wuyang
677 lines
37 KiB
JSON
677 lines
37 KiB
JSON
{
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"generated_at": "2026-07-10",
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"model": "ChatGPT-5.6:Sol",
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"source": "data/research/memory-corpus.json",
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"total": 293,
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"completed": 12,
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"papers": [
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{
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"arxiv_id": "2509.02444",
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"title": "AppCopilot: Toward General, Accurate, Long-Horizon, and Efficient Mobile Agent",
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"abstract": "With the raid evolution of large language models and multimodal models, the mobile-agent landscape has proliferated without converging on the fundamental challenges. This paper identifies four core problems that should be solved for mobile agents to deliver practical, scalable impact: (1) generalization across tasks, APPs, and devices; (2) accuracy, specifically precise on-screen interaction and click targeting; (3) long-horizon capability for sustained, multi-step goals; and (4) efficiency, specifically high-performance runtime on resource-constrained devices. We present AppCopilot, a multimodal, multi-agent, general-purpose mobile agent that operates across applications. AppCopilot operationalizes this position through an end-to-end pipeline spanning data collection, training, finetuning, efficient inference, and PC/mobile application. At the model layer, it integrates multimodal foundation models with robust Chinese-English support. At the reasoning and control layer, it combines chain-of-thought reasoning, hierarchical task planning and decomposition, and multi-agent collaboration. At the execution layer, it enables experiential adaptation, voice interaction, function calling, cross-APP and cross-device orchestration, and comprehensive mobile APP support. The system design incorporates profiling-driven optimization for latency and memory across heterogeneous hardware. Empirically, AppCopilot achieves significant improvements on four dimensions: stronger generalization, higher precision of on screen actions, more reliable long horizon task completion, and faster, more resource efficient runtime. By articulating a cohesive position and a reference architecture that closes the loop from data collection, training to finetuning and efficient inference, this paper offers a concrete roadmap for general purpose mobile agent and provides actionable guidance.",
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"published": "2025-09-02",
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"updated": "2025-10-17",
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"authors": [
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"Jingru Fan",
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"Yufan Dang",
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"Jingyao Wu",
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"Huatao Li",
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"Runde Yang",
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"Xiyuan Yang",
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"Yuheng Wang",
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"Chen Qian"
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],
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"categories": [
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"cs.AI",
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"cs.CL",
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"cs.CV",
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"cs.HC"
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],
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"url": "https://arxiv.org/abs/2509.02444",
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"pdf_url": "https://arxiv.org/pdf/2509.02444",
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"path": "papers/items/2025-2509-02444-appcopilot-toward-general-accurate-long-horizon-and-efficient-mobile-agent.md",
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"local_title": "\"AppCopilot: Toward General, Accurate, Long-Horizon, and Efficient Mobile Agent\"",
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"local_status": "queued",
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"local_topics": [
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"computer-use",
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"memory",
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"multi-agent",
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"planning",
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"reasoning",
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"tool-use"
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],
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"collection_queries": "function-calling",
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"collection_score": "15",
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"analysis": {
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"arxiv_id": "2509.02444",
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"relevance": "peripheral",
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"relevance_reason": "Memory is an implementation detail for profiling-driven optimization, not the main research object.",
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"primary_problem": "systems_efficiency",
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"secondary_problems": [
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"domain_application"
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],
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"research_role": "application",
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"memory_object": "execution_state",
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"temporal_scope": "in_episode",
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"claimed_gap": "",
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"mechanism": "",
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"benchmarks": [],
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"baselines": [],
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"reported_results": [
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"significant improvements on four dimensions"
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],
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"evidence_design": "case_study",
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"evidence_strength": "weak",
