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Works111 from public data
- CommonGen: A Constrained Text Generation Challenge for Generative Commonsense Reasoning426
A constrained text generation task, CommonGen associated with a benchmark dataset, to explicitly test machines for the ability of generative commonsense reasoning, and demonstrates that the learned generative Commonsense reasoning capability can be transferred to improve downstream tasks such as CommonsenseQA by generating additional context.
- Memory in the Age of AI Agents288
This work aims to provide an up-to-date landscape of current agent memory research, clearly delineating the scope of agent memory and distinguishing it from related concepts such as LLM memory, retrieval augmented generation (RAG), and context engineering, and articulate a forward-looking perspective on emerging research frontiers.
- SuperGPQA: Scaling LLM Evaluation across 285 Graduate Disciplines266
This work presents SuperGPQA, a comprehensive benchmark that evaluates graduate-level knowledge and reasoning capabilities across 285 disciplines and management of a large-scale annotation process, involving over 80 expert annotators and an interactive Human-LLM collaborative system, offering valuable methodological guidance for future research initiatives of comparable scope.
- OpenMoE: An Early Effort on Open Mixture-of-Experts Language Models232
This investigation confirms that MoE-based LLMs can offer a more favorable cost-effectiveness trade-off than dense LLMs, highlighting the potential effectiveness for future LLM development and proposes potential strategies for mitigating the issues found and further improving off-the-shelf MoE LLM designs.
- BERT-of-Theseus: Compressing BERT by Progressive Module Replacing230
This paper proposes a novel model compression approach to effectively compress BERT by progressive module replacing, which outperforms existing knowledge distillation approaches on GLUE benchmark, showing a new perspective of model compression.
- Agents: An Open-source Framework for Autonomous Language Agents151
Agents is an open-source library that enables non-specialists to build, customize, test, tune, and deploy state-of-the-art autonomous language agents without much coding.
- To Repeat or Not To Repeat: Insights from Scaling LLM under Token-Crisis150
This study empirically investigates the consequences of repeating pre-training data, revealing that the model is susceptible to overfitting, leading to multi-epoch degradation, and discovers that leveraging mixture-of-experts (MoE) enables cost-effective and efficient hyper-parameter tuning for computationally intensive dense LLMs with comparable trainable parameters.
- A Comprehensive Survey on Long Context Language Modeling144
A comprehensive survey on recent advances in long-context modeling for large language models, including evaluation paradigms for long-context comprehension and long-form generation, as well as behavioral analysis and mechanism interpretability of LCLMs.
- Controlled Text Generation with Natural Language Instructions139
This work presents InstructCTG, a controlled text generation framework that incorporates different constraints by conditioning on natural language descriptions and demonstrations of the constraints, and is more flexible to different constraint types than existing search-based or score-based methods.
- BERT Learns to Teach: Knowledge Distillation with Meta Learning122
Experiments show that MetaDistil can yield significant improvements compared with traditional KD algorithms and is less sensitive to the choice of different student capacity and hyperparameters, facilitating the use of KD on different tasks and models.
- AgenTracer: Who Is Inducing Failure in the LLM Agentic Systems?119
This work proposes AgenTracer, the first automated framework for annotating failed multi-agent trajectories via counterfactual replay and programmed fault injection, and develops AgenTracer-8B, a lightweight failure tracer trained with multi-granular reinforcement learning, capable of efficiently diagnosing errors in verbose multi-agent interactions.
- How Many Unicorns Are in This Image? A Safety Evaluation Benchmark for Vision LLMs116
This work introduces a comprehensive safety evaluation suite, covering both out-of-distribution (OOD) generalization and adversarial robustness, and proposes a straightforward attack strategy for misleading VLLMs to produce visual-unrelated responses.
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- BERT-based Lexical Substitution114
This work proposes an end-to-end BERT-based lexical substitution approach which can propose and validate substitute candidates without using any annotated data or manually curated resources and achieves the state-of-the-art results in both LS07 and LS14 benchmarks.
- RecurrentGPT: Interactive Generation of (Arbitrarily) Long Text107
RecurrentGPT is a language-based simulacrum of the recurrence mechanism in RNNs that uses natural language to simulate the Long Short-Term Memory mechanism in an LSTM and demonstrates the utility of borrowing ideas from popular model designs in cognitive science and deep learning for prompting LLMs.
- AutoAct: Automatic Agent Learning from Scratch for QA via Self-Planning102
AutoAct is introduced, an automatic agent learning framework for QA that does not rely on large-scale annotated data and synthetic planning trajectories from closed-source models and leverages a division-of-labor strategy to automatically differentiate based on the target task information and synthesized trajectories, producing a sub-agent group to complete the task.
- Risks of AI scientists: prioritizing safeguarding over autonomy98
The authors examine the vulnerabilities of AI scientists, propose a risk taxonomy based on user intent and impact domains, and develop a triadic safeguarding framework emphasizing human regulation, agent alignment, and environmental feedback understanding.
- Agent KB: Leveraging Cross-Domain Experience for Agentic Problem Solving92
AGENT KB is introduced, a universal memory infrastructure enabling seamless experience sharing across heterogeneous agent frameworks without retraining and establishes the foundation for collective agent intelligence through shared memory infrastructures.
- MAP-Neo: Highly Capable and Transparent Bilingual Large Language Model Series89
The MAP-Neo is the first fully open-sourced bilingual LLM with comparable performance compared to existing state-of-the-art LLMs, and is hoped to enhance and strengthen the open research community and inspire more innovations and creativities to facilitate the further improvements of LLMs.
- A Comparative Study on Reasoning Patterns of OpenAI's o1 Model88
This work compares o1 with existing Test-time Compute methods by using OpenAI's GPT-4o as a backbone on general reasoning benchmarks in three domains, and shows that the o1 model has achieved the best performance on most datasets.
- MemEvolve: Meta-Evolution of Agent Memory Systems85
This work proposes MemEvolve, a meta-evolutionary framework that jointly evolves agents'experiential knowledge and their memory architecture, allowing agent systems not only to accumulate experience but also to progressively refine how they learn from it.
- AutoKaggle: A Multi-Agent Framework for Autonomous Data Science Competitions85
This work proposes AutoKaggle, a powerful and user-centric framework that assists data scientists in completing daily data pipelines through a collaborative multi-agent system that implements an iterative development process that combines code execution, debugging, and comprehensive unit testing to ensure code correctness and logic consistency.
- X2-VLM: All-in-One Pre-Trained Model for Vision-Language Tasks75
X-VLM is presented, an all-in-one model with a flexible modular architecture, in which it is shown that the modular design of X-VLM outperforms state-of-the-art multilingual multi-modal pre-trained models without any multilingual pre-training.
- Scaling Test-time Compute for LLM Agents61
This is the first systematic exploration of applying test-time scaling methods to language agents and investigates the extent to which it improves their effectiveness, and finds that among different verification and result merging approaches, the list-wise method performs best.
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Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-12. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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