{"title": "How TTD-DR Achieves Superior Performance Compared to Traditional Research Agents", "report_sections": [{"header": "Innovative Framework Inspired by Human Writing", "content": "

TTD-DR introduces a novel framework that models the generation of research reports as an iterative diffusion process. Unlike traditional research agents that follow a linear or parallel planning process, TTD-DR is designed to mimic the human writing process. It begins with a preliminary draft\u2014a 'noisy' initial output\u2014which is then refined in successive steps. This draft-centric approach helps the agent maintain a global context throughout the report-generation process, reducing information loss and improving the overall coherence of the final document.

", "media": [{"caption": "Figure 1 | Our method is inspired by the natural human writing process, which includes planning, drafting, and multiple revisions to the draft.", "url": "https://askpandipro.s3.eu-west-1.amazonaws.com/users/1/documents/489/figures/0.png", "source_url": null, "title": null, "s3_key": "users/1/documents/489/figures/0.png", "s3_thumbnail_key": null, "type": "image", "id": 214962, "html": null, "carousel_h5": "Figure 1 | Our method is inspired by the natural human writing process, which includes planning, drafting, and multiple revisions to the draft.", "alt": null}]}, {"header": "Iterative Denoising with Retrieval", "content": "

One of the core innovations of TTD-DR is its denoising mechanism that is dynamically augmented by external information retrieval. Instead of simply refining the output repetitively, the system uses each revised report as a guide to generate targeted search queries. After the retrieval process, the new information is integrated to eliminate errors and gaps in the draft. This continuous feedback loop between refining the draft and retrieving additional data enables the report to become more detailed and accurate with each iteration. As a result, TTD-DR can incorporate up to 51.2% of the final report\u2019s information in early revision steps, thereby ensuring that critical data is captured sooner and more effectively compared to traditional models.

", "media": [{"caption": "Graph showing research accuracy versus latency for various models", "url": "https://askpandipro.s3.eu-west-1.amazonaws.com/users/1/documents/489/figures/4.png", "source_url": null, "title": null, "s3_key": "users/1/documents/489/figures/4.png", "s3_thumbnail_key": null, "type": "image", "id": 214965, "html": null, "carousel_h5": "Graph showing research accuracy versus latency for various models", "alt": null}]}, {"header": "Component-wise Self-Evolution", "content": "

TTD-DR further distinguishes itself through a self-evolutionary algorithm applied at each component of its workflow. Instead of treating the plan generation, search question generation, answer synthesis, and final report assembly as isolated tasks, TTD-DR continuously optimizes each module. The self-evolution process produces multiple variants for components like search queries and answers. It then evaluates these variants using an LLM-based judge that provides fitness scores and textual feedback. The system iteratively revises its outputs based on this feedback, merging the highest-quality variants into a single improved version. This component-wise self-evolution significantly enhances the richness and accuracy of the context provided to the report-generation process, thereby outpacing the performance of conventional research agents that do not employ such dynamic tuning.

", "media": [{"caption": "Flowcharts illustrating various research frameworks: Huggingface Open DR, GPT Researcher, Open Deep Research, and Test-Time Diffusion DR", "url": "https://askpandipro.s3.eu-west-1.amazonaws.com/users/1/documents/489/figures/1.png", "source_url": null, "title": null, "s3_key": "users/1/documents/489/figures/1.png", "s3_thumbnail_key": null, "type": "image", "id": 214961, "html": null, "carousel_h5": "Flowcharts illustrating various research frameworks: Huggingface Open DR, GPT Researcher, Open Deep Research, and Test-Time Diffusion DR", "alt": null}]}, {"header": "Enhanced Query Novelty and Early Information Integration", "content": "

The integration of denoising with retrieval and self-evolution helps TTD-DR to explore a wider range of search queries and gather more diverse data. It has been observed that the denoising process increases query novelty by more than 12 percentage points across the search iterations. This means that the system is constantly uncovering new key points and ideas that are then assimilated into the evolving draft. Moreover, by including retrieved information in the earlier stages of the search process, TTD-DR is able to guide its subsequent queries more intelligently, leading to a more comprehensive and timely refinement of the draft. This early incorporation of new data is a distinct advantage over traditional agents that may not integrate feedback as systematically.

