The paper introduces Oph‑Guid‑RAG, a multimodal retrieval‑augmented generation system tailored for ophthalmology clinical question answering. It treats each guideline page as an independent evidence unit, retrieving page images to preserve tables, flowcharts, and layout, and employs a controllable retrieval framework with routing and filtering to reduce noise. Evaluated on HealthBench, the system outperforms GPT‑5.2 and GPT‑5.4 on hard cases, achieving significant gains in overall score and accuracy, and ablation studies confirm the importance of reranking, routing, and retrieval design.
By Shuying Chen, Sen Cui, Zhong Cao
The paper introduces a digital‑twin framework that simulates opinion dynamics in real Twitter networks by assigning agents attributes such as persona, emotions, centrality, stubbornness, and influence, and using Mistral‑7B to update opinions based on memory and social exposure. Validation on COVID‑19 and U.S. election 2020 datasets shows the framework reproduces opinion trajectories, reducing prediction error by over 50% compared to classical baselines, and improves structural alignment and polarization dynamics. Ablation studies reveal that agent attributes, memory, and social exposure all contribute to predictive fidelity, with agent attributes being the most critical.
By Omran Berjawi, Giuseppe Fenza, Rida Khatoun, Sherali Zeadally
Elastic Spectral State Space Models (ES-SSM) are a train‑once, export‑many sequence modeling framework that achieves elasticity by spectrally approximating the state‑space operator. The method builds on Hankel spectral filtering, using fixed spectral channels to represent long‑range token mixing and combining input‑adaptive gates with budget dropout to enable reliable deployment across different resource budgets. ES‑SSM is evaluated on byte‑level language modeling, Long Range Arena, Speech Commands V2, and offline reinforcement learning, showing that a single trained model can be truncated to competitive compact models while maintaining smooth quality‑cost curves across a wide range of truncation levels.
By Dachuan Song, Junyu Yin, Zechen Hu, Xuan Wang
The paper introduces Poodle, a prototype for just‑in‑time model replacement (JITR) that automatically swaps a large language model with a cheaper, task‑specific model when a recurring task is detected. Poodle reduces inference time by up to 7.5× and saves over $2,200 per 1 M requests compared to a flagship hosted LLM, while maintaining competitive accuracy. The authors argue that model search and transfer learning are essential for efficiently identifying and fine‑tuning these custom models.
By Nils Strassenburg, Boris Glavic, Tilmann Rabl
The article surveys model fusion for large language models, defining it as the integration of capabilities from source models into a single target model. It categorizes prior work into three levels—parameter-level, representation-level, and behavior-level fusion—and reviews related metrics, benchmarks, and applications. The survey also outlines current challenges and future research directions, providing a comprehensive map of the field.
By Shuo Cai, Yanggan Gu, Zihao Wang, Yuanyi Wang, Yibo Yan, Wenjun Wang, Yuhang Liu, Guanghao Zhu, Sirui Huang, Ming Li, Hongxia Yang
The paper introduces CalfVO, a monocular visual odometry system that operates without camera intrinsics, test‑time optimization, bundle adjustment, or loop closure. Using a transformer, it predicts relative poses with separate rotation and translation confidences over overlapping image windows, then aggregates these predictions via a confidence‑weighted module to produce a single trajectory. CalfVO achieves the highest accuracy among calibration‑free methods across five benchmarks and runs at 53 FPS, outperforming all baselines.
By Vladimir Yugay, Duy-Kien Nguyen, Theo Gevers, Cees G. M. Snoek, Martin R. Oswald
BioPhys-Bridge is a newly released benchmark dataset designed to evaluate language models on evidence‑grounded scientific reasoning within biophysical literature. Each of its 500 cases includes evidence blocks, stable IDs, quantitative values, units, equations, assumptions, mechanisms, and next‑step decisions, covering six biological domains and nine physical model families. The dataset enforces strict quality gates and has already been evaluated against several models, with DeepSeek‑V4‑Flash achieving the highest evidence‑ID F1 score of 0.360.
By Qingyang Xu
The paper introduces a 202-scenario benchmark to evaluate how large language models (LLMs) handle safety-critical authorization decisions for vehicle voice commands. It tests two local open-weight models and three API-based LLMs, finding alignment scores ranging from 40.1% to 89.1% and noting persistent false execution errors. The study concludes that structured LLM decisions alone are insufficient for safety, recommending an independent enforcement layer to verify tool permissions and vehicle-state constraints before any vehicle function is invoked.
