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IIIT Delhi, India
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VLMs are often functionally blind, exploiting language priors instead of truly "seeing" visual data, and this problem paradoxically *worsens* as language models scale.
Scaling up LLMs doesn't uniformly improve context handling; instead, it paradoxically amplifies the tendency to copy irrelevant tokens while simultaneously improving resistance to misinformation.
Existing citation recommendation benchmarks overestimate real-world performance because they fail to account for the temporal constraints of recommending citations for *new* papers.
