CrowdCC is a benchmark that jointly evaluates corruption robustness and deployment quantization for crowd‑counting models on edge devices. It introduces 23 deployment‑motivated corruptions at three severity levels, tests 26 models across four datasets, and measures performance in FP32, FP16, and INT8 precision on A100 TensorRT engines and Jetson Orin NX hardware. The study finds that capture‑chain corruptions cause the greatest degradation, INT8 quantization alters robustness unpredictably, and that model selection must consider both corruption robustness and deployment cost together.
By S Mahmudul Hasan, Lu Peng
RoboAuto is a framework that enhances autonomous robot program search by explicitly incorporating physical understanding at the object, scene, and interaction levels. It uses a hierarchical knowledge base of executable skills and object-centric affordances, scene grounding to map knowledge to the current environment, and multi-stage planning with self-designed experiments to verify physical effects and resolve uncertainties. The approach achieves higher success rates than prior methods on multiple benchmark tasks and demonstrates transferability across simulators and embodiments.
By Buzhao Liu, Jianing Ye, Xiu Yuan, Yangtong Feng, Chengsong Huang, Tyler Dinh, Yevgeniy Vorobeychik, Ning Zhang, Chongjie Zhang
The paper introduces a unified framework for self‑driving laboratories (SDLs) that integrates experimental planning, automated execution, and measurement feedback. It comprises four components: LAM, a measurement‑conditioned policy that generates robot trajectories and typed instrument API calls; OpenSDL, an execution substrate that maps operations to heterogeneous devices and records causal events; Safe‑SDL, a runtime gate that performs pre‑dispatch checks and monitors execution with signal temporal logic; and L3‑Bench, a benchmark protocol evaluating physical outcomes, timing, reproducibility, safety, and fault recovery. Together, these elements aim to address coordination and traceability challenges in SDL workflows.
By Zihan Zhang, Zeyu Wu, Xin Zhang, Mingyu Luo, Haohui Que, Tong Zhu
The study investigates whether adding specialist language‑model contexts can enhance software assurance when deterministic tool evidence is already available. Five experiments were conducted using Qwen2.5‑Coder‑7B‑Instruct across Java warning controls, OWASP Benchmark, Defects4J test generation, SV‑COMP assertion verdicts, and misleading evidence packages. Results show that increasing context count does not improve unsafe clearance, that verifier feedback can raise correct verdicts but also incorrect ones, and that models reject most counterexamples while scope checks classify packages accurately, supporting deterministic evidence admission but highlighting limits in verdict aggregation and feedback interpretation.
By S M Abdulla Al Mamun, Akif Islam, Mohd. Ruhul Ameen
CoS‑VQ is a new vector‑quantization technique for large language models that keeps codewords low‑dimensional while jointly optimizing their hard indices and a shared scale across positions. By collecting candidates at multiple probe scales and using a tangent separable upper bound, it reduces candidate comparisons to a small set of representative intervals, enabling efficient joint index–scale updates without exhaustive enumeration. Experiments on Qwen3.8‑27B show that CoS‑VQ outperforms existing methods such as QTIP and LLVQ on language‑modeling, coding, and reasoning benchmarks, and ablations demonstrate that joint updates yield larger gains than scale‑only refinement.
By Zukang Xu, Zhixiong Zhao, Xing Hu, Zhe Jiang, Xin Si, Dawei Yang
The paper introduces SCHEMA, a topology‑aware evaluation framework designed to assess hallucinations in large language model agents used for scientific research. SCHEMA builds scientific concept graphs from benchmark seeds and literature evidence, then generates graph‑grounded tasks such as claim verification, multi‑hop reasoning, open‑ended explanation, and experimental code generation. It evaluates agents using a trajectory hallucination pipeline that assigns a topology‑weighted severity score and a multi‑agent counterfactual attribution module that identifies the causal mechanisms behind failures. The study finds that hallucinations tend to cluster around highly connected knowledge hubs and that a model’s final accuracy can be misleading, as correct answers may arise from structurally flawed reasoning.
"whyItMatters":"The findings highlight that terminal accuracy alone is insufficient for high‑stakes scientific applications, underscoring the need for mechanism‑level evaluation grounded in knowledge topology."
