*ACL Findings or TMLR? [D]

The recent Reddit post from /u/Pure-Ad9079, questioning the relative value of publication venues like TMLR (Transactions of the Machine Learning Research) versus ACL Findings, sparks a vital conversation about the evolving landscape of AI research dissemination. It's a sentiment many researchers, especially those early in their careers, grapple with: where to publish to maximize impact and visibility. The user’s expectation of a NeurIPS rejection, coupled with the decision between these alternative venues, highlights a broader shift away from the singular pursuit of top-tier conference acceptance. The core question—would you rather have TMLR or *ACL findings on your publication list?—reflects a growing recognition that impactful research can find a home in specialized, high-quality venues, even if they don’t carry the same prestige as the very top conferences. This is especially true as the volume of submissions to major conferences continues to rise, making acceptance increasingly competitive. It’s a trend we've observed before, such as in the discussion around attending ECCV and seeking social connections Is anyone esle going to ECCV and wants to get in a groupchat for socials?.

The increasing number of specialized journals and workshops offers researchers more targeted opportunities to share their work with a relevant audience. TMLR, for instance, provides a more rigorous peer-review process than some conference proceedings, potentially leading to higher quality publications. ACL Findings, as the name suggests, is designed for rapid dissemination of impactful, though perhaps not fully mature, research within the natural language processing community. The choice isn't necessarily about "better" or "worse," but rather about aligning the research with the appropriate audience and publication timeline. It’s also worth noting the potential for leveraging seemingly simpler approaches to achieve impressive results, as demonstrated by the recent discovery of beating SOTA Time Series Anomaly Detection methods with a 100-year-old algorithm You can beat SOTA Time Series Anomaly Detection methods with a 100 year old algorithm. This underscores that impact isn't solely determined by the novelty of the technique, but also by the problem addressed and the results achieved.

The significance of this discussion extends beyond individual publication decisions. It signals a broader re-evaluation of the metrics used to assess research impact. Traditionally, conference acceptance rates and citation counts have been primary indicators of success, but this focus can inadvertently incentivize researchers to prioritize flashy results over rigorous methodology or impactful applications. The willingness to consider venues like TMLR and ACL Findings suggests a move toward a more nuanced understanding of research value, one that considers the quality of peer review, the relevance of the audience, and the long-term impact of the work. It’s a move towards valuing sustained contributions and building a robust body of work, rather than chasing fleeting moments of conference glory. The rise of venues like these also allows for more focused discussion of specific problems, as evidenced by the recent publication on autonomous mathematical discovery [ [R] Autonomous Mathematical Discovery in an Open-World Multi-Agent Environment]( /post/r-autonomous-mathematical-discovery-in-an-open-world-multi-a-cmtgtz41x0vz9mi9zrnrqt4hh), which is a complex area benefiting from targeted dissemination.

Looking ahead, we anticipate continued diversification in AI research publishing. The proliferation of specialized journals and pre-print servers will likely accelerate, offering researchers increasingly granular control over how their work is disseminated. This shift will necessitate a re-evaluation of how we assess research impact, moving beyond simplistic metrics and embracing a more holistic view that considers the quality of the research, its relevance to the field, and its potential for real-world impact. A critical question to watch will be whether these specialized venues can establish their own distinct reputations and attract top-tier researchers, creating a truly multi-tiered publishing ecosystem that supports innovation across the AI landscape.