Github repo: https://github.com/yousef-rafat/the-1-1-rule submitted by /u/Otaku_7nfy [link] [comments]

Has anyone applying for a Korean visa for ICML been asked for the conference’s Business Registration Number? The ICML website explicitly states that it cannot provide the BRC so I wanted to ask how others handled this submitted by /u/No_Cardiologist7609 [link] [comments]

I'm looking for a cache simulator / benchmark suite suited to the kind of tiered ephemeral cache that LLM providers use — e.g. Anthropic's 4-tier prompt cache, where context sits across several tiers with different residency windows, costs, and eviction rules. I've already tried libCacheSim. It's a solid piece of software for classical caches (LRU, FIFO, ARC, SIEVE, S3-FIFO, W-TinyLFU, Belady oracle, plugin API, trace replay), and I got a plugin + synthetic trace working against it. But it seems fundamentally aimed at single, flat caches: One cache, not a hierarchy of tiers with different costs No notion of partial / multi-tier residency of the same object Misses are uniform-cost — no way to express "miss to L1 vs miss to L3 vs full recompute," which is the whole point in LLM prompt caching Trace model is atomic get/put, not edit streams where cached objects mutate in place No first-class support for token-weighted object sizes So it works as a baseline comparator, but it's not really the right shape for evaluating LLM-cache policies. Does anyone know of cache-testing software specifically targeting LLM-provider-style caches? Something that models multiple tiers with per-tier cost/residency, tokenised objects, and edit-driven workloads would be ideal. Academic code, research prototypes, internal tools that got open-sourced — all welcome. Even partial matches (e.g. KV-cache simulators for inference servers) would be useful pointers. submitted by /u/flatmax [link] [comments]

submitted by /u/Agitated-Ad809 [link] [comments]

A few weeks ago I shared the results of a benchmark here comparing 6 LLMs on subtitle translation, scored with two reference-free QE metrics - MetricX-24 (~13B mT5-XXL) and COMETKiwi (~10.7B XLM-R-XXL) - combined into a TQI index. Posting a follow-up because we did human review afterwards, and the result is worth discussing. The original benchmark put TranslateGemma-12b first in every language pair. The natural question: are those high scores accurate, or are the metrics insensitive in their high-confidence zone? These metrics correlate well with human judgment at the population level (that's what they're trained for), but population-level correlation doesn't tell you whether the segments they call "clean" are actually clean. So we ran the check directly. 21 English subtitle segments from one tutorial video. TranslateGemma's translations into 4 languages (ES, JA, TH, ZH-CN - Korean and Traditional Chinese got dropped). All 84 translations chosen because they passed the dashboard clean-rule (MX < 5 AND CK ≥ 0.70) in all 4 languages simultaneously. Then full MQM annotation by professional linguists - Major/Minor severity, with categories covering accuracy (mistranslation, omission, addition, untranslated), fluency (grammar, punctuation, inconsistency), style, terminology. Results under the dashboard threshold: Auto-flagged: 1/84 Human-flagged: 60/84 any-error, 13/84 Major-only Metric-blindness rate (auto-clean ∩ human-flagged / auto-clean): 59/83 = 71% any-error, 12/83 = 14.5% Major-only All 25 human-found Accuracy-class errors fell in the metric-blind quadrant. Zero overlap with the auto-flagged region (which contained one Style-category Major error). Japanese carries 10 of 15 total mistranslations across the dataset, all metric-blind, despite having the highest mean COMETKiwi (0.863) of the four languages. Caveat: small n, one model, one content set, so the numbers are directional rather than definitive. Original thread: [link] Full benchmark report: in comments. submitted by /u/ritis88 [link] [comments]

Hi, I am a student going into my first year in Ph.D in RL this September. Although each university kinda has their own reading groups, I was wondering if there is active RL Online reading group I can participate. Sadly I couldnt find any info elsewhere. Does anyone have any information regarding Online RL Reading groups? Thank you! submitted by /u/eramyu [link] [comments]

In this post, you will learn how to set up the Exa integration in Strands Agents, understand the two core tools it exposes, and walk through real-world use cases that show how agents use web search to complete multi-step tasks.

