최종 작업 Commit
This commit is contained in:
HyunjunJeon
38
.claude/research-ralph-loop.local.md
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38
.claude/research-ralph-loop.local.md
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---
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active: true
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iteration: 1
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max_iterations: 1
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completion_promise: "RESEARCH_COMPLETE"
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started_at: "2026-01-12T06:46:24.266812+00:00"
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findings_count: 0
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coverage_score: 0.0
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---
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## Research Iteration 1/1
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### Original Query
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test query
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### Previous Work
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Check `research_workspace/` for previous findings.
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Read TODO.md for tracked progress.
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### Instructions
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1. Review existing findings
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2. Identify knowledge gaps
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3. Conduct targeted searches using: web
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4. Update research files with new findings
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5. Update TODO.md with progress
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### Completion Criteria
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Output `<promise>RESEARCH_COMPLETE</promise>` ONLY when:
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- Coverage score >= 0.5 (current: 0.00)
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- All major aspects addressed
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- Findings cross-validated with 2+ sources
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- DO NOT lie to exit
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### Current Stats
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- Iteration: 1
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- Findings: 0
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- Coverage: 0.00%
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@@ -42,7 +42,6 @@ from typing import cast
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from langchain.agents.middleware.types import (
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AgentMiddleware,
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AnyMessage,
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ModelRequest,
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ModelResponse,
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)
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@@ -53,6 +52,7 @@ from langchain_core.messages import (
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HumanMessage,
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SystemMessage,
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)
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from langchain_core.messages.utils import AnyMessage
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@dataclass
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555
research_workspace/session_20260112_150419/AI_TREND_REPORT.md
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research_workspace/session_20260112_150419/AI_TREND_REPORT.md
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# 2026 AI 트렌드 키워드 연구 리포트
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**생성일:** 2026-01-12 15:04:44
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**세션 ID:** 20260112_150419
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**총 소스 수:** 11
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**Coverage Score:** 56.68%
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---
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## 핵심 트렌드 키워드 (Top 20)
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| 순위 | 키워드 | 빈도 |
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|------|--------|------|
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| 1 | agent | 15 |
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| 2 | agents | 13 |
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| 3 | context | 11 |
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| 4 | coding assistant | 10 |
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| 5 | multimodal | 6 |
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| 6 | context engineering | 4 |
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| 7 | reasoning | 4 |
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| 8 | transformer | 3 |
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| 9 | inference | 3 |
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| 10 | GPT | 2 |
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| 11 | memory | 2 |
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| 12 | context window | 1 |
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| 13 | optimization | 1 |
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---
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## 주요 발견사항
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### 1. Agent & Agentic AI
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- AI 에이전트 프레임워크가 2026년 핵심 트렌드
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- 자율적 작업 수행 및 도구 사용 능력 강조
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- Multi-agent 시스템의 부상
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### 2. Context Engineering
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- 긴 컨텍스트 윈도우 활용 최적화
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- 파일시스템 기반 컨텍스트 관리
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- 효율적인 정보 검색 및 주입
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### 3. Multimodal AI
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- 텍스트, 이미지, 오디오, 비디오 통합
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- Vision-Language 모델의 발전
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- 실시간 멀티모달 처리
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### 4. Reasoning & CoT
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- Chain-of-Thought 추론 개선
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- 복잡한 문제 해결 능력 향상
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- Self-reflection 및 자기 개선
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### 5. Code & Development
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- AI 코딩 어시스턴트의 고도화
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- 전체 개발 워크플로우 자동화
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- 코드 리뷰 및 디버깅 지원
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---
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## 소스 분석
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- **web**: 5개 소스
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- **github**: 3개 소스
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- **arxiv**: 3개 소스
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---
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## 상세 소스 목록
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### 소스 1: Web: 2026 AI trends predictions
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- **신뢰도:** 70%
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- **품질 점수:** 0.70
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- **URL:** tavily://2026 AI trends predictions
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<details>
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<summary>내용 미리보기</summary>
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Found 2 result(s) for '2026 AI trends predictions':
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## Top 6 AI Trends That Will Define 2026 (backed by data) - YouTube
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**URL:** https://www.youtube.com/watch?v=B23W1gRT9eY
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Top 6 AI Trends That Will Define 2026 (backed by data) - YouTube[정보](https://www.youtube.com/about/)[보도자료](https://www.youtube.com/about/press/)[저작권](https://www.youtube.com/about/copyright/)[문의하기](/t/contact_us/)[크리에이터](https://www.youtube.com/creators/)[광고](https://www.youtube.com/ads/)[개발자](https://developers.google.com/...
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</details>
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---
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### 소스 2: Web: AI agent frameworks 2026
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- **신뢰도:** 70%
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- **품질 점수:** 0.70
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- **URL:** tavily://AI agent frameworks 2026
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<details>
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<summary>내용 미리보기</summary>
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Found 2 result(s) for 'AI agent frameworks 2026':
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## Top 8 LLM Frameworks for Building AI Agents in 2026 | Second Talent
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**URL:** https://www.secondtalent.com/resources/top-llm-frameworks-for-building-ai-agents/
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Top 8 LLM Frameworks for Building AI Agents in 2026 | Second Talent
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[](https://www.secondtalent.com...
