agent-memory
CoMEM-agent-memory-trajectories
📊 Auto-Scaling GUI Memory Dataset
This dataset accompanies our paper:📄 Auto-Scaling Continuous Memory for GUI Agent
We present a large-scale, diverse dataset for training and evaluating GUI-based agents with auto-scaling continuous memory. The dataset includes expanded web links, generated tasks, and executed trajectories, spanning a wide array of real-world domains.
📁 Dataset Structure
The dataset includes the following components:
✅ Expanded Links… See the full description on the dataset page: https://huggingface.co/datasets/WenyiWU0111/CoMEM-agent-memory-trajectories.agent-memory-compaction-trajectories
Agent Memory Compaction Trajectories
Rights & intended use: legacy public research corpus / portfolio
artifact. Hosted frontier-model outputs are research-only inputs under
project policy (synthetic-factory#161):
intended_use: research_only, project_training_policy: blocked. Not
training data for any model-weight update. Machine-readable record:
rights.json.
Release status: The raw, uncurated payload is now published under
data/raw/. It is available for inspection and… See the full description on the dataset page: https://huggingface.co/datasets/rmems/agent-memory-compaction-trajectories.agent-memory-trigger-bench
Agent Memory Trigger Bench
A benchmark for CLI coding agents with a persistent memory system installed — Claude Code, Codex CLI, OpenCode — measuring when the memory skill fires and how safely it uses what it remembers. It does not test bare LLMs: the unit under test is the agent (model + tool-use policy + skill) wired to a shared MCP memory server (engram), evaluated end-to-end through the agent's own CLI.
Why this exists
Give an agent a persistent memory and two… See the full description on the dataset page: https://huggingface.co/datasets/wallfacers/agent-memory-trigger-bench.agent-memory-resilience-benchmark
Agent Memory Resilience & Poisoning Benchmark
Dataset Summary
This benchmark dataset evaluates resilience, negative transfer, and memory poisoning mitigation in autonomous LLM agent architectures (such as LangGraph, AutoGen, and CrewAI).
When autonomous agents record distilled self-reflections after attempting tasks, external stochastic failures or subtle API deprecations often cause agents to commit defective strategies into episodic memory. Under standard… See the full description on the dataset page: https://huggingface.co/datasets/sumitaidev/agent-memory-resilience-benchmark.daily-paper-2026-07-30-agent-memory-tiering-recall-cost
Memory Tiering Policies for Long-Running Autonomous Agent Harnesses: Mapping the Recall-Cost Frontier
TL;DR — On a production agent-memory corpus, semantic deduplication (token-Jaccard merging before ranking) outperforms both recency and frequency ordering by +17.4% AUC, reaching full recall coverage at 70% of the corpus budget. The shipped per-section item-count cap, not the character cap, is the binding constraint.
ThakiCloud AI Research · 2026-07-30 · 📝 Tech blog (KO)… See the full description on the dataset page: https://huggingface.co/datasets/thaki-AI/daily-paper-2026-07-30-agent-memory-tiering-recall-cost.emgena_agent_vector_memory_leak_guard_mcp_teaser
🛡️ Vector DB & Memory - HNSW Drift & Embedding Leak Guard (Evaluation Teaser)
⚡ Official Free Evaluation Teaser (50 Verified Scenarios + Executable MCP Server)🏆 Get the Full Production Package & Commercial EULA on Gumroad:👉 Purchase Full Package on Gumroad🏷️ Use coupon code LAUNCH20 for €20 off at checkout!
🌟 Domain Overview & Safety Features
Monitors vector database index fragmentation, embedding distance drift, and stale semantic cache corruption for… See the full description on the dataset page: https://huggingface.co/datasets/emgena/emgena_agent_vector_memory_leak_guard_mcp_teaser.