Last cycle, one of my research paper was rejected because of formatting issues. I recently heard from someone that there may be a tool or software called something like “aclpubcheck” that can be used to check whether a manuscript follows the required submission format correctly. Does anyone know the exact name of this software or tool? Also, if there is no such reliable tool, what is the best way to make sure that a paper is formatted correctly before submission? Like, how do you usually verify margins, page limits, font size, template compliance, bibliography format, and other formatting requirements before submitting to a conference or journal? submitted by /u/Distinct_Relation129 [link] [comments]

The modern ML (LLM) compiler stack is brutal. TVM is 500K+ lines of C++. PyTorch piles Dynamo, Inductor, and Triton on top of each other. I built a hackable LLM compiler from scratch and am documenting the process. It takes a small model (TinyLlama, Qwen2.5-7B) and lowers it to a sequence of CUDA kernels through six IRs. Currently, on RTX 5090, the emitted FP32 kernels run at geomean 1.11× vs PyTorch eager and 1.20× vs torch.compile, with full-block parity on TinyLlama-128 and Qwen2.5-7B at seq=128. Wins on small reductions / SDPA / kv-projections (up to 4.7×); losses on dense matmul at seq=512. Part 1 took an RMSNorm layer end-to-end and walked the upper half of that pipeline in detail. This second part closes the gap and explains Tile IR, Kernel IR, and associated lowering rules in depth. Full article: A Principled ML Compiler Stack in 5,000 Lines of Python The article focuses on producing a GPU schedule for an operation written in loop-nest form (Loop IR). Example for RMSNorm: python v0 = reciprocal(2048) for a0 in 0..32: # free for a1 in 0..2048: # reduce in2 = load x[0, a0, a1] v1 = multiply(in2, in2) acc0 <- add(acc0, v1) v2 = multiply(acc0, v0) v3 = add(v2, 1e-06) v4 = rsqrt(v3) for a2 in 0..2048: # free in3 = load x[0, a0, a2] in4 = load p_weight[a2] v5 = multiply(in3, v4) v6 = multiply(v5, in4) merged_n0[0, a0, a2] = v6 The stack mimics a sequence of optimization steps a CUDA engineer would perform when optimizing kernels: stage inputs to smem, reduce bank conflicts, increase occupancy, and so on. diff LoopOp │ ▼ [001] tileify — lift outer free Loops to thread axes [002] chunk_matmul_k — chunk the K reduce into K-outer × K-inner (intra-CTA) [003] split_matmul_k — promote the K-outer chunk loop into a grid dimension [004] cooperative_reduce — let multiple threads share one reduce; tree-merge with Combine [005] blockify_launch — pick block extents; partition free axes into BLOCK and THREAD [006] chunk_reduce — chunk non-matmul reduces so their Loads fit in shared memory [007] stage_inputs — hoist hot input slabs into Stage nodes [008] register_tile — replicate the inner tile so each thread owns a register block [009] permute_register_tile — reorder the register strip so bank-conflicting loads land on far columns [010] double_buffer — promote K-outer Stages to BufferedStage (ping-pong) [011] tma_copy — narrow eligible BufferedStages to TmaBufferedStage (sm_90+) [012] split_inner_for_swizzle — split the inner cache axis of a TmaBufferedStage for swizzle [013] async_copy — narrow the rest to AsyncBufferedStage (cp.async, sm_80+) [014] pad_smem — pad shared-memory strides to break bank conflicts [015] pipeline_k_outer — rotate the K-outer loop into prologue/steady-state/epilogue (cp.async + TMA) [016] mark_unroll — annotate small inner loops for #pragma unroll │ ▼ TileOp (fully scheduled) Each stage can be reproduced with a CLI command. For example, the stage_inputs pass stages input buffers into smem if possible and if there is a benefit in doing that (inputs are being read multiple times within CTA). To see it, the following command can be used: bash deplodock compile \ -c "torch.nn.RMSNorm(2048)(torch.randn(1,32,2048))" \ --ir tile -vv \ | awk '/^>>> t:007/,/^<<< t:007/' ```diff t:007_stage_inputs @@ matched at rms_norm (in-place) @@ @@ -2,6 +2,7 @@ v0 = reciprocal(2048) Tile(axes=(a0:256=THREAD, a1:32=BLOCK)): + x_smem = Stage(x, origin=(0, a1, 0), slab=(a2:2048@2)) StridedLoop(a2 = a0; < 2048; += 256): # reduce - in2 = load x[0, a1, a2] + in2 = load x_smem[a2] v1 = multiply(in2, in2) acc0 <- add(acc0, v1) @@ -11,5 +12,5 @@ v4 = rsqrt(v3) StridedLoop(a2 = a0; < 2048; += 256): # free - in3 = load x[0, a1, a2] + in3 = load x_smem[a2] in4 = load p_weight[a2] v5 = multiply(in3, v4) <<< t:007_stage_inputs ``` The final CUDA kernel for the RMSNorm layer: bash deplodock compile \ -c "torch.nn.RMSNorm(2048)(torch.randn(1,32,2048))" \ --target sm_120 --ir cuda c extern "C" __global__ __launch_bounds__(256) void k_rms_norm_reduce( const float* x, const float* p_weight, float* rms_norm) { float v0 = 1.0f / 2048.0f; int a1 = blockIdx.x; int a0 = threadIdx.x; int lane = threadIdx.x & 31; int warp = threadIdx.x >> 5; float acc0 = 0.0f; __shared__ float x_smem[2048]; for (int x_smem_flat = a0; x_smem_flat < 2048; x_smem_flat += 256) { float x_smem_v = x[a1 * 2048 + x_smem_flat]; x_smem[x_smem_flat] = x_smem_v; } __syncthreads(); for (int a2 = a0; a2 < 2048; a2 += 256) { float in2 = x_smem[a2]; float v1 = in2 * in2; acc0 += v1; } float acc0_w = acc0; acc0_w = acc0_w + __shfl_xor_sync(0xffffffff, acc0_w, 16); acc0_w = acc0_w + __shfl_xor_sync(0xffffffff, acc0_w, 8); acc0_w = acc0_w + __shfl_xor_sync(0xffffffff, acc0_w, 4); acc0_w = acc0_w + __shfl_xor_sync(0xffffffff, acc0_w, 2); acc0_w = acc0_w + __shfl_xor_sync(0xffffffff, acc0_w, 1); __shared__ float acc0_smem[8]; if (lane == 0) { acc0_smem[warp] = acc0_w; } __syncthreads(); for (int s = 4; s > 0; s >>= 1) { if (warp < s) { acc0_smem[warp] = acc0_smem[warp] + acc0_smem[warp + s]; } __syncthreads(); } float acc0_b = acc0_smem[0]; float v2 = acc0_b * v0; float v3 = v2 + 1e-06f; float v4 = rsqrtf(v3); for (int a2 = a0; a2 < 2048; a2 += 256) { float in3 = x_smem[a2]; float in4 = p_weight[a2]; float v5 = in3 * v4; float v6 = v5 * in4; rms_norm[a1 * 2048 + a2] = v6; } } submitted by /u/NoVibeCoding [link] [comments]

