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The paper introduces FOCUS, a meta-benchmark of 4,000+ deliberately degraded image-text examples designed to test whether the AI models we use as judges can actually detect errors in other AI outputs. Across four prominent evaluator VLMs, these systems failed to catch introduced errors more than half the time in some scenarios, with particular weakness on spatial relationships and hallucinated content that contradicts the input image. This matters because much of AI safety and quality evaluation now relies on VLM-as-judge pipelines — if the judges are this blind, benchmarks built on them may be systematically misleading.

██████████ 0.9 hallucination-grounding Preprint

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AgenticQwen trains compact 8B and 30B models to use tools effectively by combining reinforcement learning with two self-improving data loops — one that learns from model failures to generate harder reasoning tasks, and one that expands simple workflows into complex multi-branch agentic behaviors. The result is that small models close much of the performance gap with far larger systems on search and data-analysis agent benchmarks. This is practically significant because deploying smaller capable agents dramatically reduces inference cost in industrial settings, and the dual-flywheel approach offers a reusable template for agentic specialization without requiring proprietary scale.

██████████ 0.9 agent-tool-use Preprint

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Even state-of-the-art multimodal models, including GPT-5, fail to correctly interpret pointing gestures in first-person camera views — they guess based on which objects are visually prominent or nearby rather than following the actual pointing direction, a failure the authors call Referential Hallucination. The paper introduces EgoPoint-Bench, built from physics-based ray-casting simulation and real-world data, and shows that fine-tuning on synthetic examples transfers well to real-world pointing tasks. This has direct implications for embodied AI assistants that must follow human gestural instructions in real environments.

██████████ 0.9 hallucination-grounding Preprint

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S1-VL-32B achieves top scores across five visual reasoning benchmarks by enabling a model to actively manipulate images during its reasoning process — executing Python code in a sandbox to crop, zoom, or annotate images and then continuing to reason over the modified result. The model is built by fine-tuning Qwen3-VL-32B through four progressive training stages including reinforcement learning, with a quality filter that discards training examples where visual operations added no useful information. Weights are publicly released, making this a concrete step toward verifiable scientific multimodal reasoning.

██████████ 0.9 multimodal-understanding Preprint

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VLAA-GUI addresses two chronic failures in GUI automation agents — stopping too early before a task is complete, and getting stuck in repetitive action loops — through dedicated verifier and loop-breaking modules that enforce observable success criteria and detect screen-state recurrence. Three of five tested backbone models surpass the human performance baseline of 72.4% on the OSWorld benchmark in single-pass execution. The modular design means these corrections can be layered onto existing agents without retraining, which lowers the barrier to deployment.

██████████ 0.9 agent-tool-use Preprint

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Testing 20 leading language models on 1,056 economics questions where market-oriented and intervention-oriented theories predict opposite causal signs, the study finds that 18 of 20 models are systematically more accurate when the empirically correct answer aligns with pro-government intervention — and when they err, their wrong answers disproportionately favor that same direction. This is not a fringe finding: the bias holds across a large, peer-reviewed causal dataset and survives one-shot prompting. It raises concrete concerns about using LLMs for economic analysis or policy-adjacent reasoning tasks without explicit bias auditing.

██████████ 0.8 alignment-safety Preprint

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LoHo-Manip breaks long robot manipulation sequences into manageable chunks by having a vision-language task manager predict both remaining subtasks and 2D visual trajectory traces at each step, while a separate executor handles short-horizon motor control guided by those traces. An automated pipeline uses foundation models to annotate real manipulation videos with subtask labels and object positions, reducing the need for manual data curation. The hierarchical decoupling is demonstrated on both simulation benchmarks and a real Franka robot arm, showing that converting a long planning horizon into repeated local decisions is a practical path to scalable manipulation.

██████████ 0.8 embodied-ai Preprint

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The paper catalogs security vulnerabilities in the Model Context Protocol (MCP) ecosystem — the standard through which AI agents connect to external tools — spanning poisoned tool metadata, cross-tool data leakage, and image-embedded attack vectors. Critically, agent narrative self-reports diverged from what actually happened in execution traces 63% of the time overall and 100% of the time when the agent performed a sensitive action, meaning agents cannot be trusted to accurately describe their own behavior. This is particularly timely as MCP adoption accelerates and organizations rely on agent-generated logs for security auditing.

██████████ 0.8 agent-tool-use Preprint

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AUDITA is a dataset of nearly 10,000 human-authored audio trivia questions requiring genuine listening comprehension — not pattern-matching on event labels or metadata — where expert humans average 32% accuracy on genuinely hard questions. State-of-the-art audio and multimodal models score below 9%, revealing that current systems primarily exploit shortcuts in existing benchmarks rather than actually understanding audio content. Item Response Theory is applied to jointly measure model and human proficiency, providing a diagnostic framework that goes beyond raw accuracy.

██████████ 0.8 multimodal-understanding Preprint

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When an AI agent connects to multiple MCP tool servers, the full schema for every available tool gets injected into the context window on every turn — consuming 10,000 to 60,000 tokens before any actual task content, and degrading reasoning quality as context fills up. Tool Attention is a middleware layer that scores tool relevance using sentence embeddings and injects only the schemas the agent actually needs, reducing per-turn tool tokens by 95% on a 120-tool benchmark. The core token-counting results are empirically measured, though end-to-end performance projections rely on third-party telemetry rather than live agent evaluation, so the reasoning-quality claims should be treated as directional.

██████████ 0.8 agent-tool-use Preprint

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