38 citations · 38 across the 6 of their papers we have counts for
7 papers
EigenLI: Spectral Approximations to Late Interaction
Archish S, Sabyasachi Basu, Ankit Garg +2
Late-interaction models such as ColBERT achieve strong effectiveness by representing each document with many token-level vectors, but this expressivity leads to large indexing cost…
Retrieval Needs Multivectors: An Exponential Separation
Mihir Agarwal, Viraj Agrawal, Sabyasachi Basu +2
Recent works have highlighted the expressive limitations of embedding based retrieval models through both theoretical analyses and challenging benchmarks such as LIMIT. While multi…
Reading Between the Pixels: Linking Text-Image Embedding Alignment to Typographic Attack Success on Vision-Language Models
Ravikumar Balakrishnan, Sanket Mendapara, Ankit Garg
We study typographic prompt injection attacks on vision-language models (VLMs), where adversarial text is rendered as images to bypass safety mechanisms, posing a growing threat as…
On Strengths and Limitations of Single-Vector Embeddings
Archish S, Mihir Agarwal, Ankit Garg +2
Recent work (Weller et al., 2025) introduced a naturalistic dataset called LIMIT and showed empirically that a wide range of popular single-vector embedding models suffer substanti…
Incorporating Token Importance in Multi-Vector Retrieval
Archish S, Ankit Garg, Kirankumar Shiragur +1
ColBERT introduced a late interaction mechanism that independently encodes queries and documents using BERT, and computes similarity via fine-grained interactions over token-level…
Learning Arithmetic Formulas in the Presence of Noise: A General Framework and Applications to Unsupervised Learning
Pritam Chandra, Ankit Garg, Neeraj Kayal +2
We present a general framework for designing efficient algorithms for unsupervised learning problems, such as mixtures of Gaussians and subspace clustering. Our framework is based…
Alternating minimization, scaling algorithms, and the null-cone problem from invariant theory
Peter Bürgisser, Ankit Garg, Rafael Oliveira +2
Alternating minimization heuristics seek to solve a (difficult) global optimization task through iteratively solving a sequence of (much easier) local optimization tasks on differe…