Today, we're excited to announce the general availability of Claude Platform on AWS. Claude Platform on AWS is a new service that gives customers direct access to Anthropic's native Claude Platform experience through their AWS account, with no separate credentials, contracts, or billing relationships required. AWS is the first cloud provider to offer access to the native Claude Platform experience. In this post, we explore how Claude Platform on AWS works and how you can start using it today.

Hello all, I have a multidimensional time series dataset and corresponding environment videos. I want to pass them to a VLM to perform some tasks. What is the best way to pass the time series data? From the literature review, I see there are two methods: pass time series as text and plot line charts and pass those as images. Neither method performed well on my task. Appreciate any guidance. submitted by /u/zillur-av [link] [comments]

I can see 2.88 million downloads per month for small Qwen3.5 model. I tried using earlier model 0.6B in a deep resarch workflow and it was very difficult to get something done with this model . Firstly they have a very surface level understanding of concepts. Poor Semantic understand means they can get confused about the topic or the task. Json outputs are often broken . Adding a layer of checks on top took much of my time while working with these models. Slow resposne. This one depends on a lot of factors and can actullay be improved , still slow response is a buzz kill most of the time I am very curious how is the community using these models. submitted by /u/adssidhu86 [link] [comments]

In this post, we build a multimodal retrieval system for aerospace manufacturing documents using Amazon Nova Multimodal Embeddings on Amazon Bedrock and Amazon S3 Vectors. We evaluate the system on 26 manufacturing queries and compare generation quality between a text-only pipeline and the multimodal pipeline.

