AI News Today

<!-- SC_OFF --><div class="md"><p>I made this explainer for my small channel and would like feedback from people learning this, especially if anything is wrong.</p> <p>It follows one word the whole way:</p> <ul> <li>"How many r's are in strawberry?" is 8 tokens in o200k_base (the GPT-4o / GPT-5 tokenizer). " strawberry" is a single token, id 101830, so the three r's are hidden inside one id.</li> <li>Why pieces of words: letters make sequences about 4x longer, whole words need a huge vocabulary

<!-- SC_OFF --><div class="md"><p>For context, I am a new postgraduate student in machine learning (but not a very good one). I have produced some augmented datasets (TIFF files where each file contains one object to be detected / classified) which I used in training deep learning models. I used fairly aggressive cropping (random crops between 0 and 90%) and more moderate zooms (random values between 0 and 20%). My supervisor wants me to characterise these datasets to see how augmentation has ch

<!-- SC_OFF --><div class="md"><p>Hi there, wanted a suggestion from the community regarding the hardware to run local models. The first option was just to get a MacBook Pro with 32 GB RAM, and the second one was to invest into Mac Mini (256 GB SSD and 32 GB RAM), and get an iPAD as well</p> <p>PS; I have access to B200, and multiple A100s/L40s at my Univ which I can definitely use for training purposes, and I just wanted to get a device to run local inference</p> </div><!-- SC_ON --> submitted

<!-- SC_OFF --><div class="md"><p>I’ve been noticing that a lot of techniques seem to repeat across modalities with just a different name.</p> <p>For example, tokenisation, masking (MLM / MAE / audio masking), diffusion, next-token / next-frame prediction, etc.</p> <p>Is there an existing taxonomy or mental model that groups ML techniques by their underlying pattern rather than by modality (NLP / vision / audio)?</p> <p>This will help me to group the research papers and then make reference to wo

<!-- SC_OFF --><div class="md"><p>I've experimented using neuro-evolution to train AI to play a variety of different games and I'm just curious as to how or if this technique is actually really used anywhere in industry or if people are doing research on this. I'd assume it'd probably relate with reinforcement learning in some way but I'm not entirely sure about that</p> </div><!-- SC_ON --> submitted by <a href="https://www.reddit.com/user/tapopalis"> /u/tapopalis </a> <br/> <span><a href="http

<!-- SC_OFF --><div class="md"><p>Hey everyone!</p> <p>I've built and trained a small LLM (~8M parameters) from scratch using PyTorch, mainly as an experimental lab where I can implement and test ideas from different research papers.</p> <p>I've been documenting the whole journey through casual blogs, including my mistakes, experiments, and things I've learned.</p> <p>So far:</p> <ul> <li><a href="https://medium.com/@sulavstha007/i-stopped-watching-tutorials-and-built-a-transformer-5abbcfbfcc44"

<table> <tr><td> <a href="https://www.reddit.com/r/MachineLearning/comments/1xkcy83/hierarchical_detection_of_neonatal_brain/"> <img src="https://external-preview.redd.it/Foc2K0YvOCmaWMneoJBOUnuDMwE_D2LL0OrfDB1nOwI.jpeg?width=640&crop=smart&auto=webp&s=dfe059c6be2e4771fbff697cc7af0a3cdd352536" alt="Hierarchical detection of neonatal brain pathologies using a two-stage deep learning framework from magnetic resonance imaging [R]" title="Hierarchical detection of neonatal brain pathologies using a

Factory released Droid 1.0, a model-independent coding agent harness that makes programmatic tool calling the default. On Factory’s own Terminal-Bench 4 runs, Droid scores 65.2% with Claude Opus 5.5 at $4.73 per passed task. The post Meet Factory Droid 1.0: A Model-Independent Coding Agent Harness Built on Script Mode appeared first on MarkTechPost .

Vercel Labs released agent-desktop, an Apache-2.0 Rust CLI that gives any AI agent computer use on macOS, Windows and Linux by reading OS accessibility trees and acting on stable refs instead of pixels. The post Vercel Labs Open-Sources agent-desktop for Agent Computer Use appeared first on MarkTechPost .

