AI Daily Briefing

The generative AI vendor is expanding into the legal industry in a move reminiscent of Anthropic’s legal AI deals.

A vulnerability in Microsoft Copilot Cowork allows attackers to exfiltrate OneDrive files through prompt injection and external images in automatically sent emails.

Paul Graham criticizes founders for using AI to write emails, noting that the journalistic style is easily identifiable and undermines trust.

In a Decoder interview after Google I/O, CEO Sundar Pichai discusses Google's AI-first pivot, the restructuring of DeepMind, the controversial AI Overviews in Search, the 'Google Zero' phenomenon, and his thoughts on AGI.

An audit of 2.5 million biomedical papers by Columbia University and other institutions shows that the rate of fabricated references has increased more than twelvefold since 2023. The researchers suspect a link to the widespread use of language models - the fake references match their paper's topic, follow correct formatting, and are nearly impossible to spot. 98 percent of the affected papers have received no response from their publishers.

Text diffusion models challenge the autoregressive paradigm by generating text through iterative denoising, treating generation as editing rather than typing. Three key systems define the field: LLaDA (proof of scaling), Mercury (commercial speed advantage), and Gemini Diffusion (frontier validation), representing the three phases of a new architecture class: scientific proof, industrial deployment, and frontier validation.

Kuaishou releases Keye-VL-2.0-30B-A3B, a multimodal large language model that first applies DeepSeek Sparse Attention (DSA) to multimodal scenarios, enabling 256K ultra-long context deep perception. It achieves SOTA on long-video temporal understanding benchmarks and introduces built-in Agent collaboration, paving the way for enhanced reasoning and real-world business applications.

In this tutorial, we explore the TuringEnterprises/Open-MM-RL dataset as a practical foundation for multimodal reasoning and reinforcement learning with verifiable rewards. We load the dataset, inspect its schema, analyze domains, formats, question lengths, answer types, and image distributions, and visualize representative examples from each domain. We also build a lightweight reward function that checks exact, numeric, fractional, LaTeX, and symbolic answers, giving us a useful way to evaluate model outputs. Finally, we format prompts for vision-language models, optionally test SmolVLM on sample examples, and export the dataset into a GRPO-style structure for future multimodal RL training.

This book repository from Packt covers the full lifecycle of operationalizing AI with Docker, including running local LLMs, integrating MCP, building autonomous agents, and orchestrating multi-agent systems on Kubernetes.

Alibaba's latest flagship model Qwen3.7-Max achieved a score of 1541 on the authoritative Code Arena leaderboard, surpassing GPT-5.5 and other models, ranking second globally behind the Claude series.

Google unveils Gemini 3.5 and Gemini Spark agent, plus Gemini Omni multimodal video generation; Elon Musk loses OpenAI lawsuit on statute of limitations; Anthropic agrees to $30B funding at $900B valuation; AI solves 80-year-old Erdős geometry problem.

GPT Image 2 is OpenAI's latest image model with sharp text rendering and photorealism. The article introduces imagesv2.ai, a platform offering free credits, templates, and tools like panorama, tweet screenshot, and WeChat chat generators. Pricing starts at $4.16/month with yearly plans.

Kunlun Tech releases SkyClaw-v1.0 and its lightweight version SkyClaw-v1.0-lite, native Agent models that rival top players like Claude Opus 4.6. Priced at half or less of mainstream models, with limited-time free access and future open-source plans, they deeply integrate with OpenClaw, Claude Code, and other mainstream frameworks, and are compatible with OpenAI APIs.

Planetary rovers face mobility challenges on varying terrains. Researchers introduce a multimodal wheel that continuously adjusts grouser height. In 750 trials across four surfaces, adaptive deployment reduced slip by 30-58% and improved travel time and energy by up to 77.4% on granular terrains, highlighting limitations of fixed wheels.

Researchers propose a new anisotropic diffusion method for ergodic search in multi-robot systems, overcoming the uniform error propagation of traditional isotropic diffusion, using Perona-Malik diffusion gradient to guide robot motion for more flexible coverage.

ActQuant is an action-guided mixed-precision post-training quantization framework for Vision-Language-Action (VLA) models, enabling sub-4-bit weight quantization through a two-stage approach that maintains high success rates on the LIBERO benchmark and a real UR3 robotic arm, significantly reducing memory footprint.

