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A Beginner’s Guide to World Models
Learn how to simulate reality with Python The post A Beginner’s Guide to World Models appeared first on Towards Data Science.
Introducing ShipAI
Towards Data Science launches a video showcase for real-world AI work The post Introducing ShipAI appeared first on Towards Data Science.
The Four Caches in LLM Serving
As LLM applications grow more complex, inference cost and latency become increasingly important. A single request can contain thousands or even millions of tokens from system instructions, conversation history, retrieved documents, tool definitions, and user input. Reprocessing the same information again and again wastes both time and compute. Caching helps avoid this repeated work. But [ ] The post The Four Caches in LLM Serving appeared first on Analytics Vidhya.
Context Windows Don’t Know What’s Still True — I Built a Validity Layer That Does
A context window can be technically complete and still describe a world that no longer exists. I built a deterministic benchmark to measure the cost of acting on stale context. The post Context Windows Don’t Know What’s Still True — I Built a Validity Layer That Does appeared first on Towards Data Science.
Getting Started with Grok Bot
Last week, a GitHub notification arrived while I was asleep: a bug had been reproduced in staging, documented with screenshots, and assigned to the right engineer. A bot I set up four days earlier did the work overnight. That is the gap Grok Bot is designed to close. Instead of suggesting what to do, it [ ] The post Getting Started with Grok Bot appeared first on Analytics Vidhya.
I Vibe-Coded an App in Just Two Hours (And Regretted It the Next Day)
I saved my day with a mere $52 bill. But you don't have to repeat my mistake. The post I Vibe-Coded an App in Just Two Hours (And Regretted It the Next Day) appeared first on Towards Data Science.
Why Most Multi-Agent Systems Fail Even When Evaluation Passes
How to catch a payload that looks correct but isn't, using a watchdog pattern with working Python. The post Why Most Multi-Agent Systems Fail Even When Evaluation Passes appeared first on Towards Data Science.
EXAONE Forecast for Finance
arXiv:2609.04239v1 Announce Type: new Abstract: This technical report presents EXAONE Forecast for Finance (EXAONE Finance), a financial time series (TS) foundation model (TSFM) tailored to financial forecasting. Recent TSFMs achieve strong zero-shot performance through large-scale pretraining. However, they are primarily developed for general-domain TS and largely rely on self-attention backbones whose computational cost grows quadratically with sequence length and variate count. Moreover, they assume fully observed inputs and are pretrained on corpora that fail to capture the unique dynamics of financial markets. These limitations hinder their applicability to finance, where long, many-channel, intermittently observed panels are common. To address these challenges, EXAONE Finance adopts an attention-free architecture, replacing self-attention with two simple yet effective linear-time operators: 1) a causal 1D convolution for temporal mixing and 2) a group-aware pooling multi-layer pe
Spectral-Target Physical Latent Structuring for JEPA-Style World Models
arXiv:2609.04264v1 Announce Type: new Abstract: Latent world models have become increasingly popular as a method to predict and plan in latent space rather than pixel space. Recent architectures, such as LeWorldModel (LeWM), jointly train the encoder and predictor using regularization techniques like SIGReg to prevent representation collapse. Even with such regularization preventing representation collapse, we identify a new world model failure mode of \textit{physical representation laziness}, particularly noted in highly dynamic environments. For these lazy cases, the learned latent states do not collapse but nonetheless fail to represent key physical properties, causing ubiquitous downstream planning failure. To resolve this issue, we propose training-time auxiliary supervision with a lightweight "Fourier auxiliary head", which enforces physically-informed structuring of the latent space with no additional inference-time cost and can be generalized to any environment. Experimentally
From Matching Models to Recruiting Agents: A Systematized Narrative Review of AI Recruitment Systems, Evaluation, and Governance
arXiv:2609.04286v1 Announce Type: new Abstract: Artificial intelligence in recruitment has shifted the object being automated from profile pairs and ranked lists to multi-stage workflows that retrieve evidence, compare candidates, and support or execute actions. This systematized narrative review traces that development from bilateral retrieval and behavioral ranking through neural person--job matching, large language model (LLM) components, and tool-using recruiting agents. Using a purposive search and coding protocol updated through 23 July 2026, plus targeted updates through 2 September 2026, we organize 40 representative works with supporting industrial and legal sources. This synthesis is not a prevalence estimate. We analyze three coupled transitions: from similarity to reciprocal suitability, from a model to a compound workflow, and from offline prediction to evidence- and productivity-aligned evaluation. Across document understanding, retrieval, ranking, assessment, interviewin
FailureSpot: Label-Efficient Timestamp-Level Failure Detection for Vision-Language-Action Models
arXiv:2609.04277v1 Announce Type: new Abstract: Vision-language-action (VLA) policies have shown strong potential for general-purpose robotic manipulation, but they can still fail unpredictably during long-horizon execution, making reliable failure detection essential for safe deployment. Existing methods either rely on visual models that typically detect failures only after erroneous actions have occurred, or use lightweight proactive detectors trained on VLA internal representations. However, these proactive methods are often supervised with trajectory-level labels, causing normal pre-failure behavior in unsuccessful trajectories to be incorrectly labeled as failure. This supervision mismatch introduces label noise and limits both trajectory-level detection accuracy and precise timestamp-level failure localization. In this work, we study fine-grained timestamp-level VLA failure detection while addressing the cost of dense annotation. We propose a data-efficient framework that first l
ProToMEx: Rapid, Interpretable Explanations via Structured Representations
arXiv:2609.04265v1 Announce Type: new Abstract: Existing post-hoc explainers for machine learning classifiers primarily focus on feature attribution, assigning importance scores to individual features. While valuable, this approach struggles to articulate the complex, combinatorial patterns that often drive a model's decision-making process. To overcome this limitation, we introduce ProToMEx, a new paradigm for explainability that leverages Probabilistic Topic Models (PTMs). Our model-agnostic framework learns latent ''topics'' that represent distinct, high-level reasons for a classification, moving beyond simple feature importance to reveal underlying semantic structures. ProToMEx naturally provides both global explanations of a model's overall behaviour and local explanations that can disentangle multiple co-existing reasons for a specific prediction. We demonstrate empirically that ProToMEx not only produces explanations of comparable fidelity to popular methods like SHAP and LIME b