Microsoft Research and UC Berkeley released TokenEconomizer, an open-source plugin that learns from past sessions to cut coding agent token costs by 18.4% to 30.9% on SWE-Bench, with no change to the agent. The post Meet TokenEconomizer: Microsoft’s Plugin That Cuts Coding Agent Costs by Up to 30.9% appeared first on MarkTechPost .

Anthropic, OpenAI, Cloudflare and Google each released an AI agent that hunts security bugs in source code. All 4 use the same find, disprove, then patch loop. Here is how they differ, plus 12 more tools. The post AI Security Agents Compared: Claude, Codex, Mantis, Cloudflare appeared first on MarkTechPost .

While science fiction lore tells us that humans will meet extraterrestrial life in the form of little green men, the detail that should raise hairs this Halloween is this: the search for extraterrestrial intelligence took a major leap forward with AI, and it increasingly focuses on AI agents built by humans listening for AI agents […]

Evaluating Microsoft Copilot at enterprise scale requires test data that captures the full complexity of real organizations, yet real enterprise data contains sensitive information that cannot be used for evaluation under any circumstances. The post From synthetic enterprise data evaluations to real Microsoft Copilot AI confidence appeared first on Microsoft Research .

Samaya AI’s Sterling is a post-trained Nemotron 3.5 Lightning model for financial research. It scores 58.9% on FrontierFinance at $0.61 per question, beating Claude Fable 5.1’s 55.9% at $6.89. The post Samaya AI Releases Sterling: A 3B-Active Finance Research Agent Model that Beats Claude Fable 5.1 on FrontierFinance at 11x Lower Cost appeared first on MarkTechPost .

arXiv:2610.37712v1 Announce Type: new Abstract: Adaptive optimizers are widely used in deep learning, yet their behavior can degrade under heavy stochasticity and label noise where the sign and magnitude of gradients can be unstable. We study MGO-SC (Moment-Guided Optimizer with Sign-Consistency), a lightweight modification of Adam-style methods that derives a coordinate-wise consistency gate from the magnitude of a sign exponential moving average (EMA): coordinates whose gradient sign is stable

arXiv:2610.37719v1 Announce Type: new Abstract: Post-training moves a language model's weights by about a thousandth of their norm and changes its behavior sharply; pretraining moves them by a third and buys a slow drift. We introduce leverage, an exact measure of what a unit of weight change buys: the Fisher quadratic form of an update, computed with one forward-mode pass per direction, divided by the same form for random directions of the same size at the same checkpoint. Leverage orders relea

arXiv:2610.37726v1 Announce Type: new Abstract: Accurate regression need not yield the correct equation after support selection. For a median/sample-standard-deviation threshold in neural PDE discovery, we derive an exact two-signal retention boundary and a complete-vector perturbation bound. Exact advection-diffusion libraries lose diffusion despite coefficient recovery to numerical precision. True-support refits of forty saved sparse states reduce diffusion error without restoring all derivati

arXiv:2610.37727v1 Announce Type: new Abstract: In machine learning, symbolic reasoning approaches often rely on static architectures that hinder integration into deep neural networks and gradient-based optimization. To address this, we introduce the Native Predicate Neuron, a differentiable logic-reasoning neural module that enriches standard architectures with symbolic reasoning capabilities via first-order logic quantization. The proposed neuron models relational logic by learning to alternat

arXiv:2610.37733v1 Announce Type: new Abstract: This work studies context-aware selective transfer learning for multi-well groundwater anomaly forecasting in the Netherlands. Source-domain pretraining can raise average accuracy but is not uniformly reliable: it degrades performance on a substantial fraction of heterogeneous wells. Context-Aware Selective Learning (CASL) uses calibration-trained, constrained contextual blending of frozen no-transfer and fixed-transfer parents. Global Convex Blend

arXiv:2610.37737v1 Announce Type: new Abstract: Prior-Data Fitted Networks (PFNs) replace the hyperparameter refitting of Gaussian Process (GP) surrogates in Bayesian optimisation with a single forward pass, at the cost of a prior that is fixed at meta-training time. We show empirically that a PFN's predictive log-likelihood falls increasingly behind that of a refitted GP as the objective function's lengthscale grows beyond the mode of the PFN's meta-training prior, and that this deficit carries