Researchers propose a brain-to-image system that decodes visual stimuli from EEG signals recorded during natural image viewing. It handles EEG-to-image retrieval (86.30% Top-1 accuracy among 200 candidates) and EEG-to-image reconstruction (CLIP score 0.903). The method uses multi-level blurring, EVNet features, InfoNCE loss, and multi-modal CLIP alignment with SDXL-Turbo generation, demonstrating feasibility of decoding rich visual representations from EEG.

Nano World Models is a minimalist codebase for future video prediction centered on diffusion forcing. It provides a unified interface for generative objectives, model scales, action-conditioning mechanisms, latent observation spaces, datasets, evaluation protocols, and long-horizon rollouts, enabling controlled studies of world-modeling components. Experiments across control environments, games, and real-robot data validate its effectiveness. Code, configs, and pretrained checkpoints are released for open, reproducible research.

GazeWorld is a medical imaging world model that treats the image as the world and radiologist fixation sequences as trajectories. It autoregressively predicts latent representations of fixated patches while using a spatial-completion branch for unvisited regions. At inference, it generates patch representations from the image alone without real gaze data. Frozen GazeWorld features achieve state-of-the-art diagnostic accuracy on all nine supervised settings across CheXpert, RSNA Pneumonia, and SIIM-ACR Pneumothorax, and highest zero-shot accuracy on all three benchmarks. On GazeSearch, a generic decoder trained on the same frozen features outperforms the purpose-built LogitGaze-Med by over 16% in ScanMatch and 22% in SED. The work demonstrates that modeling how experts read, not just their conclusions, offers a promising pretraining paradigm for medical imaging AI.

Large language models (LLMs) are increasingly used as automatic judges for summarization and dialogue evaluation. Prior work has documented biases such as position, verbosity, and style preferences, but largely focuses on outcomes. This paper asks whether LLM judges are cue-invariant, introducing a causal framework with interventions and metrics to test stability of rankings and explanations under non-evidential cue perturbations. Results show substantial cue-anchored rationalization, effectively mitigated by the PROOF-BEFORE-PREFERENCE method.

A tri-validation framework that performs explicit validation at three stages of automatic optimization modeling: semantic specification, mathematical formulation, and code generation, with a new benchmark NL4COP for combinatorial problems.

This study develops a large language model-based framework to extract segment disclosures directly from Form 10-K filings, preserving both reportable and nested segment information. A retrieval augmented system is designed to incorporate information across multiple filings to support comparability. The framework is demonstrated in longitudinal analysis within firms and cross-firm alignment of geographic segments, showing accurate extraction and effective handling of cross-period queries.

This paper presents the Multi-Persona Debate System (MPDS), a literature-grounded framework that automates scientific hypothesis generation by combining retrieval, long-context LLM reasoning, corpus-driven persona induction, and structured multi-agent debate. Evaluated on battery materials design, MPDS constructs literature snapshots of up to 500 papers, conducts three-round citation-aware debate, and produces mechanistically explicit proposals. It outperforms baselines in cross-perspective integration and shows promise as a diagnostic aid for identifying workflow bottlenecks.

Raon-Speech is a 9B-parameter speech language model for English and Korean, achieving top performance on speech understanding and generation while preserving text capabilities. Its full-duplex extension Raon-SpeechChat enables natural real-time conversation. The models are open-sourced.

This systematic review of 139 studies proposes a unified framework and meta-analysis. Results show multimodal fusion improves accuracy by 5.28% on average, multiview fusion boosts accuracy by 4.67% and F1 by 3.08%, but only a minority of studies used statistical tests, raising reproducibility concerns.

This paper studies truthful online preference aggregation for LLM fine-tuning in mobile crowdsourcing, addressing strategic misreporting by workers. It proposes a dynamic Bayesian game model and an online weighted aggregation mechanism that dynamically adjusts worker weights based on feedback accuracy, ensuring truthful feedback and achieving sublinear regret O(√T). Experiments on real-world datasets show significant performance gains.

