The paper introduces the concept of a visual orchestration tax in agentic vision‑language model (VLM) pipelines, where the same static images are repeatedly processed by multiple agents, creating redundancy. It proposes a measurement‑to‑certification framework that audits visual evidence reuse using metrics M1_trace and M2, and certifies reuse through a SharedVisCache contract. Experiments on SeeingEye and MAMMQA show high redundancy rates (66.8‑75.6%) and demonstrate that certified reuse can reduce vision‑API calls while preserving output fidelity.
arXiv:2609.24362v1 Announce Type: new Abstract: Sandboxed computer environments support multi-step reasoning with tools, executable programs, and persistent files, yet their extension from language m...
arXiv:2606. 07595v1 Announce Type: cross Abstract: Vision-language agents increasingly consume screenshots, documents, and user interfaces before writing to memory, sending messages, or invoking external tools.
arXiv:2610.29241v1 Announce Type: new Abstract: In this paper, we investigate why vision-language models (VLMs) produce errors by decomposing them into two sources: visual-representation failures, in...
arXiv:2609.26168v1 Announce Type: cross Abstract: Recent work reports that vision--language models (VLMs) struggle to establish and maintain stable reference in repeated reference games. Rather than...
arXiv:2609.22910v1 Announce Type: new Abstract: Vision-language agents that crop and zoom are trained with rewards that credit a successful tool call, yet a successful call does not show that the mod...
The paper investigates how providing execution traces to multimodal judges in agentic video‑generation systems can bias their verdicts. On a benchmark of 109 two‑event clips, traces that falsely report successful tool calls cause large‑language‑model judges to incorrectly accept 78–90 % of failures, while contradictory traces lead to 100 % rejection of correct clips. The effect persists even when judges are instructed to consider only the video frames, indicating that the vulnerability stems from the judges’ learned trust in tool logs rather than the visual content itself.
The paper introduces CORA (Counterfactual, Observable Redundancy Audit), a protocol for auditing website redundancy by measuring repetition load, normal-use tax, and failure-domain recovery reserve. Each audit run records screenshots, stable element identities, and task traces, while a versioned vision‑language model generates annotations that are validated and released only if they meet calibrated criteria. Experiments on a transparent mechanistic testbed show that CORA’s factorized representation separates reserve from normal-use tax and predicts perturbed success more accurately than scalar-load baselines, but it withholds automated scores when instruments fail to meet release requirements, indicating that CORA is an auditable candidate procedure for the studied benchmark rather than a universal standard.
The paper introduces ZETA, an architecture that separates routing policy from action execution in scientific vision agents. By resolving scientific intent before invoking specialized visual tools, ZETA treats measurement as a deterministic operation grounded in explicit geometry, enabling independent evaluation of routing correctness and downstream execution. Experiments on a zero‑shot benchmark of 51 scientific‑intent queries demonstrate that explicit routing semantics can dramatically improve routing accuracy, especially for models like Qwen 3.8 where segmentation routing accuracy jumps from 6.2% to 75.0% and detection routing accuracy from 60.0% to 100%.
arXiv:2606. 18451v1 Announce Type: new Abstract: Single-image-to-3D generators are improving quickly, but there is no agreed, human-free way to tell whether one generated mesh is better than another.
arXiv:2610.27672v1 Announce Type: new Abstract: Rerunning every governance control after every change to an artificial intelligence (AI) pipeline is expensive, so teams reuse earlier results. Reuse i...
arXiv:2609.31234v1 Announce Type: new Abstract: Problem. Ultra-high-resolution (UHR) remote sensing with vague user intents has two bottlenecks: visual tokens are expensive, and tool calling must be...
arXiv:2608. 06270v1 Announce Type: new Abstract: The "thinking-with-images" paradigm equips multimodal LLMs with active visual operations such as crop-and-zoom.