代理工具

Agent、MCP、coding agent、工作流自動化與評測。

AgentLens Moves Coding Agent Evaluation to Production Traces, Supporting Nightly Regression Testing and Failure Diagnosis

AgentLens goes beyond checking whether an agent solves a coding task, examining the full interaction trace and actual product outcomes. The open-source benchmark can be used to compare model versions, identify behavioral regressions, and integrate evaluation into continuous integration workflows.

2026-07-23

代理工具

OmniaBench Tests General-Purpose Agents With 1,431 Cross-Scenario Tasks; Frontier Models Still Score Below 60% on Pass@1

OmniaBench introduces a cross-tool, cross-interaction benchmark for agents with explicitly defined state spaces, along with a compact 644-task hard set. The results show that planning, constraint maintenance, and post-error adaptation remain common weaknesses among frontier agents.

2026-07-23

Meta Muse Spark 1.1 Opens Model API Access, Highlighting One-Million-Token Context, Parallel Tool Calling, and Multimodal Agents

Muse Spark 1.1 is available in public preview for the first time through the Meta Model API, with an OpenAI-compatible interface. The model combines a one-million-token context window, search citations, structured outputs, and parallel tool calling into a foundation for agents.

2026-07-23

應用研究

ByteDance’s UniVR Lets Models Reason in Visual Space, Simulate Dynamics, and Plan Long-Term Actions

UniVR seeks to jointly learn complex reasoning, physical dynamics, and long-term planning solely from visual demonstrations. ByteDance has also released the 34B Planning checkpoint, providing a testable baseline for research on visual agents that do not rely on textual chains of thought.

2026-07-23

GitHub Repo

Gemma 4 12B Directly Processes Images and Audio with a Single Transformer, Reducing Multimodal Deployment Complexity

Google has released the open-weight Gemma 4 12B model, replacing separate vision and audio encoders with linear projections. The new architecture simplifies fine-tuning and local inference, but the official performance figures still require independent verification.

2026-07-23

推論系統

LiteRT.js Brings the Native LiteRT Inference Stack to the Browser, Connecting WebGPU, Wasm, and Experimental WebNN

Google has released LiteRT.js, enabling web applications to run `.tflite` models directly and use the CPU, GPU, or NPU depending on the device. It shifts browser-based AI away from JavaScript-centric runtimes toward a cross-platform native execution stack, although performance and compatibility still depend heavily on the browser and hardware.

2026-07-23

推論系統

Google Launches LiteRT.js, Bringing the Native LiteRT Inference Path to the Browser

LiteRT.js wraps Google’s cross-platform inference runtime in WebAssembly, using XNNPACK, WebGPU, and experimental WebNN for CPU, GPU, and NPU execution, respectively. It makes it easier to move existing `.tflite` models to client-side execution, but performance and compatibility still depend heavily on the browser and device.

2026-07-22