The paper compares two leading motion‑imitation reinforcement learning pipelines—HyFyDy, which uses detailed musculotendon modeling, and MuJoCo, which focuses on computational speed. Using the same human motion‑capture and EMG data, both pipelines reproduce kinematics similarly, but HyFyDy’s muscle activation predictions align more closely with experimental EMG (RMSE 0.164, r = 0.4) than MuJoCo’s (RMSE 0.344, r = 0.11). The authors conclude that HyFyDy’s higher physiological realism makes it currently more suitable for musculoskeletal modeling, though both systems need further development for GPU‑parallelizable environments and robotic assistive‑device design.
By Ayah G. Ahmad, Claire E. Borden, Maegan Tucker
SpecQuant is a training‑free framework that merges speculative decoding with multi‑parent quantization to enable adaptive, efficient inference of large language models. It generates several quantized variants (INT4, FP8, FP16) from a single base model and routes queries to the appropriate variant based on predicted complexity, using lightweight models for simple tasks and full‑precision models for complex reasoning. Evaluations on Qwen2.5 models across MMLU, AlpacaEval, and GSM8K show 35‑43% speedups with less than 2% accuracy loss, facilitating practical on‑device LLM deployment without specialized infrastructure.
By Harish KB, Jagadeeswaran M, Pradheep P, Yuvanesh S, Sivakumar T
The paper argues that zero‑shot time‑series forecasting should be treated as an evidence‑access claim rather than merely a no‑parameter‑update condition. It introduces a source‑first taxonomy that distinguishes three evidence sources—frozen LLM prior reuse, parametric time‑series pretraining, and retrieval‑augmented external memory—from the architectures that implement them. The authors further outline four audit questions—task interface, forecast object and scoring, prediction‑time context, and resource budget—to make zero‑shot leaderboards transparent and comparable.
By Delun Kong, Wanyun Ling, Chenxi Liu, Ziyue Li
FairLMs is a Python library designed to streamline fairness research in language models by unifying bias measurement, mitigation, and evaluation evidence. It offers 33 intrinsic and extrinsic metrics, 14 mitigation components across four intervention categories, 14 diagnostic tools, adapters for major Transformer architectures and hosted APIs, and benchmark loaders. The library enforces explicit declarations of model capabilities and input requirements, ensuring compatibility and reproducibility across components and datasets.
By Jiale Zhang, Michael Larionov, Zichong Wang, Zhipeng Yin, Wenbin Zhang
MemeTAG introduces a dual‑objective framework for classifying harmful memes by combining keyword generation from a pretrained Vision‑Language Model with an Aggregated Tag Inference Network (ATIN) that condenses these keywords into a rich semantic embedding. The embedding is used as a target for an auxiliary reconstruction loss, encouraging deep alignment between visual and textual features. This approach, along with a three‑stage training strategy, achieves new state‑of‑the‑art results on the HarMeme, Hateful Memes Challenge, and PrideMM datasets.
By Akshit Sharma, Prashant W. Patil
Conformal Privacy Auditing (CPA) is a distribution‑free framework that calibrates re‑identification risk for each released document against large language model (LLM)‑empowered adversaries. It outputs a conformal ambiguity set of candidate identities that is guaranteed to contain the true identity with a user‑chosen confidence level under exchangeability, along with an interpretable leakage proxy derived from the set size. CPA supports both logit‑access and sampling‑only attackers, enabling audits of both open‑source and proprietary models, and demonstrates calibrated coverage across various benchmarks and attacker configurations.
By Shuo Huang, Gholamreza Haffari, Xingliang Yuan, Ting Yu, Lizhen Qu
MemoryArena is a new evaluation gym that benchmarks agent memory in interdependent multi‑session tasks. Unlike prior benchmarks that test memorization or single‑session action in isolation, MemoryArena requires agents to acquire memory while interacting with the environment and then use that memory to guide future decisions across a range of tasks such as web navigation, planning, information search, and formal reasoning. The benchmark reveals that agents excelling on existing long‑context memory tests perform poorly here, highlighting a gap in current memory evaluation methods.
