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cs.AI2026

G-ReAct: Graph-Guided Deep Search via Structure-State Co-Evolution

Shaoxiong Yang, Mengyuan Zhang, Shaojun Lin +4

Deep search has become a fundamental capability of large language models (LLMs) for solving open-domain complex tasks. However, existing approaches typically rely on linear sequent…

cs.AI2026

Mi-Memory: A Lifecycle Memory Framework for Personal AI

Xule Liu, Hanlin Teng, Chao Li +15

Personal AI is moving beyond chat-only interaction toward continuous services that span phones, cars, homes, wearables, cameras, and tools. In this setting, memory cannot remain a…

cs.AI2026

HarnessX: A Composable, Adaptive, and Evolvable Agent Harness Foundry

Tingyang Chen, Shuo Lu, Kang Zhao +11

AI agent performance depends critically on the runtime harness, comprising the prompts, tools, memory, and control flow that mediate how a model observes, reasons, and acts. Yet to…

cs.AI2026

FSFM: A Biologically-Inspired Framework for Selective Forgetting of Agent Memory

Yingjie Gu, Wenjian Xiong, Liqiang Wang +7

For LLM agents, memory management critically impacts efficiency, quality, and security. While much research focuses on retention, selective forgetting--inspired by human cognitive…

cs.AI2026

FutureMind: Equipping Small Language Models with Strategic Thinking-Pattern Priors via Adaptive Knowledge Distillation

Shaoxiong Yang, Junting Li, Mengyuan Zhang +3

Small Language Models (SLMs) are attractive for cost-sensitive and resource-limited settings due to their efficient, low-latency inference. However, they often struggle with comple…

cs.AI2026

ICPO: Intrinsic Confidence-Driven Group Relative Preference Optimization for Efficient Reinforcement Learning

Jinpeng Wang, Chao Li, Ting Ye +3

Reinforcement Learning with Verifiable Rewards (RLVR) demonstrates significant potential in enhancing the reasoning capabilities of Large Language Models (LLMs). However, existing…