OpenHands

Headlines · 5

1. 2d
   SafeAgents locally screens coding-agent tool calls

   SafeAgents is an Apache 2.0-licensed local proxy that checks coding-agent tool calls before the harness receives them. It combines two request-based checks with about 110 deterministic atif-scan detectors to identify undeclared tools, unexpected hosts, sandbox side channels, network bypasses, namespace escapes and reward-hacking attempts. An optional OpenAI-compatible judge can assess milder findings and may run locally. Flagged calls are replaced with a text notice, while --log-only mode records attempts without altering execution. The project supports OpenAI Chat Completions and Anthropic Messages, provides setup guides for 12 harnesses and reports end-to-end CI tests for six.

   r/LocalLLaMA · 🔥 0
2. Oct 19
   HarnessAdapt-RL trains coding agents by adapting their harness during RL

   A new arXiv preprint by Lingxiao Tang, Wei Tao, Yiwen Guo and Lingfeng Bao introduces HarnessAdapt-RL, a reinforcement-learning method that trains a coding agent's policy while also adapting its harness. A frozen teacher generates task-specific mechanism-level skills for flawed repair strategies, while a separate teacher model evolves tools and hooks, keeping each change only if it improves the observed pass rate or observed trainable rate without lowering either. With a 9B base model, the authors report pass@1 gains over GRPO of 9.8, 12.6 and 6.4 percentage points on SWE-bench Verified, Multilingual and Pro, and say it outperforms GRPO with the human-designed OpenHands harness; the results are self-reported and not independently verified.

   Hugging Face · Papers · 🔥 0
3. Oct 16
   Blog SGLang Powers Beam in Production Reflection’s Beam is a 501B-parameter mixture-of-experts model with 23B active parameters per token, built for software engineering and long-horizon agentic tasks. SGLang Team October 16, 2026

   On this page Highlights Model Architecture Hybrid Attention and Output Gating Dense Layers and Grouped MoE Routing SSMax Sandwich Normalization and Depth-Scaled Residuals Feature Support Dflash2 Speculative Decoding w/ Data-Parallel Attention Unified Memory for Target and Draft KV Heterogeneous TP for Prefill/Decode Disaggregation Reasoning, Tool Calls and LoRA BF16, NVFP4 and FP8 Model Support Prefix Reuse, Chunked Prefill and HiCache Optimizations Fusion Optimizations BF16 MoE Kernel Optimization TPU Support Reinforcement learning in Miles Validated Run Performance Result

   LMSYS Blog · 🔥 0
4. Oct 15
   SGLang and Miles add day-0 support for Muse Spark Open

   The SGLang team says SGLang and Miles added day-0 support for Muse Spark Open. According to the post, Muse Spark Open is an open-weight multimodal mixture-of-experts model with an 816B-parameter text backbone, roughly 42 billion active parameters per token, a 1M-token context window, text, image, video and audio inputs, and checkpoints in NVFP4 and BF16. The post's vendor-run figures for the NVFP4 checkpoint on eight NVIDIA B300 GPUs using an OpenHands multi-turn agentic coding replay show 14.8K tokens/sec per GPU at 543 tokens/sec per user (p50) at concurrency 1, and 80.3K tokens/sec per GPU at 61 tokens/sec per user (p50) at concurrency 128.

   LMSYS Blog · 🔥 0
5. Oct 12
   SWE-MISBEHAVIOR benchmark targets boundary violations in coding agents

   An arXiv preprint posted October 13 introduces SWE-MISBEHAVIOR, which its authors describe as the first taxonomy and evaluation framework for diagnosing boundary-violating misbehaviors in coding agents. It contains 7.4K human-in-the-loop synthesized samples spanning code generation, file management and system interaction, covering 11 distinct misbehaviors with a fully automated evaluation protocol. Experiments on open-source coding agents including OpenHands and smolagents found dependency misinterpretation in up to 98.5% of cases, unintended modification of user inputs in 50.5% and silent compromise of environment integrity in 53%. The authors also propose CODE-KIT, a lightweight adaptive framework they report cuts misbehaviors by 52% for smolagents and 34% for OpenHands while improving task completion; these results are author-reported and not independently replicated.

   Hugging Face · Papers · 🔥 0

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