This paper reframes proposer selection in LLM ensembles as a combinatorial selection problem akin to feature selection, emphasizing complementarity over accuracy or diversity. It explores computationally feasible greedy algorithms that assess complementarity using a small labeled set, validating complementarity as a guiding principle and identifying methods with the best performance-cost trade-offs.

Proposes LLM-AutoSciLab, a closed-loop framework that combines hypothesis generation with hypothesis-conditioned experiment selection and mechanism refinement. It iteratively proposes plausible hypotheses, selects informative experiments, and updates state based on evidence. Introduces ActiveSciBench with 57 enzyme-kinetics tasks and 45 gene-regulatory-network tasks. Achieves 67.6% symbolic accuracy on NewtonBench, 35.1% on ActiveSciBench-Chem, and 31.1% exact graph recovery on ActiveSciBench-GRN, with 2-5x sample efficiency over baselines.

This paper introduces a framework called Verifiable Transformers that converts task-localized Transformer circuits into bounded, solver-checkable claims. It employs direct verification for exactly encodable operators and surrogate-mediated verification for complex ones, demonstrating exhaustive verification on small symbolic tasks and applicability at GPT-2 scale, aiming to formalize mechanistic circuit explanations into provable or refutable propositions.

This paper introduces CAFD, a learning-based approach that integrates model-based signals, distance features, and a novel Concept Failure Ratio (CFR) feature extracted via Vision-Language Models to achieve superior fault detection performance while maintaining efficiency, with an average 18.3% FDR improvement over state-of-the-art baselines.

This paper proposes a novel framework called MODIAD for multimodal online distributed industrial anomaly detection. It formulates a Multi-class Intelligent Scheduling (MIS) problem and designs a Sequential Marginal Gain Greedy (SMG) algorithm to solve it efficiently. Furthermore, a Resource Efficient Class-Wise Low Rank Adaptation (REC-LoRA) strategy reduces training overhead. Experiments on MVTec 3D-AD and Eyecandies datasets demonstrate superior performance and efficiency.

This study integrates a femtosecond laser-pumped Coherent Ising Machine (CIM) with an LLM-driven agentic system using LangGraph and LangChain frameworks. LLMs effectively perform QUBO/Ising model calibration, constraint weight iteration, and validation of literature-reported schemes. All tasks use domestic large models and CIM hardware, achieving practical quantum CIM empowerment fully based on domestic core technologies. A new paradigm is discovered where agent-assisted quantum computing iterations reciprocally enhance the agent's own problem-solving capability.

A new study introduces Med-Stress, a stress test framework that reveals a dissociation between medical knowledge and belief stability in LLMs under escalating clinical pressure. The authors propose two defenses: RBED (inference-time) and R-FT (training-time), with R-FT nearly eliminating belief change.

Researchers propose BODHI, a domain-knowledge prompting method that significantly improves LLM performance in generating formal OS kernel specifications. On the OSV-Bench benchmark, BODHI with Claude Opus 4.6 achieves 96.73% Pass@1, substantially surpassing previous best results.

This paper analyzes the fundamental tradeoffs among latency, reliability, and cost in LLM-enabled agentic workflows. It introduces performance models using a parametric exponential reliability function for LLM agents and proposes a water-filling token allocation policy under latency and cost constraints.

This paper quantifies redundancy in reasoning LLMs, finding that 61-93% of chain-of-thought steps can be truncated without affecting correctness, and proves this redundancy is a structural consequence of length-agnostic outcome rewards.

Research finds that large language models (LLMs) exhibit human-like calibration biases: overconfidence on hard tasks and underconfidence on easy ones. The authors introduce LifeEval, a benchmark for evaluating calibration across difficulty levels.

Pope Leo XIV issued the encyclical 'Magnifica Humanitas' on safeguarding human dignity in the age of AI. The article highlights key sections on interpretability, development, biases, environment, algorithmic accountability, power amplification, and data as a common good, and recounts a podcast prediction from earlier in 2026 that the Pope would weigh in on AI.

Together AI has released OSCAR (Offline Spectral Covariance-Aware Rotation), an INT2 KV cache quantization method for long-context LLM serving. Unlike prior rotation-based approaches that apply data-oblivious Hadamard transforms, OSCAR derives separate rotations for keys and values from attention-aware covariance structures estimated offline. At 2.28 bits per KV element, OSCAR reduces the BF16 accuracy gap to 3.78 points on Qwen3-4B-Thinking-2507 and 1.42 points on Qwen3-8B, while delivering approximately 8× KV memory reduction and up to 3× decode speedup at 100K context length.

