3,690 items tracked: 31 reported by two or more outlets, the rest by a single source. Most-corroborated first.
Text prompting is an indirect interface for poster generation, requiring users to encode inherently two-dimensional composition intent into a one-dimensional sequence of words.
Training AI agents with reinforcement learning can be challenging because their tools, context, and decision-making are managed by complex frameworks.
College Planner is coming to ChatGPT for Teens to help students manage college applications, alongside new flashcards, quizzes, and a teen AI council.
Radisson partnered with Accenture to build a ChatGPT plugin using OpenAI technology, helping travelers find, compare, and book hotels while planning their trips.
arXiv:2610.08215v2 Announce Type: replace Abstract: Learning from experience is essential for LLM agents to adapt to unfamiliar and dynmaic environments.
arXiv:2610.07289v1 Announce Type: cross Abstract: Manual repair of program failures is time-consuming and disruptive for software developers, particularly during the pre-submit phase where test failures occur within cont…
GPT‑6 is rolling out globally in ChatGPT with Intelligent UI, delivering faster responses with visuals and interactive experiences you can explore and use directly.
The evaluation of coding agents generally strives to be as realistic as possible.
Skeleton-based reasoning prompting is a promising training-free approach for structuring LLM reasoning, but prior work largely assumes an English-centric setting.
Latent world models have shown a remarkable ability to predict future states and to plan in the real world.
A large language model can only use the text that fits in its context window, and it recomputes its internal key-value (KV) state for a prompt every time the prompt is sent.
Pixel-space diffusion models avoid the lossy VAE of latent models, which suggests an advantage on downstream tasks where fine-grained detail matters.
Methods that recover individual latent variables of the world, from nonlinear ICA to dictionary learning and causal representation learning, anchor the latents to observations through reconstruction, auxiliary supervisio…
Training and evaluating interactive language agents typically requires rich user interactions, yet collecting human feedback is expensive and difficult to scale.
Dual-process agents pair a fast policy with a slow deliberative model.
Large language model agents can improve across tasks by retaining reusable skills distilled from prior interactions.
Flow policies capture rich and diverse action distributions, and fine-tuning them with off-policy RL to improve beyond the demonstrations has drawn growing interest.
Multi-teacher on-policy distillation (MOPD) is used in two settings.
Test-time scaling (TTS) improves the reasoning capabilities of large language models by allocating additional inference computation.
Memory-augmented reinforcement learning strengthens LLM agents' ability to solve complex long-horizon tasks.
Much knowledge work produces new deliverables from files a workspace already holds, and LLM agents are beginning to take such work over.
Recent advances in multimodal foundation models have made them capable generalist physical agents for a range of manipulation tasks.
Dense visual text requires image generators to reproduce long strings across multiple regions with correct placement and legibility.
Real-time robot control demands enough visual history to infer motion and task progress, but processing that history can delay action.
Agentic systems use large language models (LLMs) to carry out concrete tasks.
OpenAI publishes new results on open problems in mathematics from an internal frontier model and shares Lean proof formalizations and research details on GitHub.
Learn how OpenAI and Ironclad are training and evaluating AI agents on complex contracting workflows to advance computer use for professional work.
Improving embodied agents often focuses on optimizing the underlying model through training, while the surrounding agent harness that controls planning, context, and tool use is typically engineered.
Multimodal large language models have become the dominant paradigm for visual understanding, but incur substantial costs by encoding inputs into dense, fixed-size patch tokens.
Automated research systems increasingly run LLM agents over long horizons, but more inference does not by itself produce more progress: agents replay growing histories, duplicate one another's work, or stop experimenting…
Typed decision models answer a declared question without generating text: a decision head returns a probability for each of the declared options in a single forward pass.
Reinforcement learning with verifiable rewards (RLVR) turns agent experience into learning signals primarily through scalar outcome rewards after interaction.
AI agents increasingly operate in environments where they can diagnose failures and improve through experience, yet existing evaluations largely measure what an agent can do at a fixed point in time rather than how effec…
A central challenge for scientific agents is to turn analytical experience into reusable expertise grounded in physical evidence.
Reconstructing an editable CAD model from a 3D shape remains a challenging engineering task.
Astra can act, yet reliable manipulation depends on the system through which it observes and controls the world.
Recent game design agents have made substantial progress in generating playable games.
Recent advances in world models have enabled increasingly realistic visual synthesis.
State tracking requires an update at every input, but the depth a Transformer applies to each token is fixed regardless of sequence length.
Assistants from 2011 answered and waited, and agents from 2023 did a task and stopped.
Asynchronous reinforcement learning (RL) for large language model (LLM) agents trains one policy on trajectories generated by another: rollouts come from stale checkpoints, and the inference engine's probabilities differ…
Static-analysis checker synthesis requires agents to interpret a defect specification, inspect a repository, implement analyzer-specific logic, and refine the checker through repeated compilation and analysis feedback.
LLM agents often lack the operational knowledge to act reliably in new environments, as they must discover specific tool behaviors or environment conventions on their own.
Sensors are useful not only for understanding the world but also for deciding what to do next.
Large language models (LLMs) have become increasingly capable problem solvers, but being able to solve a problem is not the same as being able to teach it.
Agentic reinforcement learning (RL) disaggregates training from rollout, so each policy update must reach the rollout clusters before the next batch.
Mixture-of-Experts (MoE) transformers scale capacity by activating only a few experts per token, but this sparsity creates a hidden reliability problem: when routing is imperfect, load-balanced models may send tokens to…
Enterprise AI agents that share a memory store face two unaddressed risks: sensitive data can leak through legitimately computed results the requester could not derive, and departments can silently compute a same-named k…
Wrapping an image generation model in an agentic harness can effectively boost Text-to-Image task performance: the harness can leverage memory, skills, workflow orchestration, result verification, and iterative refinemen…
``What are the irreducible conditions that are sufficient to produce an outcome?'' is one of the most common questions that recur across computation and science.