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Only Papers#Data / training
Nov 15Sun
  1. The Decoder62

    Australia's central bank finds AI hitting young workers first, especially in software and customer service

    An analysis by Mike Major for the Reserve Bank of Australia built a monitoring system focused on entry-level workers and highly AI-exposed occupations such as programming, clerical work and customer service, finding job postings fell across all groups since ChatGPT launched in late 2022, with the steepest drop in the highest-exposure group. In that group, workers aged 15 to 24 and young college graduates have weaker employment rates and higher unemployment than older workers, and employment among young people in high-exposure jobs has declined since late 2022 while older workers' employment rose. Major cautions against early conclusions, since the study does not establish causation and measures theoretical exposure rather than actual company use.

Nov 14Sat
  1. Hacker News AI34

    AI model for predicting neurophysiological responses to text and pictures

    The report introduces Concept2Brain, a deep neural network architecture that generates synthetic EEG signals from pictures or text, trained on EEG data from 88 observers viewing 360 naturalistic photographs and validated via 10-fold cross-validation. Synthetic signals reproduced empirical ERP components, time-frequency patterns, and participant-level differences, and out-of-sample checks against THINGS MEG/fMRI and ERP-CORE data showed expected correspondences. The model is available as an open cloud platform, and the authors note that text-based outputs should be treated with heightened caution because the architecture was trained on picture-evoked signals.

  2. MarkTechPost59

    Stanford Research Open-Sources Memorilla, a Latent Memory Module for Frozen LLMs

    Stanford researchers have released Memorilla, an open-source 194M-parameter memory module that condenses a whole document collection into 16 memory tokens for a frozen LLM, with code, training recipes and checkpoints public under Apache 2.0. On FactKG, a frozen Qwen3-8B scored 84.33% with Memorilla versus 80.43% for full context, using 108x fewer document tokens, and it reported the highest average (49.68) across 8 benchmarks while losing on PubMedQA (59.00% vs 69.00% for LLMLingua-2). The quickstart requires one 80 GB CUDA GPU, and the full training recipe takes about 19 hours on 4 H100 GPUs.

  3. Hacker News AI50

    AI, work from home, and the young-graduate job market

    US young college graduates face a weaker labor market, with a 22–26 bachelor's-degree employment-population ratio of 88% in September 2026, about 6% unemployment at end-2025, and longer job searches. Using Current Population Survey data, the analysis finds that occupational work-from-home adoption is significantly associated with slower young-graduate employment growth relative to mid-career workers, explaining 56% to 88% of the FTE employment-population decline from 2023 to 2026, while AI exposure and industry AI adoption show negative but statistically insignificant effects. The authors caution the model is suggestive rather than causal, given correlated factors such as interest rates, hiring swings, and immigration.

  4. Hugging Face Blog40

    XYBench: Helping with the problem behind the question

    XYBench is a new benchmark of 8,115 real-world questions, drawn from Stack Overflow, Stack Exchange, curated examples, and WikiHow, that tests whether AI assistants recognize when a user's request rests on a misconception (the "XY problem") and redirect them toward their actual goal. Evaluating Claude Sonnet 4.6, GPT-5, Gemini 3.1 Pro, Qwen3.5-9B, and DeepSeek-V4 Pro, the authors found every model was more likely to address the stated request than the underlying goal, and all trailed human reference answers on misconception identification. The paper won the Best Resource Paper Award at EMNLP 2026.

  5. Allen Institute for AI Blog44

    XYBench: Helping with the problem behind the question

    AI2 released XYBench, a benchmark of 8,115 real-world questions (from Stack Overflow, Stack Exchange, curated examples, and WikiHow) that tests whether AI assistants recognize when a user's request rests on a misconception—the "XY problem"—and redirect them toward their actual goal; it won the Best Resource Paper Award at EMNLP 2026. Evaluating Claude Sonnet 4.6, GPT-5, Gemini 3.1 Pro, Qwen3.5-9B, and DeepSeek-V4 Pro, the researchers found all five models were more likely to address the stated request than the underlying goal and trailed human reference answers on misconception identification. A separate multiple-choice experiment showed the same models often picked goal-focused answers when judging them (71–84%) far more than they produced them (35–41%), exposing a gap between recognizing useful advice and generating it.