arXiv:2610.37796v1 Announce Type: new Abstract: Recent work has hypothesised that large language models may represent some concepts as multi-dimensional manifolds instead of single directions. Using these manifolds to interpret or steer representations often assumes that moving along them corresponds to a continuous change in the represented concept. In this work, we test this continuous manifold assumption on the prototypical weekday manifold. Despite its striking circular geometry, we find tha

arXiv:2610.37819v1 Announce Type: new Abstract: Quantum machine learning (QML) is increasingly explored for medical classification, yet its behaviour under subgroup-aware fairness criteria remains understudied. We present an empirical fairness audit of classical and quantum classifiers for medical imaging, assuming QML is not inherently fair. Two scenarios are evaluated: dermatological lesions on DDI (sensitive attribute: Fitzpatrick skin type) and chest X-rays on CheXpert-derived data (sensitiv

arXiv:2610.37823v1 Announce Type: new Abstract: Event cameras, spiking cochleas and similar sensors emit asynchronous events, yet most learning systems first bin them into frames or packets, so the output depends on how the stream was sliced. Networks whose state is updated exactly at every event and evolves in closed form between events are invariant to this slicing. We ask what more makes the output (i) independent of the processing order of simultaneous events and (ii) continuous in the time

arXiv:2610.37828v1 Announce Type: new Abstract: An AI developer often has several candidate versions of a system and can deploy a mixture of them, sending each request to a randomly chosen version, as in A/B testing. Each stakeholder (e.g., a safety team, a regulator, or a customer with private test data) accepts a mixture exactly when its expected performance (e.g., its rate of harmful replies) meets the stakeholder's own standard, which the developer can learn only by asking whether a proposed

arXiv:2610.37842v1 Announce Type: new Abstract: As growing compute budgets outpace the growth of high-quality training data, large language models must be trained with both compute and data constraints in mind. Unfortunately, existing prescriptions for data-constrained scaling suffer from two important limitations. First, past works hold key hyperparameters such as weight decay fixed with scale, despite the fact that optimal hyperparameters vary wildly with model size and number of epochs. Secon

arXiv:2610.37880v1 Announce Type: new Abstract: Equation learning seeks to discover compact, interpretable equations from data using various machine-learning techniques. However, few workflows discover and interpret learned equations in sparse, high-dimensional materials datasets. We present nn-EQL, an equation-learning framework that generates candidate equations through iterative training, pruning, and retraining of a neural network with customizable, user-specified neuron-level activation fun

arXiv:2610.37903v1 Announce Type: new Abstract: Metis is a bet that better data, better training methods and new architectures can buy what a bigger parameter count cannot, at a fraction of the cost. Metis-1.6 is its latest step: two language models built from scratch by a single researcher, Praxis (2.94B parameters, 0.46B active per pass) and Logos (10.23B parameters, 1.18B active per pass). Both run a hybrid Mamba-2 and attention mixture-of-experts stack twice for every token, so each token ge

arXiv:2610.37909v1 Announce Type: new Abstract: Distributed fire-monitoring systems may involve many sensing devices reporting over a shared uplink, even though the operational objective is often to identify regions exhibiting evidence of fire rather than to recover device-specific measurements. We introduce TERRA (Task-Oriented Edge Representation and Regional Aggregation), an edge-AI framework that combines task-oriented local inference, identity-free region-tagged reporting, and permutation-i

arXiv:2610.37910v1 Announce Type: new Abstract: Stellarators offer a promising magnetic confinement approach for achieving controlled nuclear fusion. However, stellarator design remains challenging, as evaluating configuration performance requires computationally expensive, high-fidelity simulations. In particular, key performance metrics derived from guiding center simulations are costly to include directly in the design optimization loop, and are almost exclusively used as diagnostics. Machine

arXiv:2610.37934v1 Announce Type: new Abstract: Pre-training foundational models is computationally expensive as models scale. In this work, we introduce Bi-Phase Training (BPT), a parameter-efficient pre-training method that preserves the high-rank learning dynamics observed in full-parameter optimization with substantially reduced trainable parameters. BPT parameterizes each weight update as a composition of constrained high-rank left/right transformations via trainable diagonal matrices and l

