The paper introduces ICRL4AHT, a large-scale benchmark for evaluating In-Context Reinforcement Learning (ICRL) in Ad-Hoc Teamwork (AHT) scenarios using Overcooked-V2. It provides a diverse teammate suite, a reproducible pipeline, and evaluates history-conditioned ICRL algorithms such as Algorithm Distillation and Decision-Pretrained Transformer. The results show that these methods often perform worse than random baselines and do not improve with longer horizons, underscoring the difficulty of strategic inference under partial observability in AHT.
By Yuheng Jing, Kai Li, Ziwen Zhang, Jiajun Zhang, Zeyao Ma, Jiaxi Yang, Lei Zhang, Zhe Wu, Jinmin He, Junliang Xing, Jian Cheng
The paper examines whether human agreement and return association can be used interchangeably as criteria for validating sentiment tools in financial NLP. Using a large corpus of securities class action messages linked to abnormal stock returns, the authors compare five sentiment instruments and find that the relationship between human agreement and predictive validity varies with sampling conventions and score representations. They conclude that benchmark agreement establishes semantic validity but does not guarantee predictive rankings, and that message volume in a spam‑heavy conversation does not predict market damage or settlement size.
By AS Aravinthakshan, Laven Srivastava, Harsh Nandwani
Foundations of Large Language Models is a book that focuses on core concepts of large language models rather than exhaustive coverage of the latest technologies. It is organized into six chapters covering pre‑training, generative models, prompting, alignment, inference, and reasoning. The book targets college students, professionals, and practitioners in NLP and related fields, serving as a reference for anyone interested in large language models.
By Tong Xiao, Jingbo Zhu
The paper introduces Growing Harness, a training method that transforms recurring control logic in large language model agents into reusable executable code, reducing reliance on the model for task-specific decisions. By using strategy-free scaffolds, failure-guided code repair, and success-first gating, the approach learns a shared harness that improves performance across multiple benchmarks and model sizes. Experiments on BrowseComp-Plus and WebArena-Verified show significant gains in success rates and substantial reductions in LLM calls and inference cost compared to traditional tool‑calling agents.
By Laizhen Li, Jiarui Li, Juanjuan Zhao, Kejiang Ye, Ye Li, Cheng-zhong Xu, Xitong Gao
TIDE (Temporal Incremental Draft Engine) is a serving‑engine‑native framework that integrates online draft adaptation into high‑performance LLM inference. By reusing intermediate hidden states from the target model as training signals, TIDE avoids extra target model computation and serving‑time overhead, activating speculation and draft training only when beneficial. On heterogeneous GPU clusters, TIDE achieves up to 1.66× higher throughput than no‑speculation baselines, reduces training time by up to 3.02×, cuts storage needs by 24×, and improves system throughput by up to 1.22×.
By Jiyoung Park, Hankyu Jang, Changseok Song, Wookeun Jung
The paper introduces CodeScan, a black-box, vulnerability-oriented scanning framework designed to detect data poisoning and backdoor attacks in code generation large language models (LLMs). CodeScan operates by analyzing structural similarities across multiple code generations, normalizing them with abstract syntax tree (AST) techniques, and then applying LLM-based vulnerability analysis to identify recurring insecure patterns. Evaluations on 117 models across three architectures and multiple sizes show over 97% detection accuracy with fewer false positives compared to prior methods.
By Shenao Yan, Shan Jin, Shimaa Ahmed, Sunpreet Singh Arora, Yiwei Cai, Yizhen Wang, Yuan Hong
The paper introduces Auto‑Robotist, a self‑evolving large language model (LLM) agent that transforms evolutionary robot design search traces into an explicit natural‑language skill library. Each skill records a structural archetype, evidence‑grounded rules, and supporting designs, enabling the agent to retrieve and condition LLM edits during search while still using a genetic algorithm for exploration. Experiments on seven EvoGym tasks show that Auto‑Robotist outperforms standard genetic algorithms, especially when transferring learned skills to larger design spaces.
By Yunfei Wang, Xiaohao Xu, Yang Li, Xiaonan Huang
The paper investigates converting a large pretrained transformer (1.4 B parameters) into a smaller sibling (410 M) by studying representation alignment and parameter projection. It finds that dense weight projection destroys structure, and that a low‑budget, structure‑aware compensation—separating least‑squares function alignment from variance‑preserving rescaling—yields significant gains on token‑efficient training, outperforming subcloning and standard distillation pipelines at matched budgets.
By Ravi Satya Durga Prasad Yenugula
This study benchmarks transformer models for Bangla medical named entity recognition (NER), comparing BanglaBERT, multilingual BERT (mBERT), XLM‑RoBERTa, and GPT‑4o mini under zero‑shot and few‑shot prompting. Across a full test set of 3,179 samples, fine‑tuned XLM‑RoBERTa achieves a new state‑of‑the‑art F1‑score of 0.5959, while BanglaBERT lags with 0.4937, suggesting that domain diversity outweighs language specificity. The analysis shows high performance on Medicine and Specialist entities (F1 > 0.83) but lower accuracy on Symptoms (F1 0.4367), and demonstrates that fine‑tuned transformers outperform prompt‑only approaches by a factor of 3.76.
By Rakib Abdullah, Md. Maruful Islam Maruf
BLADE is a variational model for knowledge graph completion that separates latent truth from graph recording and uses distilled offline language‑model judgments as a frozen teacher regularizer. The model provides calibrated probabilities and epistemic uncertainty through posterior samples, while the teacher is only an optional triage factor during inference. Across five benchmarks, BLADE matches ranking performance and significantly reduces expected calibration error, improving ECE, Brier score, and NLL over several baselines, and shows strong performance under controlled missingness and leakage stress tests.
