STAGformer is an efficient spatio-temporal agent graph transformer achieving global modeling with linear complexity via a two-step agent attention mechanism, outperforming state-of-the-art on NYC Citi-Bike and Chicago Divvy-Bike datasets.

This paper proposes a multi-objective reliability-based portfolio optimization framework using deep reinforcement learning (MORP-DRL) that jointly optimizes expected return and downside risk. It incorporates three risk measures (variance, CVaR, EVaR), models market uncertainty with GARCH and extreme value theory, and uses PPO under practical constraints, outperforming NSGA-II on ten global equity indices across different market regimes.

D2PO (Dynamic Direct Preference Optimization) is a framework for optimizing diffusion sampling policies, including timestep schedules and classifier-free guidance weights. It addresses the limitation of student-teacher regression where low-NFE student samplers sacrifice high-frequency texture fidelity. By reformulating optimization as preference alignment using energy-based models and dynamic preferences, it achieves better perceptual quality. Experiments show superiority over regression schedulers under low-NFE constraints.

Researchers propose NEST, a framework that addresses dataset-level distribution shifts by identifying distinct operational regimes via unsupervised clustering and using a regime-oriented mixture-of-experts architecture. It achieves state-of-the-art performance on long-term forecasting tasks across network traffic and physical phenomena benchmarks.

Marginal conformal prediction, used in drug discovery, can severely undercover minority classes in imbalanced datasets despite achieving global coverage targets. A class-conditional (Mondrian) approach restores per-class reliability.

TriRoute is a lightweight unified controller that jointly coordinates attention mode, expert selection, and KV-cache bit-width for every token at every layer, outperforming independent optimizations under matched compute and memory budgets.

A study on arXiv shows AI agents often fail in dynamic coordination due to neglecting implicit social norms. From pedestrian-vehicle interaction experiments, researchers identified three principles: outcome predictability, value alignment, and advantage awareness. Incorporating these into LLMs boosted closed-loop task scores nearly fourfold, outperforming human-human interactions by 43%, paving the way for more natural AI integration.

We present the Large Behavioral Model (LBM) that learns customer decision making directly from large-scale retail transactions through a unified Person-Environment formulation. Using behavioral profiles from historical purchases and retrieval-augmented generation, LBM is trained with continued pre-training, supervised fine-tuning, and reinforcement learning with verifiable rewards. It outperforms frontier general-purpose language models on purchase prediction, basket completion, and other retail tasks, with strong zero-shot and fine-tuned transfer across retailers. Ablation studies highlight continued pre-training as the main driver of generalization, and reinforcement learning improves reliance on behavioral evidence.

A new study reveals that optimizing the orchestration layer (Harness) can significantly reduce costs and improve efficiency in enterprise agentic AI, more than model choice alone. Experiments show the Writer Agent Harness cuts cost per task by 41%, time by 44%, and tokens per task by 38%, while maintaining quality.

Recent advances in AI for Mathematics have focused largely on autoformalization and theorem proving. This paper proposes a ReAct-style agentic setup combining LLM reasoning with verifiable feedback from SageMath and Context7 documentation. Evaluated on research-level problems from RealMath, the setup shows substantial performance gains averaging 9.7 pp, with GPT-5.5 achieving 75.2% solve rate. Accepted to ICML 2026 3rd AI for Math Workshop.

Researchers propose two cost-effective agent architectures—Explorer-Definer Pipeline and Reflective Orchestrator—achieving 57.50% and 67.25% pass@2 on ARC-AGI-1 at $0.25 and $0.62 per task respectively, without benchmark-specific training or heavy test-time compute.

QANTIS treats a quantum processor as a calibrated belief-update service for autonomous systems under partial observability. Using IBM Heron hardware on the Tiger POMDP, the study shows that all-step fixed-point amplification preserves the posterior across sequential steps, with hardware posteriors matching exact Bayes posteriors in all decision checks. Boundary-aware BIQAE stabilizes amplitude estimation, and a rare-event sweep maps sample complexity for one-in-a-million evidence.

Researchers introduced a scalable Hybrid Agent-based and Language-driven Epidemic (HALE) modeling framework that leverages large language models (LLMs) to predict human decision-making in agent-based modeling (ABM), with a proof-of-concept simulation of COVID-19 in Salt Lake County, UT.

This paper provides a theoretical analysis of in-context search in LLMs, modeling it as approximate inference over reasoning traces. It shows that when reflections reliably localize early mistakes, in-context search yields exponential improvements with only polynomial sequential attempts; otherwise, no asymptotic benefit over parallel sampling. Gains are robust, learnable, and connect to optimal policy in reinforcement learning.

AgentLens is a production-assessed benchmark for interactive code agents that evaluates the entire trajectory — instruction following, tool usage, self-verification, error recovery, and communication — rather than just pass/fail. It pairs formal verification with LLM-written trajectory reviews and side-by-side comparisons to provide readable explanations of scores. Useful for model diagnosis, version comparison, and regression detection. Open-sourced on GitHub.