By Zexue He, Yu Wang, Churan Zhi, Yuanzhe Hu, Tzu-Ping Chen, Lang Yin, Ze Chen, Tong Arthur Wu, Siru Ouyang, Zihan Wang, Jiaxin Pei, Julian McAuley, Yejin Choi, Alex Pentland
OmniVBench introduces a comprehensive benchmark and a large-scale dataset for omni reference-to-video (R2V) generation, addressing gaps in existing evaluations that focus only on limited reference types and holistic consistency. The benchmark expands evaluation across 7 task families and 18 fine-grained tasks, covering content, motion, style, structure, narrative, and multi-reference settings, and employs a factor‑grounded evaluation with 12,172 checklist items to assess preservation, disentanglement, and routing of reference factors. The accompanying Omni‑R2V Dataset provides 340K training samples derived from professional video footage, along with task‑specific pipelines for scalable data construction, enabling broader research and revealing performance gaps in current R2V models.
By Wenxue Li, Peiyan Guan, Haoyang Jiang, Junxian Cai, Hualuo Liu, Chunjie Zhang, Chong Guan, Songlian Li, Taiyi Wu, Yongjian Yu, Xiaotong Zhao, Alan Zhao, Eric Liu, Xi Chen, Yu Liu, Lei Zhu
The paper introduces an extension of the Decoupled Vision Transformer (DC‑ViT) to handle multi‑channel imaging (MCI) data, where each channel carries a distinct semantic signal. By tokenizing each channel separately and then decoupling intra‑channel from inter‑channel updates, the model preserves channel‑specific features. The authors further enable independent per‑channel masking by solving a linear assignment between retained patches, allowing masked training without restricting visible tokens. Experiments on fluorescence microscopy, imaging mass cytometry, and satellite imaging datasets demonstrate that this approach outperforms the strongest Multi‑Channel Vision Transformer baselines on both classification and dense‑prediction tasks.
By Umar Marikkar, Sameed Husain, Muhammad Awais, Sara Atito
The paper introduces SceneLM, a vision‑language model that maintains an open‑vocabulary 3D scene map using only a structured text list of objects as persistent memory. The model updates this textual map by adding, editing, and removing objects for each input image, learning the process through supervision tasks and an automatic annotation pipeline. Evaluations on language‑grounded retrieval and localization benchmarks show competitive performance with traditional mapping systems while producing a 6‑12× more compact representation, and the model can run online on an edge device such as a quadruped robot.
By Adam Lilja, Fabio H\"ubel, Siming He, Junsheng Fu, Claire Tomlin, Lars Hammarstrand, Jitendra Malik, Jonas Frey, Marco Pavone
RobotEQ-Video is a new video-centric benchmark designed to advance Social Proactive Intelligence (SPI) by moving beyond static image analysis. It introduces a hierarchical world-state taxonomy with 6 domains, 20 dimensions, 142 level‑1 attributes, and 816 level‑2 attributes, and includes over 2,000 videos annotated with 100,000+ human labels and 16,000+ behavior‑properness tags. Evaluation shows existing systems underperform humans, highlighting the need for richer video data and comprehensive scenario coverage in SPI research.
By Xinyi Che, Zheng Lian, Kuofei Fang, Xuehao Wang, Xinghai Gao, Junqing Wu, Chuyu Wu, Liyi Liu, Yanhan Huang, Keyi Xie, Haomin Ouyang, Jinyang Wu, Fan Zhang, Runhao Zeng, Xun Yang, Bin He
TatBLiMP is the first benchmark of linguistic minimal pairs for the Tatar language, covering 16 morphosyntactic phenomena across 1,248 sentence pairs that differ by a single morpheme. Each pair contains one grammatical and one ungrammatical sentence, with the ungrammatical version generated by a deterministic perturbation and ratified by a native speaker. The benchmark evaluates models by comparing their assigned probabilities, allowing assessment without text generation or parsing, and tracks performance across from-scratch, cross‑lingual, and multilingual large language models.
By Ilshat Saetov, Dmitry Gaynullin
The paper investigates how Large Language Models can help detect misconfigurations in Kubernetes, the dominant platform for orchestrating containerized applications in cloud‑native environments. It presents a taxonomy of common misconfiguration types, evaluates existing detection tools, and analyzes which Kubernetes objects are most vulnerable and how severe the issues can be. The study demonstrates that advanced machine learning, particularly LLMs, can offer new insights and improve the effectiveness of misconfiguration detection methods.
