SaaS‑Bench evaluates AI agents on 23 real SaaS applications and 106 cross‑app, long‑horizon tasks, revealing that even the strongest model, Claude Opus 4.7, passes fewer than four percent of tasks and exposing four structural failure modes that separate benchmark scores from true office productivity.
HiF-VLA introduces a motion‑centric bidirectional spatiotemporal reasoning framework with a joint‑expert module that simultaneously predicts future visual motion and generates high‑precision action sequences, eliminating visual redundancy, cutting inference latency and memory usage, and achieving superior success rates on long‑horizon benchmarks such as CALVIN and LIBERO‑LONG.
Stanford, Berkeley and Nvidia introduce LLM‑as‑a‑Verifier, a verification framework that scales verification compute, uses fine‑grained score tokens, repeated checks and criteria decomposition to boost agent performance, eliminate scoring ties and achieve SOTA results on Terminal‑Bench, surpassing Claude Mythos and GPT‑5.5 while improving safety in long‑horizon tasks.
The MANSION framework creates a highly realistic, multi‑scene simulation that lets robots train for long‑duration, cross‑environment tasks, dramatically cutting real‑world trial costs and narrowing the sim‑to‑real gap for embodied intelligence.
LongHorizonUI tackles the steep success‑rate drop of GUI agents on tasks longer than 10‑15 steps by introducing three tightly coupled modules—enhanced perception, deep reflective decision, and compensatory execution—and validates the approach on the new LongGUIBench benchmark with consistent performance gains across both app and game scenarios.
