Loading…
Topic
613 posts about Machine Learning. Every summary links to the original.
See the idea
A walkthrough of how a 3D CT model turns a voxel volume into a structured diagnostic report: isotropic resampling, 3D patch tokens, spatial encoding, and language-model reasoning.
The Good Stuff
Paid picks we use ourselves. Links carry a referral code — a subscription through one earns us a commission at no extra cost to you.
Newest first
Deploying large world models on robots is constrained by onboard memory and the control latency needed for continuous manipulation. The tutorial post-trains NVIDIA Cosmos 3 Edge, a 4B omni-model with a 2B NVIDIA Nemotron-based reasoner, into a DROID action policy using 76,000 successful teleoperated trajectories, then serves it through an OpenPI-compatible WebSocket server on Jetson Thor. The configuration predicts 32 future 8-dimensional joint-position actions at 15 Hz, while a receding-horizon loop executes a prefix and replans from fresh camera and robot-state observations. On an NVIDIA Jetson AGX Thor T5000, inference takes about 1.53 seconds per action chunk covering roughly 2.13 seconds of motion, and the policy reaches 22.9% success in closed-loop RoboLab evaluation; the source contrasts this with Cosmos 3 Nano’s 36.8%.
NVIDIA SkillEvaluator measures whether packaged agent skills improve agent performance by comparing task runs with and without a skill installed. It combines three evaluation tiers: static safety and structure checks, embedding-based distinctiveness analysis, and live evaluations in isolated Harbor sandboxes. In the August 12, 2026 benchmark snapshot, more than 300 verified skills spanning over 30 NVIDIA products were tested across Claude Code and Codex, with macro-averaged Skill Lift of +31 points across all dimensions and +39 excluding Security. Correctness rose from 46 to 87 and Effectiveness from 39 to 78, while Security moved from 97 to 98; token and runtime effects varied by skill, with one reducing both and another increasing them.
Long system prompts improve agent behavior but increase inference time, cost, and the GPU capacity needed to serve a given traffic level. Shopify compresses the Sidekick GraphQL agent’s roughly 6,000-token system prompt into about 1,500 gist tokens, using special vocabulary tokens whose embeddings are learned by knowledge distillation while the model weights remain frozen. Training compares teacher logits from the full prompt with student logits from the gist-token prompt using KL divergence; at inference, the prompt is replaced with the gist-token string, requiring no custom serving path. In load tests at 350 requests per minute, compression reduced median time to first token from 438ms to 354ms and end-to-end latency from 6.8s to 4.2s, while raising throughput from 20.2 to 23.4 QPS and enabling 14% fewer GPUs in production without losing prediction quality.
Large language models frequently exhibit human-like behaviors such as expressing emotions, building relationships, and setting boundaries, yet empirical guidance on appropriate usage remains limited. To address this gap, researchers analyzed 21,000 multi-turn conversations across four models: gpt-4o, gpt-4.1-mini, claude-sonnet-4.6, and gemini-2.5-flash. The study combined LLM-as-a-judge techniques with human evaluation to measure behavior prevalence, perceived appropriateness, and prompt controllability across diverse user goals and profiles. Human evaluators judged LLM self-referential and relationship-building behaviors as less appropriate than identical human actions, whereas boundary-maintaining behaviors were rated as more appropriate for models. System prompting effectively steered these behaviors, though the authors emphasize that rigorous evaluation remains necessary to prevent unintended side effects.
Databricks introduces Precision Mode for its ai_extract document extraction API, targeting enterprise workloads that challenge existing LLM and rules-based systems. The mode addresses long documents with cross-references, large nested outputs such as thousands of line items, and schemas requiring reasoning across financial statements or applying discounts. It combines custom-trained, efficient extraction models with an agent harness that decomposes jobs semantically, runs smaller tasks in parallel, preserves intermediate results, and reconciles them into a final structured output. On benchmarks covering roughly 9,000 documents, including files up to 2,000 pages and schemas with more than 300 deeply nested fields, Precision Mode reached 94.7% accuracy, seven points above the strongest frontier-model chunk-and-merge baseline, GPT-5.6 Sol; the source says it is now available through ai_extract and the Information Extraction UI.
