LLM & SLM 研究日报
算法·训练·推理 —— 大语言模型与小语言模型的前沿研究
生成时间: 2026/7/20 09:00:08
📊 今日概况
| 方向 | 论文数 |
|---|---|
| 🧮 算法与架构 | 12 |
| 🏋️ 训练方法 | 3 |
| ⚡ 推理优化 | 4 |
| 总计扫描 | 50 |
📝 论文列表
🧮 算法与架构 (12 篇)
1. Beyond the Leaderboard: Design Lessons for Trustworthy Multimodal VQA
- arXiv: 2607.15241 Kimi解读
- 摘要: multimodal,trustworthy,leaderboard,vqa,healthcare,evidence,medico,mediaeval,lessons,beyond
- 关键词: multimodal,trustworthy,leaderboard,vqa,healthcare,evidence,medico,mediaeval,lessons,beyond
2. TikStance: A Multimodal and Hierarchical Dataset for Multi-target Stance Analysis in TikTok Political Conversations
- arXiv: 2607.15240 Kimi解读
- 摘要: stance,political,tikstance,tiktok,conversations,target,biden,multimodal,trump,audiovisual
- 关键词: stance,political,tikstance,tiktok,conversations,target,biden,multimodal,trump,audiovisual
3. T^2MLR: Transformer with Temporal Middle-Layer Recurrence
- arXiv: 2607.15178 Kimi解读
- 摘要: middle,recurrence,layer,reasoning,t2mlr,2mlr,transformer,token,transformers,portantly
- 关键词: middle,recurrence,layer,reasoning,t2mlr,2mlr,transformer,token,transformers,portantly
4. Digital Pantheon: Simulating and Auditing Coalition Formation with LLM Agents
- arXiv: 2607.15095 Kimi解读
- 摘要: manifesto,coalition,party,negotiation,ideological,partisan,formateur,dpo,lineage,rag
- 关键词: manifesto,coalition,party,negotiation,ideological,partisan,formateur,dpo,lineage,rag
5. Show Me How You Reason and I'll Tell You Who You Are: Reasoning Graphs for Robust LLM Authorship Attribution
- arXiv: 2607.14905 Kimi解读
- 摘要: llm,authorship,paraphrasing,reasoning,attribution,obfuscation,percentage,tell,imaginable,generated
- 关键词: llm,authorship,paraphrasing,reasoning,attribution,obfuscation,percentage,tell,imaginable,generated
6. CoTu at EXACT 2026: Neuro-Symbolic Reasoning for Transparent Educational QA
- arXiv: 2607.14735 Kimi解读
- 摘要: cotu,2026,reasoning,symbolic,neuro,answer,educational,emits,deduction,transparent
- 关键词: cotu,2026,reasoning,symbolic,neuro,answer,educational,emits,deduction,transparent
7. Gold-Guided Programmatic Distillation for Financial Reasoning over Hybrid Tables and Text
- arXiv: 2607.14709 Kimi解读
- 摘要: reasoning,programmatic,teacher,distillation,gold,financial,tat,rationales,student,verified
- 关键词: reasoning,programmatic,teacher,distillation,gold,financial,tat,rationales,student,verified
8. D-cut: Adaptive Verification Depth Pruning for Batched Speculative Decoding
- arXiv: 2607.14647 Kimi解读
- 摘要: cut,decoding,draft,speculative,verification,pruning,tokens,drafts,request,drafting
- 关键词: cut,decoding,draft,speculative,verification,pruning,tokens,drafts,request,drafting
9. On-Policy Delta Distillation
- arXiv: 2607.15161 Kimi解读
- 摘要: distillation,policy,delta,reasoning,opd,teacher,signal,reward,naver,post
- 关键词: distillation,policy,delta,reasoning,opd,teacher,signal,reward,naver,post
10. Multimodal Semantic-Aware Contrastive Learning For False Negative Mitigation in 3D Medical Imaging
- arXiv: 2607.14995 Kimi解读
- 摘要: multimodal,contrastive,false,semantic,pediatric,samples,negatives,radiology,negative,semantically
- 关键词: multimodal,contrastive,false,semantic,pediatric,samples,negatives,radiology,negative,semantically
