All tags
Topic: "harness-engineering"
not much happened today
qwen-3.7 claude-opus-4.6 gpt-5.5 mythos quest-2b-35b deepseek google-deepmind langchain-ai anthropic openai alibaba sakana-ai stanford oxford ai2 harness-engineering agent-infrastructure coding-benchmarks security-guidance long-horizon-memory context-compression sleep-phase math-problem-solving fact-seeking citation-grounding science-evaluation sebastienbubeck
Harness engineering is emerging as the key differentiator for coding agents, emphasizing the stack of model + harness + eval loop over just stronger base models. DeepSeek is building a harness team to optimize interaction and verification loops, while Google's Gemini Managed Agents and LangChain formalize harness concepts like context governance and dynamic skill routing. New benchmarks like DeepSWE align closely with real developer experience, with Qwen3.7 Max and Claude Opus 4.6 showing strong agentic coding performance. Anthropic introduced a security-guidance plugin for Claude Code reducing security PR comments by 30–40%, and OpenAI highlighted GPT-5.5 in Codex for improved document parsing. In research, Claude Mythos solved Erdős problem #90 with a cleaner proof path than previous models, showing latent capabilities unlocked by appropriate harnesses. The paper "Language Models Need Sleep" proposes a sleep-like consolidation phase for long-horizon memory, addressing bottlenecks in persistent context storage. Open research agents like QUEST (2B–35B parameters) advance long-horizon fact-seeking and citation grounding, while the CUSP benchmark from Sakana/Stanford/Oxford/AI2 evaluates current model capabilities in science.
MiniMax 2.7: GLM-5 at 1/3 cost SOTA Open Model
minimax-m2.7 sonnet-4.6 glm-5 mimo-v2-pro mamba-3 qwen-3.5 kimi-k2.5 gpt-5.4-mini minimax xiaomi artificial-analysis ollama trae yupp openrouter vercel zo opencode kilocode cartesia self-evolving-agents reasoning cost-efficiency token-efficiency hybrid-architecture harness-engineering agent-harnesses skills memory-optimization architecture feedback-loops api inference execution-environment
MiniMax M2.7 is the headline model release, described as a "self-evolving agent" with strong performance metrics including 56.22% on SWE-Pro, 57.0% on Terminal Bench 2, and parity with Sonnet 4.6. It features recursive self-improvement in skills, memory, and architecture. Artificial Analysis places M2.7 on the cost/performance frontier with an Intelligence Index score of 50, matching GLM-5 (Reasoning) but at a fraction of the cost. Distribution is available via platforms like Ollama cloud and OpenRouter. Xiaomi’s MiMo-V2-Pro is noted as a serious Chinese API-only reasoning model with a score of 49 on the Intelligence Index and favorable token efficiency. Cartesia’s Mamba-3 is highlighted as an SSM optimized for inference-heavy use, with early reactions focusing on hybrid transformer architectures like Qwen3.5 and Kimi Linear. The report emphasizes a shift from prompting to harness engineering, where the execution environment and agent harnesses, including skills and MCP, are becoming key differentiators in AI system design. This includes discussions on tools, repo legibility, constraints, and feedback loops, with mentions of DSPy and GPT-5.4 mini as important components in this evolving landscape.