arXiv AI
CausalCache: Conditional High-Fidelity Restoration for Long-Horizon GUI Agents
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arXiv AI
Jul 29
Desktop-Delta Bench: Do Computer-Use Models Understand Desktop GUI Transitions?
arXiv:2607. 26041v1 Announce Type: new Abstract: Computer-use agents (CUAs) increasingly act through desktop GUIs to complete long-horizon tasks.
arXiv Computer Vision
Sep 10
TRACE: Trajectory-robust Admission with Evidence Ordering for Efficient GUI Agents
arXiv:2609.10297v1 Announce Type: new Abstract: GUI agents accumulate high-resolution screenshots as the trajectory unfolds, increasing inference latency and memory usage. Training-free visual token...
arXiv Computation and Language
Sep 1
When History Is Multimodal: Rethinking Context Management for Long-Horizon Agents
arXiv:2608.29897v1 Announce Type: new Abstract: Long-horizon agents need a context manager to compress growing interaction histories into a bounded working context, via passive strategies or active s...
arXiv Machine Learning
Jul 20
Presentation, Not Mechanism: A Render Confound in Deprecation-Aware Memory Evaluation
arXiv:2607. 16019v1 Announce Type: new Abstract: AI systems increasingly retrieve from records that revise themselves: issue threads, encyclopedic histories, policy logs, and long conversations.
arXiv AI
Aug 14
Beyond Retrieval: Query-Conditioned Reuse of Long-Horizon Agent Trajectories
arXiv:2608. 12847v1 Announce Type: new Abstract: Retrieval can identify a past trajectory that may matter, yet it does not specify how an acting agent should use that trajectory after users, entities, constraints, or environment state have changed.
arXiv AI
Sep 17
Contiguity, Not Importance: Budgeted Repair of Stale KV Caches After Document Edits
The paper investigates how to efficiently repair stale key-value (KV) caches in retrieval‑augmented generation systems after document edits. It proposes a budgeted in‑place recomputation approach and evaluates training‑free position‑selection policies on a factual RAG benchmark. Across three model families, a contiguous edit‑local window consistently recovers most of the post‑edit answer quality while being 13–21 times faster than a full re‑prefill, though its effectiveness diminishes when answer‑bearing text moves downstream.