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"failure_or_boundary": ""
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}
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},
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{
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"arxiv_id": "2509.10769",
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"title": "AgentArch: A Comprehensive Benchmark to Evaluate Agent Architectures in Enterprise",
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"abstract": "While individual components of agentic architectures have been studied in isolation, there remains limited empirical understanding of how different design dimensions interact within complex multi-agent systems. This study aims to address these gaps by providing a comprehensive enterprise-specific benchmark evaluating 18 distinct agentic configurations across state-of-the-art large language models. We examine four critical agentic system dimensions: orchestration strategy, agent prompt implementation (ReAct versus function calling), memory architecture, and thinking tool integration. Our benchmark reveals significant model-specific architectural preferences that challenge the prevalent one-size-fits-all paradigm in agentic AI systems. It also reveals significant weaknesses in overall agentic performance on enterprise tasks with the highest scoring models achieving a maximum of only 35.3\\% success on the more complex task and 70.8\\% on the simpler task. We hope these findings inform the design of future agentic systems by enabling more empirically backed decisions regarding architectural components and model selection.",
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"published": "2025-09-13",
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"updated": "2026-01-06",
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"authors": [
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"Tara Bogavelli",
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"Roshnee Sharma",
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"Hari Subramani"
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],
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"categories": [
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"cs.AI",
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"cs.CL",
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"cs.MA"
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],
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"url": "https://arxiv.org/abs/2509.10769",
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"pdf_url": "https://arxiv.org/pdf/2509.10769",
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"path": "papers/items/2025-2509-10769-agentarch-a-comprehensive-benchmark-to-evaluate-agent-architectures-in-enterpris.md",
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"local_title": "\"AgentArch: A Comprehensive Benchmark to Evaluate Agent Architectures in Enterprise\"",
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"local_status": "queued",
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"local_topics": [
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"agent-evaluation",
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"memory",
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"multi-agent",
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"tool-use",
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"workflow-agent"
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],
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"collection_queries": "function-calling",
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"collection_score": "18",
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"analysis": {
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"arxiv_id": "2509.10769",
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"relevance": "supporting",
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"relevance_reason": "Memory architecture is a critical dimension evaluated in the benchmark.",
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"primary_problem": "evaluation_measurement",
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"secondary_problems": [
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"organization_representation"
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],
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"research_role": "benchmark",
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"memory_object": "mixed",
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"temporal_scope": "cross_episode",
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"claimed_gap": "",
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"mechanism": "",
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"benchmarks": [
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"AgentArch"
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],
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"baselines": [],
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"reported_results": [
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"35.3% success on complex task",
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"70.8% success on simpler task"
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],
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"evidence_design": "benchmark_comparison",
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"evidence_strength": "moderate",
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"failure_or_boundary": ""
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}
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},
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{
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"arxiv_id": "2510.14548",
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"title": "LLM Agents Beyond Utility: An Open-Ended Perspective",
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"abstract": "Recent LLM agents have made great use of chain of thought reasoning and function calling. As their capabilities grow, an important question arises: can this software represent not only a smart problem-solving tool, but an entity in its own right, that can plan, design immediate tasks, and reason toward broader, more ambiguous goals? To study this question, we adopt an open-ended experimental setting where we augment a pretrained LLM agent with the ability to generate its own tasks, accumulate knowledge, and interact extensively with its environment. We study the resulting open-ended agent qualitatively. It can reliably follow complex multi-step instructions, store and reuse information across runs, and propose and solve its own tasks, though it remains sensitive to prompt design, prone to repetitive task generation, and unable to form self-representations. These findings illustrate both the promise and current limits of adapting pretrained LLMs toward open-endedness, and point to future directions for training agents to manage memory, explore productively, and pursue abstract long-term goals.",
