The article argues that instead of ending quarterly earnings reports, the SEC should move toward more frequent monthly, weekly, or real-time reporting, leveraging AI to automate the process. The author shares personal experience showing that frequent reporting reduces short-term focus, and suggests that companies should stop providing guidance to end short-termism. The piece also discusses the UK's failed experiment with semi-annual reporting and draws parallels to recent blowups in private credit.

The author shares how he built a tool called baro using the Mozaik framework to maximize his Claude Code subscription by enabling parallel agentic workflows. He details the journey from sequential use to a system that automatically generates pull requests without human supervision, highlighting the key architectural decisions that solved coordination, memory sharing, and state management issues.

Facing the rapid rise of AI, Silicon Valley's leaders, such as Sam Altman and Dario Amodei, are proposing measures like universal basic income, a shorter work week, and taxing billionaires to mitigate societal disruption and wealth concentration.

Kuaishou is reportedly spinning off its video generation model Kling AI with a $20 billion valuation, nearly 70% of Kuaishou's own market cap. Kling AI has achieved $500 million annualized revenue and plans to launch IPO early next year.

Sherloq is an AI sales tool that monitors LinkedIn for buying signals from prospects, such as interactions with competitors and job changes, notifying B2B sales reps of warm leads and automatically generating personalized outreach materials that integrate directly into CRM systems.

State and federal energy ministers agreed that datacentres should fully offset their electricity demand through investments in new renewable generation and energy storage.

Building AI agents revealed that real work requires institutional controls—ownership, authorization, review, closure, and replay—not just agent capabilities. The author proposes 'AI institutions' with primitives like WorkRecord and VerifierDecision, tested on a prediction-market desk, and offers five checks to ensure trustworthy AI work.

Skymizer unveils HTX301 PCIe AI accelerator using 28nm chips and LPDDR memory, delivering 700B parameter LLM inference at 240W, challenging Nvidia and AMD.

A Chinese journalist rents Mark Zuckerberg's former home and delves into the community of young, talented, and highly sought-after Chinese AI researchers in Silicon Valley, revealing how they leverage their math skills and work ethic to become tech stars amidst geopolitical tensions.

Let a fleet of parallel agents test your app in minutes.

Peekaboo v3 enables AI agents to see the screen and control the mouse and keyboard on Mac, achieving true desktop manipulation with pixel-level screenshots and UI element reading.

BEACON is a theory-driven framework for training generative robot policies with abundant source demonstrations but limited target demonstrations. It casts cross-domain co-training as a discrepancy-aware importance-reweighting problem, jointly learning a diffusion-based visuomotor policy and per-sample source weights to minimize an objective informed by target-domain generalization guarantees. Across sim-to-sim, sim-to-real, and multi-source manipulation settings, BEACON improves robustness and data efficiency over baselines, implicitly achieving feature alignment.

Flow matching policies suffer from a train-inference mismatch that causes compounding trajectory errors. This work proposes four complementary remedies: auxiliary rectified flow velocity regression, multi-step trajectory consistency training, velocity field regularization, and fourth-order Runge-Kutta inference. Paired with a dual-view 3D point cloud encoder, the method achieves substantial gains on real-robot tasks, including raising long-horizon success rates from 0% to 70% and 60%, and reaching 100% on precision tool placement.

Researchers propose LE-PAVD, a hybrid model integrating physics priors with learned components to improve vehicle dynamics prediction for high-speed autonomous racing. On unseen tracks, it reduces average displacement error by 16.1%, final displacement error by 20.6%, and yaw-rate RMSE by 91.3% versus a deep dynamics baseline, while using 21.6% fewer FLOPs and achieving ~1.5x faster inference. In closed-loop simulations, lap times improve by 17.4% on a training track and 9.5% on a test track with no boundary violations.

Researchers propose a hierarchical language-driven framework using two large language model modules (a high-level planning agent and a low-level spatial reasoning sub-module) to improve natural human-robot interaction for service robots. The system processes natural language commands via ReAct-style prompts, integrates YOLOX-GDRNet for object detection and pose estimation, and achieves an 86% task success rate across 24 test scenarios.

A research team proposes the AL-DRL framework for autonomous gastric navigation using wireless capsule endoscopy. It leverages stable landmark coordinates to achieve over 97% coverage in simulations within 50 seconds and 87% coverage in ex-vivo experiments, reducing procedure time by 53% compared to expert manual control. The method significantly outperforms standard DRL agents.

This paper argues that the Structural Model of General Intelligence (SMGI) can provide a formal account of admissible runtime change for robotic services, complementing the Robotic Service Ontology (RoSO). By embedding RoSO as a typed semantic layer into SMGI, the authors establish a RoSO-to-SMGI adequacy theorem, identity-preserving reconfiguration criteria, and compositional conditions for globally admissible updates.

