@AstroHanRay: We ran an actual A/B benchmark test for active tool pruning, comparing 121 Terminal Bench tasks: - Performance: no regression (even slight improvement +2.48pp) - Token consumption: reduced by 41.7…
Summary
A/B testing for agent active tool pruning shows: across 121 Terminal Bench tasks, performance slightly improves (+2.48 percentage points), token consumption reduces by 41.7%, and cost decreases by 31.6%.
View Cached Full Text
Cached at: 06/28/26, 10:07 PM
We conducted an A/B benchmark test on active tool pruning, comparing 121 Terminal Bench tasks:
- Performance: No regression (slight improvement of +2.48pp)
- Token usage: Reduced by 41.7%
- Cost: Reduced by 31.6%
kabikabi (@jakevin7): There’s an unspoken default assumption in building agents: the tool result is important, and the model must read the full text before continuing inference.
Recently, I discovered this assumption might be wrong.
https://t.co/3wF7yrD3ES — star welcome
In maka, we aggressively prune tool results
Similar Articles
TUA-Bench: A Benchmark for General-Purpose Terminal-Use Agents
TUA-Bench is a comprehensive benchmark for evaluating general-purpose terminal-use agents across diverse digital activities and specialized workflows, revealing significant performance gaps among current frontier agents.
GTA-2: Benchmarking General Tool Agents from Atomic Tool-Use to Open-Ended Workflows
GTA-2 introduces a hierarchical benchmark for evaluating general tool agents across atomic tool-use and open-ended workflows, revealing a significant capability cliff where frontier models achieve only 14.39% success on complex tasks despite reasonable atomic performance.
@wsl8297: When running complex tasks with AI agents, the most painful thing is often not that the model isn't strong enough, but that as the conversation gets longer, the context starts to overflow. You have to keep filling in background details, re-explaining the process, plus the redundant logs from tool calls — tokens just gush out like a broken pipe. Recently, I saw TencentDB Agent Memory open-sourced by Tencent...
Tencent has open-sourced TencentDB Agent Memory, which solves the AI agent long-context overflow problem through hierarchical memory management (symbolic short-term memory + hierarchical long-term memory). Benchmarks show token consumption reduced by up to 61% and task success rate improved by over 50%.
@BohuTANG: After quantification, it turns out that pi's current 4 tools (read / bash / edit / write) plus glob and grep can go up another level. These two are high-frequency tools, but relying on the LLM to piece together a usable bash to replace them is actually quite difficult — it often...
BohuTANG found through quantitative analysis that adding dedicated grep and glob functions to the pi tool reduces the detours of LLM-generated bash and improves efficiency.
@AYi_AInotes: Damn, this open-source tool directly reduces token consumption by 95%. This might be the most ruthless LLM cost-reduction tool this year. Netflix engineers open-sourced Headroom, which wraps a local Agent around Codex, Cursor, OpenClaw, Hermes, or Claude code…
Netflix engineers open-sourced the Headroom tool, which automatically compresses LLM input context during local preprocessing, reducing token consumption by up to 95%. It is compatible with mainstream AI coding tools like Codex and Cursor, and works without any code modifications.