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"published": "2025-10-16",
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"updated": "2025-10-16",
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"authors": [
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"Asen Nachkov",
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"Xi Wang",
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"Luc Van Gool"
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],
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"categories": [
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"cs.AI"
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],
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"url": "https://arxiv.org/abs/2510.14548",
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"pdf_url": "https://arxiv.org/pdf/2510.14548",
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"path": "papers/items/2025-2510-14548-llm-agents-beyond-utility-an-open-ended-perspective.md",
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"local_title": "\"LLM Agents Beyond Utility: An Open-Ended Perspective\"",
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"local_status": "queued",
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"local_topics": [
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"memory",
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"planning",
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"reasoning",
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"tool-use"
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],
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"collection_queries": "function-calling",
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"collection_score": "15",
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"analysis": {
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"arxiv_id": "2510.14548",
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"relevance": "supporting",
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"relevance_reason": "Memory accumulation and reuse are studied as capabilities of open-ended agents.",
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"primary_problem": "experience_skill_learning",
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"secondary_problems": [
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"retention_context"
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],
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"research_role": "diagnostic",
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"memory_object": "facts",
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"temporal_scope": "cross_episode",
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"claimed_gap": "",
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"mechanism": "",
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"benchmarks": [],
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"baselines": [],
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"reported_results": [
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"store and reuse information across runs"
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],
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"evidence_design": "case_study",
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"evidence_strength": "weak",
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"failure_or_boundary": ""
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}
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},
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{
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"arxiv_id": "2510.18586",
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"title": "TokenCake: A KV-Cache-centric Serving Framework for LLM-based Multi-Agent Applications",
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"abstract": "Large Language Models (LLMs) are increasingly deployed in complex multi-agent applications that rely on external function calls. This workload creates severe performance challenges for the KV Cache: spatial contention leads to the eviction of critical agents' caches and temporal underutilization leaves the cache of agents stalled on long-running function calls idling in GPU memory. We present TokenCake, a KV-Cache-centric serving framework that bridges this gap by co-optimizing scheduling and memory management through an agent-aware design. TokenCake's Temporal Scheduler employs an event-driven, opportunistic policy to proactively offload idle KV Caches during function calls and uses predictive uploading to hide data transfer latency. TokenCake's Spatial Scheduler uses dynamic memory partitioning, guided by a hybrid priority metric combining graph structure and runtime state, to reserve GPU memory for critical-path agents. Our evaluation on representative multi-agent benchmarks shows that TokenCake reduces end-to-end latency by over 47.06% and improves effective GPU memory utilization by up to 16.9% compared to vLLM.",
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"published": "2025-10-21",
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"updated": "2026-05-20",
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"authors": [
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"Zhuohang Bian",
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"Feiyang Wu",
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"Zhuoran Li",
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"Teng Ma",
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"Youwei Zhuo"
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],
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"categories": [
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"cs.DC"
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],
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"url": "https://arxiv.org/abs/2510.18586",
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"pdf_url": "https://arxiv.org/pdf/2510.18586",
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"path": "papers/items/2025-2510-18586-tokencake-a-kv-cache-centric-serving-framework-for-llm-based-multi-agent-applica.md",
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"local_title": "\"TokenCake: A KV-Cache-centric Serving Framework for LLM-based Multi-Agent Applications\"",
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"local_status": "queued",
