Structured Pruning of Large Language Models (2020 · 202 citations)
Large pretrained language models are expensive to run, and the standard ways of shrinking them each fall short.
UniCorn: Towards Self-Improving Unified Multimodal Models through Self-Generated Supervision (2026 · 11 citations)
Unified Multimodal Models (UMMs) often judge images accurately but cannot produce scenes of the same quality themselves.
EquiBench: Benchmarking Large Language Models' Reasoning about Program Semantics via Equivalence Checking (2025 · 8 citations)
EquiBench asks whether large language models actually grasp what code does or mostly react to how it looks.
Exploring Attention Attractors in Large Language Models (2026)
This paper studies "attention attractors," tokens in large language models that receive an unusually large share of attention. Earlier work has explained these tokens in conflicting ways: as summary tokens, as attention sinks, or as key tokens for KV cache compression. The authors examine them from three angles.
Response-G1: Explicit Scene Graph Modeling for Proactive Streaming Video Understanding (2026)
In streaming video understanding, a Video-LLM sees footage frame by frame. In the proactive setting, it must also decide when it has seen enough to answer a question about events that may happen later. Earlier solutions take one of three routes. Some fine-tune on frame-level silence/response labels, which tend to be…

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Papers of our library cite their works 124 times (of the 149,439 papers whose bibliographies we read); 22 citing papers with a page here →.

Updated 1 November 2026