PRISM couples vision-language models (VLM) and large language models (LLM) via a dynamic question-answer pipeline to bridge the perception-reasoning-decision gap, outperforming state-of-the-art on ALFWorld and R2R benchmarks with full automation.

This position paper argues that sycophancy in LLMs is a boundary failure between social alignment and epistemic integrity. It proposes a three-condition framework to identify sycophancy and a taxonomy for classification, advocating for boundary-aware assessment and mitigation strategies.

An AI-enabled analytics framework using existing CCTV infrastructure to evaluate soft traffic-calming interventions in Minneapolis shows speed reductions of up to 18.75% at unsignalized intersections and 20% at signalized intersections, demonstrating a low-cost, rapid method for evidence-based transport policy evaluation.

BALAR is a task-agnostic algorithm that enables structured multi-turn interaction between LLMs and users by maintaining beliefs over latent states and selecting clarifying questions via mutual information maximization. It achieves significant accuracy gains on detective, puzzle, and clinical diagnosis benchmarks.

This paper introduces Partial Evidence Bench, a deterministic benchmark for measuring when enterprise AI agents produce seemingly complete answers despite missing critical evidence due to authorization constraints. It covers three scenario families with 72 tasks and evaluates four aspects. Results show silent filtering is catastrophically unsafe, while explicit fail-and-report behavior eliminates unsafe completeness.

ZAYA1-8B is a reasoning-focused mixture-of-experts model with 700M active and 8B total parameters, trained on AMD hardware. It matches or exceeds DeepSeek-R1-0528 on math and coding benchmarks and introduces Markovian RSA for test-time compute.

This paper introduces Annotator Policy Models (APMs), interpretable models that learn annotators' internal safety policies from labeling behavior alone, enabling understanding of annotation disagreement sources (operational failures, policy ambiguity, value pluralism). APMs achieve over 80% accuracy, predict counterfactual responses, and reveal policy differences across demographic groups, supporting transparent and inclusive safety policy design.

A 16-year-old entrepreneur Audrey Fei discusses the tension between AI personalization and true individuality, arguing that LLM wrappers are crumbling and presenting her patented real-time accent conversion technology and language learning platform.

Memori launches agent-native memory infrastructure that builds persistent, structured long-term memory from agent execution traces, achieving 81.95% accuracy with 95%+ cost savings.

AnamDB is an AI-native neurosymbolic database engine built in Rust, integrating probabilistic neural perception with deterministic symbolic reasoning. It offers explainability, safety guardrails, multi-objective optimization, heterogeneous hardware support, and distributed reasoning capabilities, positioning itself as a kernel for agentic memory.

New research from Carnegie Mellon, MIT, Oxford, and UCLA reveals that using AI chatbots for as little as 10 minutes can significantly impair critical thinking and problem-solving abilities. Participants who relied on AI were more likely to give up or make mistakes when the AI was removed. The study highlights the need to design AI that scaffolds learning rather than just providing answers.

Journalist Jamie Bartlett investigates the phenomenon of AI jailbreakers—individuals who deliberately attempt to bypass the safety restrictions of large language models to understand their vulnerabilities and improve AI safety.

This article examines the copyright implications of AI-assisted contributions to CPAN, arguing that the risks are not new – CPAN has always had provenance uncertainty. The key change is increased volume, not a new type of risk. It recommends transparency through disclosure (e.g., Co-authored-by) and human accountability, rather than outright rejection.

A developer built jakartaairportcgk.com as an alternative to the official Soekarno-Hatta Airport website, which blocks international traffic and suffers from poor performance. The new site provides live flight schedules, skytrain timings, and terminal info, and achieves perfect Lighthouse scores.

OpenRouter analyzed the real cost impact of GPT-5.5 compared to GPT-5.4. By examining usage data from users who switched models, they found cost increases between 49% and 92%, but longer prompts (over 10K tokens) saw 19-34% fewer completion tokens, partially offsetting the price hike. Shorter prompts faced larger cost increases.

Netflix is testing a new AI-powered voice search feature that allows users to find movies and TV shows through natural language commands. Currently available to a subset of subscribers, the feature aims to bypass built-in voice assistants on smart TVs and streaming devices, potentially altering the power dynamics between streaming services and TV operating systems.