In this post, we dive deep into the architecture and techniques we used to improve Miro’s bug routing, achieving six times fewer team reassignments and five times shorter time-to-resolution powered by Amazon Bedrock.

Amazon Quick helps turn your large enterprise data into fast and accurate AI-powered decisions. In this post, you will learn about five new capabilities of Amazon Quick that accelerate how data professionals deliver trusted AI-powered insights at enterprise scale.

An interactive visualisation of Jensen–Shannon divergence - the symmetric, always-finite cousin of KL. Shape two distributions and watch JSD, its ceiling of one bit, and the per-point contribution respond in real time. https://robotchinwag.com/posts/jensen-shannon-divergence-visualisation/ Feedback welcome. submitted by /u/ancillia [link] [comments]

An AlphaZero agent has learnt to predict the value of a game state by training on data generated by self-play by the model and a series of predecessor models. By construction, this value should reflect the probability of winning against a copy of itself starting from the given state. To be more precise, the value measures the state's average strength against opponent players collected among all the predecessors of the current model. This average depends on the manner in which the training data is sampled from the pool of self-play data (using a rolling window of self-play by the latest x models, putting more emphasis on recent models by geometric weighting, etc.). In each round of self-play, we can think of the agents (a copy for each player) making moves following a strategy, albeit a stochastic one (unless the temperature parameter is zero), defined by the PUCT function for the predicted values and policies, but that this strategy is a little perturbed by the addition of some proportion of Dirichlet noise. The purpose of this perturbation is to give the model an opportunity to find successful actions by chance and not get trapped into some rigid, possibly narrow, pattern of playing. Because of role of noise in deciding which move to make, the formulation above that the value reflects the chances of winning against the model itself is an over-simplification. The data on which the value prediction is based does include "outlier" moves, and - as far as I've understood - this is a heuristic argument for the claim that the model makes its predictions based on experience of playing against a variety of different players. However, due to the moves that differ the most from the "predicted" ones being outliers, such moves also have a correspondingly small impact on the value predictions: it is the agent's own playing style, and the historical development of said style, that governs value predictions. So, if the agent meets a strong opponent, either a human being or an algorithm with a strong track record, why should AlphaZero's value prediction be a reliable measure of the agent's chances of winning against this opponent from the given position? Experience has shown AlphaZero to indeed outperform both human players and other algorithms in a variety of games. I wonder if this success is also to be expected a priori, or is it conceivable that AlphaZero could even fail miserably in some game against a specific algorithm whose moves, though occurring in AlphaZero's training data pool, occur so infrequently that they don't make any significant impact on the predictions? submitted by /u/YamEnvironmental4720 [link] [comments]

I completed my bachelors recently and I plan to applying to a masters program either this cycle or the next. Unfortunately, I did not publish any papers or do any research during my undergrad. Right now I’m in a research internship which is coming to and soon and it’s unlikely that I’ll get to publish a paper. I would like to know if reproducing results from a known paper for validation or extension or a comparative analysis counts as credible research. It’s the only thing I could find to do independently. submitted by /u/UmbraShield [link] [comments]

Scale AI and similar services charge a lot for annotation. MTurk is cheap but the quality is horrible for anything requiring real domain understanding. For small teams that need a few thousand labeled examples to calibrate their evals or fine tune a model, there seems to be no good middle ground. How is everyone handling this? Are you doing it manually or has anyone found something that actually works? submitted by /u/Neil-Sharma [link] [comments]

