Here is your today’s AI Dev Brief from Marktechpost, covering core research, models, infrastructure tools, and applied updates for AI developers and researchers.
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OpenAI Releases GPT-5.5, a Fully Retrained Agentic Model That Scores 82.7% on Terminal-Bench 2.0 and 84.9% on GDPval
OpenAI has released GPT-5.5, its first fully retrained base model since GPT-4.5, rolling out today to Plus, Pro, Business, and Enterprise users in ChatGPT and Codex. Built for agentic computer work, the model is designed to understand complex goals, use tools, check its own work, and complete multi-step tasks — including writing and debugging code, browsing the web, analyzing data, and operating software — with minimal human direction. It scores 82.7% on Terminal-Bench 2.0 (vs. Claude Opus 4.7 at 69.4% and Gemini 3.1 Pro at 68.5%), 84.9% on GDPval, 78.7% on OSWorld-Verified, and 58.6% on SWE-Bench Pro, while matching GPT-5.4's per-token latency and using significantly fewer tokens to complete the same Codex tasks.......… Read the full analysis/article here.
Alibaba Qwen Team Releases Qwen3.6-27B: A Dense Open-Weight Model Outperforming 397B MoE on Agentic Coding Benchmarks
Alibaba's Qwen Team has released Qwen3.6-27B, a 27B dense open-weight Vision-Language Model that delivers flagship-level agentic coding performance under Apache 2.0. Built on a hybrid architecture of 64 layers alternating between Gated DeltaNet linear attention and Gated Attention with a native 262,144-token context window, the model scores 77.2 on SWE-bench Verified, 59.3 on Terminal-Bench 2.0 (matching Claude 4.5 Opus), and 1487 on QwenWebBench — outperforming both its predecessor Qwen3.5-27B and the much larger Qwen3.5-397B-A17B MoE on several agentic coding benchmarks.......… Read the full analysis/article here.
Mend Releases AI Security Governance Framework Covering Asset Inventory, Risk Tiering, AI Supply Chain Security, and Maturity Model
Mend has released AI Security Governance: A Practical Framework for Security and Development Teams, an 18-page operational guide for AppSec leads, CISOs, engineering managers, and data scientists trying to close the gap between fast AI adoption and slow governance. The framework covers six areas: building an AI asset inventory spanning IDE tools, third-party APIs, open-source models, SaaS-bundled AI, and autonomous agents; a five-dimension risk scoring model (Data Sensitivity, Decision Authority, System Access, External Exposure, Supply Chain Origin) that classifies assets into three governance tiers; least-privilege access controls and output filtering for AI-generated content; supply chain security through an AI Bill of Materials (AI-BOM); three-layer monitoring for prompt injection, model drift, and behavioral manipulation that traditional SIEM rules miss; and a four-stage AI Security Maturity Model — Emerging, Developing, Controlling, Leading — mapped directly to NIST AI RMF, OWASP AIMA, ISO/IEC 42001, and the EU AI Act.......… Read the full analysis/article here.
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Xiaomi Releases MiMo-V2.5-Pro and MiMo-V2.5: Matching Frontier Model Benchmarks at Significantly Lower Token Cost
Xiaomi's MiMo team has released two new models — MiMo-V2.5-Pro and MiMo-V2.5 — that bring open agentic AI to frontier territory. MiMo-V2.5-Pro scores 57.2 on SWE-bench Pro, 63.8 on Claw-Eval, and 72.9 on τ3-Bench, matching Claude Opus 4.6 and GPT-5.4 while using 40–60% fewer tokens per trajectory, and can autonomously complete long-horizon tasks spanning more than a thousand tool calls — demonstrated by building a complete SysY compiler in Rust (233/233 tests, 672 tool calls, 4.3 hours) and a full desktop video editor (8,192 lines of code, 1,868 tool calls, 11.5 hours). MiMo-V2.5, meanwhile, is natively omnimodal — trained from scratch to process vision, audio, and text — with a 1M-token context window, scoring 87.7 on Video-MME and 23.8 on Claw-Eval Multimodal (matching Claude Sonnet 4.6), and delivering MiMo-V2.5-Pro-level coding performance on everyday tasks at half the cost.......… Read the full analysis/article here.
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Project Notebooks/Tutorials
▶ A Coding Tutorial on OpenMythos on Recurrent-Depth Transformers with Depth Extrapolation, Adaptive Computation, and Mixture-of-Experts Routing Codes Tutorial
▶ How to Design a Production-Grade CAMEL Multi-Agent System with Planning, Tool Use, Self-Consistency, and Critique-Driven Refinement Codes Tutorial
▶ A Detailed Implementation on Equinox with JAX Native Modules, Filtered Transforms, Stateful Layers, and End-to-End Training Workflows Codes Tutorial
▶ A Coding Implementation to Build a Conditional Bayesian Hyperparameter Optimization Pipeline with Hyperopt, TPE, and Early Stopping Codes Tutorial
▶ A Coding Implementation on Qwen 3.6-35B-A3B Covering Multimodal Inference, Thinking Control, Tool Calling, MoE Routing, RAG, and Session Persistence Codes Tutorial
▶ A Coding Implementation on Microsoft’s Phi-4-Mini for Quantized Inference Reasoning Tool Use RAG and LoRA Fine-Tuning Codes Tutorial
▶ A Coding Tutorial for Running PrismML Bonsai 1-Bit LLM on CUDA with GGUF, Benchmarking, Chat, JSON, and RAG Codes Tutorial
▶ A End-to-End Coding Guide to Running OpenAI GPT-OSS Open-Weight Models with Advanced Inference Workflows Codes Tutorial
▶ A Coding Implementation to Build Multi-Agent AI Systems with SmolAgents Using Code Execution, Tool Calling, and Dynamic Orchestration Codes Tutorial