Is Grep All You Need? How Agent Harnesses Reshape Agentic Search

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Sen, Sahil, Kasturi, Akhil, Lumer, Elias, Gulati, Anmol, Subbiah, Vamse Kumar
Format:	Preprint
Published:	2026
Subjects:	Computation and Language
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Chronos: Temporal-Aware Conversational Agents with Structured Event Retrieval for Long-Term Memory
by: Sen, Sahil, et al.
Published: (2026)

Ask Early, Ask Late, Ask Right: When Does Clarification Timing Matter for Long-Horizon Agents?
by: Gulati, Anmol, et al.
Published: (2026)

Tool-to-Agent Retrieval: Bridging Tools and Agents for Scalable LLM Multi-Agent Systems
by: Lumer, Elias, et al.
Published: (2025)

Agent-as-a-Graph: Knowledge Graph-Based Tool and Agent Retrieval for LLM Multi-Agent Systems
by: Nizar, Faheem, et al.
Published: (2025)

Agentic Jackal: Live Execution and Semantic Value Grounding for Text-to-JQL
by: Murali, Vishnu, et al.
Published: (2026)

Jackal: A Real-World Execution-Based Benchmark Evaluating Large Language Models on Text-to-JQL Tasks
by: Frank, Kevin, et al.
Published: (2025)

ScaleMCP: Dynamic and Auto-Synchronizing Model Context Protocol Tools for LLM Agents
by: Lumer, Elias, et al.
Published: (2025)

MemTool: Optimizing Short-Term Memory Management for Dynamic Tool Calling in LLM Agent Multi-Turn Conversations
by: Lumer, Elias, et al.
Published: (2025)

Don't Break the Cache: An Evaluation of Prompt Caching for Long-Horizon Agentic Tasks
by: Lumer, Elias, et al.
Published: (2026)

Cited but Not Verified: Parsing and Evaluating Source Attribution in LLM Deep Research Agents
by: Onweller, Hailey, et al.
Published: (2026)

Toolshed: Scale Tool-Equipped Agents with Advanced RAG-Tool Fusion and Tool Knowledge Bases
by: Lumer, Elias, et al.
Published: (2024)

Graph RAG-Tool Fusion
by: Lumer, Elias, et al.
Published: (2025)

Beyond Rows to Reasoning: Agentic Retrieval for Multimodal Spreadsheet Understanding and Editing
by: Gulati, Anmol, et al.
Published: (2026)

Rethinking Retrieval: From Traditional Retrieval Augmented Generation to Agentic and Non-Vector Reasoning Systems in the Financial Domain for Large Language Models
by: Lumer, Elias, et al.
Published: (2025)

From Rows to Reasoning: A Retrieval-Augmented Multimodal Framework for Spreadsheet Understanding
by: Gulati, Anmol, et al.
Published: (2026)

Comparison of Text-Based and Image-Based Retrieval in Multimodal Retrieval Augmented Generation Large Language Model Systems
by: Lumer, Elias, et al.
Published: (2025)

Are Retrials All You Need? Enhancing Large Language Model Reasoning Without Verbalized Feedback
by: Potamitis, Nearchos, et al.
Published: (2025)

Agents Are All You Need for LLM Unlearning
by: Sanyal, Debdeep, et al.
Published: (2025)

More Agents Is All You Need
by: Li, Junyou, et al.
Published: (2024)

Contrast Is All You Need
by: Kilic, Burak, et al.
Published: (2023)

Attention Is All You Need But You Don't Need All Of It For Inference of Large Language Models
by: Tyukin, Georgy, et al.
Published: (2024)

Not All Tokens Are What You Need In Thinking
by: Yuan, Hang, et al.
Published: (2025)

Increasing the Thinking Budget is Not All You Need
by: Iacobacci, Ignacio, et al.
Published: (2025)

Training on the Benchmark Is Not All You Need
by: Ni, Shiwen, et al.
Published: (2024)

ViC: Virtual Compiler Is All You Need For Assembly Code Search
by: Gao, Zeyu, et al.
Published: (2024)

BEYONDWORDS is All You Need: Agentic Generative AI based Social Media Themes Extractor
by: Ghali, Mohammed-Khalil, et al.
Published: (2025)

Grimoire is All You Need for Enhancing Large Language Models
by: Chen, Ding, et al.
Published: (2024)

Verbalized Algorithms: Classical Algorithms are All You Need (Mostly)
by: Lall, Supriya, et al.
Published: (2025)

Addition is All You Need for Energy-efficient Language Models
by: Luo, Hongyin, et al.
Published: (2024)

Greed is All You Need: An Evaluation of Tokenizer Inference Methods
by: Uzan, Omri, et al.
Published: (2024)

Agentic Harness Engineering: Observability-Driven Automatic Evolution of Coding-Agent Harnesses
by: Lin, Jiahang, et al.
Published: (2026)

Rho-1: Not All Tokens Are What You Need
by: Lin, Zhenghao, et al.
Published: (2024)

Predictable Emergent Abilities of LLMs: Proxy Tasks Are All You Need
by: Zhang, Bo-Wen, et al.
Published: (2024)

Beyond Fixed Length: Bucket Pre-training is All You Need
by: Yang, Qing, et al.
Published: (2024)

Selected Languages are All You Need for Cross-lingual Truthfulness Transfer
by: Liu, Weihao, et al.
Published: (2024)

Attention Smoothing Is All You Need For Unlearning
by: Zade, Saleh Zare, et al.
Published: (2026)

Memory Is All You Need: Testing How Model Memory Affects LLM Performance in Annotation Tasks
by: Timoneda, Joan C., et al.
Published: (2025)

Tensor Product Attention Is All You Need
by: Zhang, Yifan, et al.
Published: (2025)

s3: You Don't Need That Much Data to Train a Search Agent via RL
by: Jiang, Pengcheng, et al.
Published: (2025)

State and Memory is All You Need for Robust and Reliable AI Agents
by: Muhoberac, Matthew, et al.
Published: (2025)