arXiv AI July 15, 2026 By Brenda Lelis, Rodrigo Cabral-Carvalho

RCWT: Measuring Task-Budget Displacement from Coordination Content in LLM Calls

arXiv:2607. 12216v1 Announce Type: cross Abstract: Multi-agent and memory-augmented LLM systems often place coordination content, shared state, prior discussion, tool outputs, summaries, and role instructions, inside the same finite prompt used for the current task.

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arXiv Machine Learning

Sep 15

BudgetBench: A Budget-Tiered Protocol and Pilot Harness for Memory Strategy Evaluation in Local Large Language Model Agents

arXiv:2609.13149v1 Announce Type: new Abstract: For local large language model agents, active context is a scarce resource: memory capacity, prefill latency, cache growth, and service objectives all...

By Aditya Karnam Gururaj Rao, Arjun Jaggi

llms agents nlp

arXiv Computation and Language

Sep 15

When Agents Slow Down: Understanding LLM Agents' Test-Time Strategies via Elo-per-token Analysis

arXiv:2609.15309v1 Announce Type: new Abstract: Large language model (LLM) agents allocate test-time compute adaptively as they revise solutions, use tools, explore alternatives, and decide when to s...

By Kaiyuan Liu, Qiuyang Mang, Bo Peng, Wenhao Chai, Hanchen Li, Shreyas Pimpalgaonkar, Luke Zettlemoyer, Alex Dimakis, Alvin Cheung

llms agents benchmarks

arXiv AI

Sep 10

When Does Memory Help? A Cost-Aware Evaluation of Long-Term Memory in Tool-Using LLM Agents

The paper introduces MERIT, a benchmark that evaluates the marginal benefit of long‑term memory for tool‑using large language model agents while explicitly accounting for cost. MERIT provides episodic tool‑use tasks across three domains, verifies dependence on earlier‑episode facts, and measures memory operations in tokens and dollars. Experiments on GPT‑4.1‑mini, Claude Haiku 4.5, and Claude Sonnet 5 show that memory can significantly improve task success, but its utility varies widely across models and memory implementations, and full replay is rarely cost‑effective.

By Shweta Mishra, Shashank Mishra

llms rag agents nlp benchmarks

arXiv AI

4d ago

When Should Agents Check External State? Budgeting Observations for Stored Intentions

arXiv:2609.37125v1 Announce Type: new Abstract: Prospective memory allows an agent to retain an intention tied to a future condition, but the stored intention does not reveal whether that condition c...

By Zhengkun Di, Bin Shi, Kai Sun, Yiming Xu, Bo Dong

agents benchmarks

arXiv AI

Aug 5

Distractor-Aware Truncation: Disentangling Context-Length Effects from Signal Loss in Long-Context LLM Benchmarks

arXiv:2608. 03297v1 Announce Type: new Abstract: A standard claim in the literature on retrieval-augmented and memory-augmented language models is that shorter context is better when the relevant information is preserved.

By Mohsen Arjmandi

llms rag benchmarks

arXiv AI

Sep 23

Total Cost of Agency: Exact Attribution of Memory Injection Cost in Multi-Agent LLM Workflows

arXiv:2609.23790v1 Announce Type: new Abstract: Every node in a multi-agent large language model (LLM) workflow retrieves context from memory and injects it into its prompt, where those injected toke...

By Vivek Kumar Singh, Preeti Priyam, Gautam Bhowmick

llms agents benchmarks

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Related stories

BudgetBench: A Budget-Tiered Protocol and Pilot Harness for Memory Strategy Evaluation in Local Large Language Model Agents

When Agents Slow Down: Understanding LLM Agents' Test-Time Strategies via Elo-per-token Analysis

When Does Memory Help? A Cost-Aware Evaluation of Long-Term Memory in Tool-Using LLM Agents

When Should Agents Check External State? Budgeting Observations for Stored Intentions

Distractor-Aware Truncation: Disentangling Context-Length Effects from Signal Loss in Long-Context LLM Benchmarks

Total Cost of Agency: Exact Attribution of Memory Injection Cost in Multi-Agent LLM Workflows