China is implementing a sweeping higher education reform, eliminating thousands of outdated degree programs while rapidly expanding offerings in artificial intelligence and other emerging technologies. Between 2021 and 2025, over 12,000 undergraduate programs were cut and 10,000 new ones introduced, affecting more than 30% of the country's university courses. This strategic realignment is driven by rising youth unemployment and China's ambition to become a global leader in advanced technologies. Programs in humanities and arts are disproportionately reduced, while fields like AI, robotics, and data science are prioritized to align universities with national industrial goals. This contrasts with financially-driven program cuts often seen in US higher education, as China's government-led in…

Figment is a personal AI you can text, curious about you and proactively finding ways to help while feeling like a friend. It has its own 24/7 computer and browser, and friends experienced many magical moments during two weeks of testing.

The main bottleneck for AI infrastructure is grid interconnection, not energy shortage. Queue times have ballooned from 20 months to 55 months. Market mechanisms work, but grid planning lags.

This article explores how AI automation threatens the economic development model of poor countries that rely on exporting cheap labor and services. It traces the historical ladder of development from agriculture to manufacturing to services, noting that manufacturing employment is peaking earlier in developing countries. Service exports like IT and call centers are also at risk, with early indicators showing reduced hiring of new graduates. While cheaper human labor may temporarily compete, rapidly falling AI costs could eventually eliminate that advantage, leaving poor countries with fewer pathways to growth.

A comprehensive guide to the best Character AI alternatives in 2026, comparing Polybuzz, Chai, Silly Tavern, Swerve, and chatbrat.ai, with a focus on personality, memory, and social arcade features.

SlopWatch is a Chrome extension that lets users rate webpages for AI-generated content. Community ratings produce a 'Slop Score' to indicate how likely content is AI-generated. Currently rated 4.3/5, it aims to increase transparency online.

Current AI introduces the Open Source Gap Map v0.1, a living visualization of the open source AI landscape based on surveying over 24,626 projects, assessing openness, capability, and adoption, to identify high-leverage gaps and rally community efforts.

ClawTeams is an AI employee platform for e-commerce sellers. Instead of hiring specialists or doing everything yourself, you get a coordinated AI team that thinks, plans, and executes like real employees. Just set a goal and the AI team lead manages the rest.

Ghost is a real-time AI interview coach that provides instant, natural answers during job interviews. It works in under 3 seconds, offers four tone modes, and is free to start with a pro version at $12/month.

Ant Group's Robbyant has released LingBot-VLA 2.0, an Apache-2.0 vision-language-action model for cross-embodiment robot manipulation. The 6B checkpoint is pretrained on roughly 60,000 hours of data, spanning 50,000 hours of robot trajectories across 20 robot configurations and 10,000 hours of egocentric human video. It maps every embodiment into a single 55-dimensional canonical action space, covering arms, dexterous hands, waists, heads, and mobile bases. A token-level, auxiliary-loss-free Mixture-of-Experts action expert scales capacity without adding a load-balancing loss. Dual-query distillation from LingBot-Depth and DINO-Video adds geometric and temporal supervision for future-aware control. On the GM-100 generalist benchmark it outperforms π0.5 and LingBot-VLA-1.0 on both evaluate…

KPMG survey finds nearly a third of execs struggle to understand costs as companies rethink deployments.

Ant Group's Robbyant has released LingBot-VLA 2.0, an Apache-2.0 vision-language-action model for cross-embodiment robot manipulation. The 6B checkpoint is pretrained on roughly 60,000 hours of data, spanning 50,000 hours of robot trajectories across 20 robot configurations and 10,000 hours of egocentric human video. It maps every embodiment into a single 55-dimensional canonical action space, covering arms, dexterous hands, waists, heads, and mobile bases. A token-level, auxiliary-loss-free Mixture-of-Experts action expert scales capacity without adding a load-balancing loss. Dual-query distillation from LingBot-Depth and DINO-Video adds geometric and temporal supervision for future-aware control. On the GM-100 generalist benchmark it outperforms π0.5 and LingBot-VLA-1.0 on both evaluate…

On-policy distillation provides dense per-token supervision for reasoning models, but its effectiveness varies. This research introduces a training-free diagnostic framework that quantifies the alignment between distillation signals and the ideal gradient at the per-token level. Findings show that distillation guidance aligns better on incorrect rollouts, and the optimal context depends on student capacity and task, with no universal best configuration.

This paper introduces SRLM, a framework that augments recursive language models with self-reflective program search using uncertainty signals (self-consistency, reasoning trace length, verbalized confidence). It achieves up to 22% improvement over RLMs and shows that recursion itself is not the primary driver of performance.

Learn how GPT-5.6 powers Microsoft 365 Copilot with stronger AI capabilities across Word, Excel, PowerPoint, Chat, and Cowork for faster, higher-quality work.