The paper introduces DPTM‑DT, a dual‑pretrained Transformer framework that integrates GROVER molecular graph embeddings, ESM protein language‑model embeddings, and CTD physicochemical descriptors for drug‑target prediction. It employs bidirectional cross‑modal attention to share drug‑target information and uses a single pair representation for continuous affinity regression, high‑affinity binary classification, and six‑level affinity classification. Experiments on Davis and KIBA datasets show that DPTM‑DT outperforms existing methods across regression, binary, and multiclass tasks, with ablation studies confirming the contributions of dual target representation, gated fusion, and cross‑modal attention.
By Ge Kong
The paper investigates how to balance supervised fine‑tuning (SFT) and reinforcement learning (RL) for long‑horizon advertising agents that must retrieve, reason, and execute code on distributed business data. By classifying training trajectories into Imitation, Lift, and Discovery regimes, the authors develop a diagnostic that predicts which training path to follow, and then apply a targeted SFT‑then‑RL strategy. Across 18 feature‑specific experiments on GPT‑OSS 120B, this approach yields positive gains on most advertiser skills, reduces leakage, and achieves higher mean improvements with 43% less RL compute.
By Aakash Kolekar, Sahika Genc, Bunyamin Sisman, Shahriar Shariat, Shree Vandana Kachroo, Avishek Saha, Qianli Wu, Ari Singer, Benoit Dumoulin
EvoRank is an open autonomous ranking engineer that uses an LLM-guided evolutionary loop to automatically design complete Learning-to-Rank pipelines—including features, models, losses, and ensembles—for multi-objective e-commerce search. On the Expedia ICDM 2013 dataset, EvoRank converged within 50 iterations on interpretable pipelines that outperform an Optuna-tuned LambdaMART on 60k held-out queries and rank in the top 6 % of the original competition. The authors also introduce a headroom gate that predicts whether the evolutionary loop will be worthwhile before any LLM computation, and they release the system, auditing tools, and a catalog of failure modes to help teams apply the method to their own ranking stacks.
By Rayhan Patel, Shabaz Patel
Sofroniew et al. (2026) showed that emotion concepts in Claude Sonnet 4.5 are encoded as vectors whose geometry mirrors human affect psychology. This study replicates that finding using the base pretrained model google/gemma-2-27b, generating 205,200 Claude Sonnet 4.5 stories, extracting 171 emotion vectors, and recovering a similar affective circumplex with principal components explaining comparable variance. The analysis further identifies a sharp geometric seam at layers 22‑26, demonstrates that much of the geometry already exists in static token embeddings, and shows that the geometry predicts token‑level co‑activation with high correlation.
By Adam Hollowell
arXiv:2609.22219v1 Announce Type: cross
Abstract: Evaluating an aligned language model by reading its answers assumes the answers carry the distinction the evaluator cares about. We introduce LLM end...
By Fabrizio Davide, Francesco Collova
The paper investigates whether large language models can reliably reproduce official Eurostat statistics by generating executable code. It evaluates a coding agent across four experimental conditions—task only, task plus metadata, metadata with a repair loop using execution feedback, and metadata with a retry budget but no diagnostics—using 30 natural‑language tasks spanning seven domains and datasets. Results show that success depends on semantic validation against frozen specifications, a fully specified output contract, and a retry budget, rather than on execution diagnostics alone.
By Sabina-Cristiana Necula
arXiv:2609.22239v1 Announce Type: new
Abstract: Ambient AI is increasingly adopted in healthcare to automatically generate clinical notes from patient-clinician conversations, with the potential to s...
By Jakir Hossain, Yi-Fei Zhao, Hongjian Wang, Minmei Shih, Katie Leigh Mullen, Ahmad P. Tafti, Leming Zhou, Manoj Purohit, William Hogan, Jay Zeng, Elizabeth Skidmore, Yanshan Wang
arXiv:2609.22241v1 Announce Type: new
Abstract: We present H2LooP Telecom Model v1, a domain-specialized large language models fine-tuned for the telecommunications industry. We release two domain-ad...
By Amit Singh, Vedant Nipane, Mayank Goel, Pulkit Agrawal, Sairanjan Mishra
The paper introduces CoSLR, a Human‑AI collaborative system for systematic literature reviews that incorporates mandatory human checkpoints within a three‑phase pipeline using large language models and Retrieval‑Augmented Generation. In a survey of 63 participants, 42.9 % rated the system’s usability highly, yet 34.9 % indicated they would trust AI‑generated summaries without further human verification after brief interaction. The study highlights that effective human oversight in AI‑assisted literature reviews depends on users’ willingness to engage with the checkpoints, underscoring a calibration issue that interface design must directly address.
By MD Aidul Islam, Malik Abdul Sami, Muhammad Waseem, Zeeshan Rasheed, Kai-kristian Kemell, Zheying Zhang, Pekka Abrahamsson
arXiv:2609.22249v1 Announce Type: new
Abstract: This paper treats prompt engineering as a discipline for turning informal human intent into structured AI work specifications. It develops the practice...