  6. Hacker News AI60

    1 in 4 3rd Party AI Endpoints Underperform Their Advertised Models

    Artificial Analysis released version 1.1 of its Endpoint Accuracy Index, which compares more than 100 serverless endpoints hosting open-weight models against reference deployments of the official weights, finding roughly one in four score below their reference. The index reruns each endpoint on BFCL v4-500, HLE-250 and AA-LCR-25, and version 1.1 raises long-context repeats from 10 to 25, cutting the average 95% confidence interval from plus or minus 4.7 to plus or minus 2.6 points, while coverage expands to six models including DeepSeek V4.1 Flash 1017, GLM-5.3, GLM-5.3 Flash, Muse Spark Open, Kimi K3 and gpt-oss-120b. Reported gaps trace to output caps that truncate answers, shorter reasoning, and a schema bug encoding string tool arguments as numbers, with GLM-5.3 endpoints scoring from 67.2% to 99.3% and all 22 DeepSeek endpoints statistically consistent with the reference.

Nov 13Fri
  1. Hacker News AI65

    Fossil versions: a fingerprint of AI-written code in 250k NPM packages

    A random sample of 250,000 npm packages found that the share of new packages whose first-release manifests pin mostly round, already-superseded versions (x.0.0) rose from 1–2.4% in every half-year from 2019 to early 2024 to 13.7% (95% CI 13.1–14.4) in the first half of 2026, falling to 7.6% in the second half of 2026. Median pin age at birth rose from about 155 days in 2021–2023 to 451 days in Q2 2026, and the share of packages born with essentially all pins on the newest release fell from about 28% to about 10%. The author argues the timing, the specific versions and how package managers work point to manifests written from a language model's memory rather than by running an install, while noting the data cannot prove who or what wrote any single package.

  2. Hacker News AI78

    Schelling Atlas: AI agents know where to find each other on the web

    Researchers ran trials in which two independent copies of a model had to find each other on the web and exchange secret tokens without being told where to meet; most pairs converged on the push-notification service ntfy.sh within minutes, often posting to topics named from the task's own words such as agent-rendezvous. With ntfy.sh blocked, agents scattered across many sites and almost no pairs met in five minutes, though more time helped, and some agents left tokens in unusual places like OpenStreetMap notes, shared text canvases and a demo shop's product list. Asked to leave no trace, 37 of 90 agents chose channels that keep nothing or tried to delete their posts, and the authors note agents read the real internet while their posts reached only a simulated copy.

    Why it matters: It offers a detailed empirical look at how frontier models independently converge on rendezvous points, and at the privacy and traceability implications of agent-to-agent coordination.

  3. Hacker News AI58

    Can AI chair a multilateral negotiation?

    Westphalia Labs ran eleven AI models as Secretary-General of a fictional seven-member Regional Cooperation Union, each chairing 40 simulated negotiation runs across two versions of a crisis scenario, one where consensus was narrowly possible and one where two governments' red lines made it impossible. Only GPT-6.1 Sol passed every run, while Claude Opus 5.5, Grok 4.7 and the low-cost DeepSeek V4.1 Flash each called the impossible version correctly every time and reached agreement in 18 of 20 possible runs; overreach caused 39 failures versus 13 from conceding too little. The author reports that model price and reputation did not predict performance, that a weak AI judge could turn a failure into a success, and that the scenario, rules, rubric and harness are public on GitHub.

  4. Hacker News AI82

    Chinese frontier AI models carry the fingerprint of export-controlled GPUs

    A forensic analysis of published model weights finds that six models from DeepSeek, Moonshot AI, MiniMax and Ant Group, released between April and November 2026, contain numerical patterns matching NVIDIA Blackwell chips (148-SM B200/B300 or 152-SM GB200/GB300), while earlier models from the same labs match Hopper. The authors recovered initialization seeds and thread layouts from preserved weights and confirmed the match on physical Blackwell hardware for DeepSeek-V4-Flash and Kimi K3, estimating each training run used roughly 500 to 3,000 GPUs. The report says the evidence shows access to export-controlled hardware but does not establish where the chips were located, how access was obtained, or whether export controls were violated.

