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Linsen Guo

2 accepted papers

2026

R-Horizon: How Far Can Your Large Reasoning Model Really Go in Breadth and Depth?

ICLR 2026poster

Recent trends in test-time scaling for reasoning models (e.g., OpenAI o1, DeepSeek-R1) have led to remarkable improvements through long Chain-of-Thought (CoT). However, existing benchmarks mainly focus on immediate, single-horizon tasks, failing to adequately evaluate models’ ability to understand a…

Cited by 0SourcecodeScholar
2024

Calibrating the Confidence of Large Language Models by Eliciting Fidelity

EMNLP 2024main

Large language models optimized with techniques like RLHF have achieved good alignment in being helpful and harmless. However, post-alignment, these language models often exhibit overconfidence, where the expressed confidence does not accurately calibrate with their correctness rate. In this paper,…

Cited by 4SourcePDFScholar