arXiv:2610.37936v1 Announce Type: new Abstract: Generative flows can admit different velocity fields that induce the same continuous marginal evolution but behave differently after numerical discretization. We study this finite-step non-equivalence while keeping the marginal path and numerical method fixed. Projected modified equations characterize the component of local integration error that changes the marginal law and give sufficient conditions for higher marginal convergence order. For affi

arXiv:2610.37952v1 Announce Type: new Abstract: Rounded capacity cuts (RCCs) are among the most effective cuts for capacitated vehicle-routing relaxations, but exact separation is computationally prohibitive at scale. Existing heuristic and neural separators can be fast, but often fail to produce sufficiently many effective cuts within practical separation budgets. We introduce SAG-Sep, a separator that scores node membership in RCC-inducing subsets across vehicle-count levels from a fractional

arXiv:2610.37972v1 Announce Type: new Abstract: Machine learning (ML) architectures are being developed for fast emulation of numerical simulators to accelerate both scientific discovery and engineering design. While these approaches are well-tested on popular PDE (partial differential equation) benchmarks, it is necessary to evaluate their performance on datasets that more accurately reflect industrial and engineering applications. We evaluate several computer vision-based architectures trained

arXiv:2610.37976v1 Announce Type: new Abstract: We empirically investigate how MLPs and Transformers differ when trained on data sets containing one or more features encoded by noisy k-parity. Noisy k-parity gives us hard synthetic data sets in which we can control the predictivity and availability of every feature. Across a range of data distributions, data scales and model sizes, we find large differences in the training dynamics and inductive biases of the two architectures: Transformers are

arXiv:2610.37676v1 Announce Type: new Abstract: Automated interpretability scores an explanation of a sparse-autoencoder (SAE) feature by asking a model to predict, token by token, where the feature fires on evaluation examples or to judge whether marked tokens fit it. We find that the evaluation data standard pipelines build can be scored without reading the explanation. On Neuronpedia's stored fuzzing runs for 500 random GPT-2 small features, 91.3% of activating examples are cropped with the p

arXiv:2610.37679v1 Announce Type: new Abstract: Tool-using language agents are vulnerable to indirect prompt injection, and a growing literature probes model internals to catch unsafe actions before they execute, with sharply conflicting results. We argue much of the conflict is an evaluation artifact, and propose a double-stratification protocol: separate injection exposure from behavioral commitment, in both the evaluation and the training population. On the primary Qwen AgentDojo grid, monito

arXiv:2610.37680v1 Announce Type: new Abstract: Closing the gap between frontier and smaller language models is usually approached by distillation: fine-tuning the small model (student) on a frontier model's (teacher) outputs, which binds transfer to training compute and leaves the transferred knowledge largely opaque. We propose Artifact Distillation: a teacher solves training instances and externalizes its problem-solving procedure into an artifact bank that a student queries at inference time

arXiv:2610.37681v1 Announce Type: new Abstract: Large reasoning models have recently demonstrated remarkable capabilities in solving complex tasks, where supervised fine-tuning (SFT) on long chain-of-thought data serves as a crucial foundation for eliciting and enhancing their reasoning abilities. Despite rapid progress in both improving and analyzing reasoning-oriented SFT, the field still lacks a systematic survey that consolidates its fast-growing literature. To fill this gap, we present a co

arXiv:2610.37682v1 Announce Type: new Abstract: Listwise preference feedback offers richer supervision for large language model (LLMs) alignment than pairwise comparisons, but a full ranking also reveals more sensitive user preferences. Existing privacy-preserving alignment methods focus mainly on pairwise feedback, while listwise alignment under local differential privacy (LDP) remains unexplored. Extending LDP to this listwise domain raises a critical challenge: the high sensitivity of the ful

arXiv:2610.37683v1 Announce Type: new Abstract: Despite their strong performance, large language models remain highly sensitive to prompt formulation. This issue is typically addressed at inference-time through manual prompt optimization, which introduces overhead and lacks scalability. Conversely, we focus on train-time approaches that build prompt robustness into the model during instruction tuning. A common strategy is to mix multiple paraphrases of each prompt into training. We demonstrate t