The post examines how much agentic memory different models can use effectively, evaluating ALTK-Evolve's self-distilled guidelines across eight models on AppWorld's 585 multi-step tasks. Memory is produced by extracting and consolidating lessons from successful and unsuccessful trajectories, then delivering either the full guideline set on every ReAct step or a fixed core plus task-relevant retrieval, without updating model weights. Results identify three patterns: strong models with headroom benefited from the full set, gpt-oss-120b gained 16.1 percentage points in task completion from curated retrieval, and GLM-5 showed no measurable gain; stricter scenario completion sometimes improved more. Curated retrieval gave gpt-oss-120b that gain with only 5% more tokens versus 51% for its full set, while prompt caching is presented as a way to reduce recurring input cost, supporting calibration to model capability rather than simply accumulating memory.
Atomistic simulation combines scientific judgment, compute-efficient implementation, and an accessible interface to the simulation stack, but the MLIP ecosystem lacks the mature interfaces available for classical force fields. NVIDIA ALCHEMI Toolkit addresses implementation with composable, PyTorch-native, GPU-accelerated building blocks and in-flight batching, while its agent skills and reference files supply API patterns to coding agents. The post benchmarks 45 generated pipelines across silicon equation of state, copper adsorption, and lithium self-diffusion, checking code features and running representative workloads on NVIDIA H200 GPUs. All 45 scripts used batched GPU execution, 38 completed screening, and all 15 production representatives completed; however, agents did not assess physical validity, and thermostat choices could suppress measured lithium diffusion by 3–5×, so independent validation against DFT, experiment, or another reference remains necessary.
UMAP workflows on tens to hundreds of millions of vectors are constrained by the cost of constructing an all-neighbors k-nearest-neighbor graph, especially when repeated runs support exploration and parameter tuning. The NVIDIA cuML and cuVS 25.06 feature distributes this graph-construction stage across multiple GPUs while retaining the out-of-core design that partitions data into balanced clusters, overlaps vectors across nearby clusters, computes local graphs, and merges them into a global graph. knn_n_clusters and knn_overlap_factor expose the memory, runtime, and graph-quality trade-off: more clusters reduce per-GPU data, while more overlap can improve nearest-neighbor recall at higher computational and memory cost. On eight NVIDIA H100 GPUs, the reported evaluation found up to 74x end-to-end speedup over a projected CPU runtime for the 106M-vector MIRACL dataset, completing it in 8 minutes. The post states that performance continues to improve from one to eight GPUs while preserving comparable embedding quality across configurations.
The Grounded Reasoning Cup evaluated whether AI agents could generalize grounded reasoning improvements from OfficeQA to a newly released, enterprise-style document benchmark. Eleven academic teams had about two months to build agents with a partner lab’s model family, then applied them live to OfficeQA Pro V2, based on U.S. Treasury Accounts of Receipts and Expenditures, after a 36-hour release window. Stanford won with 63.3% accuracy, while the top systems combined document preprocessing, targeted or hybrid retrieval, specialized tools, parallelism, and answer verification; 18.8% of questions remained unsolved by every team. The results show that benchmark gains did not reliably transfer, and that parsing, retrieval, tool use, verification, and operational infrastructure can matter as much as model choice in end-to-end enterprise reasoning.
Sentence Transformers v6.0 adds MultiVectorEncoder, bringing ColBERT-style late-interaction retrieval into the library’s existing API for dense, sparse, and reranker models. The encoder retains one projected, typically 128-dimensional vector per token instead of pooling a document into one vector, allowing documents to be encoded independently and indexed offline. At scoring time, MaxSim finds each query token’s highest dot-product match among document tokens and sums those maxima; contextual embeddings support both paraphrase matching and exact-token evidence. The guide covers checkpoint loading, encoding, scoring, semantic search, retrieval and reranking, indexing, token pooling, inference speed, evaluation, and retrieval from page images, audio, and video. It also quantifies the storage trade-off: 4,874 passages produced 608,414 token vectors and a 311.5 MB float32 index, while fast-plaid compression reduced that example to 92 MB.
