This paper investigates whether six offline RL losses (SFT, RFT, DFT, RIFT, Offline GRPO, DPO) are mechanistically distinct in weight-space geometry when used for reasoning distillation. Using identical math rollouts from Qwen3-4B, they find SFT, RFT, and RIFT have nearly colinear deltas; DFT diverges; Offline GRPO adds orthogonal components; and DPO lies in a near-orthogonal subspace with highest accuracy but a mode-connectivity barrier.

This paper presents an automated large-scale search pipeline for heterogeneous 4-Expert Mixture-of-Experts (MoE4) architectures within the LEMUR neural network dataset ecosystem. Over 28 days on an NVIDIA RTX 4090, the pipeline generated 4,463 candidate models and evaluated 1,021. A critical coverage bias was discovered: due to alphabetical enumeration, the search space was anchored to the AirNet family. Within this scope, ShuffleNet and MobileNetV3 ensembles achieved highest average accuracy of 0.632, while FractalNet and MNASNet were identified as low-yield families.

This study constructs large-scale algorithm co-occurrence networks in NLP using deep learning on full-text papers. It analyzes network structure and centrality to assess collective influence over decades, finding that classic, high-performing, and cross-period algorithms dominate, and declining influence first loses core network position.

A new method called SGPO improves LLM reasoning by replacing trajectory imitation with strategy distillation, achieving better generalization and outperforming baselines on math benchmarks.

A hybrid model using ensemble feature selection (ANOVA and mutual information) and Harris Hawks optimization-tuned logistic regression predicts mental health risk in female sex workers (FSWs). Achieved 95.78% accuracy on 3,005 FSWs, identifying post-traumatic stress, client violence, and occupational factors as key depression drivers. XAI enables early intervention and targeted care.

Recommender systems often induce filter bubbles and semantic homogenization by optimizing solely for immediate engagement. This paper introduces a multi-objective reinforcement learning framework that treats engagement, diversity, and fairness as distinct reward signals using a Pareto-DQN agent. Experiments on MovieLens show that hypervolume-based action selection disrupts feedback loops leading to semantic collapse, achieving societal gains with minimal engagement impact.

This paper investigates whether language model (LM) agents can assist in explaining circuit components after they have been localized in mechanistic interpretability. The authors introduce AgenticInterpBench, a benchmark of 84 semi-synthetic transformer circuits with 163 component-level annotations, and HyVE (Hypothesize, Validate, Explain), an agentic explainer that iteratively observes, hypothesizes, and causally validates. Experiments across four LM backbones show that HyVE recovers useful explanations, but no backbone is uniformly best; failures mainly occur in the validation step. A case study on an arithmetic circuit in Llama-3-8B demonstrates extension to naturally trained models. LM agents are promising but reliable validation remains a key obstacle.

A new study shows that reinforcement learning on beneficial behavior in realistic domains can produce broad and persistent alignment generalization, with interventions limited to health improving non-health alignment evaluations and resistance to adversarial attacks.

Researchers propose a hierarchical multi-agent reinforcement learning framework that enforces hard safety constraints via a constraint manifold at low level while enabling effective coordination through high-level policy learning. The approach provides theoretical safety guarantees, stationary learning dynamics, and achieves competitive performance with nearly perfect safety rates and strong generalization.

This paper explores the nature of AI agents, distinguishing between 'agentic' systems with engineered workflows and 'agentive' systems with endogenous capabilities. It proposes the Goal-Identity-Configurator (GIC) architecture and emphasizes auditability, controllability, and safety of autonomous systems under human oversight.

A new framework extracts rule-grounded reasoning traces from classical planners to supervise driving VLA models, ensuring structurally coupled reasoning and motion generation, with significant performance gains.

RIFT-Bench is a graph representation-driven methodology for dynamic red-teaming that enables unified security evaluations across diverse agentic AI architectures. It operates in two automated phases—Discovery and Scanning—and supports evaluation of mitigation strategies, demonstrating effectiveness across 45 systems.