I generally work around 9–10 hours a day, but not contiguously. I can usually carve out a dedicated chunk of time in the morning, take lab or project meetings in the afternoon, and block out around 6–8 PM for commute, exercise, socializing, and dinner. I also get more work done in the evening, since my focus is often best then. On weekends, I mostly run errands and try out new food spots, but I also make sure to do at least a little bit of work every day. I try to schedule my Slurm jobs so they run when I’m not actively working, so I can collect results when I get back. When I don’t have at least some Slurm jobs going, I feel anxious. I also feel pressure to use coding agents whenever I can. At the same time, I find that these agents can create an illusion of productivity: I end up with more “dead time” where I’m just waiting for the agent to finish thinking. I’m in my 3rd year as a PhD student at a top-5 program for my field in the US, and I’ve been thinking a lot about time management recently. I'm done with classes and not TA'ing this quarter. I mainly target the 3 main ML conferences (though I would love to make every deadline consistently and don’t), plus core NLP venues and journals. submitted by /u/akardashian [link] [comments]

Hello Peeps Salman, Shuguang and Adil here from Katanemo Labs (a DigitalOcean company). Wanted to introduce our latest research on agentic systems called Signals. If you've been building agents, you've probably noticed that there are far too many agent traces/trajectories to review one by one, and using humans or extra LLM calls to inspect all of them gets expensive really fast. The paper proposes a lightweight way to compute structured “signals” from live agent interactions so you can surface the trajectories most worth looking at, without changing the agent’s online behavior. Computing Signals doesn't require a GPU. Signals are grouped into a simple taxonomy across interaction, execution, and environment patterns, including things like misalignment, stagnation, disengagement, failure, looping, and exhaustion. In an annotation study on τ-bench, signal-based sampling reached an 82% informativeness rate versus 54% for random sampling, which translated to a 1.52x efficiency gain per informative trajectory. Paper: arXiv 2604.00356. https://arxiv.org/abs/2604.00356 Project where Signals are already implemented: https://github.com/katanemo/plano Happy to answer questions on the taxonomy, implementation details, or where this breaks down. submitted by /u/AdditionalWeb107 [link] [comments]

Deepmind released a paper on D4RT at the start of this year which crucially enabled a “4D” understanding of the world via structure from motion and generating: 1. Point cloud reconstruction from 2D videos (not static scenes) 2. Camera pose estimation You could pass in a video of a dog walking on a beach and it would estimate the 3d representation of the beach and the dog at any point in time. They did not release the model though. Are there any open source, available implementations of anything similar now? submitted by /u/reddysteady [link] [comments]

Hi everyone! Parax is a library for "Parametric modeling" in JAX, attempting to bridge the approach between pure JAX PyTrees, and more object-orientated modeling approaches (e.g. using Equinox). v0.7 has been released, featuring a more polished API as well as some detailed examples in the documentation. Some of Parax's features: Derived/constrained parameters with metadata Computed PyTrees and callable parameterizations Abstract interfaces for fixed, bounded, and probabilistic PyTrees and parameters Two new examples in the docs that show off these features Bounded optimization (JAXopt) Bayesian sampling (BlackJAX) Perhaps the library is of use to someone, and feel free to leave any feedback! Cheers, Gary submitted by /u/gvcallen [link] [comments]

I know publication count is not everything, and quality, contribution, advisor/lab culture, subfield, and luck all matter a lot. But to make the comparison easier, I’m curious about the publication-count side specifically. For an ML PhD, what would you consider an average publication outcome by graduation? For example, would something like 3–5 first-author papers at A/top-tier venues* be considered roughly average, or would that already be above average in ML? By A*/top-tier, I’m thinking of venues such as NeurIPS, ICML, ICLR, CVPR, ACL, EMNLP, etc., depending on the subfield. Important: Again, I know paper count is a crude metric. I’m just trying to get a rough sense of what people in the field see as average, strong, or unusually strong. submitted by /u/Hope999991 [link] [comments]

In this post, we'll explore how we built a proof-of-concept that converts natural language queries into executable seismic workflows while providing a question-answering capability for Halliburton's Seismic Engine tools and documentation. We'll cover the technical details of the solution, share evaluation results showing workflow acceleration of up to 95%, and discuss key learnings that can help other organizations enhance their complex technical workflows with generative AI.

