How much work in progress can a workshop submission be [R]

The query from /u/strammerrammer regarding workshop submissions highlights a common tension in research: presenting work in progress versus waiting for a fully realized solution. Their situation – an algorithm partially implemented, with the core novelty residing in its potential to solve a broader set of problems – is relatable to many researchers. It’s a delicate balance, and the question of whether to submit in this state is valid. The core issue isn’t necessarily the *lack* of a complete solution, but rather the perceived lack of novelty in the current, limited form. As we’ve seen with the rapid advancements in tabular foundation models, like the recent release of TabPFN-3.5 is released as the next SOTA tabular foundation model, the field moves quickly, and incremental contributions can easily be overshadowed. However, dismissing work simply because it isn’t a finished product risks stifling valuable exploration and feedback.

The proposed approach—submitting the existing algorithm, experimental results, and a detailed plan for future development—is a reasonable one, provided it’s presented transparently and strategically. The key is to clearly articulate the *potential* novelty of the full algorithm, emphasizing why solving problems A and B together represents a significant advancement. The mathematics underpinning this plan should be robust and convincing, demonstrating a clear path toward a novel contribution. It's also important to acknowledge the limitations of the current implementation honestly. The fact that the algorithm for problem A alone isn't groundbreaking is a strength in this context; it avoids overstating the current contribution while still highlighting the exciting prospect of the combined solution. This mirrors the challenges faced by researchers navigating the complexities of conference submissions, as evidenced by the recent discussion about logistical issues at NeurIPS 2026: handling of multiple venue locations seems bad, demonstrating the importance of clear communication and realistic expectations.

The value of submitting such a proposal lies not just in potentially receiving feedback on the algorithm itself, but also in fostering a conversation around the research direction. Workshop environments often thrive on early-stage ideas and explorations. Presenting a well-defined plan, supported by preliminary results, can attract valuable insights from other researchers, potentially leading to unexpected collaborations or refinements. Think of it as a pre-publication validation exercise. Furthermore, this approach aligns with the growing trend of sharing intermediate research findings – a shift partially driven by the accessibility of resources and the need for increased transparency, as highlighted by work examining How Many Labeled Examples Does a Text Classifier Actually Need?. The willingness to share works-in-progress demonstrates intellectual humility and a commitment to collaborative progress.

Ultimately, the decision hinges on the workshop's specific scope and audience. If the workshop focuses on incremental improvements or methodological explorations, then submitting the partial algorithm with a clear roadmap is a viable strategy. If the workshop prioritizes fully realized, impactful results, then it might be prudent to wait. Regardless, the core takeaway is that research is rarely a linear process, and sharing works-in-progress, when done thoughtfully and transparently, can be a valuable contribution to the broader community. As AI-native tools become increasingly prevalent in research workflows, will we see a greater emphasis on iterative, publicly shared development cycles, blurring the lines between preliminary findings and final publications?