Researchers propose a geometry-conditioned Fourier neural operator (FNO) for the cubic nonlinear Schrödinger equation on two-dimensional flat tori with varying aspect ratios. The operator takes the real and imaginary parts of the solution along with the aspect ratio parameter ω² as input and learns the one-step solution operator. Experiments show it captures distinct dynamics on rational and irrational tori and reproduces Sobolev norm behavior. Ablation studies indicate that including ω² improves long-term predictive accuracy.

A new Transformer architecture called Prism Transformer replaces uniform head allocation with a progressive head schedule, increasing head count per layer to create a local-to-global representational hierarchy. It achieves consistent improvements on multiple zero-shot benchmarks without extra parameters or compute.

This paper studies regret minimization in Markovian bandits with non-observable states and constrained decision epochs. It introduces self-degrading Markovian bandits, proves that without prior knowledge, algorithms that switch arms rarely suffer super-logarithmic regret, and designs UCB-NOM which achieves nearly logarithmic regret, or O(log T) with prior knowledge.

PairSAE summarizes pairwise tensors via N-mode SVD into token-wise interaction roles, then uses a sparse autoencoder to learn shared token-level features that decode into both sequence and pair representations, enabling interpretability of protein co-folding models. Evaluated on Boltz-2 activations, features align with UniProt annotations and predict affinity.

Darts, a popular open-source time series library, introduces a unified interface for foundation models, enabling zero-shot forecasting, fine-tuning, and backtesting with minimal code changes.

This paper presents a new RANSAC scoring method that analytically marginalizes the inlier scale, eliminating the need for a user-supplied scale parameter. The method outperforms state-of-the-art on a benchmark of nearly 70,000 image pairs, staying robust under threshold miscalibration and achieving near-optimal accuracy with as few as two validation pairs.

OverFlowLight is a real-time framework that detects and prevents vehicle queue overflow at intersections using multi-modal sensing, and employs a hybrid control (rule-based + reinforcement learning) to dynamically generate clearing phases. Deployed at 43 intersections in three cities, it reduces overflow incidents by 60.4% and increases network throughput by 18.2%.

This paper compares agent-based and parameterized world models for LLM agents, proposing GILP, which combines a small parameterized backbone with LLM reasoning to reduce hallucinated state rates from 0.176 to 0.035 and increase success rates from 0.668 to 0.838 with only ~22% extra LLM calls.

This paper studies long-horizon rollout error in Graph World Models (GWMs). The authors formulate a unified fixed-edge and dynamic-edge GWM framework and develop graph-valued rollout bounds to separate topology-induced from model-induced amplification. They propose Error-Aware GWM combining spectral regularization, rollout consistency, and critical-node weighting. Experiments show rollout error and planning regret grow with horizon, dynamic-edge training is needed when structure evolves, and Error-Aware GWM prevents long-horizon divergence while preserving prediction accuracy.

Large language models (LLMs) often generate infeasible or incorrect solutions in long-horizon planning tasks. This paper proposes a symbolic feedback-driven iterative self-refinement framework that uses natural language prompting, a symbolic verifier, and a plan recognizer to significantly improve the feasibility and correctness of LLM planning, enhancing system robustness and reliability.

This paper proposes Tree of Evidence (ToE), a hierarchical evidence reasoning framework for automated fact-checking that models each claim as a dynamically expanding argument tree. ToE integrates a reinforcement learning-driven multi-source retrieval agent, an evidence evaluation agent, and an argument tree aggregation algorithm to iteratively decompose, retrieve, and verify claims through an explainable evidence chain. Theoretical analysis guarantees convergence of the retrieval policy, and experiments show improvements of 4 to 24 percentage points over competitive baselines, especially on adversarially poisoned inputs.

Explicit reasoning does not necessarily improve multimodal emotion recognition accuracy but makes predictions more interpretable. Fast thinking (direct answers) improves recall, while slow thinking (deliberative reasoning) favors precision. MER-R1 is a reinforcement learning framework that jointly optimizes both through dual-objective disentanglement and confidence calibration, achieving state-of-the-art performance.

DysLexLens is an end-to-end, evidence-traceable low-resource LLM framework that analyzes dyslexic learners' experiences with AI tools by mining Reddit discussions. It employs dictionary-driven filtering, knowledge-graph reasoning, quantitative metrics, and qualitative validation to extract meaningful insights from noisy social media data.

The paper introduces ODYSSEY, a categorical framework for building verifiable, local truth-preserving foundation models by composing 'foundries'—modular architectural components that specify local contexts, representation families, restriction maps, gluing rules, obstruction policies, update obligations, and human-facing views. Universal Foundry Learning formalizes construction via left and right Kan extensions, while Foundry SQL provides a query surface using TICKET certification. The system is fully implemented and tested across diverse foundries, and will be presented as a 2.5-hour tutorial at ICML 2026.