", "media": [{"caption": "Figure 6 | Query domain distribution of the evaluation sets: LongForm Research (left) and H LE -search (right), both demonstrating diverse domain coverage.", "url": "https://askpandipro.s3.eu-west-1.amazonaws.com/users/1/documents/489/figures/3.png", "source_url": null, "title": null, "s3_key": "users/1/documents/489/figures/3.png", "s3_thumbnail_key": null, "type": "image", "id": 214963, "html": null, "carousel_h5": "Figure 6 | Query domain distribution of the evaluation sets: LongForm Research (left) and H LE -search (right), both demonstrating diverse domain coverage.", "alt": null}]}, {"header": "Superior Performance on Benchmarks", "content": "

Empirical results clearly demonstrate the performance benefits of TTD-DR. When compared with established systems like OpenAI Deep Research and other recent deep research agents, TTD-DR has shown remarkable gains. For instance, in side-by-side evaluations for long-form research report tasks, TTD-DR achieved a win rate of 69.1% over its competitors. It also outperformed other systems across a variety of benchmarks, including those requiring multi-hop reasoning and complex reasoning to generate short-form answers. These improvements are a direct consequence of the integrated iterative refinement and the continuous feedback provided by both the denoising and self-evolution processes. The robust performance across diverse evaluation metrics underscores the strength of the TTD-DR framework in addressing the limitations faced by traditional research agents.

", "media": [{"caption": "Figure 11 | Helpfulness, Comprehensiveness, and side-by-side rating between Report A and B. Report are simplified for clarify purpose.", "url": "https://askpandipro.s3.eu-west-1.amazonaws.com/users/1/documents/489/figures/7.png", "source_url": null, "title": null, "s3_key": "users/1/documents/489/figures/7.png", "s3_thumbnail_key": null, "type": "image", "id": 214966, "html": null, "carousel_h5": "Figure 11 | Helpfulness, Comprehensiveness, and side-by-side rating between Report A and B. Report are simplified for clarify purpose.", "alt": null}]}, {"header": "Efficient Test-Time Scaling", "content": "

An important aspect of TTD-DR\u2019s design is its efficiency in test-time compute scaling. By integrating both denoising with retrieval and self-evolution, the system achieves significant performance gains without incurring excessively high latency. The iterative nature of the process means that even with additional computation steps\u2014up to a fixed number of revision cycles\u2014the performance improvement per unit increase in latency is substantial. This efficiency, showcased by its steep performance improvement curve in Pareto frontier analyses, means that TTD-DR not only delivers high-quality research outputs but does so in a time-efficient manner compared to traditional methods that may require more extensive processing without equivalent gains.

", "media": [{"caption": "Figure 13 | Pareto frontier between DR agent performances and latency for HLE-search. The dots from left to right represent 1) Gem in i -2.5 -pro w/ search tool, 2) Backbone DR Agent , 3) +Self -evolut ion and 4) +D iffus ion w ith Retrieval, which shows our final algorithm is most efficient in terms of test-time scaling (steepest slope).", "url": "https://askpandipro.s3.eu-west-1.amazonaws.com/users/1/documents/489/figures/9.png", "source_url": null, "title": null, "s3_key": "users/1/documents/489/figures/9.png", "s3_thumbnail_key": null, "type": "image", "id": 214970, "html": null, "carousel_h5": "Figure 13 | Pareto frontier between DR agent performances and latency for HLE-search. The dots from left to right represent 1) Gem in i -2.5 -pro w/ search tool, 2) Backbone DR Agent , 3) +Self -evolut ion and 4) +D iffus ion w ith Retrieval, which shows our final algorithm is most efficient in terms of test-time scaling (steepest slope).", "alt": null}]}, {"header": "Conclusion", "content": "

TTD-DR outperforms traditional research agents by fundamentally rethinking the way research reports are generated. Emulating human cognitive patterns, the system starts with a draft that is iteratively refined through denoising with retrieval, ensuring that global context is maintained and enhanced with each revision. Its component-wise self-evolution further boosts the quality of each step in the research workflow by exploring diverse alternatives and integrating the best outcomes. Coupled with early incorporation of new information and efficient test-time scaling, TTD-DR consistently delivers better performance as measured by higher win rates and improved accuracy on comprehensive benchmarks. This innovative approach not only advances the state-of-the-art in deep research agent design but also paves the way for more adaptable and effective automated research solutions.

", "media": []}]}

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