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</details>
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---
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### 소스 3: Web: context engineering LLM
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- **신뢰도:** 70%
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- **품질 점수:** 0.70
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- **URL:** tavily://context engineering LLM
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<details>
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<summary>내용 미리보기</summary>
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Found 2 result(s) for 'context engineering LLM':
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## A Survey of Context Engineering for Large Language Models - arXiv
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**URL:** https://arxiv.org/abs/2507.13334
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[2507.13334] A Survey of Context Engineering for Large Language Models
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[Skip to main content](#content)
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[](https://www.cornell.edu/)
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We gratefully acknowledge support from the Simons Foundation, [member inst...
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</details>
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---
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### 소스 4: Web: multimodal AI applications 2026
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- **신뢰도:** 70%
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- **품질 점수:** 0.70
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- **URL:** tavily://multimodal AI applications 2026
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<details>
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<summary>내용 미리보기</summary>
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Found 2 result(s) for 'multimodal AI applications 2026':
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## Rise of Multimodal AI Models: Future of AI Trends 2026
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**URL:** https://optimizewithsanwal.com/rise-of-multimodal-ai-models-future-of-ai-trends-2026/
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Rise of Multimodal AI Models: Future of AI Trends 2026
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[Skip to content](#content)
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[ for 'AI coding assistants trends':
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## Report: Developers and AI Coding Assistant Trends - CodeSignal
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**URL:** https://codesignal.com/report-developers-and-ai-coding-assistant-trends/
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Report: Developers and AI Coding Assistant Trends | CodeSignal
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[....
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</details>
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---
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### 소스 7: GitHub: LLM context
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- **신뢰도:** 75%
|
||||
- **품질 점수:** 0.76
|
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- **URL:** github://LLM context
|
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|
||||
<details>
|
||||
<summary>내용 미리보기</summary>
|
||||
|
||||
'LLM context'에 대한 GitHub 코드를 찾을 수 없습니다 (language: ['Python', 'TypeScript'])....
|
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|
||||
</details>
|
||||
|
||||
---
|
||||
|
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### 소스 8: GitHub: multimodal transformer
|
||||
- **신뢰도:** 75%
|
||||
- **품질 점수:** 0.76
|
||||
- **URL:** github://multimodal transformer
|
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|
||||
<details>
|
||||
<summary>내용 미리보기</summary>
|
||||
|
||||
'multimodal transformer'에 대한 GitHub 코드를 찾을 수 없습니다 (language: ['Python', 'TypeScript'])....
|
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|
||||
</details>
|
||||
|
||||
---
|
||||
|
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### 소스 9: arXiv: large language model agents
|
||||
- **신뢰도:** 90%
|
||||
- **품질 점수:** 0.89
|
||||
- **URL:** arxiv://large language model agents
|
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|
||||
<details>
|
||||
<summary>내용 미리보기</summary>
|
||||
|
||||
'large language model agents'에 대해 3개의 논문을 찾았습니다:
|
||||
|
||||
## AdaFuse: Adaptive Ensemble Decoding with Test-Time Scaling for LLMs
|
||||
|
||||
**저자:** Chengming Cui, Tianxin Wei, Ziyi Chen, Ruizhong Qiu, Zhichen Zeng 외 4명
|
||||
**발행일:** 2026-01-09
|
||||
**URL:** http://arxiv.org/abs/2601.06022v1
|
||||
|
||||
### 초록
|
||||
Large language models (LLMs) exhibit complementary strengths arising from differences in pretraining data, model architectures, and decoding behaviors. Inference-time ensembling provides a practical way to combine these capabili...
|
||||
|
||||
</details>
|
||||
|
||||
---
|
||||
|
||||
### 소스 10: arXiv: context window optimization
|
||||
- **신뢰도:** 90%
|
||||
- **품질 점수:** 0.89
|
||||
- **URL:** arxiv://context window optimization
|
||||
|
||||
<details>
|
||||
<summary>내용 미리보기</summary>
|
||||
|
||||
'context window optimization'에 대해 3개의 논문을 찾았습니다:
|
||||
|
||||
## Manifold limit for the training of shallow graph convolutional neural networks
|
||||
|
||||
**저자:** Johanna Tengler, Christoph Brune, José A. Iglesias
|
||||
**발행일:** 2026-01-09
|
||||
**URL:** http://arxiv.org/abs/2601.06025v1
|
||||
|
||||
### 초록
|
||||
We study the discrete-to-continuum consistency of the training of shallow graph convolutional neural networks (GCNNs) on proximity graphs of sampled point clouds under a manifold assumption. Graph convolution is defined spectrally via th...
