The paper investigates how modern selective state space models (SSMs), like Mamba2, behave in distributed learning settings. It derives architecture‑aware gradient and smoothness bounds for single‑ and multi‑layer selective SSMs, and provides convergence bounds for FedAvg and FedProx that incorporate recurrent stability, input‑dependent discretization, and state‑projection norms. Numerical experiments validate the theoretical bounds and compare nine federated learning algorithms on Mamba2 language modeling across six text domains, demonstrating that SSM‑specific bounds help interpret practical federated learning behavior.
By Adam Piaseczny, Md Kamran Chowdhury Shisher, Shiqiang Wang, Christopher G. Brinton
The paper introduces PROVE-REC, a two‑pass framework that generates verifiable preference proofs for large language model (LLM) recommendation systems. In Pass A, the model condenses user interaction histories into a compact proof of positive and avoidance claims linked to specific evidence entries. Pass B then uses only this proof and its evidence to predict the next item, ensuring the recommendation follows the reasoning path. Verification steps compare masked evidence and removed claims to confirm grounding and influence, while a ranking‑preservation objective retains useful historical information. Experiments on diverse real‑world datasets show PROVE‑REC outperforms strong baselines by up to 7.45%, producing claims that are both better grounded and more influential to recommendation quality.
By Yu Hou, Nathaniel Kang, Pengkai Wang, Hua Li
The paper introduces an AI-driven framework for thermal‑aware capacity planning in data centers, enabling temperature predictions in milliseconds by learning from key parameters such as rack power, server placement, and HVAC settings. The model is validated against high‑fidelity CFD simulations and achieves a 10,000‑fold speedup while maintaining high accuracy for unseen designs. This rapid prediction capability allows designers and operators to instantly optimize workload distribution and cooling efficiency.
By Yixing Li, Mark Fenton, Matthew Kaufeler, Ka Ming Leung, Xin Ai, Zhiyu Zeng
RASPER is a reward‑aligned summarizer that tailors the extraction of information from unstructured discharge notes to improve downstream clinical predictions. It uses a tunable LLM summarizer trained with reinforcement learning, where the reward comes from the loss of a downstream predictor, and incorporates patient‑specific context via a longitudinal encoder that soft‑prompts the summarizer with structured codes. The approach consistently outperforms strong baselines on readmission prediction and medication recommendation tasks in the MIMIC‑III and MIMIC‑IV datasets.
By Arya Hadizadeh Moghaddam, Mohsen Nayebi Kerdabadi, Chen Chen, Dongjie Wang, Zijun Yao
DyadMem introduces a new benchmark for evaluating how long‑term agents remember user‑specific relational information, called User‑conditioned Relational Agent Memory (URAM). The dataset contains 3,065 episodes, 50,961 sessions, and 61,210 QA pairs, with detailed annotations for memory capture, update, and recall across multi‑session trajectories. Experiments on 20 models show strong performance in a gold‑memory setting but a significant drop in full‑pipeline QA, highlighting gaps in current LLM memory capabilities.
By Yifei Tao, Xinyu Zhong, Henry Hengyuan Zhao, Fanyi Wang, Tengda Guo, Wentao Qiu, Ying Wang, Liujian Tang
The paper introduces ComInsight, a method for generating analytical reports from relational tables by composing verifiable atomic insights. It defines atomic insights as minimal executable analytical units, enumerates them from the database schema, and organizes them into a multi-relational insight graph. Composition operators then fuse these atomic nodes into higher-order conclusions, each accompanied by executable SQL and fine-grained provenance, achieving superior factual correctness, novelty, and structural completeness on three benchmarks.
By Teng Lin, Xinyu Liu, Nan Tang
The paper examines how different author representations affect zero‑shot authorship attribution (AA). It compares a label‑only prompting baseline with three strategies: representative writing samples, LLM‑generated descriptions, and style embeddings (LISA). The study finds that incorporating author‑specific representations improves performance, with a two‑stage LISA framework achieving the best results, while LLM‑generated descriptions offer a more compact but slightly less accurate alternative.