By Xinshun Feng, Ziqi Miao, Lijun Li, Jing Shao
The paper introduces LAnR (Latent Abstraction for Retrieval-Augmented Generation), a unified framework where a single large language model jointly encodes, retrieves, and generates within its latent space. Instead of generating natural language queries, LAnR produces dense retrieval vectors from a designated [PRED] token and matches them against encoded document representations from the same model. An adaptive MLP control head decides when sufficient evidence has been retrieved, eliminating the need for a separate retriever and explicit token-level stopping reasoning. Experiments on six QA benchmarks show that LAnR outperforms existing RAG methods while improving inference efficiency by reducing retrieval calls and tightening model integration.
By Ha Lan N. T, Minh-Anh Nguyen, Dung D. Le
arXiv:2609. 32499v2 Announce Type: replace-cross Abstract: Continually adapting large language models requires acquiring new knowledge while preserving previously learned capabilities.
By Rongguang Ye, Zhan Zhuang, Yichen Wu, Ming Tang, Kede Ma
The study presents a multimodal machine‑learning framework that combines genomic sequences, epidemiological indicators, wastewater surveillance, and clinical rash images to detect measles and infer transmission pathways. In a three‑stage pipeline, the authors achieved high accuracy in classifying transmission linkage (AUC 0.924, F1 0.912), moderate success in predicting outbreak risk (AUC 0.707), and near‑perfect recall for aligning genomic and imaging embeddings (Recall@1 0.985). The results highlight the complementary strengths of genomic data for linkage prediction and wastewater data for outbreak‑risk forecasting.
By Jiada Li
BE-CTDE introduces a blockchain‑enabled framework for multi‑agent reinforcement learning that couples behavioral verification with a token‑based incentive system. The approach uses a verifier to compare executed trajectories against shared‑policy recommendations, and the resulting flags drive token mechanisms such as slashing, burning, rewards, and compensation. Experiments on multi‑agent path‑finding show that BE‑CTDE achieves higher success rates and lower collision rates than state‑of‑the‑art baselines, even when adversaries dominate the population.
By Chih-Wei Huang, Hsin-Chieh Lee, Yun-Chen Yu, Ibrahim Althamary, Rongxing Lu, Phone Lin
Bastion is a budget‑aware speculative decoding framework that uses tree‑structured block diffusion drafting to improve the efficiency of language model inference. It dynamically builds query‑dependent trees, balancing draft quality with hardware constraints, and incorporates an acceptance surrogate, an online latency estimator, and an adaptive best‑first expansion to maximize speed without sacrificing model distribution. The method achieves up to a 6.61× speedup over standard autoregressive decoding and outperforms state‑of‑the‑art block‑diffusion baselines by 39% across various benchmarks and GPU architectures.
By Soowon Oh, Nam Cao, Yujin Kim, Hojung Jung, Huzama Ahmad, Sangmin Bae, Se-Young Yun
The paper introduces gated graph attention and a learnable temperature to enhance standard graph attention networks. Gating filters out unreliable feature dimensions, while the temperature parameter adjusts the sharpness of attention coefficient distributions. Experiments on node classification and heterophilic benchmarks show that these modifications improve performance and robustness, with theoretical analysis supporting the observed gains.
By Zhongtian Ma, Hao Wu, Yexin Zhang, Qiaosheng Zhang, Chao Gao
The paper introduces Latent Visual Contrastive Reasoning, a branch-aware latent reasoning framework that generates multiple question-conditioned latent visual branches as soft hypotheses over image tokens. It trains these branches using bounding‑box evidence annotations and a soft contrastive branch‑evidence alignment objective, encouraging evidence‑relevant branches to align with annotated visual regions while contrasting against negative evidence. Experiments on perception‑intensive visual reasoning benchmarks demonstrate that the method improves performance over the corresponding backbone and single‑path latent reasoning baselines at both 3B and 7B scales.