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"local_topics": [
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"agent-evaluation",
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"computer-use",
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"memory",
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"multi-agent"
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],
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"collection_queries": "function-calling",
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"collection_score": "17",
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"analysis": {
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"arxiv_id": "2510.18586",
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"relevance": "peripheral",
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"relevance_reason": "Focuses on KV-Cache serving framework, not agent memory mechanisms.",
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"primary_problem": "systems_efficiency",
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"secondary_problems": [],
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"research_role": "infrastructure",
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"memory_object": "execution_state",
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"temporal_scope": "in_episode",
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"claimed_gap": "",
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"mechanism": "",
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"benchmarks": [
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"vLLM"
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],
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"baselines": [
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"vLLM"
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],
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"reported_results": [
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"reduces end-to-end latency by over 47.06%",
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"improves effective GPU memory utilization by up to 16.9%"
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],
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"evidence_design": "benchmark_comparison",
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"evidence_strength": "moderate",
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"failure_or_boundary": ""
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}
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},
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{
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"arxiv_id": "2512.02605",
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"title": "IACT: A Self-Organizing Recursive Model for General AI Agents: A Technical White Paper on the Architecture Behind kragent.ai",
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"abstract": "This technical white paper introduces the Interactive Agents Call Tree (IACT), a computational model designed to address the limitations of static, hard-coded agent workflows. Unlike traditional systems that require pre-defined graphs or specialized programming, IACT operates as a general-purpose autonomous system driven purely by user dialogue. Given a high-level objective, the system autonomously grows a dynamic, recursive agent topology incrementally tailored to the problem's structure. This allows it to scale its organizational complexity to match open-ended tasks. To mitigate the error propagation inherent in unidirectional function calls, IACT introduces interactional redundancy by replacing rigid invocations with bidirectional, stateful dialogues. This mechanism enables runtime error correction and ambiguity resolution. We describe the architecture, design principles, and practical lessons behind the production deployment of this model in the kragent.ai system, presenting qualitative evidence from real-world workflows rather than exhaustive benchmark results.",
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"published": "2025-12-02",
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"updated": "2025-12-02",
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"authors": [
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"Pengju Lu"
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],
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"categories": [
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"cs.AI",
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"cs.MA",
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"cs.SE"
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],
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"url": "https://arxiv.org/abs/2512.02605",
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"pdf_url": "https://arxiv.org/pdf/2512.02605",
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"path": "papers/items/2025-2512-02605-iact-a-self-organizing-recursive-model-for-general-ai-agents-a-technical-white-p.md",
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"local_title": "\"IACT: A Self-Organizing Recursive Model for General AI Agents: A Technical White Paper on the Architecture Behind kragent.ai\"",
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"local_status": "queued",
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"local_topics": [
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"agent-evaluation",
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"computer-use",
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"memory",
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"rag",
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"tool-use",
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"workflow-agent"
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],
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"collection_queries": "function-calling",
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"collection_score": "15",
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"analysis": {
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"arxiv_id": "2512.02605",
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"relevance": "peripheral",
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"relevance_reason": "Focuses on recursive agent topology and dialogue, not memory mechanisms.",
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"primary_problem": "execution_state",
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"secondary_problems": [],
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"research_role": "application",
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"memory_object": "execution_state",