By Nudrat Habib, Tosin Adewumi, Sana Sabah Al-Azzawi, Marcus Liwicki, Elisa Barney
The Writerslogic team participated in the CLEF 2026 SimpleText shared task, tackling both text simplification (Task 1) and complexity spotting (Task 2). For simplification, they built a multi‑candidate pipeline with GPT‑4o‑mini, selecting the best candidate via a reference‑free heuristic, and their Claude Sonnet 4 submission achieved a SARI of 47.43 and BLEU of 14.21, ranking third overall on the Task 1 leaderboard. For complexity spotting, they fine‑tuned a DeBERTa‑v3‑large NLI model on 350 K labeled pairs, achieving a macro F1 of 0.8081 (0.8085 in an ensemble) on binary over‑generation identification and 0.804 accuracy on multi‑class error classification, placing them second among unique teams.
By David L. Condrey
The paper introduces the Language Model Agent Machine (LAM), a framework that quantifies the computational resources consumed by language‑model agents’ harnesses—such as bounded context, memory, tools, and verification. It presents four main theoretical results: (1) communication costs mapped to pebbling lower bounds, (2) memory‑access separations revealing a linear gap between random and non‑speculative access, (3) recomputation bounds for bit‑reversal DAGs that show when storing intermediate state is beneficial, and (4) reliability bounds that yield optimal verification intervals. Experiments on GPT‑6 Astra validate the theory’s predictions for communication, reliability, and trade‑offs in practical tasks.
By Zelin Zhao (Georgia Institute of Technology), Xinyu Guo (Georgia Institute of Technology), Jingyuan Zhang (Georgia Institute of Technology), Yuxuan Zhang (Etude AI), Yongxin Chen (Georgia Institute of Technology)
The article reviews the challenges and opportunities of detecting social bots amid the rise of advanced AI chatbots. It highlights gaps in current detection methods, especially regarding AI-generated conversations, and identifies emerging trends such as synthetic data generation, multimodal cross‑platform detection, low‑resource language support, and federated learning approaches. The authors propose these directions as promising avenues for future research.
By Emilio Ferrara
BioMol-MQA is a new question‑answering dataset focused on polypharmacy that combines a multimodal knowledge graph—containing both text and molecular structure—with challenging questions designed to test large language models’ ability to retrieve and reason over this diverse information. The dataset highlights the limitations of current retrieval‑augmented generation systems, which typically handle only single‑modality text, by demonstrating that existing LLMs perform poorly unless provided with the necessary multimodal background data. This underscores the need for more robust RAG frameworks capable of integrating multiple data types for accurate responses.
By Saptarshi Sengupta, Shuhua Yang, Paul Kwong Yu, Fali Wang, Suhang Wang
The paper introduces Percept-V, a dataset of 6,000 program-generated images across 30 domains designed to test simple visual perception skills from the TVPS-4 framework. Experiments show that state‑of‑the‑art multimodal large language models perform poorly compared to humans, especially as image complexity increases, and that fine‑tuning yields only limited generalization to related datasets. The study highlights specific perception skills that remain challenging for current models.
By Samrajnee Ghosh, Ashish Goswami, Naman Agarwal, Hemanshu Garg, Chinmay Mittal, Mausam, Parag Singla
The paper introduces a PyTorch library that unifies 55 hyperspectral image models across six deep‑learning paradigms, providing a common registry, automatic tensor adaptation, and standardized constructors. It includes 24 benchmark scenes from various sensors, with tools for data preprocessing, patch extraction, and rigorous train‑test partitioning to avoid overlap. Experiments on 1,320 model‑scene combinations show that scene difficulty outweighs architecture choice, with no single paradigm dominating and small models performing competitively with much larger ones.
By Tanishq Rachamalla, Aryan Das, Srishti Kaushik, Swalpa Kumar Roy
EditHero is presented as the first benchmark for long-horizon, part-level 3D editing, featuring natural-language instructions and target images for both geometry and texture. The benchmark uses a deterministic assembly engine that produces the exact target after each edit, and every sequence is manually reviewed. It compares non-agentic top‑down methods with LLM/VLM agent bottom‑up approaches, finding that the latter follow instructions more closely and preserve unedited parts better, though each edit takes minutes.