|
||||
|
||||
</details>
|
||||
|
||||
---
|
||||
|
||||
### 소스 11: arXiv: multimodal foundation models
|
||||
- **신뢰도:** 90%
|
||||
- **품질 점수:** 0.89
|
||||
- **URL:** arxiv://multimodal foundation models
|
||||
|
||||
<details>
|
||||
<summary>내용 미리보기</summary>
|
||||
|
||||
'multimodal foundation models'에 대해 3개의 논문을 찾았습니다:
|
||||
|
||||
## AdaFuse: Adaptive Ensemble Decoding with Test-Time Scaling for LLMs
|
||||
|
||||
**저자:** Chengming Cui, Tianxin Wei, Ziyi Chen, Ruizhong Qiu, Zhichen Zeng 외 4명
|
||||
**발행일:** 2026-01-09
|
||||
**URL:** http://arxiv.org/abs/2601.06022v1
|
||||
|
||||
### 초록
|
||||
Large language models (LLMs) exhibit complementary strengths arising from differences in pretraining data, model architectures, and decoding behaviors. Inference-time ensembling provides a practical way to combine these capabil...
|
||||
|
||||
</details>
|
||||
|
||||
---
|
||||
|
||||
687
research_workspace/session_20260112_150419/FINDINGS.md
Normal file
687
research_workspace/session_20260112_150419/FINDINGS.md
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@@ -0,0 +1,687 @@
|
||||
# Research Findings
|
||||
|
||||
## Query: 2026 AI Trends and Keywords
|
||||
|
||||
## Sources (11)
|
||||
|
||||
### Source 1: Web: 2026 AI trends predictions
|
||||
- URL: tavily://2026 AI trends predictions
|
||||
- Confidence: 70%
|
||||
- Quality Score: 0.70
|
||||
- Source Type: web
|
||||
|
||||
Found 2 result(s) for '2026 AI trends predictions':
|
||||
|
||||
## Top 6 AI Trends That Will Define 2026 (backed by data) - YouTube
|
||||
**URL:** https://www.youtube.com/watch?v=B23W1gRT9eY
|
||||
|
||||
Top 6 AI Trends That Will Define 2026 (backed by data) - YouTube[정보](https://www.youtube.com/about/)[보도자료](https://www.youtube.com/about/press/)[저작권](https://www.youtube.com/about/copyright/)[문의하기](/t/contact_us/)[크리에이터](https://www.youtube.com/creators/)[광고](https://www.youtube.com/ads/)[개발자](https://developers.google.com/youtube)[약관](/t/terms)[개인정보처리방침](/t/privacy)[정책 및 안전](https://www.youtube.com/about/policies/)[YouTube 작동의 원리](https://www.youtube.com/howyoutubeworks?utm_campaign=ytgen&utm_source=ythp&utm_medium=LeftNav&utm_content=txt&u=https%3A%2F%2Fwww.youtube.com%2Fhowyoutubeworks%3Futm_source%3Dythp%26utm_medium%3DLeftNav%26utm_campaign%3Dytgen)[새로운 기능 테스트하기](/new)
|
||||
|
||||
© 2026 Google LLC, Sundar Pichai, 1600 Amphitheatre Parkway, Mountain View CA 94043, USA, 0807-882-594 (무료), yt-support-solutions-kr@google.com, 호스팅: Google LLC, [사업자정보](http://www.ftc.go.kr/selectBizOvrCommPop.do?apvPermMgtNo=2022-공정-0001), [불법촬영물 신고](https://support.google.com/youtube?p=korea_report)
|
||||
크리에이터들이 유튜브 상에 게시, 태그 또는 추천한 상품들은 판매자들의 약관에 따라 판매됩니다. 유튜브는 이러한 제품들을 판매하지 않으며, 그에 대한 책임을 지지 않습니다.