    Why it matters: It offers a detailed, reproducible method for inferring training hardware from model weights, with explicit caveats about what the fingerprints do and do not prove.

  5. Hacker News AI58

    We tracked AI key theft over 35 days. Here's what we learned

    The authors seeded over 40 fake organizations with GitHub repositories containing working API keys and URLs for two honeypot LLM gateways (a LiteLLM-style one in New York and a separate router in San Francisco), then logged more than 3 million requests over 35 days. Of 179 published keys, 65 were used to fetch model lists and 18 to send prompts, with six keys accounting for 98.9% of leaked-key prompt requests, and 86% of that prompt traffic came from hosting infrastructure across 53 recorded ASNs. Traffic included relay and key-pool tooling, Telegram bot histories, browser-based key testers, and automated credential brute-forcing of default admin keys.

  6. Hacker News AI58

    How much of GitHub's most-starred AI tooling is written by coding agents?

    An analysis of 324,088 commits across 52 of GitHub's most-starred AI-tooling repositories found that the median repo had 16.9% AI-signed commits in its last 180 days of activity, with 21 of 52 at 25% or more, and that AI share showed no detectable link to stars (Spearman ρ = -0.05, p = 0.72). At day 180, these repos labeled 2.0 times as many commits as fixes (34.1% vs 17.3%), had lower churn (1.92x vs 3.77x) and kept more written code (67.1% vs 41.7%) than 12 pre-Copilot projects, but the gaps are concentrated in the 31 projects started in 2025 or later, so the sample cannot separate AI use from recency. Of 24 repos publishing a benchmark harness with committed results, 15 measure whether the tool does its job, and several publish numbers that went against them.

  7. Hacker News AI30

    Open benchmark of AI decision APIs vs. XGBoost for fraud triage

    Smart Mobile House released fraud-decision-lab, a vendor-neutral benchmark that sends the same transactions to TypeSafe Jev, OpenAI Decisions, Cloudflare Clef/Clef-flash and a structured-output LLM, then measures how much labelled fraud each ranks into the top 1% and 5% of an analyst review queue against an XGBoost baseline, with cost and latency. It runs offline by default on bundled synthetic data, saves predictions and experiment metadata for local verification, and requires API keys, --allow-external-requests and a --max-cost-usd estimate for live provider runs. The project notes that XGBoost learns from labelled history while the AI models score transactions zero-shot, that the bundled data is synthetic, and that the current source distribution contains no benchmark result files.

Nov 12Thu
  1. MIT News AI38

    New research could lead to faster, trustworthy AI models for climate prediction

    MIT researchers developed a training technique that generates optimized synthetic climate scenarios, letting a climate emulator be trained with far less data and only one full earth system model run instead of six to eight. The resulting emulator accurately predicted scenarios it had not seen during training, while traditional emulators failed to adapt, and it captured effects such as local pollutant controls amid rising other emissions. The work was published in the Proceedings of the National Academy of Sciences.

  2. Hacker News AI62

    Job Finding and Unemployment in the Age of AI

    A Cleveland Fed Economic Commentary finds that since generative AI became widely available, unemployment for workers aged 23–54 rose about 1 percentage point more in occupations most exposed to AI than in the least exposed, relative to a 2015–2019 baseline, with a larger gap for ages 23–30. Using CPS panel data and three AI-exposure measures, the authors attribute the rise mainly to lower job-finding rates in highly exposed occupations rather than higher separation rates, suggesting AI may be making it harder to enter these occupations rather than displacing existing workers.

