本期的 18 篇论文如下:
[00:21] 🛠 START: Self-taught Reasoner with Tools(自教工具集成推理器)
[01:03] 👓 EgoLife: Towards Egocentric Life Assistant(EgoLife:面向自我中心的生活助手)
[01:39] 📞 LLM as a Broken Telephone: Iterative Generation Distorts Information(大型语言模型作为失真传话:迭代生成对信息的影响)
[02:14] 🧠 LINGOLY-TOO: Disentangling Memorisation from Reasoning with Linguistic Templatisation and Orthographic Obfuscation(LINGOLY-TOO:通过语言模板化和正字法混淆分离记忆与推理)
[02:51] 🔄 HybridNorm: Towards Stable and Efficient Transformer Training via Hybrid Normalization(混合归一化:通过混合归一化实现稳定高效的Transformer训练)
[03:34] 🎥 Token-Efficient Long Video Understanding for Multimodal LLMs(高效的多模态大语言模型长视频理解)
[04:14] 🧠 FuseChat-3.0: Preference Optimization Meets Heterogeneous Model Fusion(FuseChat-3.0:偏好优化与异构模型融合)
[04:58] 🎮 PokéChamp: an Expert-level Minimax Language Agent(宝可冠军:一个专家级的Minimax语言代理)
[05:42] 🎧 Audio Flamingo 2: An Audio-Language Model with Long-Audio Understanding and Expert Reasoning Abilities(音频火烈鸟2:具有长音频理解和专家推理能力的音频语言模型)
[06:21] 📊 IFIR: A Comprehensive Benchmark for Evaluating Instruction-Following in Expert-Domain Information Retrieval(IFIR:评估专家领域信息检索中指令遵循的综合基准)
[07:02] 📊 Identifying Sensitive Weights via Post-quantization Integral(通过后量化积分识别敏感权重)
[07:46] 📏 L$^2$M: Mutual Information Scaling Law for Long-Context Language Modeling(L²M:长上下文语言模型的互信息缩放定律)
[08:22] 🎥 The Best of Both Worlds: Integrating Language Models and Diffusion Models for Video Generation(双剑合璧:结合语言模型与扩散模型进行视频生成)
[09:05] 🤖 Lost in Literalism: How Supervised Training Shapes Translationese in LLMs(迷失于字面主义:监督训练如何塑造LLMs中的翻译体)
[09:48] 🚀 Dedicated Feedback and Edit Models Empower Inference-Time Scaling for Open-Ended General-Domain Tasks(专用反馈和编辑模型增强开放式通用领域任务的推理时扩展)
[10:33] 🧠 Union of Experts: Adapting Hierarchical Routing to Equivalently Decomposed Transformer(专家联盟:将分层路由适应等价分解的Transformer)
[11:13] 🤖 Combining Flow Matching and Transformers for Efficient Solution of Bayesian Inverse Problems(结合流匹配与Transformer实现高效的贝叶斯反问题求解)
[11:54] 🚫 Understanding and Predicting Derailment in Toxic Conversations on GitHub(理解与预测GitHub上毒性对话中的脱轨现象)

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