By Mostafa Anouar Ghorab, Mohamed Aymen Saied
The paper shows that multi‑hop retrieval failures cluster in predictable subpopulations and formalizes this with two theoretical results: (1) confident‑failure reduction is possible only when retrieval features carry mutual information about success, and (2) no single ANN score feature dominates across all failure regimes. Building on these insights, the authors introduce RegimeAbstain, which computes a Retrieval Confidence Score (RCS) from up to nine query‑ANN structural features and uses it to calibrate an abstention policy. Across three benchmarks and two retrieval architectures, RCS achieves the best or co‑best AUC‑AC and significantly reduces the Confident‑Wrong‑Answer Rate, demonstrating its effectiveness and domain‑agnostic applicability.
By Andre Bacellar
The paper introduces Omni Demand Understanding (ODU), a benchmark designed to test whether multimodal models can infer a user's underlying demand from complex audio‑visual interactions. ODU requires models to detect the presence of a demand and infer intent using multimodal and conversational context, evaluated across single‑turn and multi‑turn scenarios. The authors built ODU‑Bench through a taxonomy‑guided approach, agentic video generation, and human‑recorded interactions, and found that even top models like Gemini 3.1 Pro recover only 44.7% of key information, with many models exhibiting high false‑trigger rates.
By Qi Chen, Yunfei Chu, Haolin He, Yifan Yang, Zihan Liu, Yuxuan Wang, Ziyang Ma, Ruiyang Xu, Meng Gao, Yinsong Yan, Ling Wang, Hui Wang, Wen Huang, Yiheng Chen, Guanrou Yang, Qiuqiang Kong, Jin Xu, Xie Chen
The paper investigates gender bias in machine translation evaluation metrics using an occupation-balanced subset of GAMBIT+ across seven English‑source language pairs, including a new German extension. It finds that masculine translations tend to receive higher scores and that biases align with stereotypical gender representations, though the strength varies by evaluator and language. The study highlights that assessing bias requires multiple dimensions beyond a single aggregate measure.
By Orfeas Menis Mastromichalakis, Giorgos Filandrianos, Wafaa Mohammed, Giuseppe Attanasio, Chrysoula Zerva
MIRAGE is a new inference-time framework that enhances large language models by using a Selector to choose effective conceptual perspectives and a Reasoner to solve tasks step-by-step, aggregating multiple perspectives when needed. It is inspired by human cognitive flexibility and is designed to improve performance on complex mathematical, scientific, and logical problems. Experiments on GSM8K, MATH500, MMLU-Pro, and Game-of-24 show that MIRAGE outperforms Chain-of-Thought and diverse prompting ensembles, boosting accuracy with minimal inference overhead.
By Arash Lagzian, Srinivas Anumasa, Dianbo Liu
The paper evaluates pre‑trained speech embeddings from four state‑of‑the‑art speech foundation models for cross‑lingual Parkinson's disease severity assessment. Experiments span three datasets in zero‑shot and k‑shot settings, showing that these embeddings can transfer meaningfully across languages, though performance varies with dataset characteristics, preprocessing, and adaptation strategy. Misclassifications linked to inter‑speaker variability and atypical speech patterns underscore the need for more robust feature extraction, modeling, and explainability to support reliable clinical insights.
By Simon Pals, Cristian Tejedor-Garcia
The paper presents Sometin Beta Pass Notin (SBPN), a multilingual ASR framework for Nigerian languages that uses a two‑stage knowledge‑distillation approach. First, student‑teacher distillation from monolingual models is conditioned on language‑specific N‑gram language models; second, iterative self‑improvement with pseudo‑labelled data further refines accuracy. The method reduces relative WER by 29% over monolingual baselines and outperforms state‑of‑the‑art multilingual models on Common Voice and FLEURS benchmarks for Yoruba, Hausa, Igbo, Nigerian Pidgin, and Nigerian English.
By Sewade Ogun
The paper introduces Adaptive World Memory 3D Foundation Model (AWM-3DFM), a memory‑centric 3D foundation model that scales to large‑scale robotic localization, reconstruction, and Gaussian rendering. It employs transformer‑based gated updates, test‑time temporal‑spatial regulation, and local submap organization to maintain persistent memory, accuracy, and consistency across long image sequences. A Gaussian reconstruction head unifies pose estimation, dense point‑cloud reconstruction, and photorealistic rendering, achieving superior trajectory accuracy, reconstruction completeness, and rendering quality on public benchmarks and diverse robotic datasets.
By Tianchen Deng, Guole Shen, Yilin Shen, Wenhua Wu, Yilin Fang, Ziqi Ma, Tianjun Zhang, Shenghai Yuan, Wolfram Burgard, Hesheng Wang