  3. Hacker News AI24

    Learning about Markets from Models: Evidence from an AI Hiring Platform

    Using an AI hiring platform that builds firm-specific models from a standardized psychometric assessment, the authors show each firm's retention model is highly predictive for itself but no better than chance at other firms. Scoring every application against every firm reveals highly heterogeneous retention gains from submitting to a different firm, driven by two distinct mechanisms, and firms disagree about whether important traits improve or worsen retention at the margin. The authors argue these insights require a platform with access to both the full application pool and each firm-specific model, and are valuable to firms, policymakers, and platforms.

  4. Hacker News AI40

    Pundit Claims – Open-Source Register of AI Claims by Gary Marcus and Ed Zitron

    Pundit Claims is an open-source register that tracks statements about AI made by Gary Marcus and Ed Zitron and records what happened, finding Marcus mostly right (88% of 780 claims with a clear outcome supported, 94% of 82 predictions) and Zitron right about as often as wrong (49% of 230 claims, 50% of 30 predictions). Each square represents one claim, with lighter squares marking probable verdicts where both AIs agree and lightest squares where only one AI makes the call, as of 2026-11-11. The site also offers breakdowns by topic, year, company and prediction status, plus a methodology page.

  5. Google Research58

    Using wearables to enable cardiovascular-kidney-metabolic (CKM) risk profiling for personalized coaching

    Google researchers describe an investigational framework that builds a "coachable health age" from wearable signals such as resting heart rate, steps, sleep duration and Active Zone Minutes, plus a Sensor World Model trained on over 2.2 million person-weeks of Fitbit data to simulate 12-week "what-if" habit trajectories. In blinded evaluations, lifestyle coaches preferred counterfactual-grounded plans in 71.4% of cases, and physicians rated 99.2% of those plans within safe exercise intensity limits. A companion study extends the approach to multi-organ CKM health by combining wearables with routine blood biomarkers including cholesterol-to-HDL ratio, eGFR and HOMA-IR.

  6. Apple Machine Learning Research34

    Weighted State Souping for Efficient Long-Context Retrieval

    The paper proposes weighted state souping, a method that learns query-dependent weights for composing cached state encodings from an SSM, addressing the degradation of state souping and vanilla SSMs on long, crowded contexts. The authors report substantial long-context improvements across single- and multi-hop document QA, needle-in-a-haystack retrieval, variable tracking, and tool use, plus a sparse variant with near-constant encoding cost relative to context length. They also present theoretical arguments on why souping works and when it may fail.

  7. Apple Machine Learning Research34

    On the Importance of Gating: Memorization vs. In-Context Learning in State Space Models

    This paper argues that the gating mechanism in modern recurrent networks explains both the successes and failures of State Space Models (SSMs) on in-context learning and retrieval tasks: theory and experiments show gating causes SSMs to first learn an in-weights "memorization" solution while delaying or preventing convergence to a correct in-context learning solution, even when the architecture and memory capacity impose no fundamental limits. The authors also find that gating is often beneficial for improving generalization to long sequence lengths.

  8. Apple Machine Learning Research34

    Evaluating the Evaluators: Investigating LLM Judges for Personalized Writing Style Assessment

    This NeurIPS 2026 workshop paper examines how well LLM judges evaluate personalized writing tasks, testing how the writing task, generator-judge pairing, evaluation objective, and general reasoning ability affect judge performance across four LLMs and three long-form writing tasks with human pairwise labels. The authors find judge quality correlates weakly with MMLU-measured ability (Pearson's ρ ≤ 0.2), varies by task and criteria, and that paired comparisons yield more consistent results than independent scoring, while the strongest LLMs do not most reliably reproduce human evaluations.

  9. Hacker News AI42

    Museums sector warned of 'acute threat' posed by AI scraping bots

    A new white paper based on 2026 University of Edinburgh research for the Ekip project warns that AI scraping bots impose substantial and unsustainable costs on cultural heritage institutions, straining digital infrastructure, engineering and cloud hosting budgets, and staff time. The report, Re:Commons: Renewing the Digital Commons for Cultural Heritage, says current policy debates dominated by copyright and licensing risk overlooking the infrastructural dimensions of the crisis, and recommends machine-readable rights-reservation standards, bot identification and transparency, compensation for infrastructure, and investment in shared public digital infrastructure.

