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Research

Time-Division Multiplexing Layer for Continual Agents

The paper proposes a temporal organization layer for continual agents using phase-tagged micro-epochs and nested clocks to manage urgent interactions and long-term commitments. It provides a projected duty-cycle update with deterministic and stochastic guarantees, and specifies coupled busy/idle execution contracts. Synthetic experiments test descent, noise, misalignment, gain asymmetry, resource coupling, and dropped replay logs, with a frozen-backbone Qwen3-8B prototype for orchestration checks.

arxiv1d agoScore 68

continual learningagent orchestrationtemporal organization

Research Editor selected

Continual Prior Compensation for Human Motion Prediction

This paper introduces Continual Prior Compensation (CPC), a temporal continual learning framework for human motion prediction that trains models progressively across short- and long-term prediction subtasks to avoid interference. It adds a learnable Prior Compensation Factor (PCF) to mitigate prior knowledge forgetting, with a theoretical bound for joint optimization)Skip. An enhanced version, CPC++, uses fine-grained factors per subtask. Experiments on three benchmarks with multiple backbones show improved flexibility and effectiveness.

Why it matters: Provides a novel continual learning method with theoretical grounding and broad backbone compatibility, offering practical gains for HMP.

arxiv1d agoScore 82

Human Motion PredictionContinual LearningTemporal Modeling

Research

Study Finds Coding Agents Forget Previous Tasks During Continual Harness Evolution

A new empirical study investigates whether coding agent harnesses can evolve continuously across multiple tasks without catastrophic forgetting. Using six evolution directions across three software engineering datasetshare, the authors show that evolving on a new dataset can severely degrade performance on previously learned tasks—e.g., Meta-Harness drops from 94.0% to 32.0% on SWE-Bench after five rounds of further evolution. The paper provides insights for designing continual self-evolution methods for agent harnesses.

arxiv3d agoScore 78

agent self-evolutioncontinual learningcoding agents

Research Editor selected

OLGA: Online Label-Free Gradient Matching for Continual Language Model Training

OLGA is a new method for continual language model training that distills unlabeled data by using the model's own next-token predictions as gradient matching targets, removing the need for ground-truth labels. It improves accuracy by 10 percentage points over the strongest selection baseline on five text classification benchmarks with phi-1.5, using as few as five examples. In online settings, it outperforms the strongest selection-based continual baseline by 6.6 stream-AUC points, and a hybrid pipeline with selection cuts runtime by 30% while beating selection-only pipelines by up to 12.6 points.

Why it matters: This presents a concrete, novel method with clear benchmark gains and addresses practical limitations of existing continual learning approaches.

arxiv3d agoScore 82

continual learningdata distillationlanguage model training

Research

Implicit Assumptions About Tasks in Continual Learning

This paper clarifies that continual learning measurements depend on factors like data similarity, change rate, and order, which can be defined at different levels: underlying data, partition, or learner. The authors show these levels often don't coincide and are usually left implicit, leading to ambiguous results. Through teacher-student experiments, they demonstrate that changing granularity, order, or similarity structure alters measured outcomes. They also review benchmarks to see which properties are stated, noting that for foundation models with undisclosed data, properties are inherited by the model's initial state.

arxiv4d agoScore 72

continual learningevaluation methodologytask design