AI Research

AI teams often hit a 'latent capability ceiling' where larger models no longer improve task accuracy, with scaling data showing diminishing returns beyond 100B parameters and 61% of tasks exhibiting non-monotonic scaling…

A new open-source repository, the Agent Engineering Roadmap, provides a structured, beginner-friendly guide for building production-ready AI agents, covering topics from single agents to multi-agent colonies and producti…

A 428-billion-parameter open-weight model achieved 59.0% on SWE-Bench Pro, outperforming GPT-5.5's 58.6%, after being compressed from 855GB to 128GB. The model's efficiency and performance mark a significant advance in A…

Researchers at arXiv find that verifying coding agent outputs is now harder than generating them, as foundation models improve. They argue that no fixed reward function remains effective as policy capability grows, and v…

Researchers introduced EVOM, an agentic meta-evolution framework that uses an LLM-based design agent to automate the discovery of high-performance actor-critic architectures for reinforcement learning. EVOM outperformed …

Researchers introduced ContextForge, a system that recycles context for long-horizon LLM inference by combining structured query generation, external memory retrieval, and controlled synthesis. In a 15-turn conversationa…

Researchers introduced Mesh-RL, a spatial domain-decomposition framework that partitions environments into overlapping subgrids to accelerate temporal-difference learning in reinforcement learning. The method improved co…

Researchers introduced Neural Voxel Dynamics, a self-supervised framework that learns implicit 3D physics from video by lifting 2D features into a volumetric latent space. The method achieves long-term structural stabili…

Researchers introduced the Frame Forgetting Network (FFN) for test-time training on long videos, using only three frames per sliding window to reduce computational cost. The model defines a surprise metric to adaptively …

Researchers at arXiv propose a multi-dimensional evaluation framework for AI agents beyond accuracy saturation, using CORE-Bench Hard as a case study. They introduce CORE-Bench v1.1 and an out-of-distribution suite, find…

Researchers introduced CRISP, a new evaluation paradigm that diagnoses visual spatial intelligence in vision-language models by measuring consistency between implicit perception and explicit reasoning. The study found th…

Researchers introduced AlgoEvolve, an LLM-driven evolutionary framework that generates and iteratively improves algorithmic trading strategies expressed as Python code. The system exhibits emergent regime-adaptive strate…

Researchers introduced PhyEditBench, a benchmark for evaluating physics-aware image editing in generative models, revealing significant limitations in current state-of-the-art methods. The benchmark includes 238 real-wor…

Researchers developed a hybrid approach combining image processing and deep learning to assess fruit freshness, achieving over 90% accuracy in real-time classification of apples and oranges. The method uses a CNN for bin…

Researchers introduced OpenFinGym, a unified gym environment for evaluating large language model agents across multiple quantitative-finance tasks including forecasting, trading, and fraud detection. The platform address…

Researchers introduced a Geometry-Aware Monte Carlo Tree Search (MCTS) framework to solve extremal combinatorial geometry problems, achieving new best-known results on five out of six problems, including configurations o…

New research from arXiv reveals that larger language models consistently outperform smaller ones on reasoning benchmarks by an average of 6-7%, with the key advantage being constraint-guided reasoning—the ability to iden…

Researchers proposed Dynamic-dLLM, a training-free framework to accelerate Diffusion Large Language Models (dLLMs) by dynamically allocating cache budgets and calibrating decoding thresholds. The method achieves over 3x …

Researchers at arXiv found that large language models (LLMs) perform well on text-only statics problems but their accuracy drops when diagrams are added and multi-step reasoning is required. The performance decline is at…

A study comparing LLM-generated research methods against 1,000 arXiv computer-science papers found that LLMs suggest a much narrower range of methods, with provider choice showing the strongest imbalance. Academic models…