# Pushing Test-Time Scaling Limits of Deep Search with Asymmetric Verification

arXiv: https://arxiv.org/abs/2510.06135 · HTML: https://mlsanity.com/paper/2510.06135/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Placed in Test-time compute and efficient thinking, one of 17 directions in LLM reasoning.
- Builds on 18 of its 28 references that are in the map.
- First posted on arXiv 7 Oct 2025.

## Placed in

- LLM reasoning / [Test-time compute and efficient thinking](https://mlsanity.com/lr/test-time-compute-and-efficient-thinking/)

## Builds on (most cited first, 18 of 18)

- [Qwen3 Technical Report](https://mlsanity.com/paper/2505.09388/) (2025)
- [DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning](https://mlsanity.com/paper/2501.12948/) (2025)
- [Self-Consistency Improves Chain of Thought Reasoning in Language Models](https://mlsanity.com/paper/2203.11171/) (2022)
- [Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters](https://mlsanity.com/paper/2408.03314/) (2024)
- [s1: Simple test-time scaling](https://mlsanity.com/paper/2501.19393/) (2025)
- [OpenAI o1 System Card](https://mlsanity.com/paper/2412.16720/) (2024)
- [Kimi k1.5: Scaling Reinforcement Learning with LLMs](https://mlsanity.com/paper/2501.12599/) (2025)
- [BrowseComp: A Simple Yet Challenging Benchmark for Browsing Agents](https://mlsanity.com/paper/2504.12516/) (2025)
- [Kimi K2: Open Agentic Intelligence](https://mlsanity.com/paper/2507.20534/) (2025)
- [GLM-4.5: Agentic, Reasoning, and Coding (ARC) Foundation Models](https://mlsanity.com/paper/2508.06471/) (2025)
- [WebThinker: Empowering Large Reasoning Models with Deep Research Capability](https://mlsanity.com/paper/2504.21776/) (2025)
- [WebSailor: Navigating Super-human Reasoning for Web Agent](https://mlsanity.com/paper/2507.02592/) (2025)
- [Beyond Ten Turns: Unlocking Long-Horizon Agentic Search with Large-Scale Asynchronous RL](https://mlsanity.com/paper/2508.07976/) (2025)
- [BrowseComp-ZH: Benchmarking Web Browsing Ability of Large Language Models in Chinese](https://mlsanity.com/paper/2504.19314/) (2025)
- [xbench: Tracking Agents Productivity Scaling with Profession-Aligned Real-World Evaluations](https://mlsanity.com/paper/2506.13651/) (2025)
- [WebExplorer: Explore and Evolve for Training Long-Horizon Web Agents](https://mlsanity.com/paper/2509.06501/) (2025)
- [Diving into Self-Evolving Training for Multimodal Reasoning](https://mlsanity.com/paper/2412.17451/) (2024)
- [B-STaR: Monitoring and Balancing Exploration and Exploitation in Self-Taught Reasoners](https://mlsanity.com/paper/2412.17256/) (2024)

## Built on by (most cited first, 12 of 12)

- [ParallelMuse: Agentic Parallel Thinking for Deep Information Seeking](https://mlsanity.com/paper/2510.24698/) (2025)
- [InteractComp: Evaluating Search Agents With Ambiguous Queries](https://mlsanity.com/paper/2510.24668/) (2025)
- [Symphony: A Cognitively-Inspired Multi-Agent System for Long-Video Understanding](https://mlsanity.com/paper/2603.17307/) (2026)
- [AgentSwing: Adaptive Parallel Context Management Routing for Long-Horizon Web Agents](https://mlsanity.com/paper/2603.27490/) (2026)
- [Argus: Evidence Assembly for Scalable Deep Research Agents](https://mlsanity.com/paper/2605.16217/) (2026)
- [Deep Search with Hierarchical Meta-Cognitive Monitoring Inspired by Cognitive Neuroscience](https://mlsanity.com/paper/2601.23188/) (2026)
- [AREX: Towards a Recursively Self-Improving Agent for Deep Research](https://mlsanity.com/paper/2607.21461/) (2026)
- [Diagnosing and Mitigating Context Rot in Long-horizon Search](https://mlsanity.com/paper/2606.29718/) (2026)
- [On the Overscaling Curse of Parallel Thinking: System Efficacy Contradicts Sample Efficiency](https://mlsanity.com/paper/2601.21619/) (2026)
- [When and Why LLM Causal Priors Help: Closed-Loop Prior Selection for Amortized Causal Inference](https://mlsanity.com/paper/2609.06941/) (2026)
- [TRACE: Trajectory Selection for Parallel Scaling of Search Agents](https://mlsanity.com/paper/2609.39912/) (2026)
- [VeriHarness: Scaling Agentic Verification for Long-Horizon Tasks](https://mlsanity.com/paper/2610.00972/) (2026)

## Abstract (arXiv)

Test-time compute can be scaled both sequentially and in parallel. Sequential scaling involves lengthening the generation process, while parallel scaling involves verifying and selecting among multiple candidate outputs. Combining these two strategies has led to the most powerful AI systems, such as Grok 4 Heavy and GPT-5 Pro. In certain contexts (e.g., solving Sudoku puzzles), verifying responses can be substantially easier than generating them. This property, referred to as asymmetric verification, highlights the strong potential of test-time scaling (TTS). In this work, we study both sequential and parallel TTS of deep search agents, motivated by the intuition that verification in this setting is often much easier than generation. In experiments, we first show that sequential scaling methods, such as budget forcing, can be effective initially but soon degrade performance. Leveraging asymmetric verification, however, we are able to achieve substantial improvements by allocating only a modest amount of compute to the verifier. We conduct experiments with flagship open-source models and extend them to their ``Heavy'' variants through TTS. These deep research agents achieve gains of up to 27 absolute points on benchmarks such as BrowseComp. Remarkably, as an open-source alternative, GLM-4.5 Heavy reaches accuracy of bf 54.0% on BrowseComp and bf 66.0% on GAIA, placing it comparable to the best proprietary choices such as OpenAI Deep Research. Tongyi-DeepResearch Heavy further achieves bf 69.0% accuracy on BrowseComp, greatly surpassing the best proprietary results.