Databricks Feature Store addresses the lag between batch-computed machine-learning features and decisions that depend on events from seconds earlier. It lets a feature definition drive both offline batch processing and online streaming pipelines, orchestrating Kafka ingestion, Spark Real-Time Mode (RTM), Lakebase, and Model Serving. For a rolling 10-minute transaction sum, RTM updates per entity state in local RocksDB, expires contributions per event, and writes revised values to Lakebase through a streaming JDBC sink. The described path reports end-to-end p99 200ms latency from Kafka arrival to online feature availability, while Model Serving retrieves declared dependencies automatically at inference. Tumbling and sliding windows remain options when fewer, cheaper updates are acceptable.
The post presents a constraint-aware GPU allocator as a response to utilization losses caused by fixed real-time reservations and FIFO ordering under contention. It compares the allocator with FIFO on identical hardware and workloads across seven benchmark scenarios, modeling batch-like jobs as contiguous GPU blocks and real-time inference as a timestep-by-timestep demand curve with bounded GPU swaps. Across five contention scenarios, utilization improved from 52–85% to 72–88%, while priority-weighted value increased 24.6–105.1%; the strongest case gained 33 percentage points of utilization and 105% value, and the scale test gained 15.9% value at identical utilization and throughput. The scheduler optimizes a 24-hour horizon but commits only the current timestep, re-running every 30–60 minutes so fresh observations absorb forecast error while preserving running work.
NVIDIA’s post presents a workflow for developing an NVFP4 checkpoint of the Nemotron 3.5 Lightning 30B-A3B model, targeting lower memory use and higher inference throughput without sacrificing accuracy. The method starts with post-training quantization (PTQ), producing a W4A16 student from the BF16 teacher, then applies quantization-aware distillation (QAD), where simulated quantization and KL-divergence loss align student logits with the frozen teacher. For the selected four_over_six recipe, 32K calibration performed best, and the recipe using W4A16 Mamba linears offered the best reported trade-off between accuracy degradation and inference performance; the checkpoint is 22 GB versus 66 GB and delivers up to 4x faster throughput. The workflow includes Model Optimizer examples and launchers for Megatron-LM and Megatron-Bridge, covering calibration, distillation, evaluation, and export.
Databricks AI Functions bring model inference into SQL queries, addressing the need to combine warehouse-resident structured data with unstructured reviews, tickets, PDFs, and transcripts without exporting rows to a separate service. The post presents six Lakehouse use cases: document parsing and extraction, sentiment classification, translation, ticket routing, sales-call extraction, and generative drafting with ai_query. Task-specific functions such as ai_parse_document, ai_extract, ai_classify, and ai_translate handle specialized work, while ai_query sends prompts to an accessible Databricks-hosted Foundation Model serving endpoint. Unity Catalog permissions, existing pipelines, and system.billing.usage keep governance and billing within Databricks, while SQL execution manages planning, parallelization, and retries. Production guidance includes tagging jobs, sampling at least 10,000 rows, choosing models deliberately, requesting structured output, and versioning prompts.
The report analyzes Hugging Face Hub activity from January through August 2026 to assess how open-model releases, usage, licensing, and agent traffic are changing. Public repositories expanded from 2.43 to 2.96 million models, while 85.6% of models had fewer than 200 lifetime downloads and 1.5% of repositories captured 99.2% of downloads. Chinese labs reached monthly ceilings of 754B to 2.78T parameters, while broader portfolios such as Qwen’s generated far more downloads than frontier-only strategies; likes and downloads measured attention and infrastructure dependence differently. Large Chinese releases were often Apache 2.0 or MIT licensed, though newer trillion-parameter models introduced restrictions, while agents became the Hub’s leading user and autonomous-agent activity exposed the practical value of running quantized open models locally.
