Xuan Zhou

Ryan Wong, Jiawei Wang, Junjie Zhao et al.

From professional research to everyday planning, many tasks are bottlenecked by wide-scale information seeking, which is more repetitive than cognitively complex. With the rapid development of Large Language Models (LLMs), automated search agents powered by LLMs offer a promising solution to liberate humans from this tedious work. However, the capability of these agents to perform such "wide-context" collection reliably and completely remains largely unevaluated due to a lack of suitable benchmarks. To bridge this gap, we introduce WideSearch, a new benchmark engineered to evaluate agent reliability on these large-scale collection tasks. The benchmark features 200 manually curated questions (100 in English, 100 in Chinese) from over 15 diverse domains, grounded in real user queries. Each task requires agents to collect large-scale atomic information, which could be verified one by one objectively, and arrange it into a well-organized output. A rigorous five-stage quality control pipeline ensures the difficulty, completeness, and verifiability of the dataset. We benchmark over 10 state-of-the-art agentic search systems, including single-agent, multi-agent frameworks, and end-to-end commercial systems. Most systems achieve overall success rates near 0\%, with the best performer reaching just 5\%. However, given sufficient time, cross-validation by multiple human testers can achieve a near 100\% success rate. These results demonstrate that present search agents have critical deficiencies in large-scale information seeking, underscoring urgent areas for future research and development in agentic search. Our dataset, evaluation pipeline, and benchmark results have been publicly released at https://widesearch-seed.github.io/

Kexin Pei, Zhou Xuan, Junfeng Yang et al.

Detecting semantically similar functions -- a crucial analysis capability with broad real-world security usages including vulnerability detection, malware lineage, and forensics -- requires understanding function behaviors and intentions. This task is challenging as semantically similar functions can be implemented differently, run on different architectures, and compiled with diverse compiler optimizations or obfuscations. Most existing approaches match functions based on syntactic features without understanding the functions' execution semantics. We present Trex, a transfer-learning-based framework, to automate learning execution semantics explicitly from functions' micro-traces and transfer the learned knowledge to match semantically similar functions. Our key insight is that these traces can be used to teach an ML model the execution semantics of different sequences of instructions. We thus train the model to learn execution semantics from the functions' micro-traces, without any manual labeling effort. We then develop a novel neural architecture to learn execution semantics from micro-traces, and we finetune the pretrained model to match semantically similar functions. We evaluate Trex on 1,472,066 function binaries from 13 popular software projects. These functions are from different architectures and compiled with various optimizations and obfuscations. Trex outperforms the state-of-the-art systems by 7.8%, 7.2%, and 14.3% in cross-architecture, optimization, and obfuscation function matching, respectively. Ablation studies show that the pretraining significantly boosts the function matching performance, underscoring the importance of learning execution semantics.