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A Day in the Life of a Data Scientist in 2026
How AI has massively changed my day-to-day workflow The post A Day in the Life of a Data Scientist in 2026 appeared first on Towards Data Science.
RAG Workflow and Loop Engineering: The Dispatcher That Decides When to Loop and When to Stop
Enterprise Document Intelligence [Vol.1 #13] - Putting the patterns together, and why this is what “agentic RAG” should look like The post RAG Workflow and Loop Engineering: The Dispatcher That Decides When to Loop and When to Stop appeared first on Towards Data Science.
My Model Was Cheating on Its Own Test
A preprocessing pipeline let my car price model peek at the test set before the exam, and the twelve points of R squared it cheated its way to The post My Model Was Cheating on Its Own Test appeared first on Towards Data Science.
I Made an LLM Lay Siege to My Minecraft House
Can a language model do live adversarial level design? Yes, emphasis on the adversarial part The post I Made an LLM Lay Siege to My Minecraft House appeared first on Towards Data Science.
NVIDIA Nemotron 3.5 Lightning: The AI Agent Workhorse
Long-running AI agents often spend most of their time on routine execution rather than difficult reasoning. After making a plan, they may perform hundreds of tool calls, file reads, validations, commands, and formatting steps, so using a frontier reasoning model for every action can become unnecessarily slow and expensive. NVIDIA’s Nemotron 3.5 Lightning takes a [ ] The post NVIDIA Nemotron 3.5 Lightning: The AI Agent Workhorse appeared first on Analytics Vidhya.
LoKiFormer: Locality-aware Attention with Decoupled Knowledge Memory for Efficient Large Language Model Pretraining
arXiv:2608.12419v1 Announce Type: new Abstract: Large language models (LLMs) have achieved remarkable breakthroughs across various applications. However, their architectures remain inefficient in pretraining due to two main limitations: (i) self-attention lacks an explicit inductive bias for locality, leading to redundant modeling of sequence-internal local information; (ii) mixture-of-experts (MoE) implicitly couples knowledge storage with computational pathways, hindering flexible access to sequence-external global knowledge. To overcome these limitations, we propose LoKiFormer, a novel LLM architecture that augments the standard decoder with two dedicated modules: 1) Local Fusion Attention (LFA), which incorporates a convolutional fusion to attention, explicitly capturing local patterns and allowing the attention to operate on more informative representations; 2) Knowledge Memory Module (KMM), which introduces a parametric key-value memory that explicitly stores global knowledge in
Diagnostic Foundation for Evaluating LLMs' Research Integrity as Co-Scientists
arXiv:2608.12345v1 Announce Type: new Abstract: Language models are increasingly deployed as co-scientists, yet their ability to uphold research integrity under institutional pressure remains unmeasured. We introduce IntegrityBench, a benchmark evaluating misconduct classification, ethical action reasoning and artifact-grounded decision making across 36 paired tasks under a 5-level implicit-explicit pressure protocol spanning 3 domains and 4 research stages. Evaluating 18 frontier model variants, we find that under peak pressure, models fail roughly 1 in 3 integrity-critical decisions, and neither scale nor reasoning ability reliably mitigates this. Explicit pressures induce compliance with misconduct, while implicit contextual reframing more often causes over-refusal of legitimate research tasks. Interestingly, models failing to classify research requests accurately perform equally or better on artifact-grounded decision making (85.7 vs. 79.4), suggesting the three facets are structur
RoboSynChallenge: Mastering Real-World Dexterity via Generalizing Synthesized Manipulation Skills
arXiv:2608.12416v1 Announce Type: new Abstract: Achieving generalizable robotic manipulation remains a central challenge in embodied intelligence. Despite rapid advances in model architectures and learning algorithms, progress is often limited by the scarcity and narrow diversity of real-world data. The RoboSynChallenge competition introduces a unified benchmark to evaluate and advance the generalizability of manipulation policies across a spectrum of tasks, environments, and difficulty levels. To alleviate the shortage of realistic data, the challenge integrates large-scale synthetic data generation with standardized real-world robotic evaluation. Participants are encouraged to leverage synthesized state-action trials to improve general-purpose policy learning, while final assessments are conducted exclusively on unseen real-world manipulation environments. Baseline implementations, including Transformer-, Diffusion-, Vision-Language-Action, and World-Action-Model-based policies, are
Attune: A Self-Annotation Tool for Understanding Robot Operator Attention Profiles
arXiv:2608.12650v1 Announce Type: new Abstract: Deploying robot fleets in complex, real-world environments requires human operators to supervise multiple robots simultaneously. Managing operator attention is a fundamental challenge of designing multi-robot supervision interfaces, encompassing both feed layout and feed content (i.e., robot behavior design). Thus far, designers lack empirical guidance on the latter-how to change a robot's behavior to capture, sustain, or relinquish operator attention during multi-robot supervision. In our vision of the future, designers should be able to use this guidance to calibrate robot behavior to different operator attention profiles. Treating operator eye gaze as a robot behavior design clue, we created a pre-deployment elicitation tool called Attune. Attune automatically identifies when meaningful gaze shifts occur, provides AI assistance for annotating why shifts occurred, and outputs a summary of operator gaze patterns for operator review. We e
Position: Reasoning is a Learnable Rule-Based Process
arXiv:2608.12325v1 Announce Type: new Abstract: Autonomous reasoning is among the most scientifically and economically motivating topics in AI today. Historically the purview of symbolic AI, recent advances have mainly emerged from deep probabilistic generative models. Despite immense interest and rapid progress, the generative AI community has not clearly converged on operational definitions for reasoning and often implicitly rejects the historical treatment of this topic in logic and verifiable automated reasoning. This position contends that definitional ambiguity leaves the construct validity of reasoning evaluation unverifiable, undermining quantifiable progress toward trustworthy autonomous reasoning. We also contend that this ambiguity is addressable. To that end, we provide (1) operational definitions based on a synthesis of the literature, positioning valid and sound reasoning as a learnable rule-based process; and (2) a checklist for best practices in the communication of AI
Which Site, and When: A Free-Satellite-Data Test of Himalayan Glacial Lake Bursts, Landslides, and Ice Floods
arXiv:2608.12422v1 Announce Type: new Abstract: Two free satellite signals carry real information about glacial-lake outburst risk in the Nepal Himalaya: radar interferometry sees a moraine dam slowly sagging, and satellite weather marks the weeks when a primed lake is under stress. A companion feasibility study found that deformation indicates which lake is destabilizing and weather indicates when it is at risk, but proposed no predictive model. To address this gap, we propose and evaluate models that predict which site is susceptible and when a trigger arrives. We test three related hazards on free data alone: large moraine- and ice-dammed bursts, rainfall-triggered landslides, and smaller floods from ponds on and around a glacier. Each hazard gets two questions, never blended. Using 589 dated outbursts from HMAGLOFDB and several thousand catalogued landslides, we match each event against similar but unfailed sites, and hold every model to a strong simple baseline under spatial cross
How to Utilize OKF Efficiently to Enable Knowledge Exchange Among LLMs
Google's Open Knowledge Format (OKF) is a Markdown+YAML skeleton for sharing knowledge between humans and AI agents. This post reuses that skeleton for a very specific job — an agent-to-agent hand-off of pre-tokenized integer arrays between three Qwen2.5-Coder models (7B, 3B, 1.5B) — and shows the 28–37% TTFT reduction plus the one full-vocabulary equivalence check that keeps the whole thing safe. The post How to Utilize OKF Efficiently to Enable Knowledge Exchange Among LLMs appeared first on Towards Data Science.