模型与评测

How loveholidays is making everyone a builder with Codex

Discover how loveholidays uses OpenAI Codex to make software development accessible across the business, helping teams turn ideas into products faster.

Training and Finetuning Multi-Vector Embedding Models with Sentence Transformers

Accelerating Frontier Transformation: Reinvent customer engagement

Discover how organizations use AI to personalize engagement, strengthen relationships, and deliver more responsive experiences at scale. The post Accelerating Frontier Transformation: Reinvent customer engagement appeare

Restore LLM Inference Capacity in Seconds with Shadow Engine Recovery in NVIDIA Dynamo

When an LLM engine process fails, the standard recovery path involves a cold restart. This requires loading weights into HBM from storage, compiling kernels,...

AutoSaddler: Automatic Harness Optimization with Durable Updates from Agent Execution Traces

LLM agents remain unreliable on long-horizon tasks, where small local failures can compound over extended interactions and lead to overall task failure. Although external harnesses can substantially improve robustness, h

SecOPD: Mitigating Adaptive Prompt Injections by On-Policy Distillation

Prompt injection is listed as the \#1 threat to AI agents. When an agent accesses external data from websites, files, or emails, an attacker may inject a prompt into the data, saying, "Ignore all prior instructions and p

Recursive Experiential-Working Memory Evolution for Long-Horizon Agent Harnesses

Recursive self-improvement (RSI) remains hard in long-horizon tasks, where growing histories obscure the task state and misalign skill invocation. We introduce Recuris, a recursive Experiential-Working Memory architectur

Autonomous Mathematical Discovery in an Open-World Multi-Agent Environment

We study autonomous mathematical discovery in the Station, an open-world multi-agent environment in which AI agents from different model families pursue a shared research goal without a central coordinator or scripted pi

Annotations as Rollouts: Efficient and Scalable Reinforcement Learning for Video MLLMs

Multimodal large language models (MLLMs) have become a prevailing paradigm for unified video perception. However, post-training on large multi-task datasets remains challenging, as existing reinforcement learning methods

GigaBrain-0.7: Scaling Embodied Foundation Models to Emergent Capabilities with a Three-System Architecture

Vision-language-action (VLA) models have become a dominant paradigm for generalist embodied agents, demonstrating strong complex and long-horizon task completion in structured settings. Yet it remains an open question wh

WeMM-Embedding: WeChat Multi-Modal Embedding Technical Report

Universal multimodal embeddings are becoming a core component of modern AI systems, enabling heterogeneous content to be represented in a shared space for applications such as retrieval, recommendation, classification, a

On-Policy Self-Distillation in Diffusion Models

Reinforcement learning can align diffusion models with human preferences and task-specific objectives, but endpoint rewards do not specify how an intermediate denoising prediction should change. We introduce DiffusionOPS

CyberFactory: Scaling Cyber Security Capabilities with Instances from the Wild

As large language models (LLMs) continue to advance in coding capabilities, their potential in cybersecurity has drawn increasing research attention, with closed-source LLMs (e.g., Mythos) delivering advanced cybersecuri

Best Practice Critic Optimization

Group-based reinforcement learning methods such as GRPO for large language models avoid training a critic by sampling multiple responses for each prompt. A reliable critic could instead estimate token-level advantages fr

On-policy Distillation with Verifiable Reward

Reinforcement Learning with Verifiable Rewards (RLVR) and on-policy distillation (OPD) have become two widely adopted paradigms for post-training large language models. However, RLVR suffers from sparse task-level feedba

LAION-BVD: A 10-Million-Hour Open Video Dataset for Multimodal Pre-training

We present LAION-BVD, a large-scale open video dataset for multimodal learning, which contains 1.3B platform-specific video URLs collected from CommonCrawl. From these, we download 80M videos with a total duration of 10

Meta^n: Recursive Self-Improvement through Emergent Depth

Self-improving LLM agents refine answers, not the process that produces those answers. Systems that add a meta-level hold that level fixed, and those that edit themselves must leave part of their own editing machinery un

Game2World Engine: Unlocking In-the-Wild Gameplay Videos for World Model Training

Video games provide a scalable source of training data for video world models, offering diverse environments, complex interactions, and abundant in-the-wild gameplay videos. However, raw gameplay footage entangles the ga

From Seeing to Acting: Smart Glasses as First-Person Intelligence Platforms

Smart glasses are evolving from capture and display accessories into first-person intelligence platforms that connect human perception, persistent context, and digital or physical action. Their on-body viewpoint aligns w

MARS: Multi-Specialist LLM Relay System for Competitive Programming

Large Language Models excel at code generation, yet competitive programming exposes a persistent failure mode: existing multi-agent pipelines distribute work over generic planner, coder, and debugger roles and delegate t

AgentRoom: Concurrent Multi-Agent Coding in a CRDT-Backed Shared Workspace

Concurrent multi-agent coding promises division of labor across modules, robustness through redundancy, and parallel exploration at the natural granularity of multi-file projects. Realtime collaborative editing protocols

When "Must" Becomes "Maybe": Constraint Weakening in LLM Agent Workflows

Large language model (LLM) agents coordinate complex tasks through multi-role and multi-stage workflows. Upstream state is repeatedly transformed into intermediate language artifacts, such as summaries, plans, tickets, m

CAFE: Self-Improving Search Agents Need Co-Evolving Feedback

Outcome-supervised search agents learn when and how to retrieve evidence, but terminal rewards neither localize intermediate errors nor redirect an ongoing trajectory before those errors compound. Treating corrective fee

Automata from Agent Traces: Failure and Next-Step Prediction

LLM-based agents execute multi-step tasks, but their behavioral structure remains opaque: long unstructured traces resist the safety auditing and runtime monitoring that deployment requires. Existing approaches operate p