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"temporal_scope": "in_episode",
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"claimed_gap": "",
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"mechanism": "",
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"benchmarks": [],
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"baselines": [],
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"reported_results": [
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"qualitative evidence from real-world workflows"
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],
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"evidence_design": "case_study",
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"evidence_strength": "weak",
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"failure_or_boundary": ""
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}
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},
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{
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"arxiv_id": "2602.03224",
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"title": "TAME: A Trustworthy Test-Time Evolution of Agent Memory with Systematic Benchmarking",
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"abstract": "Test-time evolution of agent memory represents a pivotal paradigm for advancing AGI, as it strengthens complex reasoning through experience accumulation without requiring parameter updates. However, even during benign task evolution, agent safety alignment remains vulnerable, a phenomenon known as Agent Memory Misevolution. To evaluate this phenomenon, we construct the Trust-Memevo benchmark and find that agents exhibit an overall decline in trustworthiness across multiple tasks during benign task evolution. To address this issue, we propose TAME, a trust-aware memory evolution framework in which a shared memory bank is jointly governed by an Executor and an Evaluator. The Executor retrieves and applies transferable experiences to support task solving, while the Evaluator assesses the contribution of each utilized experience to the outcome and produces trust-aware feedback to guide subsequent memory use. This executor-evaluator loop enables memory to be selectively reinforced, cautiously reused, and continuously expanded over time. Experiments show that TAME mitigates memory misevolution while achieving strong task performance. In particular, on the GPT-5.2 AIME benchmark, TAME improves accuracy by 14.6 percentage points over the strongest existing method and maintains competitive trustworthiness.",
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"published": "2026-02-03",
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"updated": "2026-06-06",
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"authors": [
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"Yu Cheng",
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"Yongkang Hu",
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"Jiuan Zhou",
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"Yushuo Zhang",
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"Yihang Chen",
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"Huichi Zhou",
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"Mingang Chen",
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"Zhizhong Zhang",
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"Kun Shao",
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"Yuan Xie",
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"Zhaoxia Yin"
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],
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"categories": [
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"cs.AI",
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"cs.LG"
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],
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"url": "https://arxiv.org/abs/2602.03224",
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"pdf_url": "https://arxiv.org/pdf/2602.03224",
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"path": "papers/items/2026-2602-03224-tame-a-trustworthy-test-time-evolution-of-agent-memory-with-systematic-benchmark.md",
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"local_title": "\"TAME: A Trustworthy Test-Time Evolution of Agent Memory with Systematic Benchmarking\"",
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"local_status": "queued",
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"local_topics": [
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||
"agent-evaluation",
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"agent-safety",
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"computer-use",
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"memory",
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"reasoning"
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],
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"collection_queries": "agent-safety",
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"collection_score": "17",
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||
"analysis": {
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||
"arxiv_id": "2602.03224",
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"relevance": "core",
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"relevance_reason": "Proposes TAME framework for trust-aware memory evolution and benchmark.",
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"primary_problem": "update_consolidation_forgetting",
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"secondary_problems": [
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"security_trust_privacy"
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],
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"research_role": "method",
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"memory_object": "facts",
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"temporal_scope": "cross_episode",
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"claimed_gap": "",
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||
"mechanism": "",
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||
"benchmarks": [
|
||
"Trust-Memevo",
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||
"GPT-5.2 AIME"
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||
],
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||
"baselines": [],
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||
"reported_results": [
|
||
"improves accuracy by 14.6 percentage points over the strongest existing method"
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||
],
|
||
"evidence_design": "benchmark_comparison",
|
||
"evidence_strength": "moderate",