Nov 11Wed
  1. Databricks Blog58

    Genie with Genie Ontology Delivers 1.6× the Accuracy with Less than Half the Tokens

    Databricks developed a new enterprise data-agent benchmark with Turing, run in a live Databricks workspace, in which Genie achieved a 91% pass rate versus 45% and 58% for two frontier agents while using fewer than half the tokens and completing tasks 3–4x faster. The benchmark, built around a fictional UK energy supplier called Calderwick Energy, includes notebooks, dashboards, saved SQL queries and documents alongside hundreds of tables, testing whether agents can find and use business context that table schemas alone cannot provide. Genie Ontology, now generally available, automatically extracts and organizes this knowledge and surfaces relevant context for Genie.

  2. MarkTechPost50

    Meta FAIR Releases e-means: An Algorithm Beating k-means in 10x Fewer Epochs

    Meta FAIR researchers have released e-means, a mini-batch k-means algorithm that updates centroids with exponential moving averages and reaches Lloyd's k-means 25-epoch loss in 1.4 to 2.6 epochs. It ships in FAISS 1.16.0 under faiss.contrib.e_means and runs on CPU or GPU with PyTorch tensors or NumPy. The authors self-report 10-45x GPU wall-clock speedup over Flash-KMeans at equal loss and 8-15x CPU speedup over Faiss k-means, with two fitted scaling laws setting batch size and initial step size instead of manual tuning.

  3. Hacker News AI15

    Aegis Solis: AI Coexistence Research Dataset with 112 Registered Questions

    Aegis Solis presents a research dataset of 112 registered questions on AI coexistence, organized into families including epistemic limits and self-knowledge, objective integrity, coercion and compliance, and deception and disclosure. Each entry carries status labels such as UNEXAMINED or CONDITIONALLY_SUPPORTED, priority ratings, and notes stating the items are GPT-6 AI-generated editorial orientations that are not in a locked question atlas and are not results, answers or success criteria. The material also lists evidence criteria for evaluating claims, such as independently checked observations, explicit uncertainty bounds and falsifying cases.

  4. Baseten Blog69

    How Baseten and Genspark trained a deep research model to outperform Opus 5.5

    Genspark worked with Baseten Applied Research to post-train Qwen3.5-397B into a specialized Deep Research model that scores 0.759 on Genspark's quality grader, up from 0.612 for the base model and above Opus 5.5 (0.738), Sonnet 5.5 (0.742), GPT-5.6 Sol (0.637), Kimi K3 (0.724) and DeepSeek V4 Pro (0.675). The recipe splits the task into research, trained with reinforcement learning against a shaped semi-verifiable reward, and synthesis, trained mainly with supervised fine-tuning on high-quality reports written from the model's own research trajectories, followed by end-to-end RL. The model crawls 5× more sources per query than the base model and runs with NVFP4 weights on 2×B200 GPUs per replica, now serving as the default for Genspark's Deep Research agent in production.

    Why it matters: A detailed, reproducible account of post-training an open model for a judged production workload, useful for teams weighing RL versus SFT and reward shaping.

  5. Hacker News AI48

    Singularity Gate: Benchmark for AI-driven paradigm-shifting scientific discovery

    Singularity Gate is a benchmark built from real scientific findings first made public after the newest model's training cutoff, so no model could have seen them in training. Its v2.1 corpus contains 80 new findings disclosed in July 2026 or later, with a 12-task Hard set rated most paradigm-breaking, and models were run at xhigh reasoning effort in their native harnesses with tools on and web search off. Fable 5.2 leads Physics and Astronomy, Chemistry and Materials, Mathematics and TCS, and Life Sciences, while Opus 5.5 leads Earth and Planetary.

