reviews: 
Comments: The paper tackles item (document) retrieval problem, i.e., the algorithm should return the indices (and scores) of most relevant items given a query. In particular, the paper focuses on generative (decoder) based subset of algorithms. Current decoder-based methods follow a two-step procedure where first the index is learned over pre-trained features (e.g., DSI) and then the model is trained to generate in this space. The paper argues that the aforementioned procedure can be sub-optimal, and it proposes URI (a unified framework for generative retrieval) that simultaneously learns the index as well as the decoder. 

The model is trained in a layer-by-layer manner (in contrast to existing ones). The first layer is trained by optimizing $\mathcal{L_{E step}} + \mathcal{L_{M step}} $ - the similarity between corresponding queries and items is optimized while avoiding index collapse. The buckets are then assigned for each item via balanced assignment. First layer tokens are then passed onto second layer which is trained via an additional memory reinforcement loss term to avoid forgetting. Finally, a ranker is trained that assigns score to query item pairs on the basis of inner product between pooled embeddings.  

The results are reported on KuaiSAR, Amazon Beauty and NQ-320K datasets with focus on demonstraing the benefit of URI index as compared to the baselines (DSI, NCI, TIGER etc.,).
Strengths: 1. The paper is decently written and easy to follow.
2. The paper reports numbers on KuaiSAR, Amazon Beauty and NQ-320K datasets and it compares against leading methods including DSI and NCI. 
3. The proposed method yields good benefits when the index of baselines is replaced with the proposed URI based index as well as in terms of overall numbers. For e.g., the performance of DSI and NCI improved by ~3% on the NQ-320K datasets. The benefits are even starker on the KuaiSAR dataset where the overall performance improved by upto 14% as compared to the baselines.  
4. The paper analyses the impact of URI index as compared to other popular indices including kMeans and VQ-VAE. The APE values are reported in Table 2. The benefits of layer wise training (token consistency), Adaptive weights ($\mathcal{L}_{balance\ w}$) and hard negative mining (during ranking). 
5. The paper provides hyper-parameter details in 5.4.
Weaknesses: 1. The results section compares against various methods including DR, DSI and NCI. It shows the impact of changing the original index with URI index alongside the overall comparison. However, the overall numbers don't seem to be for the best version of respective algorithms. It is perhaps done for consistency across algorithms? For e.g., the R@20 for NQ-320K can be in range of 0.56 - 0.89 for methods including ANCE, DSI, and NCI whereas URI is 0.057 (?). Please see https://arxiv.org/pdf/2206.02743. Please clarify if I am misinterpreting something. 
2. The paper is missing the training and inference times of the proposed method. These stats will provide a fair comparison against the baselines (in addition to already included theoretical complexity).
Recommendation: 6
Reviewer confidence: 3
Soundness/correctness: 2
Clarity: 3