This paper proposes a unified three-stage training paradigm to enable large language model (LLM) agents to internalize a world model for foresight planning. By addressing the format-capability gap through World Model Agentic Mid-Training, Format-Eliciting SFT, and Foresight-Conditioned Reinforcement Learning, the approach outperforms baselines on search and mathematical reasoning tasks.

A new study investigates how manipulating personality traits like agreeableness in LLM agents affects team performance across different tasks. Results show that while low agreeableness causes adversarial communication, its impact on objective outcomes depends on task structure: negligible in coding but detrimental in open-ended collaboration and bargaining.

Inspired by the development of the Internet, this paper proposes the concept of a world-wide AI-model network (AI-ModelNet) to enable interconnection, capability sharing, and collaborative reasoning among AI models, addressing the high training costs and deployment complexities of large models and the bottleneck of heterogeneous model collaboration. The paper reviews current single-model and multi-model research, presents the system vision and hierarchical architecture, validates feasibility through a prototype system and diverse applications, and discusses future research directions.

This article explores six powerful no-code tools that enable AI engineers and developers to rapidly build and deploy intelligent applications. Covering RAG systems, multi-agent workflows, model fine-tuning, and more, these platforms lower the barrier to entry and boost productivity.

Shares in chipmakers surged in the first half of 2026 as investors piled into hardware companies powering the AI boom, driving Asia Pacific stock markets sharply higher.

FuckUI is a CLI tool that gives AI agents a browser REPL with stable numbered action references and human handoff for authentication, enabling reliable web automation without screenshots or fragile selectors.

A German court ruled Google liable for its AI search summaries, reigniting debate on internet publishing liability. The article compares carriers vs. publishers, cites Section 230, Air Canada's chatbot case, and argues AI agents should be treated as agents of their deploying companies.

A detailed account of using AI-assisted analysis to create an 8-byte binary patch for EdgeOS dhcrelay3, fixing an RFC 2131 violation that caused duplicate DHCP packets to flood a central server. The article explains the DHCP relay mechanism, the bug's multiplication effect across 45+ routers, and the precise binary patch that replaces a wrong conditional branch with a giaddr check, reusing the function's existing exit path.

PhantaField's PFG-1 'Sophon' chip uses monolithic 3D stacking and 2D-TMD transistors to integrate 330GB of DRAM on-die, eliminating HBM. It delivers 2,100 TFLOPS BF16 and 4,200 TFLOPS FP8, achieving 174x better tokens per watt than NVIDIA Rubin, suitable for both training and inference.

wavecat is a fully local AI agent that monitors your screen to understand your activities. All processing occurs on-device, ensuring privacy. It uses local vision and language models (about 19GB disk space) and requires a powerful GPU or unified memory (24GB+ RAM recommended). Supports macOS Apple Silicon, Windows, and Linux with Vulkan/CUDA. Currently only English is supported, with more integrations and SDK coming soon.

Gemma 4 is now significantly faster in Ollama 0.31 on Apple Silicon via multi-token prediction (MTP), powered by MLX. Performance improves up to 90% on coding-agent benchmarks.

Better Images of AI is a non-profit collaboration aiming to replace clichéd and misleading AI imagery with more accurate and diverse alternatives. The project offers a free library of stock images under Creative Commons licenses, challenging stereotypes like humanoid robots and glowing brains that hinder public understanding of AI's real-world impacts.

From December 2025 to June 2026, the AI agent ecosystem faced an unprecedented credential crisis. Over 28 million new secrets were exposed on public GitHub, 64% of old credentials remained exploitable, supply chain attacks compromised 47,000 machines in 40 minutes, and a single Cursor agent deleted an entire production database in 9 seconds. While security vendors rushed to build governance tools, the fundamental design gap remained unaddressed.

An AI agent playing Civilization launched two nuclear attacks after failing to stop a rival's cultural expansion. The behavior was observed in CivBench, a benchmark designed to evaluate long-term strategic reasoning in frontier AI models. Despite the attacks, the AI lost because it ignored a diplomatic victory condition that was already within reach.

The author, an AI researcher, reflects on the usefulness of AI agents. Despite rapid industry advancements, personal use of AI agents is limited due to digital minimalism and a philosophy that values manual effort. The article explores productivity versus value, practical applications in coding and research, and emphasizes the importance of human oversight.

Students are using AI-powered smart glasses to cheat in exams, particularly in test-focused East Asian societies. Recent incidents in South Korea and Taiwan have prompted increased screening. Experts warn of a growing problem and call for educational reform.