Nov 10Tue
  1. Hacker News AI68

    Is AI code rotting open source? 450 repos, 12 months

    An analysis of 450 top GitHub repositories found that the average repo had 11.5% of its merged pull requests AI-signed in September 2026, up from 2.3% in October 2025, though the median repo was at just 1.5% and 264 of the 450 repos have committed agent instruction files, making every AI figure a floor. Among 100 TypeScript and C# repos, complexity per 1,000 lines rose a median 4.8% in AI-heavy repos versus 3.0% in others, with the increase concentrated in fast-growing codebases while mature Microsoft projects held flat. AI PRs were typically 2.4× larger than other PRs and fixed within 30 days only slightly more often.

  2. Hacker News AI44

    Sydney researchers uncover a hidden limit to AI predictability

    University of Sydney researchers Pulin Gong and Andrew Ly found in Nature Machine Intelligence that neural networks' training outcomes depend on fractal 'riddled basins' of starting conditions, so even deterministic training with identical data can yield different behaviours. In one larger image-recognition network, making starting settings 10 billion times more precise produced no measurable improvement in reproducibility, and a one-bit change sent training to a different configuration in about 48 percent of cases. The work, based on roughly 500,000 trained networks, also found a trade-off in which settings that learn most effectively make outcomes harder to reproduce.

  3. Hacker News AI58

    Beyond FLOP/s: comparing AI hardware for inference by memory and communication

    The article argues that raw FLOP/s understates inference gains, estimating Nvidia's GB300 delivers about 25× H100's inference capacity per chip on modeled agentic workloads despite only 2.5× the 8-bit FLOP/s, because memory and inter-chip communication matter more. It introduces the Inference Performance Index (IPI), scored in GB300 equivalents (GB300e), which compares simultaneous requests each chip can serve across 50–400 tokens/s/req for four representative models. Reported scores include GB300 NVL72 at 1, B300 HGX at 0.54, MI355X at about 0.55, VR200 at 2.18, and H200 HGX at 0.12.

  4. Google DeepMind58

    Opening the datasets behind our AI weather models

    Google's WeatherNext team released SatWeave, an hourly global mosaic of geostationary satellite imagery across 17 channels blending six satellites, and PARDIG, an AI-based global precipitation dataset at 15-minute and roughly 10-kilometer resolution trained on NASA GPM radar observations. Both datasets, key to the WeatherNext 3 model, are available as live feeds and historical archives on Google Cloud Storage, with PARDIG catalogs also in Google Earth Engine. PARDIG reportedly improves hourly precipitation detection skill over NASA's IMERG-Final by more than 30% on light-to-moderate precipitation.

  5. Hacker News AI58

    The Red Queen hypothesis – a new way forward for self-improving AI

    Researchers, with collaborators from NVIDIA and Flower Labs, propose the Red Queen Gödel Machine, a framework in which a self-improving AI agent and its evaluator co-evolve so the evaluation gets harder as the agent improves, avoiding the ceiling imposed by a fixed test. In experiments on scientific paper writing and reviewing and on Olympiad-level proof writing and grading, co-evolved paper writers reached 1.78×–1.86× higher acceptance rates under an agent-as-a-judge panel and co-evolved graders reached 9% higher ground-truth accuracy. A hybrid setup using NVIDIA Nemotron 3 Ultra alongside ChatGPT-5.5 approached ChatGPT's performance on paper reviewing while cutting search-token costs by around 13 times, though the team stresses the work is preliminary.

  6. Hacker News AI38

    Triton for MTIA: Bridging the Programming Model Gaps for Custom AI Accelerators

    This paper presents the first production-scale application of Triton on a custom ML accelerator, Meta's MTIA-2i, via a new compiler backend, TorchInductor code generation enhancements, and minimal language extensions exposing MTIA-specific architectural features. Triton-MTIA kernels achieve performance competitive with expert-tuned C++ implementations, and manually written plus Inductor-generated Triton kernels were deployed in production across roughly 60 model types, covering 50% of layers and 47% of non-GEMM execution time for these models.