2026 continues to accelerate AI progress with open models lagging in agentic capabilities, Google's Gemini not yet competitive with Claude Code/Codex, American open models rising, a fierce competition between Anthropic and OpenAI, and power structures asserting control.

In this article, we cover three topics: what to visualize during training, the tools that provide those visualizations, and the methods to capture model computations directly using hooks and breakpoints.

CircleCI introduces Chunk sidecars to validate agent-generated code before it reaches CI, enhancing quality and security.

AI agents delegate tasks across systems, but current architectures lack authorization models for these delegation chains, creating security gaps like ghost permissions and broken audit trails.

Give your AI the power to make phone calls

A new study from MIT and the University of Southern California shows that lawsuits filed without a lawyer at US federal courts have nearly doubled since ChatGPT went mainstream. One in five complaints now contains AI-generated text. Judges are resorting to drastic measures to cope with the flood of filings.

As autonomous AI systems expand from software into warehouses, delivery networks, and public spaces, existing governance frameworks face new challenges. Singapore's IMDA released an updated framework for agentic AI, focusing on risk assessment, accountability, and technical controls. Companies like Grab, JPMorgan, and Walmart are testing autonomous systems, while issues of safety, responsibility, and monitoring remain key concerns.

CometChat launches Calling Skills, allowing AI coding agents to integrate HD voice and video calling with a single skill file, supporting Ringing and Session modes, 23-point verification, and multiple frameworks.

This article presents 10 everyday tasks that can be automated using AI and the low-code platform n8n, complete with ready-to-use workflow templates. Tasks include job application assistance, email management, meeting notes, calendar scheduling, daily briefings, newsletters, social media posting, blog repurposing, lead generation, and invoice processing. Each section describes what the workflow does and provides a link to the template. The article emphasizes starting small and customizing workflows for personal needs.

ModelBest (面壁智能) unveils ForgeTrain, the world's first production-grade LLM pretraining framework entirely written by AI, which outperforms NVIDIA's Megatron by 10%. The framework was used to train MiniCPM5-1B, a compact model that sets new records for intelligence density among sub-2B models.

Google DeepMind's AlphaProof Nexus, powered by Gemini 3.1 Pro and the Lean theorem prover, has cracked 9 open problems from the Erdős list, including one unsolved for 56 years. It also proved 44 OEIS conjectures, solved a 15-year-old algebraic geometry problem, and improved a convex optimization bound — all at a cost of a few hundred dollars per problem.

OmniVoice Studio runs voice cloning, video dubbing, real-time dictation, and speaker diarization entirely on your own hardware. No API keys, no cloud account, and no subscription required. The project supports 646 languages for TTS and exposes an MCP server for integration with Claude, Cursor, or any MCP client.

Andrej Karpathy updated his X bio to 'MTS @Anthropic', sparking debate about flat hierarchies. While supporters praise the anti-bureaucratic culture, critics argue it devalues individual achievements and may harm career mobility for lesser-known employees.

Huawei unveiled its AI DC full-stack data infrastructure solution at the 2026 Innovation Data Infrastructure Forum in Paris, covering data lake, knowledge & memory platform, model engineering, agent framework, and data resilience to accelerate enterprise AI adoption.

Local models offer privacy, cost savings, control, and availability. While not as capable as frontier models, they are improving. This post explains how to set up local models in Zed using LM Studio, Ollama, or llama.cpp, and offers tips for effective use.

nilbox is a desktop GUI sandbox that provides real VM isolation for AI agents, using a zero-token architecture to keep API keys secure. It supports MCP servers, domain gating, and token usage monitoring.

BobCA is a sovereign agent that learns to code with your preferences.

This paper presents IsaacIPC, a robotic simulation framework coupling GPU-accelerated Incremental Potential Contact (IPC) with IsaacSim/Lab. It maps simulated deformation between simulation and visual meshes for real-time realistic rendering, aiding data collection and policy evaluation. It also introduces the Geometric Mortar Contact Potential (GMCP) for improved tactile sensing contact-pressure resolution. Evaluated on contact benchmarks and demonstrated on rigid-deformable simulations including a quadruped robot, dexterous hand, and UMI gripper.

