Academic

The Synthetic Web: Adversarially-Curated Mini-Internets for Diagnosing Epistemic Weaknesses of Language Agents

arXiv:2603.00801v1 Announce Type: new Abstract: Language agents increasingly act as web-enabled systems that search, browse, and synthesize information from diverse sources. However, these sources can include unreliable or adversarial content, and the robustness of agents to adversarial ranking - where misleading information appears prominently in search results - remains poorly understood. Existing benchmarks evaluate functional navigation or static factuality but cannot causally isolate this vulnerability, and current mitigation strategies for retrieval-augmented generation remain largely untested under such conditions. We introduce Synthetic Web Benchmark, a procedurally generated environment comprising thousands of hyperlinked articles with ground-truth labels for credibility and factuality, process-level interaction traces, and contamination filtering to eliminate training-data leakage. By injecting a single high-plausibility misinformation article into a controllable search rank

S
Shrey Shah, Levent Ozgur · · 1 min read · 2 views
#cs.AI #cs.IR

arXiv:2603.00801v1 Announce Type: new Abstract: Language agents increasingly act as web-enabled systems that search, browse, and synthesize information from diverse sources. However, these sources can include unreliable or adversarial content, and the robustness of agents to adversarial ranking - where misleading information appears prominently in search results - remains poorly understood. Existing benchmarks evaluate functional navigation or static factuality but cannot causally isolate this vulnerability, and current mitigation strategies for retrieval-augmented generation remain largely untested under such conditions. We introduce Synthetic Web Benchmark, a procedurally generated environment comprising thousands of hyperlinked articles with ground-truth labels for credibility and factuality, process-level interaction traces, and contamination filtering to eliminate training-data leakage. By injecting a single high-plausibility misinformation article into a controllable search rank, we measure the causal effect of adversarial exposure in six frontier models. The results reveal catastrophic failures: accuracy collapses despite unlimited access to truthful sources, with minimal search escalation and severe miscalibration. These findings expose fundamental limitations in how current frontier models handle conflicting information, with immediate implications for deployment in high-stakes domains. Our benchmark enables systematic analysis of these failure modes and provides a controlled testbed for evaluating mitigation strategies under adversarial ranking - a gap in current research. This work establishes a reproducible baseline for developing search-robust and epistemically humble agents capable of resisting manipulation in high-stakes domains.

Executive Summary

The article introduces the Synthetic Web Benchmark, a procedurally generated environment to test language agents' robustness to adversarial ranking. The results show catastrophic failures in six frontier models, revealing limitations in handling conflicting information. The benchmark enables systematic analysis of failure modes and evaluation of mitigation strategies, providing a baseline for developing search-robust agents. The findings have immediate implications for deployment in high-stakes domains, highlighting the need for epistemically humble agents capable of resisting manipulation.

Key Points

  • ▸ Introduction of the Synthetic Web Benchmark to test language agents' robustness to adversarial ranking
  • ▸ Catastrophic failures in six frontier models despite unlimited access to truthful sources
  • ▸ Limitations in current models' ability to handle conflicting information

Merits

Comprehensive Benchmark

The Synthetic Web Benchmark provides a controlled testbed for evaluating language agents' robustness to adversarial ranking, enabling systematic analysis of failure modes

Reproducible Results

The benchmark establishes a reproducible baseline for developing search-robust and epistemically humble agents

Demerits

Limited Model Scope

The study only evaluates six frontier models, which may not be representative of all language agents

Potential Biases

The benchmark's procedurally generated environment may introduce biases or limitations not present in real-world scenarios

Expert Commentary

The article's findings underscore the critical need for language agents that can effectively navigate and evaluate the credibility of online information. The Synthetic Web Benchmark provides a valuable tool for assessing and improving the robustness of these agents. However, the study's limitations highlight the importance of ongoing research and development in this area, particularly in addressing potential biases and expanding the scope of models evaluated. Ultimately, the development of epistemically humble agents will require a multidisciplinary approach, incorporating insights from AI, cognitive science, and social epistemology.

Recommendations

  • ✓ Further research on developing more robust language agents capable of handling conflicting information and resisting manipulation
  • ✓ Expansion of the Synthetic Web Benchmark to include a broader range of models and scenarios, addressing potential biases and limitations

Sources

Related Articles