Papers with reasoning-intensive

2 papers
Efficient KL Divergence Estimation via Truncated Top-K Integration for Large Language Models (2026.acl-long)

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Challenge: Existing methods for estimating KL divergence using only top-k tokens suffer from high variance or systematic bias.
Approach: They propose a top-k Importance-weighted KL Estimator that exploits the Zipfian structure of language model distributions by integrating only the top-K tokens.
Outcome: The proposed estimator outperforms existing estimators on multiple benchmarks while exhibiting lower variance.
SIV-Bench: A Video Benchmark for Social Interaction Understanding and Reasoning (2026.findings-acl)

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Challenge: Existing benchmarks for Multimodal Large Language Models (MLLMs) have been lacking due to the rich nature of social interaction.
Approach: They propose a video benchmark to evaluate MLLMs' capabilities across social scene understanding, social state reasoning, and social dynamics prediction.
Outcome: The proposed benchmarks evaluate MLLMs' capabilities across social scene understanding, social state reasoning, and social dynamics prediction tasks.

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