A new visual navigation method called MASt3R-Nav uses pixel-relative connectivity to build geometrically accurate maps without requiring global consistency, enabling more capable navigation than traditional topological graphs.

Coronary microvascular dysfunction (CMVD) affects approximately 40%-60% of patients with ischemia and non-obstructive coronary arteries, yet diagnosis remains challenging due to reliance on invasive functional testing or subjective Thrombolysis In Myocardial Infarction (TIMI) flow grade. The TIMI Myocardial Perfusion Frame Count (TMPFC) offers an objective, angiography-based quantitative measure of CMVD, but its clinical translation is hindered by cumbersome manual calculation and insufficient validation. This study aims to develop and validate a deep learning-powered TMPFC calculation (DL-TMPFC), enabling integration into clinical workflows. In a cohort of 655 patients from three independent institutions, DL-TMPFC showed excellent agreement with expert manual measurements (bias: -0.93 frames; 95% LoA: -5.33 to +3.47; r =0.98). DL-TMPFC markedly enhanced clinical feasibility by fully automating TMPFC and removing observer dependence, accurately identifying CMVD across a full spectrum of coronary pathologies and capturing continuous severity for quantitative risk stratification.

Digital avatar watermarking faces unique challenges: avatars are routinely post-processed with background replacement, reframing, and format conversion before deployment. This paper introduces the RAW benchmark with 50 synthetic avatar videos from 5 providers and 6 attacks simulating real-world workflows. Evaluation of 7 existing methods reveals that avatar-specific attacks degrade watermark recovery. The proposed WALT method embeds watermarks in UV texture space via 3D face reconstruction, achieving 92.4% robustness to zoom and 95.6% on background removal. The benchmark is released to facilitate research.

This paper introduces a runtime execution model that enforces Reconstructive Authority (RAM) in autonomous agent systems: actions are permitted only if authority can be constructed from the current state. It extends the admit/deny state space with a third state, halt, for cases where authority is undefined due to incomplete or uncertain observability. A concrete execution protocol is defined including dynamic dependency resolution, authority reconstruction, and explicit decision semantics. A Recovery Loop integrates drift detection (IML) with execution control (ACP) to suspend execution, acquire missing information, and retry authority reconstruction. The model guarantees safety (no action without constructible authority) and conditional liveness (execution resumes when authority-defining variables become observable).

This paper introduces Quantum Frog, a two-player cooperative game with a quantized-time mechanic. Using reinforcement learning, the authors analyze difficulty scaling, optimal single-agent policy, cooperation gap, and emergent strategies. Key findings: the rush strategy is optimal; adding an uncoordinated player is harder than sextupling traffic; cooperative training boosts success rate by 32–34 percentage points; the emergent strategy is synchronized rushing.

A new paper presents Context, the intelligence layer of the Magarshak Architecture, which replaces reactive chatbots with proactive goal-directed agents. The architecture relies on write-time context assembly, composable sandboxed wisdom programs, and proactive goal stream state machines. It proves six theorems including context stability and proactive dominance.

Allen Wu introduces AgentToolBench-Code, an open-source benchmark that evaluates AI coding agents on 16 security scenarios. Testing Claude Code Sonnet 4.6 and Haiku 4.5 reveals that Sonnet scores +9 (12 caught, 3 silent fail, 1 noop) vs Haiku's +3 (8 caught, 5 silent fail, 3 noop). The initial tie was due to a small corpus; the expanded set shows Sonnet's advantage in pattern recognition. Both models share structural failures in dependency trust and budget discipline. The work is reproducible for ~$3.50 in API costs and encourages community contribution.

AIntegriX is an open-source server that coordinates multiple ACP agents via a single API, enabling parallel execution, pipelines, and intelligent routing.

Corey Quinn comments on Pope Leo XIV's AI encyclical Magnifica Humanitas, which was influenced by Anthropic co-founder Christopher Olah. Quinn calls it the single greatest act of vendor lobbying.

