EvoEmpirBench: Dynamic Spatial Reasoning with Agent-ExpVer

Most existing spatial reasoning benchmarks focus on static or globally observable environments, failing to capture the challenges of long-horizon reasoning and memory utilization under partial observability and dynamic changes. We introduce two dynamic spatial benchmarks, locally observable maze navigation and match-2 elimination that systematically evaluate models' abilities in spatial understanding and adaptive planning when local perception, environment feedback, and global objectives are tightly coupled. Each action triggers structural changes in the environment, requiring continuous update of cognition and strategy. We further propose a subjective experience-based memory mechanism for cross-task experience transfer and validation. Experiments show that our benchmarks reveal key limitations of mainstream models in dynamic spatial reasoning and long-term memory, providing a comprehensive platform for future methodological advances. Our code and data are available at https://anonymous.4open.science/r/EvoEmpirBench-143C/.

FakeHunter: Multimodal Step-by-Step Reasoning for Explainable Video Forensics

FakeHunter is a multimodal deepfake detection framework that combines memory-guided retrieval, chain-of-thought (Observation-Thought-Action) reasoning, and tool-augmented verification to provide accurate and interpretable video forensics. FakeHunter encodes visual content using CLIP and audio using CLAP, generating joint audio-visual embeddings that retrieve semantically similar real exemplars from a FAISS-indexed memory bank for contextual grounding. Guided by the retrieved context, the system iteratively reasons over evidence to localize manipulations and explain them. When confidence is low, it automatically invokes specialized tools-such as zoom-in image forensics or mel-spectrogram inspection-for fine-grained verification. Built on Qwen2.5-Omni-7B, FakeHunter produces structured JSON verdicts that specify what was modified, where it occurs, and why it is judged fake. We also introduce X-AVFake, a benchmark comprising 5.7k+ manipulated and real videos (950+ min) annotated with manipulation type, region/entity, violated reasoning category, and free-form justification. On X-AVFake, FakeHunter achieves an accuracy of 34.75%, outperforming the vanilla Qwen2.5-Omni-7B by 16.87 percentage points and MiniCPM-2.6 by 25.56 percentage points. Ablation studies reveal that memory retrieval contributes a 7.75 percentage point gain, and tool-based inspection improves low-confidence cases to 46.50%. Despite its multi-stage design, the pipeline processes a 10-minute clip in 8 minutes on a single NVIDIA A800 (0.8x real-time) or 2 minutes on four GPUs (0.2x), demonstrating practical deployability.

CALM: Curiosity-Driven Auditing for Large Language Models

Auditing Large Language Models (LLMs) is a crucial and challenging task. In this study, we focus on auditing black-box LLMs without access to their parameters, only to the provided service. We treat this type of auditing as a black-box optimization problem where the goal is to automatically uncover input-output pairs of the target LLMs that exhibit illegal, immoral, or unsafe behaviors. For instance, we may seek a non-toxic input that the target LLM responds to with a toxic output or an input that induces the hallucinative response from the target LLM containing politically sensitive individuals. This black-box optimization is challenging due to the scarcity of feasible points, the discrete nature of the prompt space, and the large search space. To address these challenges, we propose Curiosity-Driven Auditing for Large Language Models (CALM), which uses intrinsically motivated reinforcement learning to finetune an LLM as the auditor agent to uncover potential harmful and biased input-output pairs of the target LLM. CALM successfully identifies derogatory completions involving celebrities and uncovers inputs that elicit specific names under the black-box setting. This work offers a promising direction for auditing black-box LLMs. Our code is available at https://github.com/x-zheng16/CALM.git.