A systematic comparison of four asynchronous inference methods for Vision-Language-Action (VLA) models—IT-RTC, TT-RTC, VLASH, and A2C2—shows that A2C2's per-step residual correction achieves the best performance on both Kinetix and LIBERO benchmarks, maintaining over 90% solve rate up to 8-step delays. TT-RTC is the most robust training-based method with zero inference overhead, while IT-RTC degrades under high delays. The study provides unified codebases for reproducible evaluation.

This study presents a transfer learning framework for digital image forgery detection that combines compression-aware feature enhancement with CNNs. A hybrid input representation (RGB + FDIFF) and Youden Index-based threshold optimization are proposed. Evaluated on CASIA v2.0 with six architectures, DenseNet121 achieved highest accuracy and AUC, while ResNet50 had the best MCC, emphasizing that accuracy alone is insufficient for forensic applications.

This study presents a whole-body tumor segmentation method developed for the AutoPET III challenge using the nnU-Net framework with a ResNet encoder. It systematically investigates training strategies including intensity normalization, batch dice optimization, and CraveMix augmentation, achieving a Dice score of up to 0.80 and a third-place ranking. The code is publicly available.

WATCH is a framework for month-level change-event localization on PlanetScope satellite mosaics (2017-2024, 4.7 m/px) supporting three complementary scoring approaches: Temporal Embedding Distance (TED), Self-Supervised Change Detection (SSCD), and Weakly Supervised (WS) temporal localization. Benchmarked on 1,943 archaeological sites in Afghanistan, unsupervised methods (TED, SSCD) consistently outperform WS. TED with SatMAE achieves 55% exact-month recall, while TED with GeoRSCLIP, CLIP, or Satlas-Pretrain reaches 92.5% within a three-month tolerance. Directional margin analysis reveals temporal biases: SSCD offers early warning, while TED favors confirmation-oriented detection.

VT-Bench is the first unified benchmark standardizing vision-tabular discriminative prediction and generative reasoning tasks. It aggregates 14 datasets across 9 domains (over 756K samples) and evaluates 23 representative models, highlighting substantial challenges in visual-tabular learning.

Current vision language models face hallucination and robustness issues against ambiguous or corrupted modalities. We hypothesize that these issues can be addressed by exploiting the shared information between modalities to compensate for the impaired one. To this end, we analyze multimodal interactions -- redundant (shared), unique (exclusive), and synergistic (emergent) task-relevant information provided by the modalities -- to determine their impacts on model reliability. Specifically, amplifying redundant interactions would increase this exploitable shared information to resolve these issues; yet, modern instruction datasets often eliminate redundancies to prioritize visual grounding. We bridge this gap through a self-captioning workflow featuring a Multimodal Interaction Gate: a mech…

A study benchmarks ResNet backbones in RT-DETR for round object detection under environmental variations. ResNet50 excels under illumination changes (accuracy near 1.00, confidence up to 0.869, latency ~0.058-0.059 ms), while ResNet34 performs best under background changes (confidence up to 0.887). Results show optimal architecture depends on the environmental variation, with intermediate-depth models offering the best balance.

Researchers propose VLADriver-RAG, a framework that enhances vision-language-action models for autonomous driving by using retrieval-augmented generation to improve generalization in long-tail scenarios. It abstracts sensory inputs into spatiotemporal semantic graphs to filter noise and uses a scenario-aligned embedding model with graph-DTW metric alignment to prioritize topological consistency. Achieves a driving score of 89.12 on Bench2Drive, setting a new state-of-the-art.

Magis-Bench is a new benchmark evaluating LLMs on magistrate-level legal writing tasks, derived from Brazilian judicial exams. It includes 74 questions across 8 exams (2023–2025). 23 LLMs were evaluated using an LLM-as-a-judge methodology with four frontier models. Gemini-3-Pro-Preview achieved the highest average score (6.97/10), but even top models scored below 70%, indicating judicial reasoning remains challenging for current LLMs.

This paper introduces Sem-ECE, a framework for evaluating calibration of large language models in open-ended question answering. It samples answers, groups them into semantic classes, and uses frequencies as confidence. The framework outperforms existing methods and provides diagnostics for question difficulty.

This research proposes a computational model that uses LLMs to convert explanations into action plans (policy prior and value map) for planning under partial observability. Through four preregistered experiments with 1,200 explanations on 24 maps, higher-scored explanations are judged more helpful and improve navigation performance. Results show procedural explanation as utility-guided communication shaped by grounding language into action under uncertainty.

This study explores the use of large language models (LLMs) with remote sensing imagery for smart city tasks, including design suggestions, constructability assessment, land use patterns, and risk identification. It evaluates multi-scale imagery inputs and compares models like InternVL and Qwen, demonstrating the potential for AI-assisted decision-making.

AIPO is an enhanced reinforcement learning framework that improves LLM reasoning by enabling the policy model to proactively consult three collaborative agents (Verify, Knowledge, and Reasoning) during exploration, expanding its capability boundary. Experiments show consistent gains on AIME, MATH500, GPQA-Diamond, and LiveCodeBench.