This study introduces a multi-turn multi-agent dialogue framework to evaluate VLMs in spatial reasoning, showing limited improvements mainly due to visual grounding challenges.

Chalamalasetti Kranti, Sherzod Hakimov, David Schlangen

Introduced RHELM benchmark with multi-source heterogeneous data and dynamic user profiles to evaluate long-term memory in dialogue models.

Han Zhang, Zihao Tang, Xin Yu et al.

COFT employs counterfactual causal intervention with distribution-free marginal guarantees, reducing bias by 30-55% without retraining.

Arya Fayyazi, Mehdi Kamal, Massoud Pedram

LLMSurgeon formulates data mixture diagnosis as a label-shift inverse problem, achieving 94.46% accuracy on the LLMSurgeon benchmark.

Yaxin Luo, Jiacheng Cui, Xiaohan Zhao et al.

Proposes COMPOSE, a dual-graph framework combining citation and formal theorem graphs, generating plausible future theorems with 108K training pairs and 47K future papers tested.

David Busbib, Michael Werman

Proposes MedCase-Structured, a pipeline combining LLMs and terminology validation to generate HL7 FHIR R4 clinical datasets for diagnostic reasoning, with an 82.5% success rate.

Valentina Bui Muti, Eugénie Dulout, Ziquan Fu

Proposed dual-path block architecture enhances compute and capacity in LLMs at fixed FLOPs.

Markus Frey, Behzad Shomali, Joachim Koehler et al.

DirectorBench uses personalized multi-agent diagnosis and finds cross-shot transitions average only 0.256.

Jiamin Chen, Qianben Chen, Jiawen Zhang et al.

Proposed a causal intervention method for continuous variables, applied to verb bias, validating its role in in-context learning.

Zhenghao Herbert Zhou, R. Thomas McCoy, Robert Frank

Introduces PLANAHEAD framework, systematically evaluating natural language plan representations' impact on multimodal LLM web agent performance.

Alejandra Zambrano, Sara Vera Marjanovic, Imene Kerboua et al.

UKA introduces user-aware active knowledge acquisition using ToM uncertainty, boosting emotional support dialogue quality.

Mufan Xu, Kehai Chen, Jiahao Hu et al.

Proposes Draft-OPD, combining target supervision and error replay, achieving 5× speedup in large language model inference.

Haodi Lei, Yafu Li, Haoran Zhang et al.

STAMP framework trains explicit memory for mobile GUI agents using controllable virtual environments, enhancing Memory-World benchmark performance.

Junyang Wang, Haiyang Xu, Xi Zhang et al.

Proposes Bidirectional Evolutionary Search (BES), combining forward candidate evolution with backward goal decomposition to enhance exploration and verification in language models.

Guowei Xu, Zhenting Qi, Huangyuan Su et al.

OmniVerifier-M1 employs symbolic bounding boxes and decoupled reinforcement learning to enhance visual verification accuracy, achieving 0.68 on ViVerBench.

Xinchen Zhang, Bowei Liu, Jiale Liu et al.

Proposes CAPO, a cross-annotator preference optimization method, enabling LLMs to learn and reproduce stable individual explanation behaviors, outperforming prompting and SFT.

Beiduo Chen, Pingjun Hong, Ziyun Zhang et al.

FluxMem models memory as a dynamically evolving heterogeneous graph with three stages, achieving state-of-the-art results in complex reasoning and web navigation tasks.

Jizhan Fang, Buqiang Xu, Zhixian Wang et al.

GUI-CIDER enhances GUI agent training via causal internalization and density-aware exemplar reselection, boosting task success by 9.70%.

Zheng Wu, Chengcheng Han, Zhengxi Lu et al.

AI research agents generate more concentrated scientific ideas, failing to significantly broaden scientific exploration.

Yixuan Tang, Yi Yang

MATCHA employs contrastive learning to improve semantic matching, achieving over 18% better accuracy than ROUGE-L.

Siran Li, Ece Sena Etoglu, Carsten Eickhoff et al.