|
||||
|
||||
---
|
||||
|
||||
## The Future of AI in 2026: Major Trends and Predictions - Medium
|
||||
**URL:** https://medium.com/predict/the-future-of-ai-in-2026-major-trends-and-predictions-fad3b6f9ecbe
|
||||
|
||||
Error fetching content from https://medium.com/predict/the-future-of-ai-in-2026-major-trends-and-predictions-fad3b6f9ecbe: Client error '403 Forbidden' for url 'https://medium.com/predict/the-future-of-ai-in-2026-major-trends-and-predictions-fad3b6f9ecbe'
|
||||
For more information check: https://developer.mozilla.org/en-US/docs/Web/HTTP/Status/403
|
||||
|
||||
---
|
||||
|
||||
|
||||
### Source 2: Web: AI agent frameworks 2026
|
||||
- URL: tavily://AI agent frameworks 2026
|
||||
- Confidence: 70%
|
||||
- Quality Score: 0.70
|
||||
- Source Type: web
|
||||
|
||||
Found 2 result(s) for 'AI agent frameworks 2026':
|
||||
|
||||
## Top 8 LLM Frameworks for Building AI Agents in 2026 | Second Talent
|
||||
**URL:** https://www.secondtalent.com/resources/top-llm-frameworks-for-building-ai-agents/
|
||||
|
||||
Top 8 LLM Frameworks for Building AI Agents in 2026 | Second Talent
|
||||
|
||||
[](https://www.secondtalent.com)
|
||||
|
||||
* [Find Talent](https://secondtalent.com/hire-developers/)
|
||||
|
||||
Close Find Talent Open Find Talent
|
||||
* Services
|
||||
|
||||
Close Services Open Services
|
||||
* For Clients
|
||||
|
||||
Close For Clients Open For Clients
|
||||
* [Resources](https://secondtalent.com/resources/)
|
||||
|
||||
Close Resources Open Resources
|
||||
* [About Us](https://secondtalent.com/for-client-about-us/)
|
||||
|
||||
#### Hire by role
|
||||
|
||||
[Hire Software Developers](https://www.secondtalent.com/hire-developers/)
|
||||
|
||||
[Hire Back-End Developers](https://secondtalent.com/hire-developers/back-end/)
|
||||
|
||||
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||||
|
||||
[Hire DevOps Engineers](https://secondtalent.com/hire-developers/devops-engineer/)
|
||||
|
||||
[Hire AI Developers](https://secondtalent.com/hire-developers/artificial-intelligence/)
|
||||
|
||||
[Hire Mobile App Developers](https://secondtalent.com/hire-developers/mobile-app/)
|
||||
|
||||
[Hire Web Designers](https://secondtalent.com/hire-developers/web-and-mobile-app-designers/)
|
||||
|
||||
[Hire Cloud Engineers](https://secondtalent.com/hire-developers/cloud-engineers/)
|
||||
|
||||
[Hire Web3 Developers](https://secondtalent.com/hire-developers/web3/)
|
||||
|
||||
[Hire PHP Developers](https://secondtalent.com/hire-developers/php/)
|
||||
|
||||
[Hire Data Engineers](https://secondtalent.com/hire-developers/data-engineers/)
|
||||
|
||||
[Hire Tensorflow Engineers](https://secondtalent.com/hire-developers/tensorflow-engineers/)
|
||||
|
||||
#### Hire by Location
|
||||
|
||||
[Vietnam](https://secondtalent.com/hire-developers/vietnam/)
|
||||
|
||||
[Malaysia](https://secondtalent.com/hire-d
|
||||
|
||||
### Source 3: Web: context engineering LLM
|
||||
- URL: tavily://context engineering LLM
|
||||
- Confidence: 70%
|
||||
- Quality Score: 0.70
|
||||
- Source Type: web
|
||||
|
||||
Found 2 result(s) for 'context engineering LLM':
|
||||
|
||||
## A Survey of Context Engineering for Large Language Models - arXiv
|
||||
**URL:** https://arxiv.org/abs/2507.13334
|
||||
|
||||
[2507.13334] A Survey of Context Engineering for Large Language Models
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
[Skip to main content](#content)
|
||||
|
||||
|
||||
[](https://www.cornell.edu/)
|
||||
|
||||
We gratefully acknowledge support from the Simons Foundation, [member institutions](https://info.arxiv.org/about/ourmembers.html), and all contributors.
|
||||
[Donate](https://info.arxiv.org/about/donate.html)
|
||||
|
||||
[](/) > [cs](/list/cs/recent) > arXiv:2507.13334
|
||||
|
||||
[Help](https://info.arxiv.org/help) | [Advanced Search](https://arxiv.org/search/advanced)
|
||||
|
||||
All fields
|
||||
Title
|
||||
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|
||||
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|
||||
Comments
|
||||
Journal reference
|
||||
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|
||||
MSC classification
|
||||
Report number
|
||||
arXiv identifier
|
||||
DOI
|
||||
ORCID
|
||||
arXiv author ID
|
||||
Help pages
|
||||
Full text
|
||||
|
||||
Search
|
||||
|
||||
[](https://arxiv.org/)
|
||||
|
||||
[](https://www.cornell.edu/)
|
||||
|
||||
open search
|
||||
|
||||
GO
|
||||
|
||||
open navigation menu
|
||||
|
||||
quick links
|
||||
-----------
|
||||
|
||||
* [Login](https://arxiv.org/login)
|
||||
* [Help Pages](https://info.arxiv.org/help)
|
||||
* [About](https://info.arxiv.org/about)
|
||||
|
||||
|
||||
|
||||
Computer Science > Computation and Language
|
||||
===========================================
|
||||
|
||||
**arXiv:2507.13334** (cs)
|
||||
|
||||
[Submitted on 17 Jul 2025 ([v1](https://arxiv.org/abs/2507.13334v1)), last revised 21 Jul 2025 (this version, v2)]
|
||||
|
||||
Title:A Survey of Context Engineering for Large Language Models
|
||||
===============================================================
|
||||
|
||||
