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Vulnerability rediscovery is becoming a measurable benchmark for frontier models, and a Chinese model matching a top US lab result suggests the capability gap on security-relevant reasoning has narrowed. Security researchers and red teams can weigh these pass@3 scores when picking models for bug-hunting workflows. The poster argues the result reflects data scale rather than copying a competitor's outputs.

Teortaxes@teortaxesTex11 sourcesSource

Agent benchmarks that measure office-app workflows, rather than chat or coding, are becoming a separate leaderboard from traditional evals, and Mistral is staking its position on it. Developers building automation agents on Gmail, Sheets or Salesforce should check whether the benchmark's task design matches their use cases before switching models. Comparisons against Kimi K3 and DeepSeek V4 Pro also frame it as a European lab competing on agentic execution.

官方账号Mistral AI@MistralAI11 sourcesSource

Training on internalized skill workflows lets a 35B open-weights model beat far larger proprietary models on agentic benchmarks, shifting the edge from raw model scale to curated environment data. Builders running Claude Code or Codex harnesses can reuse the released environments and trajectories for their own SFT or RL pipelines. The results give Qwen-based deployments a concrete benchmark point against Claude Sonnet 4.6 and DeepSeek V4 Pro.

The key finding is that skills can be internalized as model weights rather than injected at inference time, since the fine-tuned agent beats skill-assisted base models even without the skills provided. Agent engineers and fine-tuning practitioners can reuse the released environments and trajectories for SFT or RL with outcome-based rewards. The result also shows a 35B open Qwen-based model outscoring much larger closed models on SkillsBench, a useful cost comparison for teams building coding and tool-use agents.

This matters because it introduces a new tool, RoofLang, that could significantly improve the performance of LLMs like DeepSeek V4-series by optimizing their inference architecture, potentially leading to faster processing and better user experience.

The method significantly boosts performance on long-context reasoning tasks without changing transformer architecture, benefiting developers working with complex document analysis.

27B的小模型追平了GPT-5.6和GLM-5.2，参数还小几十倍，值得看看。