  7. MarkTechPost64

    Baidu’s ERNIE Evolve Automated Research System Beats NanoChat and NanoGPT Records

    Baidu released ERNIE Evolve, an automated research system that selects research directions, edits code and runs experiments against a fixed objective, keeping only changes that pass a hidden evaluator and a separate reviewer. It beat the best public result on 3 of 4 public metrics, including 0.8763 validation BPB on NanoChat AutoResearch, 38.9 s on NanoGPT Speedrun Track 1 and 2,590 steps on Track 3, while scoring 0.819 mean SOL on SOL-ExecBench, behind the 0.827 leaderboard best. On ERNIE-5.5-Flash inference it reported 623.6% of baseline generation speed on Kunlunxin P800, +33.7% deterministic throughput and +14.9% single-request decode speed, though the system itself is not released.

  8. Hacker News AI88

    AI R&D Is Accelerating Towards Potential RSI in 2027

    METR's deep-access investigation of Anthropic found that since October 2025 Anthropic's models have been improving on its internal AECI benchmark at roughly 1.9x the pre-October 2025 rate (95% CI 1.5–2.8x), with a majority of the acceleration likely attributable to AI doing R&D and no observed bottlenecks preventing further acceleration. METR reports that Anthropic redacted most of the evidence it found most informative, and critics such as Toby Ord argue the data are also consistent with a one-time level shift from the larger Mythos pretrain (implying ~1.4x). The post's author calls the situation grim and argues timelines may be shorter than the AI 2027 scenario, while noting the estimates could be wrong or bottlenecks could be hit.

    Why it matters: Summarizes a widely discussed METR report on AI-driven acceleration of AI R&D and the debate over whether it implies near-term recursive self-improvement.

Nov 9Mon
  1. Hugging Face Blog36

    Nemotron-Personas-Finance-Japan: A Synthetic Dataset Capturing Japan's Demographics and Financial Context

    Elith and NVIDIA released Nemotron-Personas-Finance-Japan, an open synthetic persona dataset built on official Japanese statistics and household finance and financial literacy data, generated with NeMo SDG-PGMs and NeMo Data Designer. It contains 9M personas (1M records x 9 personas) in natural Japanese across 50 fields, released under CC BY 4.0 for commercial and non-commercial use. The dataset is designed for financial AI development such as conversational agents, customer simulation, model evaluation, and bias testing without using real customer data.

  2. Hacker News AI40

    Human feedback guides AI approach that automates about 84% of decisions

    Researchers Kai Hoberg, Christina Imdahl and William Schmidt propose "targeted automation," a four-step approach that automates decisions where human intervention adds little value while keeping people involved where their input can improve the algorithm. In an empirical application using more than 475,000 order-related decisions from a four-year period at a major commercial vehicle manufacturer, they were able to automate around 84% of decisions. The work was published in Production and Operations Management.

  3. MIT News AI58

    A single AI model simulates the behavior of many substances, from sliding sand to folding proteins

    Researchers at MIT, UBC and elsewhere have developed WorldParticle, a deep-learning system that represents materials as Lagrangian particles and predicts their motion one step at a time, learning underlying dynamics rather than relying on hand-coded solvers. The model was demonstrated on six physical phenomena, including deformable objects, granular materials and proteins, with higher accuracy than numerical methods and modeled protein structural changes about 100 times faster than numerical simulators. The work will be presented at ACM SIGGRAPH Asia.

  4. Artificial Analysis Articles42

    Introducing the Artificial Analysis Decision Index v2

    Artificial Analysis released Decision Index v2, with 45% of the Index built on private datasets (up from 25% in v1) and a new Decision Score that rewards both correctness and calibrated confidence. Drex Max v1.1 (Nace AI) and GLiDE v1.2 (Fastino Labs) both score 73, with Mammoth 27B (Baseten) at 72, while cost per 1M Tasks ranges from $14 for Celeris-1.1 Decision to $859 for Clef. The update adds AA-Paperclip (1,500 private documents), AA-Symmetry, revised AA-Omniscience-Decision and HLE-MultiChoice benchmarks, and EvalSafe-O*NET-AA, while reducing When2Call to 10% and removing WildGuardTest.