Tencent Hunyuan's team, together with several institutions, topped the Hugging Face paper rankings on July 13, bringing Long-Horizon-Terminal-Bench (LHTB) — an evaluation system designed specifically for "long-horizon terminal Agents", aimed directly at the blind spots of current Agent evaluation. Existing Terminal-Bench-type benchmarks mostly stay at minute-level, single-step success judgments — they neither give intermediate-process signals nor make most exploration actions anything more than "reward-less trial and error". LHTB's core design is "dense grading": 46 long-horizon tasks cover 9 categories including experiment reproduction, software engineering, multimodal analysis, interactive games, and scientific computing; each problem is split into sub-tasks that can be scored independently — partial credit can be earned, and the Agent can also see where it got stuck. The cost is also direct: an average of 9.9M tokens, 231 episodes, 85.3 minutes of execution time per task — 1–2 orders of magnitude higher than SWE-Bench-type benchmarks. Across 15 frontier models, even the strongest only scored 15.2% pass@1 (0.95 threshold) and 10.9% pass@1 (full-score threshold); the field average was only 4.3% and 1.7%. These numbers expose not "models aren't strong enough" but "the compound failure modes of long-horizon tasks": with long-context management + planning + debugging tightly coupled, any one error will drag down the entire chain. The authors simultaneously open-sourced the evaluation, Agent framework, and failure-mode classification — essentially putting the entry ticket for "the next SWE-Bench" on the table. For vendors currently betting on Coding Agents, this leaderboard will sooner or later need a direct response.