Evolutionary Algorithms

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Period ending 2026-09-21

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Evolution

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Self-Evolving Agents

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Evolutionary Search

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Self-Evolution

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Alphaevolve

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Co-Evolution

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Genetic Algorithms

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Evolving

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Multi-Agent Evolution

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807 papers

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Overcoming State Inertia: Minimally Invasive Temporal Alignment for Evolving Contexts
Dec 3, 2025Yijun LiaoEfficient Long-Context Inference Memory-Augmented Large Language Models
ATHENA: Agentic Team for Hierarchical Evolutionary Numerical Algorithms
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Computing Evolutionarily Stable Strategies in Multiplayer Games
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Nov 20, 2025Peng Xia, Kaide Zeng, Jiaqi Liu +5Self-Evolving Agents
Feature weighting for data analysis via evolutionary simulation
Nov 9, 2025Aris Daniilidis, Alberto Domínguez Corella, Philipp WissgottMulti-Objective Optimization Weighted
Evolution Strategies at Scale: LLM Fine-Tuning Beyond Reinforcement Learning
Sep 29, 2025Xin Qiu, Yulu Gan, Conor F. Hayes +6Large Language Model Fine-Tuning Evolutionary Search
A Systematic Survey on Large Language Models for Evolutionary Optimization: From Modeling to Solving
Sep 10, 2025Yisong Zhang, Ran Cheng, Guoxing Yi +1Evolutionary Search Llm-Based Optimization Modeling
Illuminating the Three Dogmas of Reinforcement Learning under Evolutionary Light
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GeLaCo: An Evolutionary Approach to Layer Compression
Jul 14, 2025David Ponce, Thierry Etchegoyhen, Javier Del SerLarge Language Model Compression Context Compression
ESSA: Evolutionary Strategies for Scalable Alignment
Jul 6, 2025Daria Korotyshova, Boris Shaposhnikov, Alexey Malakhov +7Large Language Model Alignment Large Models
Consensus-based optimization for closed-box adversarial attacks and a connection to evolution strategies
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Serious Games: Human-AI Interaction, Evolution, and Coevolution
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Asynchronous Federated Continual Segmentation with Evolving Clients and Label Spaces
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( $θ_l, θ_u$ )-Parametric Multi-Task Optimization: Joint Search in Solution and Infinite Task Spaces
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Evolutionary Guided Decoding: Iterative Value Refinement for LLMs
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Investigating Hyperparameter Optimization and Transferability for ES-HyperNEAT: A TPE Approach
Date pendingRomain Claret, Michael O'Neill, Paul Cotofrei +1Hyperparameter Optimization Evolutionary Search
Dynamic Multi-Agent Pickup and Delivery in Robotic Cellular Warehousing Systems
Date pendingCheng Ren, Ming Li, Xinping Guan +1Multi-Robot Systems Warehouse Automation
Self-Evolving Embodied Agents via Skill-Harness Evolution
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Dec 3, 2025 · cs.CLJ/K move · Enter open · S save

Overcoming State Inertia: Minimally Invasive Temporal Alignment for Evolving Contexts

Yijun Liao

Long-context dialogue systems suffer from state inertia, where models over-attend to history and fail to adapt to evolving intents. We demonstrate that standard alignment methods like DPO and even recent long-context optimization techniques struggle to resolve this without incurring a severe contextual alignment tax--a substantial perplexity surge caused by disrupting pre-trained priors. To address this, we propose DZ-TiDPO, a minimally invasive framework that synergizes conflict-aware optimization (during training) with a structural temporal attention bias. This design effectively decouples state updating from general linguistic modeling. Experiments on Multi-Session Chat and our new Inertia Challenge (IC-Bench) show DZ-TiDPO preserves structural coherence while resolving inter-turn conflicts. Crucially, our framework supports dual inference strategies: a negligible-latency static mode for general robustness and a precision-focused dynamic mode for micro-semantic conflicts. Furthermore, our scaling analysis reveals a capacity-stability trade-off, confirming that highly capable mid-sized models (7B) can efficiently internalize temporal alignment. Code and data are available at: https://github.com/lyj20071013/DZ-TiDPO.

Efficient Long-Context Inference Memory-Augmented Large Language Models Evolving

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