By Diba Afroze, Xingli Zhang, Yazhou Tu, Xiali Hei
The paper evaluates three approaches—LLM-Z3 full replanning, IPyHOPPER hierarchical repair, and iTIMO local-revision—for revising travel itineraries after disruptions such as flight cancellations or hotel unavailability. Using two TREK-derived benchmark sets (500 single-disruption cases and 200 compound-disruption cases), the study compares effectiveness, plan stability, and computational cost. Results show LLM-Z3 with Gemini achieves the highest compound-disruption success, IPyHOPPER nearly matches single-disruption success while preserving more of the original itinerary, and iTIMO makes fewer edits but consumes more tokens, offering practical guidelines for balancing feasibility recovery, commitment preservation, and computational cost.
By Xiaofei Yuan, Yan Zhang, Shaobo Qiao, Huangleshuai He, Leyan Ni, Mingchen Ju, Lujia Yang, Sijia Xu, Yifu Tang, Zhengyi Yang
WiCleanData is a refined version of Wikidata that eliminates type inconsistencies and constraint violations. The authors built an automated pipeline that cleans the taxonomy with language‑model assistance, aggregates type constraints hierarchically, and filters facts to ensure no type violations remain. The resulting knowledge graph is publicly available through a web interface for easy exploration and downstream use.
By Yiwen Peng (IP Paris), Marc Jeanmougin (IP Paris), Thomas Bonald (IP Paris)
The study compares human deliberation in Wason selection tasks with large language model (LLM) agent groups that are seeded with participants’ pre-discussion beliefs. Across various scoring definitions, human consensus rates ranged from 24.0% to 57.0%, whereas LLM agents consistently achieved higher consensus, with gaps of 34–44 percentage points in two sensitivity analyses. Even when early stopping was removed or memorizable answers were eliminated, LLM groups still reached near-unanimous agreement, often on incorrect answers, indicating that simulated consensus does not reflect collective accuracy.
By Tengfei Shao
KoNeoBench is a curated dataset designed to evaluate large language models’ understanding of Korean neologisms. It contains 1,785 recently attested Korean words from online news since 2020, each accompanied by usage examples, word‑formation analyses, and dictionary‑style definitions. The authors define four evaluation tasks, report results from recent models and a human baseline, and find that current LLMs struggle with recovering source components, distinguishing semantic categories, and generating accurate definitions.
By Soha Lee, Soojin Lee, Heesung Yang, Hyunju Song, Hyunji Lee, Jinsan An, Jeongwan Shin, Jin Hyun Park, Jun Lee, Hyeyoung Park, Kilim Nam
SCICONVBENCH is a benchmark designed to evaluate large language models (LLMs) on multi‑turn clarification tasks in computational science. It focuses on two key abilities: eliciting missing information (disambiguation) and resolving contradictory requests (inconsistency resolution) across four domains—fluid mechanics, solid mechanics, materials science, and partial differential equations. The benchmark pairs a structured task ontology with a rubric‑based evaluation framework, measuring LLM performance in clarification behavior, conversational grounding, and final‑specification fidelity, and reveals that even top models only resolve about 52.7% of disambiguation cases in fluid mechanics while often making ungrounded assumptions.
By Nithin Somasekharan, Youssef Hassan, Shiyao Lin, Gihan Panapitiya, Patrick Emami, Anurag Acharya, Sameera Horawalavithana, Shaowu Pan
The paper investigates how reinforcement learning can be effectively applied to diffusion models for visual tasks, focusing on the role of likelihood estimation. By systematically separating policy‑gradient objectives, likelihood estimators, and rollout sampling schemes, the authors find that using an evidence lower bound (ELBO) based likelihood estimator computed from the final generated sample is the key factor for stable and efficient RL optimization, outweighing the choice of loss function. Experiments on SD 3.5 Medium across multiple reward benchmarks confirm that this approach improves GenEval scores from 0.24 to 0.95 in 90 GPU hours, outperforming existing methods such as FlowGRPO and the current state‑of‑the‑art without reward hacking.
