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DS-Lighting: Making Agent Harnesses Explicit for Data-Science Automation
arXiv:2608.28590v1 Announce Type: new Abstract: Large Language Model (LLM) agents have shown promise for automating data-science workflows, yet their end-to-end performance depends critically on the agent harness that represents tasks, manages execution state, constrains output artifacts, and provides evaluation feedback. Existing data-science agents often leave this harness implicit, making results difficult to reproduce, compare, and attribute across heterogeneous tasks. We introduce DS-Lighting, a unified harness toolkit that makes harness design explicit for data-science automation. DS-Lighting decomposes the harness into four reusable layers: data, workflow, execution, and evaluation, and represents diverse agents as executable operator programs that support both predefined pipelines and adaptive search. We further integrate multiple open-source data-science benchmarks into an MLE-Bench-style task format, enabling controlled comparison under a shared task interface, sandboxed runt
Your LLM Can Return Perfect JSON and Still Be Wrong
What I learned after thinking more carefully about Structured Outputs on messy, incomplete data The post Your LLM Can Return Perfect JSON and Still Be Wrong appeared first on Towards Data Science.
FAQ as RAG: When You Get to Design the Corpus
Enterprise Document Intelligence [Vol.1 #B2] - The FAQ inverts every brick of the standard RAG pipeline. Parsing is trivial, retrieval doubles as a cache, and few-shot prompting becomes a retrieval problem too The post FAQ as RAG: When You Get to Design the Corpus appeared first on Towards Data Science.
Why RAG Complexity Should Be Earned
A framework for building RAG pipelines that introduces complexity in response to observed failure modes, from lexical and hybrid search to reranking and agentic information seeking The post Why RAG Complexity Should Be Earned appeared first on Towards Data Science.
AgentOps Is Not MLOps: What Breaks in Your Monitoring Stack When Agents Go to Production
The five MLOps monitoring assumptions agents break, and which inherited signals now pass failed runs as healthy. The post AgentOps Is Not MLOps: What Breaks in Your Monitoring Stack When Agents Go to Production appeared first on Towards Data Science.
Beyond Relative Geometry: Metric-Aware Geometry Perception for Robotics
arXiv:2608.27497v1 Announce Type: new Abstract: Recent embodied models increasingly leverage geometric representations to improve spatial reasoning and robotic manipulation. However, existing reconstruction methods only reconstruct relative geometry with arbitrary scales, causing predicted object dimensions and spatial distances to vary across scenes, viewpoints, and input configurations. This inconsistency prevents geometric perception from being directly aligned with robotic actions defined on the real-world scale. To address this limitation, we propose Metric-Aware Geometry Perception (MAGP), an end-to-end, plug-and-play framework for metric geometry reconstruction that can be seamlessly integrated into robotic policies. At its core, Metric Scale Equivariant Augmentation encourages the model to reconstruct metric geometry from camera parameters and depth observations, ensuring that the reconstructed geometry follows the metric scale specified by observations. Flexible Metric Conditi
Quantization-Triggered Backdoors in Language Models: Cross-Quantizer Transferability and the Validation--Deployment Gap
arXiv:2608.27512v1 Announce Type: new Abstract: Post-training quantization is often treated as a semantically neutral optimization for edge deployment of Large Language Models. When a full-precision source checkpoint is evaluated and quantization is applied downstream without equivalent re-evaluation, this workflow creates a structural validation--deployment gap: because quantization is a many-to-one mapping over parameter space, source-precision certification does not guarantee behavioral equivalence in the deployed configuration. We formalize this gap through Quantization Behavioral Equivalence Classes (QBECs) and prove that QBEC membership does not imply behavioral equivalence, providing a theoretical basis for quantization-triggered backdoor attacks. Building on a three-stage adversarial fine-tuning framework, we embed latent malicious payloads into models that satisfy the source-precision checks used in our evaluation, yet activate targeted adversarial behavior upon INT8 or 4-bit
Remote Human and Robot Interaction for Greenhouse Gardening Using Virtual Reality
arXiv:2608.27545v1 Announce Type: new Abstract: This study evaluates the effectiveness of remote human-robot interaction using virtual reality for leaf inspection and soil moisture assessment in a greenhouse environment. The robotic system comprised an unmanned ground vehicle and a robotic manipulator equipped with cameras, governed by kinematic models for navigation and manipulator control. Fourteen distinct plants were inspected across two experiments utilizing VR teleoperation, guided by a set of pre-specified research questions and hypotheses. In the leaf inspection experiments, cycle completion times varied from 3.3 to 8.0 s, and plant-based disease detection was achieved up to 88% accuracy; diseased-spot detection improved numerically in the second experiment, though this change was not statistically significant (p=0.378). For soil moisture assessment, the experiments achieved successful determination of watering needs in up to 64.3% of plants (9 of 14), with consistent success o
Marginal Coverage Credit Reduces Redundant Exploration in Parallel State-Entropy Optimization
arXiv:2608.27507v1 Announce Type: new Abstract: Policy Gradient for Parallel State Entropy maximization (PGPSE) expands state-space coverage by training independently parameterized policies in replicated copies of the same environment. However, its pooled team-entropy score measures only collective exploration and cannot identify policies that contribute non-redundant coverage. We introduce Marginal Coverage Credit for PGPSE (MCC-PGPSE), which combines leave-one-policy-out coverage with state-owner specialization to estimate policy-specific credit. MCC-PGPSE preserves PGPSE's pooled objective and redistributes non-negative auxiliary intrinsic rewards according to these credits without changing their total mass. This redistribution is designed to discourage redundant visitation and promote complementary coverage. We evaluated MCC-PGPSE in controlled environments, seven public discrete-state benchmarks, and representative Room and Maze settings from the original PGPSE protocol. Across al
Top 7 Free AI Automation Courses with Certificates
You don’t need to know anything about AI automation to get started. There are plenty of free courses that can take you from the basics to building your own automations, even if you’re starting from zero. Some are completely beginner-friendly, while others are better once you’re comfortable with the basics. We’ve picked courses covering popular [ ] The post Top 7 Free AI Automation Courses with Certificates appeared first on Analytics Vidhya.
8 Tips for Writing Effective Agent Instructions
Quick and simple tips to help you write better agent instructions The post 8 Tips for Writing Effective Agent Instructions appeared first on Towards Data Science.
Context Engineering Is Changing. Here’s What It Means for Data Scientists
How to apply the latest context engineering guidelines to your day-to-day data science work The post Context Engineering Is Changing. Here’s What It Means for Data Scientists appeared first on Towards Data Science.