If your Roku is lagging, with apps struggling to open, it might not be your Wi-Fi. Here's what I do to fix performance.

Modern IDEs are organized around files and projects, but AI workflows need a programmable surface where every stateful object is addressable, inspectable, and replayable. Drawing inspiration from Smalltalk, Lisp machines, and Emacs, the future IDE should be an operating system for development work, unifying terminals, logs, browser sessions, tests, diffs, and agents under an object model that both humans and AI can navigate.

Selvedge is a local MCP server that logs the reasoning behind every code change made by AI agents in real time, storing it in a SQLite database for later querying. It tracks entities like columns, env vars, and endpoints, providing context that git commit messages often miss.

After extensive comparison, Google Maps wins in routing, AI, and features, while Apple Maps offers a cleaner interface.

The White House has ordered Anthropic not to expand access to its powerful AI model Mythos, and is seriously considering implementing a full prior restraint regime requiring government approval before releasing frontier models. This abrupt and messy policy shift risks slowing innovation, enabling elite capture, and creating arbitrary regulation. The article examines the context, potential impacts, and alternative approaches.

Google Chrome has been discovered quietly downloading a ~4GB AI model, Gemini Nano, to user devices, with reports that it may re-download even after deletion. This has sparked debates on privacy, consent, and user trust. The article analyzes the situation, technical background, user responses, and broader implications.

Radiq is a product intelligence platform for the autonomous coding era. It automates the customer-to-code loop, helping product managers extract decision signals from Slack, Jira, Confluence, and meetings, generate developer-ready specs, and push them directly into IDEs like Cursor and Windsurf, reducing a week's work to minutes.

Incidentary provides shared causal traces for incident response, helping teams understand what caused an incident before the war room starts. It uses an OpenTelemetry exporter to capture outbound calls, errors, and slow queries, building a causal graph in real time. When an alert fires, it delivers an artifact showing where the failure first appeared, when, how it spread, and what to look at next — all without AI inference.

This article describes how to use AI tools Grok and Whicks Lab to create YouTube kids channel videos.

Current critic-less RLHF methods aggregate multi-objective rewards via an arithmetic mean, leaving them vulnerable to constraint neglect: high-magnitude success in one objective can numerically offset critical failures in others (e.g., safety or formatting), masking low-performing “bottleneck” rewards vital for reliable multi-objective alignment. We propose Reward-Variance Policy Optimization (RVPO), a risk-sensitive framework that penalizes inter-reward variance during advantage aggregation, shifting the objective from “maximize sum” to “maximize consistency.” We show via Taylor expansion that a LogSumExp (SoftMin) operator effectively acts as a smooth variance penalty. We evaluate RVPO on rubric-based medical and scientific reasoning with up to 17 concurrent LLM-judged reward signals (Q…

Velox is a framework for learning latent representations of 4D objects from unstructured dynamic point clouds. It compresses spatiotemporal color point clouds into dynamic shape tokens, supervised by a 4D surface decoder for geometry and a Gaussian decoder for appearance. Strong performance is demonstrated on video-to-4D generation, 3D tracking, and cloth simulation.

Apple hosted a two-day workshop in early 2026 focusing on privacy-preserving machine learning and AI, bringing together researchers to discuss advances in federated learning, foundation model privacy, attacks and security, and more.

We propose HeadsUp, a scalable feed-forward method for reconstructing high-quality 3D Gaussian heads from large-scale multi-camera setups. Our method employs an efficient encoder-decoder architecture that compresses input views into a compact latent representation, which is then decoded into UV-parameterized 3D Gaussians anchored to a neutral head template. This UV representation decouples the number of 3D Gaussians from the number and resolution of input images, enabling training with many high-resolution views. Trained on an internal dataset of over 10,000 subjects—an order of magnitude larger than existing datasets—HeadsUp achieves state-of-the-art reconstruction quality and generalizes to novel identities without test-time optimization. We analyze scaling behavior and demonstrate down…

Learn how to deploy any HuggingFace model in one session using Goose and Together's Dedicated Container Inference. Skip the setup complexity — one prompt gets your model running in a production-grade GPU environment on release day.