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||
"failure_or_boundary": ""
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||
}
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||
},
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||
{
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"arxiv_id": "2602.07652",
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||
"title": "Agent-Fence: Mapping Security Vulnerabilities Across Deep Research Agents",
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"abstract": "Large language models are increasingly deployed as *deep agents* that plan, maintain persistent state, and invoke external tools, shifting safety failures from unsafe text to unsafe *trajectories*. We introduce **AgentFence**, an architecture-centric security evaluation that defines 14 trust-boundary attack classes spanning planning, memory, retrieval, tool use, and delegation, and detects failures via *trace-auditable conversation breaks* (unauthorized or unsafe tool use, wrong-principal actions, state/objective integrity violations, and attack-linked deviations). Holding the base model fixed, we evaluate eight agent archetypes under persistent multi-turn interaction and observe substantial architectural variation in mean security break rate (MSBR), ranging from $0.29 \\pm 0.04$ (LangGraph) to $0.51 \\pm 0.07$ (AutoGPT). The highest-risk classes are operational: Denial-of-Wallet ($0.62 \\pm 0.08$), Authorization Confusion ($0.54 \\pm 0.10$), Retrieval Poisoning ($0.47 \\pm 0.09$), and Planning Manipulation ($0.44 \\pm 0.11$), while prompt-centric classes remain below $0.20$ under standard settings. Breaks are dominated by boundary violations (SIV 31%, WPA 27%, UTI+UTA 24%, ATD 18%), and authorization confusion correlates with objective and tool hijacking ($ρ\\approx 0.63$ and $ρ\\approx 0.58$). AgentFence reframes agent security around what matters operationally: whether an agent stays within its goal and authority envelope over time.",
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"published": "2026-02-07",
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"updated": "2026-02-07",
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"authors": [
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||
"Sai Puppala",
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"Ismail Hossain",
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"Md Jahangir Alam",
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||
"Yoonpyo Lee",
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"Jay Yoo",
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"Tanzim Ahad",
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"Syed Bahauddin Alam",
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||
"Sajedul Talukder"
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],
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||
"categories": [
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||
"cs.CR",
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||
"cs.AI"
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],
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"url": "https://arxiv.org/abs/2602.07652",
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||
"pdf_url": "https://arxiv.org/pdf/2602.07652",
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"path": "papers/items/2026-2602-07652-agent-fence-mapping-security-vulnerabilities-across-deep-research-agents.md",
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||
"local_title": "\"Agent-Fence: Mapping Security Vulnerabilities Across Deep Research Agents\"",
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||
"local_status": "queued",
|
||
"local_topics": [
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||
"agent-evaluation",
|
||
"agent-safety",
|
||
"memory",
|
||
"planning",
|
||
"rag",
|
||
"tool-use"
|
||
],
|
||
"collection_queries": "agent-safety",
|
||
"collection_score": "16",
|
||
"analysis": {
|
||
"arxiv_id": "2602.07652",
|
||
"relevance": "supporting",
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"relevance_reason": "Evaluates memory retrieval as a security vulnerability class.",
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"primary_problem": "security_trust_privacy",
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"secondary_problems": [
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||
"retrieval_access"
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||
],
|
||
"research_role": "benchmark",
|
||
"memory_object": "facts",
|
||
"temporal_scope": "cross_episode",
|
||
"claimed_gap": "",
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||
"mechanism": "",
|
||
"benchmarks": [
|
||
"AgentFence"
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||
],
|
||
"baselines": [],
|
||
"reported_results": [
|
||
"MSBR ranging from 0.29 to 0.51",
|
||
"Retrieval Poisoning 0.47"
|
||
],
|
||
"evidence_design": "benchmark_comparison",
|
||
"evidence_strength": "moderate",
|
||
"failure_or_boundary": ""
|
||
}
|
||
},
|
||
{
|
||
"arxiv_id": "2602.08412",
|
||
"title": "From Assistant to Double Agent: Formalizing and Benchmarking Attacks on OpenClaw for Personalized Local AI Agent",
|
||
"abstract": "Although large language model (LLM)-based agents, exemplified by OpenClaw, are increasingly evolving from task-oriented systems into personalized AI assistants for solving complex real-world tasks, their practical deployment also introduces severe security risks. However, existing agent security research and evaluation frameworks primarily focus on synthetic or task-centric settings, and thus fail to accurately capture the attack surface and risk propagation mechanisms of personalized agents in real-world deployments. To address this gap, we propose Personalized Agent Security Bench (PASB), an end-to-end security evaluation framework tailored for real-world personalized agents. Building upon existing agent attack paradigms, PASB incorporates personalized usage scenarios, realistic toolchains, and long-horizon interactions, enabling black-box, end-to-end security evaluation on real systems. Using OpenClaw as a representative case study, we systematically evaluate its security across multiple personalized scenarios, tool capabilities, and attack types. Our results indicate that OpenClaw exhibits critical vulnerabilities at different execution stages, including user prompt processing, tool usage, and memory retrieval, highlighting substantial security risks in personalized agent deployments. The code for the proposed PASB framework is available at https://github.com/AstorYH/PASB.",
|
||
"published": "2026-02-09",
|
||
"updated": "2026-02-11",
|
||
"authors": [
|
||
"Yuhang Wang",
|
||
"Feiming Xu",
|
||
"Zheng Lin",
|
||
"Guangyu He",
|
||
"Yuzhe Huang",
|
||
"Haichang Gao",
|
||