Handshake AI is hiring students and graduates to remotely test large language models for $30/hour, no expertise required. The fellowship offers flexible, part-time work to help improve AI systems, but questions remain about its long-term career value.

AI was expected to enhance decision-making, but new concerns suggest it could make leaders more reckless by fostering over-reliance and moral detachment.

Anthropic's Mythos AI model identified vulnerabilities in classified US government systems during a test with intelligence agencies. Senator Warner disclosed the finding, while tensions rise between Anthropic and the Trump administration over AI restrictions.

The article discusses China's mineral supply threats to the EU and the rise of AI warfare technology in Japan and on WeChat.

Amazon Prime Day 2026 offers deep discounts on portable power stations and batteries from EcoFlow, Jackery, and Anker, with savings up to 50%.

Anthropic launched Claude Tag, a Slack-integrated AI that acts as a virtual employee with persistent context and memory. It can break down tasks, search channels, and support handoffs. The launch aims to boost enterprise adoption ahead of Anthropic's expected IPO.

As artificial intelligence reshapes the global economy, Chinese universities are rapidly restructuring their academic offerings, cutting foreign language and translation programs while launching new majors in areas like "embodied intelligence" and "low-altitude economy." This shift seeks to prepare students for an AI-driven future and reflects broader trends in higher education worldwide.

This article satirizes the overuse of LLMs to generate low-quality content and proposes a passive-aggressive shame scheme using specific emojis, while also offering healthier alternatives like positive reinforcement and establishing social norms.

A developer built an MCP server that indexes all repositories, supports natural language and structured search, and automates batch PR creation and status tracking, solving the tedious process of manually managing 30+ repos.

Upbound Inc. today released Modelplane, a new open-source tool for managing AI inference clusters. It builds on the company's Crossplane project to simplify multi-cloud inference workloads, auto-scale resources, and reduce latency with distributed caching.

KrosAI provides AI phone infrastructure for Africa, LATAM, and MENA, enabling AI agents to operate on dedicated telecom networks suited for emerging markets.

As AI agents increasingly interact with each other rather than humans, the challenge of gathering software requirements diminishes because programs are more predictable and have clearly defined specifications. This shift may reduce the importance of traditional requirements-gathering, enabling faster and more reliable development.

NVIDIA and AWS collaborate to provide scalable, low-latency AI infrastructure with new EC2 G7 instances featuring Blackwell GPUs, GPU-accelerated vector indexing in OpenSearch Serverless powered by cuVS, and AWS achieving NVIDIA Exemplar Cloud status for GB300 training.

The author found that separating search and AI queries yields better results, choosing DuckDuckGo for private search and Perplexity for AI queries, and shares how to set up custom search engines in browsers.

Scaling laws are one of the most critical empirical findings in deep learning, describing power-law relationships between model size, data, compute, and loss. This article reviews the development from early theory to modern empirical studies, including Kaplan et al.'s classic scaling laws and the Chinchilla scaling laws, and discusses key findings such as compute-optimal allocation.

Modal and Decagon collaborated to cut inference latency by 100ms using speculative decoding, outperforming proprietary providers. The article details the low-latency playbook including optimization of communication, host overhead, prefill, and decode latencies, with a focus on custom speculative decoding models (DFlash) for big wins.

GitKraken introduces Code Flow, a framework to manage the increased volume of code from AI agents, focusing on visibility, governance, and integration. It also launches Kepler ADE, GitKraken Desktop 12, and GitLens 18.

At the Bio International Convention, Nvidia unveiled the BioNeMo Agent Toolkit to bring agentic AI into biotech. The toolkit turns large language models into domain-specific AI agents that can execute end-to-end biology and chemistry workflows, while optimizing for speed, accuracy, and cost. It aims to compress drug discovery timelines, lower barriers, and shift the industry toward a network of specialized agents.