11. Leveraging Instruction Tuning and Merging for Reasoning Model Adaptation
- arXiv: 2607.14895 Kimi解读
- 摘要: rlm,reasoning,rlms,domains,tuning,instruction,verifiable,performance,coding,unverifiable
- 关键词: rlm,reasoning,rlms,domains,tuning,instruction,verifiable,performance,coding,unverifiable
12. GAttNHP: Group Attention Neural Hawkes Process for Extrapolation Reasoning in Temporal Knowledge Graphs
- arXiv: 2607.14733 Kimi解读
- 摘要: gattnhp,hawkes,tkg,attention,chains,group,temporal,tailed,quantile,arrival
- 关键词: gattnhp,hawkes,tkg,attention,chains,group,temporal,tailed,quantile,arrival
🏋️ 训练方法 (3 篇)
1. Digital Pantheon: Simulating and Auditing Coalition Formation with LLM Agents
- arXiv: 2607.15095 Kimi解读
- 摘要: manifesto,coalition,party,negotiation,ideological,partisan,formateur,dpo,lineage,rag
- 关键词: manifesto,coalition,party,negotiation,ideological,partisan,formateur,dpo,lineage,rag
2. Leveraging Instruction Tuning and Merging for Reasoning Model Adaptation
- arXiv: 2607.14895 Kimi解读
- 摘要: rlm,reasoning,rlms,domains,tuning,instruction,verifiable,performance,coding,unverifiable
- 关键词: rlm,reasoning,rlms,domains,tuning,instruction,verifiable,performance,coding,unverifiable
3. Innocuous-Seeming Data, Latent Ideology: Ideological Generalisation in Finetuned LLMs
- arXiv: 2607.14888 Kimi解读
- 摘要: ideological,finetuning,generalisation,innocuous,seeming,prompting,ideology,sycophantic,finetuned,shifts
- 关键词: ideological,finetuning,generalisation,innocuous,seeming,prompting,ideology,sycophantic,finetuned,shifts
⚡ 推理优化 (4 篇)
1. D-cut: Adaptive Verification Depth Pruning for Batched Speculative Decoding
- arXiv: 2607.14647 Kimi解读
- 摘要: cut,decoding,draft,speculative,verification,pruning,tokens,drafts,request,drafting
- 关键词: cut,decoding,draft,speculative,verification,pruning,tokens,drafts,request,drafting
2. Routing Ceilings Are Domain-Independent: Structural Prior Injection in Code Security Vulnerability Detection
- arXiv: 2607.14628 Kimi解读
- 摘要: cwe,sair,vulnerability,vudenc,cheatsheets,cheatsheet,collapse,cve,structural,code
- 关键词: cwe,sair,vulnerability,vudenc,cheatsheets,cheatsheet,collapse,cve,structural,code
3. Kernel weighted importance sampling for off-policy evaluation in contextual bandits
- arXiv: 2607.15067 Kimi解读
- 摘要: wis,importance,vanilla,sampling,kernel,weighted,policy,bandits,contextual,evaluation
- 关键词: wis,importance,vanilla,sampling,kernel,weighted,policy,bandits,contextual,evaluation
4. Causal Inference for Sequential Settings under Interference and Latent Confounding
- arXiv: 2607.14940 Kimi解读
- 摘要: causal,settings,latent,confounders,sequential,interference,confounding,outcome,outcomes,units
- 关键词: causal,settings,latent,confounders,sequential,interference,confounding,outcome,outcomes,units
分析生成失败
📚 附录
筛选关键词
算法: attention mechanism, mixture of experts, MoE, sparse attention, flash attention, rotary position, RoPE, grouped query, GQA, KV cache …
训练: pre-training, pretraining, post-training, fine-tuning, finetuning, supervised fine-tuning, SFT, alignment, RLHF, DPO …
推理: inference, serving, latency, throughput, speculative decoding, batching, continuous batching, PagedAttention, vLLM, quantization …
本报告由 OpenClaw 自动生成 | LLM & SLM Research Daily