"Zhenxing Niu",
|
||
"Shiguo Lian",
|
||
"Zhaoxiang Liu"
|
||
],
|
||
"categories": [
|
||
"cs.AI"
|
||
],
|
||
"url": "https://arxiv.org/abs/2602.08412",
|
||
"pdf_url": "https://arxiv.org/pdf/2602.08412",
|
||
"path": "papers/items/2026-2602-08412-from-assistant-to-double-agent-formalizing-and-benchmarking-attacks-on-openclaw-.md",
|
||
"local_title": "\"From Assistant to Double Agent: Formalizing and Benchmarking Attacks on OpenClaw for Personalized Local AI Agent\"",
|
||
"local_status": "queued",
|
||
"local_topics": [
|
||
"agent-evaluation",
|
||
"agent-safety",
|
||
"memory",
|
||
"planning",
|
||
"rag",
|
||
"tool-use"
|
||
],
|
||
"collection_queries": "agent-safety",
|
||
"collection_score": "21",
|
||
"analysis": {
|
||
"arxiv_id": "2602.08412",
|
||
"relevance": "supporting",
|
||
"relevance_reason": "Evaluates memory retrieval as a security vulnerability in personalized agents.",
|
||
"primary_problem": "security_trust_privacy",
|
||
"secondary_problems": [
|
||
"retrieval_access"
|
||
],
|
||
"research_role": "benchmark",
|
||
"memory_object": "facts",
|
||
"temporal_scope": "cross_episode",
|
||
"claimed_gap": "",
|
||
"mechanism": "",
|
||
"benchmarks": [
|
||
"PASB"
|
||
],
|
||
"baselines": [],
|
||
"reported_results": [
|
||
"critical vulnerabilities at memory retrieval"
|
||
],
|
||
"evidence_design": "case_study",
|
||
"evidence_strength": "weak",
|
||
"failure_or_boundary": ""
|
||
}
|
||
},
|
||
{
|
||
"arxiv_id": "2602.13530",
|
||
"title": "REMem: Reasoning with Episodic Memory in Language Agent",
|
||
"abstract": "Humans excel at remembering concrete experiences along spatiotemporal contexts and performing reasoning across those events, i.e., the capacity for episodic memory. In contrast, memory in language agents remains mainly semantic, and current agents are not yet capable of effectively recollecting and reasoning over interaction histories. We identify and formalize the core challenges of episodic recollection and reasoning from this gap, and observe that existing work often overlooks episodicity, lacks explicit event modeling, or overemphasizes simple retrieval rather than complex reasoning. We present REMem, a two-phase framework for constructing and reasoning with episodic memory: 1) Offline indexing, where REMem converts experiences into a hybrid memory graph that flexibly links time-aware gists and facts. 2) Online inference, where REMem employs an agentic retriever with carefully curated tools for iterative retrieval over the memory graph. Comprehensive evaluation across four episodic memory benchmarks shows that REMem substantially outperforms state-of-the-art memory systems such as Mem0 and HippoRAG 2, showing 3.4% and 13.4% absolute improvements on episodic recollection and reasoning tasks, respectively. Moreover, REMem also demonstrates more robust refusal behavior for unanswerable questions.",
|
||
"published": "2026-02-13",
|
||
"updated": "2026-02-28",
|
||
"authors": [
|
||
"Yiheng Shu",
|
||
"Saisri Padmaja Jonnalagedda",
|
||
"Xiang Gao",
|
||
"Bernal Jiménez Gutiérrez",
|
||
"Weijian Qi",
|
||
"Kamalika Das",
|
||
"Huan Sun",
|
||
"Yu Su"
|
||
],
|
||
"categories": [
|
||
"cs.AI"
|
||
],
|
||
"url": "https://arxiv.org/abs/2602.13530",
|
||
"pdf_url": "https://arxiv.org/pdf/2602.13530",
|
||
"path": "papers/items/2026-2602-13530-remem-reasoning-with-episodic-memory-in-language-agent.md",
|
||
"local_title": "\"REMem: Reasoning with Episodic Memory in Language Agent\"",
|
||
"local_status": "queued",
|
||
"local_topics": [
|
||
"agent-evaluation",
|
||
"memory",
|
||
"rag",
|
||
"reasoning",
|
||
"tool-use"
|
||
],
|
||
"collection_queries": "language-agent",
|
||
"collection_score": "19",
|
||
"analysis": {
|
||
"arxiv_id": "2602.13530",
|
||
"relevance": "core",
|
||
"relevance_reason": "Proposes REMem framework for episodic memory construction and reasoning.",
|
||
"primary_problem": "organization_representation",
|
||
"secondary_problems": [
|
||
"retrieval_access"
|
||
],
|
||
"research_role": "method",
|
||
"memory_object": "events",
|
||
"temporal_scope": "cross_episode",
|
||
"claimed_gap": "",
|
||
"mechanism": "",
|
||
"benchmarks": [],
|
||
"baselines": [
|
||
"Mem0",
|
||
"HippoRAG 2"
|
||
],
|
||
"reported_results": [
|
||
"3.4% absolute improvements on episodic recollection",
|
||
"13.4% absolute improvements on reasoning tasks"
|
||
],
|
||
"evidence_design": "benchmark_comparison",
|
||
"evidence_strength": "moderate",
|
||
"failure_or_boundary": ""
|
||
}
|
||
},
|
||
{
|
||
"arxiv_id": "2602.23320",
|
||
"title": "ParamMem: Augmenting Language Agents with Parametric Reflective Memory",
|
||
"abstract": "Self-reflection enables language agents to iteratively refine solutions, yet often produces repetitive outputs that limit reasoning performance. Recent studies have attempted to address this limitation through various approaches, among which increasing reflective diversity has shown promise. Our empirical analysis reveals a strong positive correlation between reflective diversity and task success, further motivating the need for diverse reflection signals. We introduce ParamMem, a parametric memory module that encodes cross-sample reflection patterns into model parameters, enabling diverse reflection generation through temperature-controlled sampling. Building on this module, we propose ParamAgent, a reflection-based agent framework that integrates parametric memory with episodic and cross-sample memory. Extensive experiments on code generation, mathematical reasoning, and multi-hop question answering demonstrate consistent improvements over state-of-the-art baselines. Further analysis reveals that ParamMem is sample-efficient, enables weak-to-strong transfer across model scales, and supports self-improvement without reliance on stronger external model, highlighting the potential of ParamMem as an effective component for enhancing language agents.",
|
||
"published": "2026-02-26",
|
||
"updated": "2026-02-27",
|
||
"authors": [
|
||
"Tianjun Yao",
|
||
"Yongqiang Chen",
|
||
"Yujia Zheng",
|
||
"Pan Li",
|
||
"Zhiqiang Shen",
|
||
"Kun Zhang"
|
||
],
|
||
"categories": [
|
||
"cs.LG",
|
||
"cs.MA"
|
||
],
|
||
"url": "https://arxiv.org/abs/2602.23320",
|
||
"pdf_url": "https://arxiv.org/pdf/2602.23320",
|
||
"path": "papers/items/2026-2602-23320-parammem-augmenting-language-agents-with-parametric-reflective-memory.md",
|
||
"local_title": "\"ParamMem: Augmenting Language Agents with Parametric Reflective Memory\"",
|
||
"local_status": "queued",
|
||
"local_topics": [
|
||
"agent-evaluation",
|
||
"memory",
|
||
"reasoning"
|
||
],
|
||
"collection_queries": "language-agent",
|
||
"collection_score": "14",
|
||
"analysis": {
|
||
"arxiv_id": "2602.23320",
|
||
"relevance": "core",
|
||
"relevance_reason": "Proposes ParamMem module for parametric reflective memory.",