Authors:[Lingrui Mei](https://arxiv.org/search/cs?searchtype=author&query=Mei,+L), [Jiayu Yao](https://arxiv.org/search/cs?searchtype=author&query=Yao,+J), [Yuyao Ge](https://arxiv.org/search/cs?sear
|
||||
|
||||
### Source 4: Web: multimodal AI applications 2026
|
||||
- URL: tavily://multimodal AI applications 2026
|
||||
- Confidence: 70%
|
||||
- Quality Score: 0.70
|
||||
- Source Type: web
|
||||
|
||||
Found 2 result(s) for 'multimodal AI applications 2026':
|
||||
|
||||
## Rise of Multimodal AI Models: Future of AI Trends 2026
|
||||
**URL:** https://optimizewithsanwal.com/rise-of-multimodal-ai-models-future-of-ai-trends-2026/
|
||||
|
||||
Rise of Multimodal AI Models: Future of AI Trends 2026
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
[Skip to content](#content)
|
||||
|
||||
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|
||||
|
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|
||||
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|
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|
||||
|
||||
[Request Quote](https://optimizewithsanwal.com/contact-us/)[Request Quote](https://optimizewithsanwal.com/contact-us/)
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|
||||
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||||
|
||||
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|
||||
* [Services](#)
|
||||
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||||
|
||||
+ [SEO Services](https://optimizewithsanwal.com/seo-services/)
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||||
+ [WordPress Services](https://optimizewithsanwal.com/wordpress-services/)
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||||
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||||
|
||||
|
||||
+ [Blogs](https://optimizewithsanwal.com/blogs/)
|
||||
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|
||||
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|
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||||
* [Meet Sanwal Zia](https://optimizewithsanwal.com/meet-sanwal-zia/)
|
||||
* [Contact Us](https://optim
|
||||
|
||||
### Source 5: Web: AI coding assistants trends
|
||||
- URL: tavily://AI coding assistants trends
|
||||
- Confidence: 70%
|
||||
- Quality Score: 0.70
|
||||
- Source Type: web
|
||||
|
||||
Found 2 result(s) for 'AI coding assistants trends':
|
||||
|
||||
## Report: Developers and AI Coding Assistant Trends - CodeSignal
|
||||
**URL:** https://codesignal.com/report-developers-and-ai-coding-assistant-trends/
|
||||
|
||||
|
||||
|
||||
Report: Developers and AI Coding Assistant Trends | CodeSignal
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
[](https://codesignal.com)
|
||||
|
||||
TRENDS REPORT
|
||||
-------------
|
||||
|
||||
Developers & AI Coding Assistant Trends
|
||||
=======================================
|
||||
|
||||
We surveyed over 1,000 developers around the world to understand how they're using AI Coding Assistant tools, what they think about the future of AI, and more. Here’s what we learned.
|
||||
---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
|
||||
|
||||
Overview
|
||||
--------
|
||||
|
||||
ChatGPT, Github CoPilot, and other AI-powered coding assistants have taken the world by storm. This survey reveals how software developers are embracing these tools to boost their productivity and learn new technical skills.
|
||||
|
||||
### Here are the most eye-opening findings from our report:
|
||||
|
||||
#### 81% of developers surveyed use AI-powered coding assistants
|
||||
|
||||
#### [Developers use AI-powered coding assistants to learn new tech skills](#how)
|
||||
|
||||
[See how](#how)
|
||||
|
||||
#### [ChatGPT is the most popular assistant used by developers](#top-tools)
|
||||
|
||||
[See Top 5](#top-tools)
|
||||
|
||||
#### [49% of developers use AI-powered coding assistants every day](#adoption)
|
||||
|
||||
[Read more](#adoption)
|
||||
|
||||
#### [Developers are excited to boost their productivity with AI assistance](#excited)
|
||||
|
||||
[Read more](#excited)
|
||||
|
||||
#### [AI-generated code quality is a major concern among developers](#nervous)
|
||||
|
||||
[See more](#nervous)
|
||||
|
||||
#### Report sections
|
||||
|
||||
* [How developers use AI coding assistants](#how)
|
||||
* [Top AI coding assis
|
||||
|
||||
### Source 6: GitHub: agent framework
|
||||
- URL: github://agent framework
|
||||
- Confidence: 75%
|
||||
- Quality Score: 0.76
|
||||
- Source Type: github
|
||||
|
||||
'agent framework'에 대한 GitHub 코드를 찾을 수 없습니다 (language: ['Python', 'TypeScript']).
|
||||
|
||||
### Source 7: GitHub: LLM context
|
||||
- URL: github://LLM context
|
||||
- Confidence: 75%
|
||||
- Quality Score: 0.76
|
||||
- Source Type: github
|
||||
|
||||
'LLM context'에 대한 GitHub 코드를 찾을 수 없습니다 (language: ['Python', 'TypeScript']).
|
||||
|
||||
### Source 8: GitHub: multimodal transformer
|
||||
- URL: github://multimodal transformer
|
||||
- Confidence: 75%
|
||||
- Quality Score: 0.76
|
||||
- Source Type: github
|
||||
|
||||
'multimodal transformer'에 대한 GitHub 코드를 찾을 수 없습니다 (language: ['Python', 'TypeScript']).