By Ibne Farabi Shihab, Rabeya Bosri Tamanna, Abdo El Karaky, Sanjeda Akter, Anuj Sharma
CataOPD introduces a new framework for improving large language model reasoning by combining reinforcement learning and on‑policy distillation. The method treats the teacher as a catalyst that expands the student’s reachability, using Self‑Rescue Routing to find correct trajectories through additional on‑policy sampling and Catalytic‑Guided Self‑Resolution to elicit verified student trajectories. Barrier‑Weighted Internalization further focuses updates on decisive tokens, leading to better performance on unseen problems and improved out‑of‑distribution generalization.
By Wenjin Liu, Chenxi Wang, Jiapu Wang, Zhe Cui, Anh Tuan Luu, Haoran Luo
The article examines how classical test theory reliability statistics misrepresent the performance of large language model (LLM) judges. It shows that internal‑consistency coefficients, the dependability index, and Livingston‑Lewis accuracy each conflate judge error with item design or criterion validity, making it impossible to attribute a single reliability value to the judge alone. The authors argue that such misattribution can influence deployment decisions and documentation.
By Louis Yiven Zhu
arXiv:2609. 30085v1 Announce Type: new Abstract: In multi-target regression, correlated targets are often coupled through multi-output Gaussian processes with an intrinsic model of coregionalisation (GP-ICM), assuming that sharing statistical strength improves overall performance.
By Fangqin Zhou, Joaquin Vanschoren
The paper presents a graph-based framework for large-scale railway network management, combining a hierarchical Bayesian model with a Gaussian Process on a graph kernel to infer spatially correlated maintenance environments from Swiss Federal Railways data. It introduces a topology-aware Multi-Agent Reinforcement Learning system that uses graph neural networks and Transformers to optimize network-level policies. The approach demonstrates scalability via zero-shot transfer learning, enabling agents trained on small network segments to perform effectively on unseen large networks, outperforming heuristics and standard MARL baselines while reducing training time.
By Giacomo Arcieri, Gregory Duth\'e, Christophe Muller, Konstantinos G. Papakonstantinou, Daniel Straub, Eleni Chatzi
The paper investigates how providing execution traces to multimodal judges in agentic video‑generation systems can bias their verdicts. On a benchmark of 109 two‑event clips, traces that falsely report successful tool calls cause large‑language‑model judges to incorrectly accept 78–90 % of failures, while contradictory traces lead to 100 % rejection of correct clips. The effect persists even when judges are instructed to consider only the video frames, indicating that the vulnerability stems from the judges’ learned trust in tool logs rather than the visual content itself.
By Jian Xu
EvoTreeNAD is a genealogy‑guided evolutionary algorithm that autonomously discovers neural architectures without a predefined seed or search space. Starting from an empty root, it builds a persistent genealogy where each node represents a complete architecture; top‑percentile values from nodes and descendants steer lineage selection. The method combines an Idea Agent that proposes variants and a Code Agent that implements them, with theoretical analysis showing stationary variation regimes and empirical results demonstrating superior performance on CIFAR‑10/100 and MedMNIST‑v2 tasks.
By Lishan Yu, Derek Jiu, Qizhen Lan, Xiaoqian Jiang
The paper introduces ELF-REG, a scaling of continuous diffusion language models (dLMs) for reasoning tasks. By aligning representations and using a frozen autoregressive teacher to supervise intermediate denoiser features, ELF-REG achieves higher pass@1 rates on GSM8K, MATH-500, HumanEval, and MBPP compared to prior dLMs. The method also enables strong low‑NFE performance through early‑stop decoding of intermediate predictions.
By Zeyu Michael Li, William Xingxu Chen, Bingshuo Qian, Jiayin Liu, Xiang Cheng
The paper presents an inference-only pipeline that extends the frozen NER model MahaNER‑BERT to document‑level prediction using overlapping sliding windows, eliminating the need for retraining or architectural changes. The approach is evaluated on six document‑level corpora derived from the MahaNER test set, employing two repetition strategies (Normal Repeat and Random Repeat) at three length levels and various window configurations. Results show the model maintains a macro F1‑score of up to 0.8902 with minimal variation, outperforming non‑windowed methods by avoiding boundary‑fragmentation errors and achieving more stable document‑level performance.
By Hariom Ingle, Ronit Ghode, Ishwari Gondkar, Jidnyasa Harad, Ravindra Murumkar, Raviraj Joshi
CORDIAL is a calibration method for large language models that treats ordinal outputs as noisy measurements of true labels and corrects them using a small, interpretable channel of five parameters. The method can be calibrated from only a few labels and preserves first‑order stochastic order. Experiments on Amazon reviews and CMU‑MOSEI transcripts with four LLMs show that CORDIAL achieves the lowest log loss in most settings, outperforming other calibrators when the calibration set is small, and can also learn priors from other tasks or fuse multiple LLMs.
By Xiangwei Wang, Peng Wang, Saman Halgamuge
The paper investigates how to detect text generated by large language models (LLMs) when the data has been edited or contaminated. By modeling human and machine text as finite-order Markov processes with Huber contamination, the authors derive an exact boundary that determines when reliable detection is possible. They show that a clipped likelihood-ratio test can achieve vanishing worst‑case errors below this boundary and that clipping improves robustness across several detectors and datasets, yielding significant gains in true‑positive rates at a fixed false‑positive rate.
By Jiaxun Li, Saptarshi Chakraborty, Ambuj Tewari