FluxMem: Connectivity-Evolving Memory Framework for LLM Agents
FluxMem proposes a heterogeneous graph-based memory framework for LLM agents that continuously evolves its topology through three stages: initial connection formation, feedback-driven refinement, and long-term consolidation. Unlike static memory repositories, FluxMem repairs missing links, prunes interference, aligns abstraction granularity, and distills successful trajectories into reusable procedural circuits. The system is guided by a single metric for memory generalizability and evolutionary maturity, achieving state-of-the-art results on LoCoMo, Mind2Web, and GAIA benchmarks.
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MemOS: Self-Evolving Memory OS for LLM Agents with Hybrid Retrieval and Token Savings
MemOS is an open-source TypeScript project providing a memory operating system layer for LLM and AI agents, featuring ultra-persistent memory, hybrid retrieval, and cross-task skill reuse. The project claims 35.24% token savings through its memory management approach. It has accumulated 9,329 GitHub stars with moderate daily momentum (+67). The system targets agent memory persistence and efficiency as a foundational infrastructure component.
Infini Memory: Topic-structured persistent memory architecture for long-term LLM agents
Researchers propose Infini Memory, a persistent memory architecture for LLM agents that organizes memory as topic-structured documents rather than isolated records or summaries. New observations are staged in a buffer and periodically consolidated, while retrieval uses iterative agentic tool calls instead of a single lookup step. The system achieves 64.7% on MemoryAgentBench, with ablations showing complementary gains from topic-structured maintenance and iterative evidence inspection.
EvoArena benchmark and EvoMem memory paradigm for LLM agents in dynamic environments
Researchers introduce EvoArena, a benchmark suite that evaluates LLM agents in dynamic environments by modeling changes as progressive update sequences across terminal, software, and social domains. Alongside it, they propose EvoMem, a patch-based memory paradigm that records memory evolution as structured update histories to help agents reason about environmental change. Current agents score only 39.6% average accuracy on EvoArena, while EvoMem yields consistent gains on EvoArena and also improves performance on GAIA and LoCoMo benchmarks. The work highlights a significant gap between static-benchmark performance and real-world dynamic deployment requirements.
Mem-π: Adaptive Memory for LLM Agents via On-Demand Generation and Decoupled RL
Mem-π introduces a framework where a dedicated language or vision-language model generates context-specific guidance for LLM agents on demand, rather than retrieving static entries from episodic memory banks. The system is trained with a decision-content decoupled reinforcement learning objective that jointly learns when to generate guidance and what to generate, enabling abstention when generation would not help. Evaluated across web navigation, terminal-based tool use, and text-based embodied interaction benchmarks, Mem-π achieves over 30% relative improvement on web navigation tasks compared to retrieval-based and prior RL-optimized memory baselines.
LongMINT: Benchmark for Evaluating Memory Under Multi-Target Interference in Long-Horizon Agent Systems
LongMINT is a new benchmark designed to evaluate memory-augmented agents in realistic long-horizon settings where information is repeatedly updated and interferes across memories. It contains 15.6k QA pairs over contexts averaging 138.8k tokens (up to 1.8M tokens), spanning domains including state tracking, multi-turn dialogue, Wikipedia revisions, and GitHub commits. Evaluation of 7 representative systems—including vanilla long-context LLMs, RAG, and memory-augmented agent frameworks—reveals consistently low average accuracy of 27.9%, with performance particularly degraded on multi-target aggregation tasks and when earlier facts are revised by subsequent context. The analysis identifies retrieval and memory construction as the primary bottlenecks.
MemTrace: Framework for Tracing and Attributing Errors in LLM Memory Systems
MemTrace introduces a framework that converts LLM memory pipelines into executable memory evolution graphs to enable fine-grained error tracing and root-cause attribution. The authors construct MemTraceBench, a benchmark covering Long-Context, RAG, Mem0, and EverMemOS memory systems, to systematically characterize memory failure modes such as information loss and retrieval misalignment. An automatic attribution method iteratively traces operation subgraphs to pinpoint failures, and the resulting signals are used to guide prompt optimization in a closed-loop system that improves end-task performance by up to 7.62%.
FORGE: Self-Evolving Agent Memory via Population Broadcast Without Weight Updates
FORGE (Failure-Optimized Reflective Graduation and Evolution) is a staged, population-based protocol that evolves prompt-injected natural-language memory for hierarchical ReAct agents without any gradient updates. It wraps a Reflexion-style inner loop where a reflection agent converts failed trajectories into textual heuristics or few-shot demonstrations, then propagates the best-performing instance's memory across a population between stages. Evaluated on CybORG CAGE-2 (a stochastic network-defense POMDP), FORGE improves average return by 1.7–7.7× over zero-shot and 29–72% over Reflexion across all 12 model-representation conditions tested with four LLM families. Notably, weaker models benefit disproportionately, suggesting the method may help close capability gaps rather than amplify already-strong models.
GitOfThoughts: Git-based agent memory substrate with sobering findings on memory utility for novel problems
Researchers introduce GitOfThoughts, a system that stores LLM reasoning trees as git repositories, enabling replayable, auditable, and mergeable agent memory at low engineering cost. Across five memory substrates (none, markdown, vector, graph, git), two benchmarks, and two model scales with pre-registered replications, the paper finds that no memory format reliably improves accuracy on novel problems. Memory only helps above a 'copyability threshold' (similarity >~0.8), where retrieved cases are near-duplicates of the current problem — and even then, the gain is answer retrieval rather than method transfer. The paper also documents a retracted result and refuted hypothesis, modeling a rigorous evaluation standard.