By Erfan Loweimi, Hadi Daneshvar, Samira Loveymi, Samir Ouelha, Zhengjun Yue, Hajar Mozaffar, Saturnino Luz
The study presents a deployment‑aware framework for forecasting spring discharge and groundwater levels in the Edwards Aquifer over 1‑12 week horizons using 79 years of hydroclimatic data. Five machine‑learning families—extreme gradient boosting, extremely randomized trees, LSTM, CNN, and Transformers—were compared, with extreme gradient boosting consistently delivering the highest reliability (R² ≥ 0.94) and strong agreement with operational drought thresholds. The validated models were integrated into a five‑agent operational architecture that automates data acquisition, model selection, prediction, threshold monitoring, verification, literature retrieval, and reporting.
By Pramod Lekhak, Chetan Sharma, Hakan Ba\c{s}a\u{g}ao\u{g}lu, F. Paul Bertetti, Debaditya Chakraborty
CALM is a reproducible hybrid framework that combines an optional large language model (LLM) activity planner with calibrated stochastic choice, shared network feedback, memory and habit, typed feasibility checks, and deterministic offline replay. It executes a closed traveler‑day loop and evaluates each generative module against an empirical, reproducible baseline, using the 2024 New York City Citywide Mobility Survey data. The framework demonstrates significant improvements in mode‑choice accuracy, quantifies trade‑offs through live‑LLM ablation, and supports controlled stress testing and deterministic replay of downstream simulations.
By Yezhou Cheng
Teacher Should Think Ahead: Adaptive Continuations for Reliable On-Policy Distillation explores how on‑policy distillation (OPD) can be improved by addressing teacher uncertainty that contracts as the teacher continues from a student‑generated prefix. The authors identify Teacher Uncertainty Contraction (TUC) and theoretically analyze its variance‑bias trade‑off, leading to the proposal of Adaptive‑Continuations On‑Policy Distillation (AC‑OPD). Experiments on mathematical reasoning and code generation show that AC‑OPD consistently outperforms standard OPD, with controlled‑continuation and matched‑budget analyses supporting the adaptive‑continuation design.
By Jingang Zhou, Yuyi Zhou, Haiyang Guo, Xukai Wang, Shuai Feng, Sirui Gao, Jian Xu, Qingpei Guo, Xu-Yao Zhang
arXiv:2609.22271v1 Announce Type: new
Abstract: Multimodal stroke recurrence prediction requires effective integration of heterogeneous clinical and imaging data, yet modality imbalance often causes...
By Christian Gapp, Elias Tappeiner, Martin Welk, Karl Fritscher, Stephanie Mangesius, Constantin Eisenschink, Philipp Deisl, Michael Knoflach, Astrid E. Grams, Elke R. Gizewski, Rainer Schubert
arXiv:2609.22302v1 Announce Type: new
Abstract: Medical vision-language models can propose how urgently a skin lesion should be reviewed, but the local service retains authority to accept or replace...
By Flint Xiaofeng Fan, Cheston Tan, Yew-Soon Ong, Roger Wattenhofer
arXiv:2609.22327v1 Announce Type: cross
Abstract: Visual graph reasoning requires answering graph-theoretic questions directly from graph images, where graph topology and state are conveyed visually...
By Rongzheng Wang, Zhe Wang, Ke Qin, Rongwei Wang, Muquan Li, Yizhuo Ma, Yihong Huang, Jielei Wang, Shuang Liang
arXiv:2609.22332v1 Announce Type: cross
Abstract: Generalizable robot manipulation requires predicting how a scene will evolve, identifying where interactions are feasible, and determining how to act...
By Jiadi You, Qize Yu, Yue Chen, Minghong Cai, Zhide Zhong, Yuran Wang, Bowen Ping, Jiaqi Liang, Zhenhao Shen, Haodong Yan, Yinchuan Li, Ruihai Wu, Xiaojuan Qi, Yingcong Chen
The paper demonstrates that a preference‑optimization objective can learn to distinguish reliable from unreliable sources by installing a prior‑dependent reliability switch. By training on data where a source’s stated reliability is paired with its answer, the model learns to flip its response only when the stated reliability exceeds a threshold that grows with the model’s prior. Experiments on Qwen2.5‑7B‑Instruct and Llama‑3.1‑8B show that this switch generalizes to unseen reliability values and follows stated reliability over role prestige, whereas supervised imitation fails to learn it.
By Sen Yang, Yuen-Hei Yeung
arXiv:2609.22566v1 Announce Type: cross
Abstract: Knowledge distillation (KD) aims to compress high-performance teacher LLMs into lightweight students. However, distilled students often exhibit subst...
By Dileesha Kannangara, Sanghamitra Dutta
arXiv:2609.22603v1 Announce Type: new
Abstract: Summarization ships in countless production systems, making model selection a routine decision that depends on measuring summary quality. Existing metr...
By Nikhil Reddy Pottanigari, Ramin Fahimi, Noah Bolger, Sepideh Kharaghani, Ying Zhang