LLaDA2.2-flash

LLaDA2.2-flash LLaDA2.2-flash** is an agent-oriented diffusion language model in the LLaDA2 series. By introducing **Levenshtein Editing** (with `DELETE` and `INSERT` control tokens) to diffusion language modeling, it represents the LLaDA2 series' first step in agentic applications, including long-context tool use, multi-turn interaction, and robust error correction.For more information, please refer to our technical report. 📊 Benchmarks The following tables compare **LLaDA2.2-flash** and **Ling-2.6-flash** in terms of agentic benchmark scores and throughput (TPS). Agentic benchmark scores** | Benchmark | LLaDA2.2-flash | Ling-2.6-flash | | --- | ---: | ---: | | SWE-bench Verified | 49.28 | 61.20 † | | SWE-bench Pro | 30.10 | 31.88 | | SWE-bench Multilingual | 25.00 | 33.73 | | τ²-Bench | 80.33 | 76.36 † | | Claw-Eval | 64.22 | 64.56 † | | PinchBench | 81.66 | 81.30 † | | MCP-Atlas | 46.21 | 41.12 | | BFCL-V4 | 60.78 | 66.81 | **LLaDA2.2-flash evaluation setup:** The SWE-bench series was evaluated using the Claude Code scaffold. Across all benchmarks, we used a 128K context window with `temperature=1.0`, `block_length=32`, `threshold=0.5`, and `editing_threshold=0.0`. Each score represents the average of five runs. † The Ling-2.6-flash score on SWE-bench Verified is taken from the Ling and Ring 2.6 Technical Report, where it was obtained using the OpenHands scaffold. The Ling-2.6-flash scores on τ²-Bench, Claw-Eval, and PinchBench are also sourced from the technical report, whereas its SWE-bench Pro and SWE-bench Multilingual scores were evaluated by us using the same Claude Code scaffold as LLaDA2.2-flash. Throughput (TPS)** | Benchmark | LLaDA2.2-flash (TPS) | Ling-2.6-flash (TPS) | | --- | ---: | ---: | | SWE-bench Verified | 519.0 | 303.2 | | SWE-bench Pro | 485.3 | 283.4 | | SWE-bench Multilingual | 459.5 | 200.6 | | τ²-Bench | 592.8 | 334.9 | | BFCL-V4 | 703.82 | 331.5 | **Ling-2.6-flash evaluation setup:** MTP was enabled with 4 draft tokens. More results wil