AI research papers.

In collaborative tasks with asymmetric information, participants coordinate their understanding through interaction.

Mixture-of-experts (MoE) inference on consumer hardware is bounded by weight memory: a 35B-class model is 19.5GB at 4-bit, and sparsity shrinks the compute per token, not the bytes that must be held.

Autonomous research loops such as AutoResearch show that one coding agent can improve a training setup unattended.

Recent Omni-Modal Generative Models (Omni-Models) have advanced content generation toward unified modeling of text, images, video, and audio.

Action tokenizers play a central role in autoregressive vision-language-action (VLA) models, determining both the targets for policy training and the executable commands recovered from predicted tokens.

As corporate AI adoption continues to grow, enterprise-grade LLM agents are being deployed into sensitive contexts such as hiring, healthcare, and finance.

Preparing high quality training data for foundation models requires scalable pipelines that transform heterogeneous documents and videos into structured records.

In reinforcement learning for large language models, Proximal Policy Optimization (PPO) commonly uses a critic to estimate state values and reduce the variance of policy updates.

Large audio-language models have shown promise for anti-fraud detection by directly processing speech and reasoning over fraud-related evidence.

Current Mixture-of-Agents (MoA) paradigms generally treat query routing and agent fine-tuning as separate processes, limiting their ability to respond to evolving agent capabilities.

Coding agents are typically evaluated with desired behavior specified through issues or instructions.

Scientific code repositories encode decades of human knowledge in executable models, methods, and tools.

Enabling robots to adapt to unfamiliar environments as readily as humans remains a moonshot goal of embodied AI.

Intelligent systems that act in the world require image understanding that is both comprehensive and spatially grounded.

Direct preference alignment methods are widely used to align large language models (LLMs) with human preferences because of their computational and memory efficiency.

Large language models (LLMs) provide a flexible interface for long-horizon robot planning, but generated plans often fail to respect embodiment constraints, recover from planning errors, or reason effectively under parti…

Test-time scaling can improve large language model reasoning by generating and combining multiple candidate responses.

Business intelligence (BI) is a cornerstone of enterprise decision-making and is widely used by enterprise users in software such as Power BI and Tableau.

Few-shot learning research is predominantly evaluated on accuracy alone, with limited attention to the parameter and training-sample budgets required to reach that accuracy - a real constraint for practitioners without l…

Traditional multimodal representation learning and generation are two stages: a contrastive or self-supervised visual encoder is trained first, followed by a separate downstream generative model.

Language model-generated rubrics are increasingly used as reward signals for rubric-based reinforcement learning, LLM-as-a-judge evaluation, and automated grading.

Platform abuse campaigns conceal redirection instructions with emojis, homophones, character decomposition, and redundant symbols, then route users through disguised links to services associated with pornography, fraud,…

A walking robotic hand must use the same fingers to move its body, support its weight, and interact with the environment.

3D hand mesh reconstruction is a challenging yet essential task for downstream applications, including human-robot interaction and AR/VR.

We introduce LimiX-2, a new model in the LimiX family, developed through model and data scaling guided by our previously established scaling laws.

LLM assistants are widely used for daily social advice, yet evaluating their social reasoning in such consultation settings remains challenging since (i) it requires setups where the assistant learns about social situati…

Large language model (LLM) trading agents can combine market data, news, and executable analysis, but their behavior is often controlled by static hand-written tool-use policies that are fixed before deployment.

When agentic sessions run to a million tokens with many sessions resident at once, the KV cache and the index that ranks it live in host memory, and the scan that ranks all n keys for a top-k step becomes the traffic tha…

GUI agents execute long-horizon tasks on dynamic graphical user interfaces, where pop-ups, delayed loads, and relocated widgets routinely invalidate plans fixed before execution.

Reliable confidence estimation is increasingly central to the trustworthy deployment of language models: a calibrated estimate of the probability that an output is correct decides what to ship, what to escalate, and what…

We introduce Zing-0.5, a 5B autoregressive world model designed for playability: users can explore generated worlds, influence unfolding events, and respond to the resulting feedback through joint keyboard and online tex…

We present TAPe+ML v3, a compact computer vision system based on TAPe (Theory of Active Perception), a structured representation that encodes relations among perceptual elements before recognition.

The rapid progress of artificial intelligence is reshaping robotics and accelerating the adoption of learning-based approaches.

Face Image Quality Assessment determines the suitability of captured face images for automated face recognition (FR), a critical capability for reliable biometric systems.

We present NanoForecast v0.5, a 6.5M-parameter forecaster that competes with models 31x its size (TimesFM, 200M parameters) after training pipeline fixes and no architecture change.

Chain-of-thought reasoning has revolutionized natural language processing by enabling large language models (LLMs) to decompose problems into intermediate steps before answering.

Scaling large language models efficiently has motivated sparse capacity mechanisms such as Mixture-of-Experts and, more recently, conditional memory: token-indexed embedding tables that augment the backbone with cheap pa…

BraTS-GoAT evaluates tumor segmentation across heterogeneous populations.

Data agents aim to fulfill natural-language instructions over heterogeneous data, including tables, files, and databases.

Diffusion Transformers deliver state-of-the-art video generation, but their long spatiotemporal sequences make attention the dominant deployment cost, and a deployable low-bit kernel must be accurate and fast.

Scientific agents contribute to hypothesis discovery by synthesizing evidence, assessing proposals, and developing new explanations.

Reinforcement learning with verifiable rewards (RLVR) improves the reasoning capabilities of large language models (LLMs).

We study a practical question: can a small correction module fix errors in a frozen language model's outputs without degrading its base capabilities?

Computer-use agents are increasingly evaluated in realistic desktop environments, but existing benchmarks provide limited coverage of professional engineering workflows whose outputs are persistent, structured artifacts.

Masked diffusion language models (dLLMs) generate text by iteratively denoising masked tokens with bidirectional attention.

Training a Mixture-of-Experts (MoE) model at long context or large batch size fails as soon as any one component's peak allocation exceeds device memory, so the target is every peak at once, not the average footprint.

Recently, linear attention layers have been increasingly adopted to replace softmax attention at scale for long-context modeling.

Unified multimodal large language models (MLLMs) and multi-agent systems have advanced visual generation.

Natural interaction in digital and physical environments requires continuous perception and timely responses.

Few-shot in-context learning (ICL), the capacity of a model to infer abstract patterns from input-output examples provided in its prompt and apply them to new inputs, has been extensively studied in large language models…