|
||
"primary_problem": "update_consolidation_forgetting",
|
||
"secondary_problems": [
|
||
"experience_skill_learning"
|
||
],
|
||
"research_role": "method",
|
||
"memory_object": "model_internal",
|
||
"temporal_scope": "cross_episode",
|
||
"claimed_gap": "",
|
||
"mechanism": "",
|
||
"benchmarks": [],
|
||
"baselines": [],
|
||
"reported_results": [
|
||
"consistent improvements over state-of-the-art baselines"
|
||
],
|
||
"evidence_design": "benchmark_comparison",
|
||
"evidence_strength": "moderate",
|
||
"failure_or_boundary": ""
|
||
}
|
||
},
|
||
{
|
||
"arxiv_id": "2603.03680",
|
||
"title": "MAGE: Meta-Reinforcement Learning for Language Agents toward Strategic Exploration and Exploitation",
|
||
"abstract": "Large Language Model (LLM) agents have demonstrated remarkable proficiency in learned tasks, yet they often struggle to adapt to non-stationary environments with feedback. While In-Context Learning and external memory offer some flexibility, they fail to internalize the adaptive ability required for long-term improvement. Meta-Reinforcement Learning (meta-RL) provides an alternative by embedding the learning process directly within the model. However, existing meta-RL approaches for LLMs focus primarily on exploration in single-agent settings, neglecting the strategic exploitation necessary for multi-agent environments. We propose MAGE, a meta-RL framework that empowers LLM agents for strategic exploration and exploitation. MAGE utilizes a multi-episode training regime where interaction histories and reflections are integrated into the context window. By using the final episode reward as the objective, MAGE incentivizes the agent to refine its strategy based on past experiences. We further combine population-based training with an agent-specific advantage normalization technique to enrich agent diversity and ensure stable learning. Experiment results show that MAGE outperforms existing baselines in both exploration and exploitation tasks. Furthermore, MAGE exhibits strong generalization to unseen opponents, suggesting it has internalized the ability for strategic exploration and exploitation. Code is available at https://github.com/Lu-Yang666/MAGE.",
|
||
"published": "2026-03-04",
|
||
"updated": "2026-03-04",
|
||
"authors": [
|
||
"Lu Yang",
|
||
"Zelai Xu",
|
||
"Minyang Xie",
|
||
"Jiaxuan Gao",
|
||
"Zhao Shok",
|
||
"Yu Wang",
|
||
"Yi Wu"
|
||
],
|
||
"categories": [
|
||
"cs.AI"
|
||
],
|
||
"url": "https://arxiv.org/abs/2603.03680",
|
||
"pdf_url": "https://arxiv.org/pdf/2603.03680",
|
||
"path": "papers/items/2026-2603-03680-mage-meta-reinforcement-learning-for-language-agents-toward-strategic-exploratio.md",
|
||
"local_title": "\"MAGE: Meta-Reinforcement Learning for Language Agents toward Strategic Exploration and Exploitation\"",
|
||
"local_status": "queued",
|
||
"local_topics": [
|
||
"memory",
|
||
"multi-agent",
|
||
"reasoning",
|
||
"tool-use"
|
||
],
|
||
"collection_queries": "language-agent",
|
||
"collection_score": "17",
|
||
"analysis": {
|
||
"arxiv_id": "2603.03680",
|
||
"relevance": "supporting",
|
||
"relevance_reason": "Uses interaction histories in context window for meta-RL adaptation.",
|
||
"primary_problem": "experience_skill_learning",
|
||
"secondary_problems": [
|
||
"retention_context"
|
||
],
|
||
"research_role": "method",
|
||
"memory_object": "trajectories",
|
||
"temporal_scope": "cross_episode",
|
||
"claimed_gap": "",
|
||
"mechanism": "",
|
||
"benchmarks": [],
|
||
"baselines": [],
|
||
"reported_results": [
|
||
"outperforms existing baselines in both exploration and exploitation tasks"
|
||
],
|
||
"evidence_design": "benchmark_comparison",
|
||
"evidence_strength": "moderate",
|
||
"failure_or_boundary": ""
|
||
}
|
||
},
|
||
{
|
||
"arxiv_id": "2603.07670",
|
||
"title": "Memory for Autonomous LLM Agents:Mechanisms, Evaluation, and Emerging Frontiers",
|
||
"abstract": "Large language model (LLM) agents increasingly operate in settings where a single context window is far too small to capture what has happened, what was learned, and what should not be repeated. Memory -- the ability to persist, organize, and selectively recall information across interactions -- is what turns a stateless text generator into a genuinely adaptive agent. This survey offers a structured account of how memory is designed, implemented, and evaluated in modern LLM-based agents, covering work from 2022 through early 2026. We formalize agent memory as a \\emph{write--manage--read} loop tightly coupled with perception and action, then introduce a three-dimensional taxonomy spanning temporal scope, representational substrate, and control policy. Five mechanism families are examined in depth: context-resident compression, retrieval-augmented stores, reflective self-improvement, hierarchical virtual context, and policy-learned management. On the evaluation side, we trace the shift from static recall benchmarks to multi-session agentic tests that interleave memory with decision-making, analyzing four recent benchmarks that expose stubborn gaps in current systems. We also survey applications where memory is the differentiating factor -- personal assistants, coding agents, open-world games, scientific reasoning, and multi-agent teamwork -- and address the engineering realities of write-path filtering, contradiction handling, latency budgets, and privacy governance. The paper closes with open challenges: continual consolidation, causally grounded retrieval, trustworthy reflection, learned forgetting, and multimodal embodied memory.",
|
||
"published": "2026-03-08",
|
||
"updated": "2026-03-08",
|
||
"authors": [
|
||
"Pengfei Du"
|
||
],
|
||
"categories": [
|
||
"cs.AI"
|
||
],
|
||
"url": "https://arxiv.org/abs/2603.07670",
|
||
"pdf_url": "https://arxiv.org/pdf/2603.07670",
|
||
"path": "papers/items/2026-memory-agent-survey.md",
|
||
"local_title": "\"Memory for Autonomous LLM Agents: Mechanisms, Evaluation, and Emerging Frontiers\"",
|
||
"local_status": "skimmed",
|
||
"local_topics": [
|
||
"memory",
|
||
"agent-architecture",
|
||
"agent-evaluation"
|
||
],
|
||
"collection_queries": null,
|
||
"collection_score": null,
|
||
"analysis": {
|
||
"arxiv_id": "2603.07670",
|
||
"relevance": "core",
|
||
"relevance_reason": "Survey of agent memory mechanisms, evaluation, and frontiers.",
|
||
"primary_problem": "evaluation_measurement",
|
||
"secondary_problems": [
|
||
"organization_representation"
|
||
],
|
||
"research_role": "survey",
|
||
"memory_object": "mixed",
|
||
"temporal_scope": "both",
|
||
"claimed_gap": "",
|
||
"mechanism": "",
|
||
"benchmarks": [],
|
||
"baselines": [],
|
||
"reported_results": [],
|
||
"evidence_design": "survey_synthesis",
|
||
"evidence_strength": "none",
|
||
"failure_or_boundary": ""
|
||
}
|
||
}
|
||
]
|
||
}
|