By Kegeng Tang, Jingbo Wang, Yangming Zhang, Huanshu Zhang, Shaogang Ren, Zihao Wang
The paper investigates how agentic life‑science benchmarks assess reasoning over evidence by independently varying the identity of a named entity and the evidence provided. Experiments on a clinical‑outcome benchmark and tasks from Biomni and LAB‑Bench show that blinding the identity has little effect when the prompt contains decisive evidence, but dramatically reduces performance when the prompt is mainly a name and candidate list. Across 96 LAB‑Bench LitQA2 questions, the performance gap widens under a fixed passage‑redaction scheme, and a no‑evidence recall probe on the clinical benchmark matches prediction accuracy and recovers a database‑specific programme count.
By Danai Brilli, Maria Georgila
The paper proposes an explicit pixel‑level regional representation for video quality assessment called a Quality‑Relevance Heatmap (QRH), which fuses localized degradation evidence with perceptual priors guided by a vision‑language model, all without pixel‑level annotations. Two deterministic fragment‑sampling strategies are introduced: distribution‑preserving sampling approximates the original QRH‑score distribution without retraining, while quality‑aware sampling focuses on high‑QRH regions and is used with fine‑tuning. Experiments on the DOVER model across multiple benchmarks show that distribution‑preserving sampling closely matches predictions from random sampling, and quality‑aware sampling improves most evaluated metrics, demonstrating the utility of explicit regional representations for downstream VQA tasks.
By Fuyi Yang, Lei Yang, Andrew Liu, Chenyu Wei, Xiaoyi Guo, Ming Kai Hsu
HoloRecon proposes a panoramic reconstruction framework that merges ORB-based sparse mapping with a fine-tuned feed-forward model to achieve accurate localization and globally consistent dense mapping over long sequences. By using predicted local geometry for 3D verification of feature matches, the method establishes long-range camera constraints even between distant revisits, calibrates depths with sparse landmarks, and optimizes a global pose graph. The authors also introduce PanoRecon, a seven-scene benchmark for evaluating global consistency, and report that HoloRecon achieves the lowest mean absolute trajectory error and high F‑1 Score among evaluated methods.
By Zhuoxiao Li, Xinyi Liu, Hongye Hou, Jing OU, Kang Du, Shuai Zhang, Dongli Wu, Taoyu Wu, Siyuan Feng, Xiaohui Zhu, Wufan Zhao
SplatScout is a new query‑driven, closed‑loop approach for part‑level referring segmentation in Gaussian scenes, placing perception under vision‑language model control. It actively selects views, directs segmentation, and refines predictions by inspecting rendered 3D results. On the newly introduced PartRef‑ScanNet++ benchmark, SplatScout achieves 0.538 mIoU, outperforming existing methods and demonstrating the benefits of iterative refinement and effective tool‑feedback utilization.
By Yihan Zang, Da Li, Shinkyu Park, Ivan Viola, Dominik Engel
FastBEVFusion is a lightweight fusion system for real‑time 3D object detection that combines LiDAR and camera data without depth estimation. It projects modality‑specific features into semi‑aligned BEV spaces and fuses them with a dynamic cross‑attention module, achieving over 50 FPS on the nuScenes dataset. The method also demonstrates strong robustness to calibration errors.
By Tom Sanitz, Emre G\"ulsoylu, Simone Frintrop, Christian Wilms
The paper presents an incremental Learner Digital Twin that uses a lightweight knowledge graph to provide evidence‑grounded early warning in learning analytics. The twin is updated at temporal checkpoints by replaying learning traces and assessment records, enabling both feature extraction and explanation grounding. Evaluated on a public benchmark, the graph‑mediated approach preserves predictive performance while generating alerts that link risk decisions to specific evidence items and raw records.
By Ngoc Luyen Le, Marie-H\'el\`ene Abel, Bertrand Laforge
The paper introduces a clipped barrier policy gradient method that incorporates a convex regularizer to manage failure risk in budget-constrained operations. By adding a barrier that grows as the budget depletes and clipping rewards below a threshold, the approach ensures that the learned policy maximizes utility while keeping risk within a bounded margin. The authors prove exponential convergence of the training dynamics under convex regularization and demonstrate the method on several benchmark tasks.
By Pekka Malo, Lauri Viitasaari, Antti Suominen, Eeva Vilkkumaa, Olli Tahvonen