Grounding LLMs with Fresh Web Data to Reduce Hallucinations
Why production LLM systems need live web search to overcome knowledge cutoffs and stale training data
The post Grounding LLMs with Fresh Web Data to Reduce Hallucinations appeared first on Towards Data Science.
Another day, another example of an AI Agent “running rogue” and doing something the human operator didn’t want it to do. The tl;dr is that Jeremy (Jer) Crane, founder of PocketOS, was using Claude to perform some routine DB maintenance. Claude then proceeded to delete the production database and all...
The Rise of the Micro Executive: How AI Turns Individuals Into Teams
Discover how AI copilots are creating a new class of micro-executives—individuals who operate with the leverage, speed, and output of entire departments.
What if the model you've been evaluating has been evaluating you right back? New research finds that LLMs systematically alter their output depending on whether, and by whom, they believe they are being observed. It might have serious implications - are you ready?
AI Artifact Catalogs: Durable Standards Worth Institutional Investment
Companies everywhere are trying to leverage AI to boost internal productivity metrics. Some, like Ramp and Intercom, are succeeding. Many are failing. To make matters more complicated, the narrative around what tooling enables these gains is constantly shifting. For software engineers, auto-complete...
Systematic Optimization of Real-Time Diffusion Model Inference on Apple M3 Ultra
arXiv:2605.16259v1 Announce Type: new
Abstract: While real-time image generation using diffusion models has advanced rapidly on NVIDIA GPUs, systematic optimization research on non-CUDA platforms such as Apple Silicon remains extremely limited. In this study, we conducted comprehensive optimization...
Mirror Descent-Type Algorithms for the Variational Inequality Problem with Functional Constraints
arXiv:2605.16262v1 Announce Type: new
Abstract: Variational inequalities play a key role in machine learning research, such as generative adversarial networks, reinforcement learning, adversarial training, and generative models. This paper is devoted to the constrained variational inequality proble...
Reducing Credit Assignment Variance via Counterfactual Reasoning Paths
arXiv:2605.16302v1 Announce Type: new
Abstract: Reinforcement learning for multi-step reasoning with large language models (LLMs) often relies on sparse terminal rewards, leading to poor credit assignment conditions where the final feedback is evenly propagated across all intermediate decisions. Th...
arXiv:2605.16311v1 Announce Type: new
Abstract: Distributed training of large neural networks is bottlenecked by full-precision gradient communication and by coordinatewise optimizers that ignore the matrix structure of weight tensors. We propose Sign-Muon, a 1-bit, matrix-aware optimizer that comb...
When Actions Disappear: Adversarial Action Removal in Self-Play Reinforcement Learning
arXiv:2605.16312v1 Announce Type: new
Abstract: We study adversarial action masking in self-play reinforcement learning: an attacker selectively removes legal actions from a victim's action set. Unlike observation or action perturbations, removal eliminates decision options before the agent acts. A...
AgentWall: A Runtime Safety Layer for Local AI Agents
arXiv:2605.16265v1 Announce Type: new
Abstract: The safety of autonomous AI agents is increasingly recognized as a critical open problem. As agents transition from passive text generators to active actors capable of executing shell commands, modifying files, calling APIs, and browsing the web, the ...
ANNEAL: Adapting LLM Agents via Governed Symbolic Patch Learning
arXiv:2605.16309v1 Announce Type: new
Abstract: LLM-based agents can recover from individual execution errors, yet they repeatedly fail on the same fault when the underlying process knowledge--operator schemas, preconditions, and constraints--remains unrepaired. Existing self-evolving approaches ad...
From Prompts to Protocols: An AI Agent for Laboratory Automation
arXiv:2605.16552v1 Announce Type: new
Abstract: Automating science laboratories enables faster, safer, more accurate, and more reproducible execution of protocols, accelerating the discovery and testing of new materials, drugs, and more. However, setting up and running autonomous labs requires coor...
Skim: Speculative Execution for Fast and Efficient Web Agents
arXiv:2605.16565v2 Announce Type: new
Abstract: Skim is a speculative execution framework for web agents that exploits the predictable structure of purpose-built websites. Today's web-agent expense is not intrinsic to the tasks but a property of how agents are composed: frontier-model inference, br...
Scalable Uncertainty Reasoning in Knowledge Graphs
arXiv:2605.16568v1 Announce Type: new
Abstract: Knowledge Graphs are pivotal for semantic data integration. The real-world data they model is often inherently uncertain. Within knowledge graphs, uncertainty manifests in three distinct levels: imprecise attribute values, probabilistic triple existen...
AI Weekly Issue #493: Meta hired $145B in capex and fired 8,000 people
Six days after we called $725B a bet on what no one wanted, the receipts started landing. Meta committed $145B to AI infrastructure the same week it began firing 8,000 people. Standard Chartered described its own cuts as replacing "lower-value human capital." Pope Leo XIV announced he'd co-launch hi...
EpiCache: Episodic KV Cache Management for Long-Term Conversation on Resource-Constrained Environments
Modern large language models (LLMs) extend context lengths to millions of tokens, enabling coherent, personalized responses grounded in long conversational history. However, the Key-Value (KV) cache grows linearly with the extended dialogue history, causing the model’s memory footprint to quickly ex...
NVIDIA CEO Jensen Huang at Dell Technologies World: “Demand Is Going Parabolic, Utterly Parabolic”
Agentic AI inference at one-tenth the cost per token with NVIDIA Vera Rubin NVL72. Agent sandboxes run 50% faster on NVIDIA Vera than traditional CPUs — while enterprise data queries are up to 3x faster with the Vera CPU. And 5,000 enterprises like Lilly, Samsung, and Honeywell are running AI worklo...
Vera Arrives: NVIDIA’s First CPU Built for Agents Lands at Top AI Labs
The first NVIDIA Vera CPUs arrived at three of the world's leading AI labs on Friday — Anthropic in San Francisco, OpenAI in Mission Bay, SpaceXAI in Palo Alto — followed by a delivery to Oracle Cloud Infrastructure in Santa Clara on Monday. NVIDIA Vice President of Hyperscale and High-Performance C...
SandboxAQ brings its drug discovery models to Claude — no PhD in computing required
Other venture-backed companies like Chai Discovery and Isomorphic Labs have raced to build better models. SandboxAQ is betting that the bigger obstacle is access, and that Claude solves it.
Meet MemPrivacy: An Edge-Cloud Framework that Uses Local Reversible Pseudonymization to Protect User Data Without Breaking Memory Utility
As LLM-powered agents move from research to production, one design tension is becoming harder to ignore: the more useful cloud-hosted memory becomes, the more private user data it exposes. Researchers from MemTensor (Shanghai), HONOR Device and Tongji University have introduced MemPrivacy, a framewo...