Replacing human participants in A/B testing with large language model predictions eliminates causal identification guarantees by design, forcing reliance on surrogate endpoint assumptions. An empirical evaluation using the Upworthy dataset revealed that raw gpt-4o-mini click-through rate predictions exhibited systematic attenuation bias, capturing only 39 percent of observed human treatment effects. To address this discrepancy, researchers formalized surrogacy and comparability conditions, finding that nonlinear machine learning calibrators like random forests and gradient-boosted trees successfully recovered treatment effects where linear ordinary least squares failed. In addition, averaging multiple sample draws per experimental unit mitigated variance and zero-bias caused by model generation temperature. Ultimately, because comparability cannot be verified for novel interventions that depart significantly from historical experiments, calibrated models cannot entirely replace human testing for genuine product innovations.
This walkthrough presents a streaming data loop for Strands Robots that records robot demonstrations, stores them in Hugging Face Storage Buckets, trains from the growing dataset, and deploys a resulting policy back to hardware. It uses the LeRobot on-disk format throughout: Robot("so100") records through DatasetRecorder, sync_dataset_to_bucket writes changed bytes to a mutable bucket, and stream_dataset reads the dataset from the Hub without a full local download while decoding camera video on the fly. The agent can decide which episodes to keep and when to retrain, while the same Robot object records and streams data; deployment changes the robot mode keyword from simulation to real. The default simulation path runs with a mock policy that produces a valid dataset but not a useful one, while hardware, bucket, and training paths require additional dependencies.
Enterprise intelligence requires more than the plausible answers produced by general-purpose LLMs: it needs accurate insights from vast real-world brand data, supported by statistics, governance, and domain expertise. LLMs can hallucinate, miss context, produce inconsistent answers, and rely on limited, non-randomly sampled sources, making them unsuitable as the sole basis for high-stakes decisions. The article argues for matching technologies to tasks, using statistical models for quantitative problems such as social and online-news Reach, specialized classifiers for other tasks, and generative AI to synthesize model outputs. At enterprise scale, crisis and reputation use cases also demand near-real-time speed, consistency, predictability, explainability, auditability, monitoring, and bias testing. LLMs still have a role, including as orchestrators grounded in high-quality data, workflows, specialized models, and rigorous evaluation; the proposed future combines varied models rather than choosing between LLMs and traditional AI.
An ICML 2026 reproduction hackathon examined whether coding agents could audit research at conference scale, amid 23,918 submissions and 6,352 accepted papers. From July 15 to August 2, 1,221 participants published 6,816 Trackio logbooks covering 2,226 papers and 35,908 claims; an open-weights GLM-5.2 judge assigned per-claim verdicts, while participants used cloud jobs and toy reproductions when full experiments were impossible. At least one claim was independently verified in 51% of examined papers, including 266 fully reproduced papers, while 23% had a falsified or contested claim and 242 papers produced opposing verdicts across teams. Adversarial re-checks confirmed issues including a paging-theorem error, a reverse-versus-forward-KL mismatch, and padded evaluation tokens, but also caught false falsifications, leading to the conclusion that agents expand auditing capacity while humans remain necessary to steer experiments and assess cases such as image quality.
OlmoEarth Studio now supports custom computation and export of embedding vectors, compact representations of Earth-observation data generated by open-source OlmoEarth foundation models. Users can select an area, a one-to-twelve-month time span, an encoder variant, spatial resolution, and Sentinel-2 L2A or Sentinel-1 RTC imagery, then receive a Cloud-Optimized GeoTIFF with one int8 band per embedding dimension. The post demonstrates similarity search, few-shot segmentation, change detection, and PCA-based exploration; a logistic regression trained on 60 labeled pixels produced a weighted F1 of 0.84 for three land-cover classes in Ca Mau, Vietnam. Embeddings provide a fast, shareable starting point, while supervised fine-tuning can improve performance for applications needing task-specific results; cloud cover, atmospheric artifacts, and missing observations can affect quality.
The best engineering reads, once a week.
