13 stories tagged with #continual, in publish-time order across the WeSearch catalog. Tag pages update as new stories ingest.
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OpenEvoShield: Dual Non-Stationary Continual Defense for Open-World Multi-Agent System Attacks
arXiv:2607.19351v1 Announce Type: new Abstract: LLM-based multi-agent systems (LLM-MAS) are increasingly deployed in safety-critical applications, where adversaries inject maliciou…
HERO: A Heterogeneity-Aware Benchmark Library for Federated Continual Learning
Federated continual learning (FCL) evaluates how distributed clients learn from changing data streams while retaining previously learned knowledge. Existing evaluations are difficu…
Multi-Lora-Continual-Learning
Research lab and product company building the platform for continual learning.…
Trajectory, founded by ex-DeepMind, Apple, and OpenAI staff to train "continual learning" models on user interactions, raised a $15M seed at a $115M valuation (Maxwell Zeff/Wired)
ICCU: In-Context Continual Unlearning via Pattern-Induced Refusal Rules
Machine unlearning aims to remove the influence of specific data from trained language models. In real-world deployments, unlearning requests often arrive sequentially, which chall…
Continual Speaker Identity Unlearning with Minimal Interference
Machine unlearning removes designated concepts or knowledge from pre-trained models. Recent work has extended this paradigm to speaker identity unlearning in zero-shot text-to-spee…
DRIVE: Modeling Skills at the Reasoning and Interaction Levels for Web Agents under Continual Learning
Web agents require both high-level reasoning (for task decomposition) and low-level interactions (for page elements manipulation) to conduct different tasks. However, these knowled…
Continual Harness: A reset-free self-improving harness for embodied agents
A reset-free self-improving harness for embodied agents. Pokémon Red, Emerald, and the Gemini Plays Pokémon experiments.…
SOLAR: A Self-Optimizing Open-Ended Autonomous Agent for Lifelong Learning and Continual Adaptation
Despite the remarkable success of large language models (LLMs), they still face bottlenecks while deploying in dynamic, real-world settings with primary challenges being concept dr…
CP-MoE: Consistency-Preserving Mixture-of-Experts for Continual Learning
Catastrophic forgetting remains a major obstacle to continual learning in large language models (LLMs) and vision--language models (VLMs). Although Mixture-of-Experts (MoE) archite…
Tunable MAGMAX: Preference-Aware Model Merging for Continual Learning
Continual learning (CL) aims to train models sequentially on multiple tasks while mitigating catastrophic forgetting of previously learned knowledge. Recent advances in large pre-t…
Shapley Neuron Values for Continual Learning: Which Neurons Matter Most?
Continual learning enables neural networks to learn tasks sequentially without forgetting previously acquired knowledge. However, neural networks suffer from catastrophic forgettin…
Self-Distillation Enables Continual Learning [PDF]
Continual learning, enabling models to acquire new skills and knowledge without degrading existing capabilities, remains a fundamental challenge for foundation models. While on-pol…