In this post, you will learn how to secure reserved GPU capacity for short-term workloads using Amazon Elastic Compute Cloud (Amazon EC2) Capacity Blocks for ML and Amazon SageMaker training plans. These solutions can address GPU availability challenges when you need short-term capacity for load testing, model validation, time-bound workshops, or preparing inference capacity ahead of a release.

In this post, you will learn how to implement reinforcement learning with verifiable rewards (RLVR) to introduce verification and transparency into reward signals to improve training performance. This approach works best when outputs can be objectively verified for correctness, such as in mathematical reasoning, code generation, or symbolic manipulation tasks. You will also learn how to layer techniques like Group Relative Policy Optimization (GRPO) and few-shot examples to further improve results. You’ll use the GSM8K dataset (Grade School Math 8K: a collection of grade school math problems) to improve math problem solving accuracy, but the techniques used here can be adapted to a wide variety of other use cases.

Today, we're announcing a preview of Amazon Bedrock AgentCore Payments, a new set of features in Amazon Bedrock AgentCore that enables AI agents to instantly access and pay for what they use. AgentCore Payments was developed in partnership with Coinbase and Stripe.

Tomofun, the Taiwan-headquartered pet-tech startup behind the Furbo Pet Camera, is redefining how pet owners interact with their pets remotely. To reduce costs and maintain accuracy, Tomofun turned to EC2 Inf2 instances powered by AWS Inferentia2, the Amazon purpose-built AI chips. In this post, we walk through the following sections in detail.

Hapag-Lloyd's Digital Customer Experience and Engineering team, distributed between Hamburg and Gdańsk, drives digital innovation by developing and maintaining customer-facing web and mobile products. In this post, we walk you through our generative AI–powered feedback analysis solution built using Amazon Bedrock, Elasticsearch, and open-source frameworks like LangChain and LangGraph

Today, we’re excited to announce that Amazon SageMaker AI MLflow Apps now support MLflow version 3.10, bringing enhanced capabilities for generative AI development and streamlined experiment tracking to your generative AI workflows. Building on the foundations established with Amazon SageMaker AI MLflow Apps, this latest version introduces powerful new features for observability, evaluation, and generative

We’re announcing OS Level Actions for AgentCore Browser. This new capability unblocks these scenarios by exposing direct OS control through the InvokeBrowser API, so agents can interact with content visible on the screen, not only what's accessible through the browser's web layer. By combining full-desktop screenshots with mouse and keyboard control at the OS level, agents can observe native UI, reason about it, and act on it within the same session. This post walks through how OS Level Actions work, what actions are supported, and how to get started.

AI agents in production require secure access to external services. Amazon Bedrock AgentCore Identity, available as a standalone service, secures how your AI agents access external services whether they run on compute platforms like Amazon ECS, Amazon EKS, AWS Lambda, or on-premises. This post implements Authorization Code Grant (3-legged OAuth) on Amazon ECS with secure session binding and scoped tokens.

In this post, you will learn how you can use Amazon Nova Foundation Models in Amazon Bedrock to apply generative AI techniques for both business protection and enhancement. You can identify obvious and disguised attempts at direct contact while gaining valuable insights into customer sentiment and service improvement opportunities.

Business leaders across industries rely on operational dashboards as the shared source of truth that their teams execute against daily. But dashboards are built to answer known questions. When teams need to explore further, ad-hoc, multi-dimensional, or unforeseen questions, they hit a bottleneck. They wait hours or days for BI teams to build new views

Generate recommendations from production traces, validate them with batch evaluation and A/B testing, and ship with confidence. AI agents that perform well at launch don’t stay that way. As models evolve, user behavior shifts, and prompts get reused in new contexts they were never designed for. Agent quality quietly degrades. In most teams, the improvement

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FastAPI has become one of the most popular ways to serve machine learning models because it is lightweight, fast, and easy to use.

A stateless AI agent has no memory of previous calls.

Zero-shot text classification is a way to label text without first training a classifier on your own task-specific dataset.

Calling a large language model API at scale is expensive and slow.

You've probably written a decorator or two in your Python career.

The open-weights model ecosystem shifted recently with the release of the

Language models (LMs), at their core, are text-in and text-out systems.

Most