---
title: "Furu Wei — papers, page 3 — Elamur Research"
description: "Furu Wei, Microsoft: 57 papers on Elamur Research (2011–2026), h-index 85 — affiliations, topics, co-authors and who cites them in our library."
canonical: "https://research.elamur.ai/free/en/authors/furu-wei/page/3/"
---
[Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/authors/furu-wei/page/3/)

[home](https://md.research.elamur.ai/free/en/) / [authors](https://md.research.elamur.ai/free/en/authors/) / [Furu Wei](https://md.research.elamur.ai/free/en/authors/furu-wei/) / page 3

# Furu Wei · page 3

57 papers in our library · 2011–2026 · page 3 of 3

[The profile, affiliations and co-authors →](https://md.research.elamur.ai/free/en/authors/furu-wei/)

## Papers

The most cited first (OpenAlex and our library).

[← prev](https://md.research.elamur.ai/free/en/authors/furu-wei/page/2/) [1](https://md.research.elamur.ai/free/en/authors/furu-wei/) [2](https://md.research.elamur.ai/free/en/authors/furu-wei/page/2/) **3**

51. **[Examining False Positives under Inference Scaling for Mathematical Reasoning](https://md.research.elamur.ai/free/en/papers/wang-2025-examining-false-positives-under-inference/)** *(2025 · 9 citations)* Most math benchmarks for language models award credit by matching the final answer, usually through heuristic rules, and never check the derivation.
52. **[ALYMPICS: LLM Agents Meet Game Theory](https://md.research.elamur.ai/free/en/papers/mao-2025-alympics-llm-agents-meet-game/)** *(2025 · 7 citations)* Empirical game theory usually needs experiments with human participants, which are costly, slow and ethically demanding.
53. **[ShifCon: Enhancing Non-Dominant Language Capabilities with a Shift-based Multilingual Contrastive Framework](https://md.research.elamur.ai/free/en/papers/zhang-2025-shifcon-enhancing-non-dominant-language/)** *(2025 · 6 citations)* Multilingual LLMs fine-tuned on translated data (MSFT) still lag behind in non-dominant languages compared with English. Producing more high-quality translated supervision is costly and error-prone. ShifCon addresses this by changing how the model processes these languages internally rather than by adding data.
54. **[Rectified Sparse Attention for Efficient Long-Sequence Generation](https://md.research.elamur.ai/free/en/papers/sun-2026-rectified-sparse-attention-efficient-long/)** *(2026 · 1 citation)* Large language models that produce very long outputs, such as extended reasoning chains, are slowed down by having to read the entire KV cache at every decoding step.
55. **[Towards Stable and Effective Reinforcement Learning for Mixture-of-Experts](https://md.research.elamur.ai/free/en/papers/zhang-2026-stable-effective-reinforcement-learning-mixture/)** *(2026 · 1 citation)* The paper addresses why reinforcement learning with verifiable rewards (RLVR) often breaks down when the policy is a Mixture-of-Experts (MoE) model, frequently ending in sudden reward collapse.
56. **[Learning to Refine: Self-Refinement of Parallel Reasoning in LLMs](https://md.research.elamur.ai/free/en/papers/wang-2026-learning-refine-self-refinement-parallel/)** *(2026)* Common test-time scaling strategies, such as majority voting and Best-of-N, can only pick from the candidates a model produces.
57. **[Two Pathways to Truthfulness: On the Intrinsic Encoding of LLM Hallucinations](https://md.research.elamur.ai/free/en/papers/luo-2026-two-pathways-truthfulness-intrinsic-encoding/)** *(2026)* Probing classifiers trained on LLM hidden states can detect hallucinations, but it has been unclear where these truthfulness signals come from.

[← prev](https://md.research.elamur.ai/free/en/authors/furu-wei/page/2/) [1](https://md.research.elamur.ai/free/en/authors/furu-wei/) [2](https://md.research.elamur.ai/free/en/authors/furu-wei/page/2/) **3**