ROBOMARKET
Stay Informed
Daily coverage from 5886 trusted sources — robotics, AI agents, and industry analysis for the GCC.
Last Updated: 06:00 AM (1h ago)
Auto-refresh every 2 hours
AI Slop Is in Your Training Dataset Now. I Tested Three Ways to Spot It.
My AI detectors flagged many genuine reviews, and filtering them made the sentiment model less accurate. The post AI Slop Is in Your Training Dataset Now. I Tested Three Ways to Spot It. appeared first on Towards Data Science.
Agentic Context Engineering (ACE): Self-Improving Language Models
Agentic Context Learning or ACE is a learning paradigm that lets an AI agent improve across tasks by editing the context it reads, while leaving model weights unchanged. The paper outlining the techniques show why full rewrites fail, how the playbook update works, and where the measured gains hold up. This article explores how ACE [ ] The post Agentic Context Engineering (ACE): Self-Improving Language Models appeared first on Analytics Vidhya.
Your LLM Has a Curved Space of Paragraphs
Inside a transformer, token index is a coordinate. Paragraph structure is what turns it into a metric. The post Your LLM Has a Curved Space of Paragraphs appeared first on Towards Data Science.
10 Things I’m Learning Beyond AI to Become More Technologically Fluent
Part 1: Understanding the technologies shaping our future The post 10 Things I’m Learning Beyond AI to Become More Technologically Fluent appeared first on Towards Data Science.
Your Model's MSE Is Lying to You: Part II
Autoregressive rollout and uncertainty propagation. Second in a series on probabilistic forecasting for physical signals. The post Your Model's MSE Is Lying to You: Part II appeared first on Towards Data Science.
RAG Isn't an Agent — I Built the Layer Between Retrieval and Action
RAG retrieves. Agents act. I built both separately, connected them explicitly, and ran the same nine tasks through all three systems. The post RAG Isn't an Agent — I Built the Layer Between Retrieval and Action appeared first on Towards Data Science.
Jev vs. LLMs: When AI moves from Generation to Decision-making
I tested TypeSafe AI’s Jev on 3,080 classification tasks to see how its accuracy, latency, calibration, and confidence compare with LLMs — and whether it works as a practical decision layer for AI systems. The post Jev vs. LLMs: When AI moves from Generation to Decision-making appeared first on Towards Data Science.
10 Solved Generative AI Projects to Boost your Profile
Projects are the bridge between learning and becoming a professional. While theory builds fundamentals, recruiters value candidates who can solve real problems. A strong, diverse portfolio showcases practical skills, technical range, and problem-solving ability. This guide compiles 10 solved projects across AI domains, from basic machine learning to advanced generative AI system. The tools and [ ] The post 10 Solved Generative AI Projects to Boost your Profile appeared first on Analytics Vidhya.
Stable and Faithful Explanations for Knowledge Tracing
arXiv:2609.28502v1 Announce Type: new Abstract: Knowledge tracing (KT) models predict student performance opaquely, limiting pedagogical action. This study contributes a validation protocol testing predictive competitiveness (RQ1), explanation stability (RQ2) and retraining-based faithfulness (RQ3) together. Thirteen behavioral features across five pedagogical themes were engineered from ASSISTments 2009 and 2012, with history features computed from temporally preceding interactions and current response latency retained only for retrospective analysis. ASSISTments 2009 was rebuilt: the uncorrected skill-builder release duplicates each multi-skill interaction across one row per skill, and because those rows share one correctness label, they leak it into preceding-interaction features. Rebuilding lowered model AUC and reordered the explanation results. An Extreme Gradient Boosting (XGBoost) model explained with Tree SHapley Additive exPlanations (TreeSHAP) was compared against four deep
Know Your Body: A Harness for Direct and Self-Improving Robot Control with VLMs
arXiv:2609.28530v1 Announce Type: new Abstract: A general-purpose vision-language model can understand a task goal without knowing how a particular robot's motion and functional parts produce the intended effect. We introduce KnowBody, a harness that makes these action-relevant body relations explicit, queryable, and revisable while keeping the model weights frozen. Initialized from one off-task trajectory, a partial body model guides action selection and the interpretation of past interactions. New evidence refines the model, and knowledge dependent on revised body estimates is rechecked before reuse. Across 32 fixed-budget trials on four real-robot tasks, initialized KnowBody achieves 75% completion versus 25% for the native harness and requires fewer planner rounds on successful trials in tasks completed by both. With persistent updates enabled, planner rounds decrease by 29-53% from the first to the fifth recorded success.
SMILESGNN: Interpretable Clinical Toxicity Prediction via SMILES-Graph Cross-Attention Fusion
arXiv:2609.28553v1 Announce Type: new Abstract: Drug toxicity prediction is critical for reducing late-stage attrition in drug discovery, yet remains challenging due to severe class imbalance, scaffold-based generalization, and the clinical need for interpretable predictions. Single-modality approaches-SMILES Transformers or graph neural networks capture complementary aspects of molecular structure, while sequence-only models cannot directly provide graph-attributed explanations. We present SMILESGNN, a multimodal architecture that fuses a SMILES Transformer encoder and a GATv2 graph encoder via cross-attention, and SMILESGNN-PT, a variant using a ChemBERTa-2 pretrained backbone. The design retains an explicit graph branch within the predictive pipeline, supporting GNNExplainer-based analysis of substructures associated with toxic predictions. On ClinTox, SMILESGNN achieves AUC-ROC 0.987 and F1 0.906 with only 0.4M parameters, performing competitively with a strong SMILESTransformer an
When Should Forecasting Agents Reason? Behavioral Stress Tests for Reliability Routing
arXiv:2609.28475v1 Announce Type: new Abstract: Forecasting agents increasingly combine language-model reasoning, retrieval, ensembling, and calibration, but it remains unclear when each behavior should be trusted. We study this question on ForecastBench-style binary forecasting tasks, treating the choice to retrieve, reason, defer to a market prior, or use a historical analog as an observable agent behavior rather than a hidden implementation detail. Our central finding is that mechanism choice is source-dependent: structured analogs dominate for some data-generating processes, while market/crowd-style and conservative baselines are better for others. We introduce ReliabilityRoute, a structural intervention that steers forecasting-agent behavior using reliability features such as historical coverage, market-prior availability, source-prior sharpness, evidence strength, evidence disagreement, and horizon. A fixed 2024-fitted rule closely matches a hand taxonomy without hard-coded sourc