Proposed 3DAxiesPrompts (3DAP) embeds 3D coordinate info into images, significantly enhancing GPT-4V's performance in 3D point reconstruction, matching, and object detection.

Dingning Liu, Xiaomeng Dong, Renrui Zhang et al.

Math-Shepherd automatically constructs process supervision data, enabling step-by-step verification and reinforcement of LLMs without human annotations.

Peiyi Wang, Lei Li, Zhihong Shao et al.

SGLang system leverages RadixAttention and compressed FSM to accelerate large language model programs, achieving up to 6.4× throughput increase.

Lianmin Zheng, Liangsheng Yin, Zhiqiang Xie et al.

TaskWeaver: a code-first autonomous agent framework supporting rich data structures and dynamic plugin invocation.

Bo Qiao, Liqun Li, Xu Zhang et al.

Proposed AgentMonitor framework achieves 89.4% F1 in safe testing of AutoGPT on the open internet.

Silen Naihin, David Atkinson, Marc Green et al.

Proposes RETROFIT-CQs, leveraging large language models to automatically extract competency questions from existing ontologies, enhancing reusability.

Reham Alharbi, Valentina Tamma, Floriana Grasso et al.

Safety-Gymnasium environment suite and SafePO library enable comprehensive safe RL research with 16 algorithms across diverse tasks.

Jiaming Ji, Borong Zhang, Jiayi Zhou et al.

Empirical analysis of GPT-4’s iterative prompting on graph coloring; reveals limited self-critique and verification abilities, external verifier boosts success to 40%.

Kaya Stechly, Matthew Marquez, Subbarao Kambhampati

MemGPT employs OS-inspired hierarchical memory and function calls to extend LLM context beyond fixed windows, improving long document analysis and multi-session chat.

Charles Packer, Sarah Wooders, Kevin Lin et al.

RoboCLIP leverages pretrained video-language models to generate rewards from a single demonstration, boosting zero-shot robot task success by 2-3x.

Sumedh A Sontakke, Jesse Zhang, Sébastien M. R. Arnold et al.

Goodtriever uses retrieval-augmented methods to reduce inference latency by 43% while maintaining state-of-the-art toxicity mitigation.

Luiza Pozzobon, Beyza Ermis, Patrick Lewis et al.

This study demonstrates that large language models (LLMs) linearly encode truth/falsehood in their internal representations, validated through transfer and causal intervention experiments, especially at scale.

Samuel Marks, Max Tegmark

Corex employs multi-model collaboration (Discuss, Review, Retrieve) to significantly improve complex reasoning, outperforming existing baselines with detailed experimental validation.

Qiushi Sun, Zhangyue Yin, Xiang Li et al.

Framework for LLM-based agents enhances potential for general intelligence.

Zhiheng Xi, Wenxiang Chen, Xin Guo et al.

NExT-GPT achieves any-to-any modality input-output with multimodal adapters and diffusion decoders, tuning only 1% of parameters.

Shengqiong Wu, Hao Fei, Leigang Qu et al.

Confucius framework enhances LLM tool usage via easy-to-difficult curriculum and introspective feedback, surpassing existing baselines.

Shen Gao, Zhengliang Shi, Minghang Zhu et al.

Unified LLM autonomous agent framework integrates perception, memory, planning, and action modules, significantly improving task performance in complex environments.

Lei Wang, Chen Ma, Xueyang Feng et al.

AI-Hilbert employs polynomial optimization combining background knowledge and experimental data to automatically derive scientific laws with formal proofs.

Ryan Cory-Wright, Cristina Cornelio, Sanjeeb Dash et al.

Evaluating LLM trustworthiness through alignment techniques, finding aligned models generally perform better in trustworthiness.

Yang Liu, Yuanshun Yao, Jean-Francois Ton et al.

Proposes staged speculative decoding with tree-structured batches and two-stage prediction, achieving 3.16x speedup on 762M GPT-2 while preserving quality.

Benjamin Spector, Chris Re