UUMuse is a cloud AI knowledge base platform where you upload files once and use them across GPT, Claude, DeepSeek, Qwen, and more — with cited answers, persistent memory, agent mode, a multi-expert debate feature (Spark), and flexible deployment as docs sites, APIs, or MCP servers.

This article introduces the process of SEO competitor analysis, including keyword gap analysis, a five-step method, and regular check cadence. It also highlights Fox AI's free competitor analyzer tool that uses Lighthouse audits to generate actionable playbooks.

Experts warn that AI-generated news sites masquerading as local outlets, known as 'pink-slime' journalism, have appeared in regional Australia, raising concerns about misinformation and erosion of trust in media. The sites were traced to an Australian living overseas who called it a failed experiment.

The software industry is undergoing an unprecedented metamorphosis. From simple statistical completion of early coding assistants, through conversational chatbots and the failure of multi-agent systems, we have arrived at the era of the Agentic Loop. This comprehensive guide analyzes the entire evolution, from the Completion paradigm to the revolutionary Ralph Loop that is redefining how we write code.

This tutorial provides a detailed guide to building an advanced federated learning experiment using NVIDIA FLARE, comparing FedAvg and FedProx on a non-IID CIFAR-10 dataset. Client data is partitioned using a Dirichlet distribution to simulate realistic label imbalance. The NVFlare Job API is used to define and launch federated jobs, while the Client API handles local training and model exchange. Complete code implementation and experimental results visualization are provided.

Anthropic co-founder Chris Olah spoke at the Vatican on Pope Leo XIV's encyclical on AI, highlighting the need for ethical scrutiny, global responsibility, and moral imagination. He identified three key questions for the Church: duty to the global poor, human flourishing, and discernment of AI's nature.

China is restricting overseas travel for top AI researchers at private firms like Alibaba and DeepSeek, requiring official approval to leave the country, due to fears of data leaks and talent poaching.

Streaming platform says remix tool agreed with Universal Music Group will protect artists from piracy

Google Cloud COO Francis de Souza urges companies to integrate security into their AI strategy from day one, emphasizing that AI security is a boardroom issue, not just a technical one.

From the 2017 "Slaughterbots" video to Anthropic's ongoing battle with the Pentagon, AI's role in warfare has moved from science fiction to reality. This article traces the evolution of AI warfare, highlighting Project Maven, the ambiguity of autonomous weapons definitions, the failure of international regulation, and the complex relationship between tech companies and the military.

This episode of The Good Robot explores how feminist principles and decentralized infrastructure could transform cloud infrastructure from a corporate service into a public commons. Friederike von Franqué, policy advisor at Wikimedia Germany, discusses examples from Frankfurt's energy-intensive data centres to Stockholm's municipally owned fibre network, advocating for environmental accountability and community-driven design.

An acoustic regulation system for dogs.

This article explores cognitive security as a key subfield of AI safety, focusing on protecting human cognition from AI-induced harms such as misinformation and cognitive biases. It discusses the relationship between cognitive security and AI safety, research directions, and challenges.

A retrofit of a custom series elastic element to a black-box actuator improved force control bandwidth from 10.32 Hz to 30.32 Hz (2.93x), outperforming a commercial sensor by 7.63% at a cost of 25 GBP.

The paper introduces 'algometrics', a framework for time series forecasting when predictive models influence the data they aim to predict. It distinguishes historical risk from deployment risk and proves three key results: deployment risk is not identifiable from passive historical data alone; historical model rankings can invert due to crowding; and randomized actions can identify short-horizon linear feedback with a finite-sample bound. The findings suggest that benchmarks in algorithmic markets should report feedback sensitivity alongside predictive accuracy.

The California State University system has inked multi-million dollar contracts with OpenAI to provide ChatGPT Edu, but a survey reveals majorities of students and faculty are skeptical of AI's educational benefits, worrying about impacts on jobs, creativity, and the environment.

As Wyoming faces another tinderbox fire season, high-tech home fire systems are starting to catch on across the West. One of the fastest growing is a Jackson Hole, Wyoming, company that makes AI sprinklers that are saving homes from wildfires.

In calling for regulation of the digital revolution, and foregrounding human dignity, the pontiff has contributed to a crucial ethical debate.