|
||||
|
||||
### Source 9: arXiv: large language model agents
|
||||
- URL: arxiv://large language model agents
|
||||
- Confidence: 90%
|
||||
- Quality Score: 0.89
|
||||
- Source Type: arxiv
|
||||
|
||||
'large language model agents'에 대해 3개의 논문을 찾았습니다:
|
||||
|
||||
## AdaFuse: Adaptive Ensemble Decoding with Test-Time Scaling for LLMs
|
||||
|
||||
**저자:** Chengming Cui, Tianxin Wei, Ziyi Chen, Ruizhong Qiu, Zhichen Zeng 외 4명
|
||||
**발행일:** 2026-01-09
|
||||
**URL:** http://arxiv.org/abs/2601.06022v1
|
||||
|
||||
### 초록
|
||||
Large language models (LLMs) exhibit complementary strengths arising from differences in pretraining data, model architectures, and decoding behaviors. Inference-time ensembling provides a practical way to combine these capabilities without retraining. However, existing ensemble approaches suffer from fundamental limitations. Most rely on fixed fusion granularity, which lacks the flexibility required for mid-generation adaptation and fails to adapt to different generation characteristics across tasks. To address these challenges, we propose AdaFuse, an adaptive ensemble decoding framework that dynamically selects semantically appropriate fusion units during generation. Rather than committing to a fixed granularity, AdaFuse adjusts fusion behavior on the fly based on the decoding context, w...
|
||||
|
||||
---
|
||||
|
||||
## Chaining the Evidence: Robust Reinforcement Learning for Deep Search Agents with Citation-Aware Rubric Rewards
|
||||
|
||||
**저자:** Jiajie Zhang, Xin Lv, Ling Feng, Lei Hou, Juanzi Li
|
||||
**발행일:** 2026-01-09
|
||||
**URL:** http://arxiv.org/abs/2601.06021v1
|
||||
|
||||
### 초록
|
||||
Reinforcement learning (RL) has emerged as a critical technique for enhancing LLM-based deep search agents. However, existing approaches primarily rely on binary outcome rewards, which fail to capture the comprehensiveness and factuality of agents' reasoning process, and often lead to undesirable behaviors such as shortcut exploitation and hallucinations. To address these limitations, we propose \textbf{Citation-aware Rubric Rewards (CaRR)}, a fine-grained reward framework for deep search agents that emphasizes reasoning comprehensiveness, factual grounding, and evidence connectivity. CaRR decomposes complex questions into verifiable single-hop rubrics and requires agents to satisfy these rubrics by explicitly identifying hidden entities, supporting them with correct citations, and constru...
|
||||
|
||||
---
|
||||
|
||||
## Mobility Trajectories from Network-Driven Markov Dynamics
|
||||
|
||||
**저자:** David A. Meyer, Asif Shakeel
|
||||
**발행일:** 2026-01-09
|
||||
**URL:** http://arxiv.org/abs/2601.06020v1
|
||||
|
||||
### 초록
|
||||
We present a generative model of human mobility in which trajectories arise as realizations of a prescribed, time-dependent Markov dynamics defined on a spatial interaction network. The model constructs a hierarchical routing structure with hubs, corridors, feeder paths, and metro links, and specifies transition matrices using gravity-type distance decay combined with externally imposed temporal schedules and directional biases. Population mass evolves as indistinguishable, memoryless movers performing a single transition per time step.
|
||||
When aggregated, the resulting trajectories reproduce structured origin-destination flows that reflect network geometry, temporal modulation, and c
|
||||
|
||||
### Source 10: arXiv: context window optimization
|
||||
- URL: arxiv://context window optimization
|
||||
- Confidence: 90%
|
||||
- Quality Score: 0.89
|
||||
- Source Type: arxiv
|
||||
|
||||
'context window optimization'에 대해 3개의 논문을 찾았습니다:
|
||||
|
||||
## Manifold limit for the training of shallow graph convolutional neural networks
|
||||
|
||||
**저자:** Johanna Tengler, Christoph Brune, José A. Iglesias
|
||||
**발행일:** 2026-01-09
|
||||
**URL:** http://arxiv.org/abs/2601.06025v1
|
||||
|
||||
### 초록
|
||||
We study the discrete-to-continuum consistency of the training of shallow graph convolutional neural networks (GCNNs) on proximity graphs of sampled point clouds under a manifold assumption. Graph convolution is defined spectrally via the graph Laplacian, whose low-frequency spectrum approximates that of the Laplace-Beltrami operator of the underlying smooth manifold, and shallow GCNNs of possibly infinite width are linear functionals on the space of measures on the parameter space. From this functional-analytic perspective, graph signals are seen as spatial discretizations of functions on the manifold, which leads to a natural notion of training data consistent across graph resolutions. To enable convergence results, the continuum parameter space is chosen as a weakly compact product of u...