By Ruihan Yu, Yu-Ju Tsai, Muyao Niu, Runyi Li, Lian Fu, Hanqing Liu, Zheng-Hui Huang, Yonghao Yu, Sho Kuno, Ming-Hsuan Yang, Kaipeng Zhang, Zhixiang Wang
MEA is a multi‑agent framework that automates the selection and configuration of explanation tools for machine learning models across tabular, text, and vision data. A Proposer agent chooses appropriate tools based on the question and modality, while an Actor agent is trained end‑to‑end for faithfulness, producing natural‑language explanations. Experiments show that MEA outperforms existing post‑hoc explainers and closed‑source baselines, achieving significant faithfulness gains on six datasets.
By Yuyang Cheng, Raghav Kaushik Ravi, Srivarshinee Sridhar, Sriparna Saha, Akash Ghosh, Chirag Agarwal
The paper introduces a Mixture-of-Agents (MoA) approach to quantify the per-token compute required by large language models. By having a panel of fifteen models of varying sizes attempt to reproduce each token, the authors define the smallest successful agent’s inference cost as the token’s sufficient compute, providing an upper bound on necessary computation. Experiments on benchmarks show that a 0.5B model can reproduce most tokens, and that the MoA-derived compute map can reduce latency in model routing and drafting tasks while improving or maintaining accuracy.
By Zhixu Du, Weijia Han, Hai Helen Li, Yiran Chen
LUMOS is a diagnostic framework that tracks how knowledge in large language models (LLMs) originates from training data and manifests in outputs, using the fully transparent OLMo 2 corpus. The study finds that while models encode rare facts with high separability (84%), they often fail to express them behaviorally (54%), and self‑reflection accuracy drops sharply on unseen content. These results show that incorporating the training‑data axis into evaluation turns speculative claims into verifiable evidence, suggesting it should become a standard part of LLM knowledge assessment.
By Seoyeon Ye, Gayoung Kim, Jiyoung Hong, Sookyung Kim, Hyunsoo Cho
The study investigates how AI agents influence each other when they disagree, measuring persuasion as the change in an agent’s decision after a single exchange. Across seven open‑weight models and three language tasks, it finds that persuasion is strong—receivers often abandon their initial judgment after seeing a peer’s answer and explanation. Surprisingly, neither certainty nor model size reliably predicts persuasion dynamics; small models can persuade and resist larger ones just as effectively, and the shift depends more on the listener’s susceptibility than the speaker’s persuasiveness.
By Frida N{\o}hr Laustsen, Marie Haahr Petersen, Victoria Popa, Ariel Flint, Romualdo Pastor-Satorras, Andrea Baronchelli, Luca Maria Aiello
MOF-VERIFY is a failure-aware agentic harness designed to improve hypothesis verification for metal‑organic frameworks (MOFs). It introduces a diagnostic benchmark with four task families—structural grounding, synthesis‑condition verification, evidence‑sufficiency verification, and MLIP‑based computational verification—to pinpoint failures in knowledge access, evidence acquisition, and reasoning. Guided by these diagnostics, MOF‑Verify addresses structural, literature, evidence‑sufficiency, and computational bottlenecks, achieving significant performance gains over direct inference and retrieval‑based baselines across multiple large language models.
By Donghyun Lee, Taehoon Lee, Geonhee Ahn, Jieun Kim, Jihyun Park, Suyeon Cho, Yoona Kim, Chaerim Shin, Hoi Ri Moon, Jonggeol Na, Sukho Hong, Jihwan Oh, Soo Kyung Kim
The paper presents an expert‑validated benchmark of 1,042 items across ten areas of Colombian law to assess the reliability of large language models (LLMs) on national legal systems outside the U.S. It evaluates 15 contemporary proprietary and open‑weight models, finding that while closed‑choice accuracy can be high (up to 0.905), free‑text factual correctness never exceeds 0.45. The study reveals a strong dissociation between answer relevance and correctness, systematic variation by legal area, and suggests that expert supervision and grounding in authoritative sources are needed for reliable Colombian legal tasks.
By Rub\'en Manrique, Michelle Castellanos, Jorge Morales, Juan David Guti\'errez, Antonio Barreto Rozo, Joaqu\'in V\'elez Navarro