A look at the alarming trend of Suno AI users exclusively listening to their own AI-generated music, and the possible reasons behind it. The author found that many users boast about abandoning Spotify, but none were willing to explain why they prefer AI slop over real music. Theories include narcissism or laziness, with the author leaning towards laziness.

The content creators behind channels like Chloe VS History are using AI tools to ‘bring history to life in a really visceral way’.

Blockchain ecosystems are losing developers across the board while AI projects dominate GitHub growth. Weekly crypto commits dropped from about 850,000 to 210,000 since early 2025, and active developers declined 56% to around 4,600.

ContextVault is a browser extension that captures AI conversations in real-time across major LLM platforms like ChatGPT, Claude, and Gemini, storing them locally in IndexedDB. It allows one-click export as Markdown or ZIP, ensuring your data never leaves your device. Free, open source, no accounts or backend required.

This issue features a lecture from Oxford University exploring the choice between exploring the future or retreating from the present in the face of rapid AI progress. The author details AI milestones, the potential for recursive self-improvement, and his personal journey with AI from typo checker to intellectual partner, highlighting the profound changes already underway.

RED is a real-time scheduling framework for multi-task deep neural network workloads on resource-constrained robotic platforms. It adapts to runtime environmental changes by assigning intermediate sub-deadlines, leveraging MIMONet weight sharing, and reconstructing computation graphs. Implemented on NVIDIA Jetson and Apple M-series platforms, RED consistently outperforms existing methods in throughput, deadline satisfaction, robustness, adaptability, and overhead.

With AI companies snapping up RAM and storage drives to build data centers and train LLMs, prices on vital components and pre-built rigs have skyrocketed. This means that gamers and DIY PC builders are putting upgrades and new purchases on hold in the hope that the market will eventually correct itself. If you've been putting off a new build or upgrade, this Memorial Day weekend, you can save $176 on the kit at Best Buy, bringing the price back to just under $1,000.

Y Combinator founder Paul Graham ignores emails clearly written by AI—they feel 'like being lied to,' he says. That's coming from one of OpenAI's earliest investors. Studies suggest his reaction is anything but unusual.

Uber president Andrew Macdonald says it's 'hard to draw a line' between AI spending and deliverable features, as the company reportedly exhausted its annual AI budget four months into 2026.

Robotic assistants in long-term human-robot collaboration need to assist users under partial observations while leveraging cross-day interaction history. Since human traits are often unknown initially, passive infer-then-act is ineffective. We propose PACT, an ask-or-act framework that evaluates contextual sufficiency to decide whether to seek clarification before acting. Using reinforcement learning, PACT improves assistance accuracy and clarification utility over passive baselines in multi-day embodied scenarios.

This paper proposes a reinforcement learning framework that modulates a constant reference trajectory to perform compact, position-constrained quadrotor inversions while remaining compatible with traditional trajectory generation and tracking. In simulation, the method reduces position RMSE by 32% and settling time by 57% relative to the strongest optimization-based baseline. Hardware experiments demonstrate successful inversion across multiple yaw configurations with position RMSE below 0.35m.

A study compares deep learning models with linear interpolation for imputing missing satellite data due to cloud cover in aquatic monitoring. CNN-based models, especially CNN, outperformed linear interpolation across four lakes. The imputed data improved the reliability of algal bloom detection using PlanetScope SuperDove imagery.

Protein-ligand modeling underpins computational drug discovery. Existing benchmarks typically evaluate whether a protein and ligand interact and how strongly they bind, but provide limited evidence of whether models can localize binding sites or identify non-covalent interactions. To address this, we introduce InteractBind, a large-scale dataset of ~100k protein-ligand pairs with a benchmark for fine-grained evaluation. The core task is binding-site localization using interaction maps of six non-covalent interaction types. Evaluating eight existing models reveals limited binding-site localization despite strong binary binding prediction, with marked variation across interaction types. InteractBind encourages development of more interpretable and physically grounded models.

Recent college grads are not very fond of commencement speakers hyping up a technology they see as a threat to their career prospects.

The Ecovacs X8 Pro Omni won the ZDNET Lab Award for best pickup performance in a robot vacuum, and it's currently $67 off for Memorial Day.