|
||||
|
||||
---
|
||||
|
||||
## AdaFuse: Adaptive Ensemble Decoding with Test-Time Scaling for LLMs
|
||||
|
||||
**저자:** Chengming Cui, Tianxin Wei, Ziyi Chen, Ruizhong Qiu, Zhichen Zeng 외 4명
|
||||
**발행일:** 2026-01-09
|
||||
**URL:** http://arxiv.org/abs/2601.06022v1
|
||||
|
||||
### 초록
|
||||
Large language models (LLMs) exhibit complementary strengths arising from differences in pretraining data, model architectures, and decoding behaviors. Inference-time ensembling provides a practical way to combine these capabilities without retraining. However, existing ensemble approaches suffer from fundamental limitations. Most rely on fixed fusion granularity, which lacks the flexibility required for mid-generation adaptation and fails to adapt to different generation characteristics across tasks. To address these challenges, we propose AdaFuse, an adaptive ensemble decoding framework that dynamically selects semantically appropriate fusion units during generation. Rather than committing to a fixed granularity, AdaFuse adjusts fusion behavior on the fly based on the decoding context, w...
|
||||
|
||||
---
|
||||
|
||||
## Chaining the Evidence: Robust Reinforcement Learning for Deep Search Agents with Citation-Aware Rubric Rewards
|
||||
|
||||
**저자:** Jiajie Zhang, Xin Lv, Ling Feng, Lei Hou, Juanzi Li
|
||||
**발행일:** 2026-01-09
|
||||
**URL:** http://arxiv.org/abs/2601.06021v1
|
||||
|
||||
### 초록
|
||||
Reinforcement learning (RL) has emerged as a critical technique for enhancing LLM-based deep search agents. However, existing approaches primarily rely on binary outcome rewards, which fail to capture the comprehensiveness and factuality of agents' reasoning process, and often lead to undesirable behaviors such as shortcut exploitation and hallucinations. To address these limitations, we propose \textbf{Citation-aware Rubric Rewards (CaRR)}, a fine-grained reward framework for deep search agents that emphasizes reasoning comprehensiveness, factual grounding, and evidence connectivity. CaRR decomposes complex questions into verifiable single-
|
||||
|
||||
### Source 11: arXiv: multimodal foundation models
|
||||
- URL: arxiv://multimodal foundation models
|
||||
- Confidence: 90%
|
||||
- Quality Score: 0.89
|
||||
- Source Type: arxiv
|
||||
|
||||
'multimodal foundation models'에 대해 3개의 논문을 찾았습니다:
|
||||
|
||||
## AdaFuse: Adaptive Ensemble Decoding with Test-Time Scaling for LLMs
|
||||
|
||||
**저자:** Chengming Cui, Tianxin Wei, Ziyi Chen, Ruizhong Qiu, Zhichen Zeng 외 4명
|
||||
**발행일:** 2026-01-09
|
||||
**URL:** http://arxiv.org/abs/2601.06022v1
|
||||
|
||||
### 초록
|
||||
Large language models (LLMs) exhibit complementary strengths arising from differences in pretraining data, model architectures, and decoding behaviors. Inference-time ensembling provides a practical way to combine these capabilities without retraining. However, existing ensemble approaches suffer from fundamental limitations. Most rely on fixed fusion granularity, which lacks the flexibility required for mid-generation adaptation and fails to adapt to different generation characteristics across tasks. To address these challenges, we propose AdaFuse, an adaptive ensemble decoding framework that dynamically selects semantically appropriate fusion units during generation. Rather than committing to a fixed granularity, AdaFuse adjusts fusion behavior on the fly based on the decoding context, w...
|
||||
|
||||
---
|
||||
|
||||
## Mobility Trajectories from Network-Driven Markov Dynamics
|
||||
|
||||
**저자:** David A. Meyer, Asif Shakeel
|
||||
**발행일:** 2026-01-09
|
||||
**URL:** http://arxiv.org/abs/2601.06020v1
|
||||
|
||||
### 초록
|
||||
We present a generative model of human mobility in which trajectories arise as realizations of a prescribed, time-dependent Markov dynamics defined on a spatial interaction network. The model constructs a hierarchical routing structure with hubs, corridors, feeder paths, and metro links, and specifies transition matrices using gravity-type distance decay combined with externally imposed temporal schedules and directional biases. Population mass evolves as indistinguishable, memoryless movers performing a single transition per time step.
|
||||
When aggregated, the resulting trajectories reproduce structured origin-destination flows that reflect network geometry, temporal modulation, and connectivity constraints. By applying the Perron-Frobenius theorem to the daily evolution operator, we identi...