By Jaemoo Choi, Yuchen Zhu, Wei Guo, Petr Molodyk, Bo Yuan, Jinbin Bai, Yi Xin, Molei Tao, Yongxin Chen
The paper introduces TriDim, a reusable block that preserves the three EEG axes—channel, short‑term temporal, and long‑term temporal—by applying feed‑forward transformations and cross‑axis attention. Stacking these blocks yields TriDimEEG, a standalone EEG decoder that outperforms fifteen other models on eight datasets, achieving a 4.3% relative accuracy gain. Replacing Transformer blocks in existing EEG foundation models with TriDim blocks improves downstream accuracy by 7.4% on average while reducing parameters by 17.0% to 47.3%.
By Shiyue Su, Song Wang, Zekai Zhan, Junjie Zeng, Ziling Lu, Zongsheng Li, Xinyuan Ye, Zhiyuan Ma, Xinke Shen, Quanying Liu
The paper introduces a simulation-driven framework for diagnosing transformer faults using dissolved-gas analysis. It employs a variational quantum classifier (VQC) with a two-qubit representation, domain-aware feature modeling from Duval geometry, and a hybrid ZX-YY feature map to capture nonlinear gas interactions. Extensive simulation, noise-aware emulation, and limited hardware tests show high diagnostic accuracy, strong generalization, and robustness to realistic noise while using minimal quantum resources.
By Huy Hoang Le, Ba Tu Phung, Dai Huynh, Kim-Anh Nguyen
The paper introduces REVEAL, a diagnostic benchmark that stresses Video‑Language Models (VidLMs) on five controlled probes—camera‑motion sensitivity, cross‑frame integration, video sycophancy, language‑only shortcuts, and temporal expectation bias—to assess how well these models encode and use visual evidence. Experiments on 12 VidLMs reveal systematic failures: some visual signals are never reliably encoded, while others are overridden by model priors, leading to performance below chance on several probes that humans solve with high accuracy. Mechanistic probes further pinpoint where and why visual evidence is lost, demonstrating that under assertive prompts a model’s output becomes nearly invariant to real versus random video input, rendering visual evidence causally inert.
By Sethuraman T V, Savya Khosla, Aditi Tiwari, Vidya Ganesh, Rakshana Jayaprakash, Aditya Jain, Vignesh Srinivasakumar, Onkar Kishor Susladkar, Srinidhi Sunkara, Aditya Shanmugham, Rakesh Vaideeswaran, Abbaas Alif Mohamed Nishar, Simon Jenni, Rohan Maheshwari, Derek Hoiem
The paper introduces Janus, a method for validating error patterns in language models by comparing error rates across predefined yes/no properties and using shuffled decoy labels to set significance thresholds. Janus requires that a pattern’s error difference surpasses the decoy-derived threshold and is replicated on held‑out data before reporting. Experiments on a controlled code‑finding task confirm several meaningful error patterns, while on MuSiQue and LongBench v2 Janus reports no confirmed patterns for the tested properties, contrasting with standard shuffling tests that sometimes confirm patterns.
By Vyzantinos Repantis, Ameya Gawde, Harshvardhan Singh
The paper introduces UC-Bench, a human‑annotated benchmark for detecting user‑side implicit conflicts in Human‑LLM dialogue, a problem largely overlooked compared to LLM‑side conflicts. Experiments show current LLMs struggle with these conflicts, especially when they stem from implicit incompatibilities in dialogue history. To address this, the authors propose SynUC, a constraint‑guided data synthesis method that generates a new training set, UC‑Data, which improves performance of lightweight LLMs on UC‑Bench compared to larger general‑purpose models and existing synthesis approaches.
By Jinqiang Wang, Tao Zhu, Huansheng Ning
JustMem is a new memory system for long‑term conversational AI that stores conversation history as compact atomic memories and adapts its access strategy to each query. It introduces two dimensions of memory access—discovery breadth and reading fidelity—implemented through LOOKUP for local evidence, COMPOSE for distributed evidence, and REPLAY for fidelity‑sensitive evidence. Experiments on LoCoMo and LongMemEval‑S show that JustMem outperforms competing memory systems in accuracy and recall while using fewer generative‑model tokens for memory construction and inference.
By Guanhua Chen, Yanting Wang, Wenjing Zhi, Lei Sha