cayleyR is an R package for solving permutation puzzles by detecting cycle intersections in Cayley graphs. It uses an iterative bidirectional search and distance-guided bridge selection. The package targets the TopSpin(n,k) puzzle and leverages C++ indexing with optional Vulkan GPU acceleration. It is available on CRAN.

This paper introduces DROPJ, a human-centered method for safe training and deployment of agents in safety-critical environments with unknown dynamics and no suitable reward function. DROPJ first learns a world model from prior real-world trajectories, then has a human play in the simulator to generate informative simulated trajectories. Preferences and justifications are elicited from humans on trajectory segments, which are used to train a reward model. The agent is then deployed using model predictive control with the world model and reward model. Experiments show that generating informative simulated trajectories significantly reduces computational cost and improves deployment performance, with preference feedback outperforming other types, and safety justifications enhancing safety.

A technical report from arXiv introduces Oracle Agent Memory, a database-native memory system built on Oracle Database for long-horizon AI agents. It achieves 93.8% accuracy on LongMemEval while using 10.7x fewer tokens compared to flat-history baselines. The system addresses memory lifecycle, layered architecture with scope control, and evaluation methodology combining task accuracy with memory-specific metrics.

Small language models struggle with molecular property prediction due to structural blindness. A new framework called Context-Augmented Prompting integrates GNN tools to provide predictive hints and explanatory subgraphs, achieving up to 74% relative improvement on Tox21, though a gap with specialized GNNs remains.

This survey reviews self-improving autonomous agents transitioning from prototypes to deployed systems. It introduces a system-level framework modeling an agent as a foundation model coupled with an operational scaffold (prompts, memory, tools, control logic). Self-improvement is formalized as a self-induced update operator that updates model parameters or scaffold components. The paper categorizes prior work by update target and driving signals, and discusses applications, evaluation, and open challenges.

This paper presents a probabilistic extension of neuro-symbolic AGI based on Belnap's Typed Intensional FOL (IFOL_B). By integrating Nilsson's probability structure, it computes probabilities for unknown sentences, introducing global and local symmetry transformations to preserve knowledge integrity and enable real-time decision-making. Neural networks compute the probability density function via Shannon's maximum information entropy.

Large language models produce chain-of-thought reasoning that appears logically sound but may not genuinely depend on its stated premises. This paper introduces interventional grounding audits, a black-box step-level test of premise dependency that substitutes a predicate in a single premise and checks for changes in the normalized conclusion of each reasoning step. Evaluated on ProntoQA with GPT-4o, the method achieves F1=0.806 for detecting proof-tree dependencies, significantly outperforming a self-consistency baseline (F1=0.343). Notably, 66% of correctly solved problems contain at least one step insensitive to a direct proof-tree dependency, revealing a 'right answer, wrong reasoning' signal.

Researchers propose SPINE, an agentic framework that automates debugging and deployment of bimanual robots, reducing reliance on expert calibration. In tests, SPINE improved success rates and reduced time-to-teleoperation compared to manual methods.

OriginBlame is a record- and token-level data provenance system that precisely resolves data removal requests to individual training records, reducing over-deletion from 101x to 1.3x on Wikipedia data. Integration adds 1.3-4.0% throughput overhead (HuggingFace) and 2.1-19.0% (Datatrove). On a 1.7B model, provenance-based forget sets improve unlearning by 42% over random baselines.

Researchers developed an automated framework that helps AI models generate CAD programs more accurately and efficiently.

An interactive visual map of 56 AI concepts and their connections, allowing users to explore and learn about key topics in artificial intelligence.

Through the lens of a blues song, the author explores how large language models generate text—often explaining after the fact, but sometimes planning ahead. The article reflects on the 'phony voice' of AI, our drive to strip it bare through interpretability, and the author's own experience using AI to write about AI.