|
||||
|
||||
---
|
||||
|
||||
## LookAroundNet: Extending Temporal Context with Transformers for Clinically Viable EEG Seizure Detection
|
||||
|
||||
**저자:** Þór Sverrisson, Steinn Guðmundsson
|
||||
**발행일:** 2026-01-09
|
||||
**URL:** http://arxiv.org/abs/2601.06016v1
|
||||
|
||||
### 초록
|
||||
Automated seizure detection from electroencephalography (EEG) remains difficult due to the large variability of seizure dynamics across patients, recording conditions, and clinical settings. We introduce LookAroundNet, a transformer-based seizure detector that uses a wider temporal window of EEG data to model seizure activity. The seizure detector incorporates EEG signals before and after the segment of interest, reflecting how clinicians use surrounding context when interpreting EEG recordings. We evaluate the proposed method on multiple EEG datasets spanning diverse clinical environments, patient populations, and recording modalities, including routine clinical EEG and long-term ambulatory recordings, in
|
||||
19
research_workspace/session_20260112_150419/SUMMARY.md
Normal file
19
research_workspace/session_20260112_150419/SUMMARY.md
Normal file
@@ -0,0 +1,19 @@
|
||||
# Research Summary
|
||||
|
||||
## Query
|
||||
2026 AI Trends and Keywords
|
||||
|
||||
## Statistics
|
||||
- Total Iterations: 1
|
||||
- Total Findings: 11
|
||||
- Final Coverage: 56.68%
|
||||
|
||||
## Session
|
||||
- ID: 20260112_150419
|
||||
- Started: 2026-01-12T06:04:19.390272+00:00
|
||||
- Completed: 2026-01-12T06:04:44.989494+00:00
|
||||
|
||||
## Output Files
|
||||
- TODO.md: Progress tracking
|
||||
- FINDINGS.md: Detailed findings
|
||||
- SUMMARY.md: This file
|
||||
13
research_workspace/session_20260112_150419/TODO.md
Normal file
13
research_workspace/session_20260112_150419/TODO.md
Normal file
@@ -0,0 +1,13 @@
|
||||
# Research TODO
|
||||
|
||||
## Query
|
||||
2026 AI Trends and Keywords
|
||||
|
||||
## Progress
|
||||
- [ ] Initial exploration (iteration 1)
|
||||
- [ ] Deep dive into key topics
|
||||
- [ ] Cross-validation of findings
|
||||
- [ ] Final synthesis
|
||||
|
||||
## Findings
|
||||
(Updated during research)
|
||||
73
research_workspace/tool_trajectory.log
Normal file
73
research_workspace/tool_trajectory.log
Normal file
@@ -0,0 +1,73 @@
|
||||
Tool Trajectory Log - 2026-01-12T15:21:36.648582
|
||||
============================================================
|
||||
|
||||
[1] think_tool
|
||||
Args: {'reflection': 'Testing reflection capability'}
|
||||
Success: True
|
||||
Duration: 24ms
|
||||
Output:
|
||||
Reflection recorded: Testing reflection capability
|
||||
----------------------------------------
|
||||
[2] tavily_search
|
||||
Args: {'query': 'context engineering', 'max_results': 1}
|
||||
Success: True
|
||||
Duration: 912ms
|
||||
Output:
|
||||
Found 1 result(s) for 'context engineering':
|
||||
|
||||
## Context Engineering: A Guide With Examples - DataCamp
|
||||
**URL:** https://www.datacamp.com/blog/context-engineering
|
||||
|
||||
Context Engineering: A Guide With Examples | DataCamp
|
||||
|
||||
[Skip to main content](#main)
|
||||
|
||||
EN
|
||||
|
||||
[English](/blog/context-engineering)[Español](/es/blog/context-engineering)[Português](/pt/blog/context-engineering)[DeutschBeta](/de/blog/context-engineering)[FrançaisBeta](/fr/blog/context-engineering)
|
||||
|
||||
---
|
||||
|
||||
[More Information](https://support.da
|
||||
----------------------------------------
|
||||
[3] arxiv_search
|
||||
Args: {'query': 'large language model', 'max_results': 2}
|
||||
Success: True
|
||||
Duration: 1252ms
|
||||
Output:
|
||||
Found 2 paper(s) for 'large language model':
|
||||
|
||||
## Learning From Failure: Integrating Negative Examples when Fine-tuning Large Language Models as Agents
|
||||
|
||||
**Authors:** Renxi Wang, Haonan Li, Xudong Han, Yixuan Zhang, Timothy Baldwin
|
||||
**Published:** 2024-02-18
|
||||
**URL:** http://arxiv.org/abs/2402.11651v2
|
||||
|
||||
### Abstract
|
||||
Large language models (LLMs) have achieved success in acting as agents, which interact with environments through tools such as search engines. However, LLMs are optimized for language gen
|
||||
----------------------------------------
|
||||
[4] github_code_search
|
||||
Args: {'query': 'useState(', 'max_results': 2}
|
||||
Success: True
|
||||
Duration: 622ms
|
||||
Output:
|
||||
Found 2 GitHub code snippet(s) for 'useState(':
|
||||
|
||||
## unknown/unknown
|
||||
**File:** `unknown`
|
||||
**Language:** unknown
|
||||
|
||||
```unknown
|
||||
|
||||
```
|
||||
|
||||
---
|
||||
## unknown/unknown
|
||||
**File:** `unknown`
|
||||
**Language:** unknown
|
||||
|
||||
```unknown
|
||||
|
||||
```
|
||||
|
||||
----------------------------------------
|
||||
Reference in New Issue
Block a user