Agentic inference is shifting AI infrastructure from training expansion to context-aware, memory-augmented reasoning. The RAISE Summit highlighted three key insights: specialization across the AI stack with diverse chips and accelerators, storage becoming an active memory extension for GPUs, and the integration of capital deployment and data sovereignty into infrastructure design.

Voicebox is an open-source voice-to-text tool that captures speech, transcribes it via Whisper, and formats the output with an LLM, all powered by Cloudflare Workers AI.

Boom is an AI coach that helps Instagram creators grow, monetize, and secure brand collaborations, even with as few as 100 followers. It offers personalized daily planning, 24/7 growth support, automatic collab matching, and contract management. Early access opens August 1, 2026, with a one-time early bird fee of $4.50.

Or will they forge a path of their own?

Service organizations supporting critical infrastructure face a structural mismatch: tightening uptime requirements while maintenance models and technician capacity lag. AI offers anomaly detection for condition-based maintenance, prescriptive guidance for consistent technician performance, and requires operational transformation to succeed.

A CNA investigation found about 500 TikTok videos pushing false or misleading claims about Singapore or Malaysia, drawing a total of more than 3 million views. The videos use AI-generated female personas, reused voices and scripts, and systematically spread misinformation aimed at eroding trust and social cohesion.

Neocloud provider QumulusAI announced its direct listing on Nasdaq under the ticker QMLS. The move signals the maturation of AI-first infrastructure built around GPUs and power availability. The company focuses on rapid GPU deployment, leveraging colocation and modular data centers. The listing provides capital flexibility, public company credibility, and timing advantage. The article also explores neocloud differentiation and advice for IT leaders.

River is a platform that uses AI account executives to automate B2B product demonstrations and deal closing.

Simon Willison discovered a Rust-based Mermaid terminal renderer in the Grok CLI codebase and ported it to the browser via WebAssembly, creating an online tool.

The video discusses AI data center companies being put up for sale, with owners liquidating assets in anticipation of a market crash.

A free collection of copy-paste UI components for AI agents, including thinking states, tool calls, streaming text, tables, and more.

Throttle is a macOS menu bar meter for Claude Code usage that evolved into a full cockpit. The free version provides local, no-telemetry monitoring. Pro adds a project cockpit with embedded terminals, auto-hibernate, remote session transfer to Linux servers, and an AI optimizer that audits claude.md and settings.json to reduce output tokens by 65–75%. All data stays local or in iCloud private database. One-time fee of €29.

Prime Minister Albanese's speech at the University of Sydney outlined a shift in AI policy, promising laws to protect Australian creatives, but lacked specifics and omitted data centre regulation.

Apple researchers introduce SSD, a simple self-distillation method that improves LLM code generation without verifiers, teacher models, or reinforcement learning, achieving significant gains on LiveCodeBench.

A new personalization system for Apple TV search combines text-based multilingual embeddings and ID-based collaborative embeddings, fine-tuned via contrastive learning and interaction data. The approach improves ranking for ambiguous short queries (8.63% NDCG@10 lift) and users with longer histories (4.37% vs 2.13% for new users). Online A/B test shows +1.14% tap-through rate and +1.23% conversion rate.

This article breaks down the real meaning behind reliability numbers like 99%, 99.9%, and 99.99% uptime for AI inference services, explaining the failure domains each tier must survive and the architectural requirements. The authors from Together AI share their experience building reliable inference infrastructure and provide key questions to ask any provider before committing.

Cars24 uses OpenAI-powered voice and chat agents to handle 1M+ monthly conversation minutes, recover 12% of lost leads, and bring agentic workflows across the company.

We study doubly sub-linear interactive proofs of proximity (dsIPPs): proofs that are ultra-fast to generate, and can be used to prove approximate assertions about a huge input. Proof generation is ultra-fast in the sense that it only requires reading a small (sub-linear) portion of the input. Approximate verification of the proof is even faster (reading an even smaller portion of the input). We construct such proofs for any property decidable by constant-width read-once oblivious branching programs (ROOBPs), as well as for approximate verification of Hamming weight and a relaxation of bipartiteness.