The paper introduces Knowledge-Aware Semantic Bridging (KASB), a framework designed to improve Retrieval-Augmented Generation (RAG) by aligning the semantic spaces of queries and retrieved contexts. KASB achieves this through intelligent fusion of generative and retrieval-based knowledge in a multistage process, aiming to enhance passage selection quality, relevance, and accuracy. The authors evaluate the method on three popular open-domain Question Answering datasets, demonstrating its effectiveness.
By Xinkai Du, Chao Lv, Yalin Sun, Quanjie Han, Lei Yao, Maosong Sun
Large Language Model judges are commonly used to rank texts via pairwise comparison, with reliability traditionally measured by position bias, transitivity, and pairwise agreement. This paper argues that these proxies are misleading because they are dominated by close‑rank‑gap pairs, which contribute little to the overall ranking, while far‑gap pairs carry the true ranking signal. Experiments on simulations and human‑rated corpora show weak correlation between the proxies and actual ranking accuracy, suggesting judges should be evaluated using rank‑gap‑conditional metrics against human rankings.
By Bruno Brocai, Maria Becker
The paper introduces SCSP, a training‑free framework that improves long‑context embeddings by selectively pooling informative tokens. SCSP partitions documents into sentence‑aware chunks, adds a semantic compression prompt to each chunk, and uses prompt‑isolated attention masks to estimate token importance. The selected tokens’ intermediate‑layer representations are aggregated to form the final embedding, yielding consistent performance gains across zero‑shot and fine‑tuned models on long‑context benchmarks.
By Zifeng Cheng, Jie Zheng, Zhiwei Jiang, Shuwen Wang, Fei Shen, Shiping Ge, Qing Gu
The paper introduces LongHarness Bench, a new benchmark designed to evaluate both the effectiveness and efficiency of language model harnesses for long-context reasoning. It features tasks that require diverse retrieval strategies—such as lexical search and semantic matching—and strategic, adaptive reasoning over global and local context, with only a small subset of the context being useful at each step. Evaluations across multiple state‑of‑the‑art models and harnesses show that even strong combinations achieve only 68% macro‑average accuracy, and that the same model can vary markedly in efficiency depending on the harness used.
By Quang Hieu Pham, Thuy Duong Nguyen, Jocelyn Qiaochu Chen, Xi Ye
TTMark introduces a pairwise watermarking framework that extends distortion‑free watermarking from single tokens to adjacent token pairs, enlarging the watermarking alphabet from V to V². By watermarking the joint distribution of consecutive tokens, the detector can exploit both token entropy and conditional entropy while maintaining distortion‑freeness. Experiments on multiple language models and datasets show that TTMARK improves detectability, robustness to edits, and localized watermark detection without degrading generation quality.
By Ruibo Chen, Zhengmian Hu, Donghang Lu, Xuehao Cui, Georgios Milis, Yihan Wu, Jian Du, Heng Huang
arXiv:2608.25466v2 Announce Type: replace-cross
Abstract: The functional annotation of genes in non-model organisms remains a significant challenge in computational biology, with 20-70% of sequenced...
By Azrin Sultana
arXiv:2502.04492v3 Announce Type: replace
Abstract: The advancement of LLMs and their accessibility have triggered renewed interest in multi-agent reinforcement learning as robust and adaptive framew...
By Selim Furkan Tekin, Gaowen Liu, Ramana Rao Kompella, Ling Liu
arXiv:2601.03418v3 Announce Type: replace
Abstract: Trustworthy clinical summarization requires every claim to be traceable to its evidence, yet existing attribution often resolves only to the senten...
By Bohao Chu, Hendrik Damm, Tabea M. G. Pakull, Sameh Frihat, Georg Lodde, Elisabeth Livingstone, Christoph M. Friedrich, Norbert Fuhr
arXiv:2604.15490v2 Announce Type: replace
Abstract: Recent developments in reasoning capabilities have enabled large language models to solve increasingly complex mathematical, symbolic, and logical...
By Eleanor M. Lin, David Jurgens
arXiv:2609.36957v1 Announce Type: new
Abstract: Direct visual readability and task-information recoverability are different quantities. Failure to decode a target from a fixed observation need not el...
By Yiwei Liu
The paper introduces Learner2Skill, a method that transforms historical learner interactions into a reusable Simulation Skill. This Skill encapsulates a learner’s current state and recurring response patterns, allowing it to evolve with new interactions and be adapted to different LLMs without reconstructing the learner from scratch. Experiments demonstrate that Learner2Skill more accurately reproduces fine‑grained learner behavior while reducing token costs and enabling cross‑LLM reuse.
By Zijian Chen, Zheng Zhang, Miao Jia, Xingchen Hu, Weibo Gao, Linan Yue
Large language models (GPT‑4o and Claude 3.7 Sonnet) were tested on two variations of a Bayesian reasoning task across 5 experiments and 5,000 trials. They performed near human levels on the task, but their reasoning was more rule‑based and rigid. The models also exhibited Bayesian hypocrisy, condemning others who used the same Bayesian reasoning, a human‑like error that highlights a dissociation between self‑performance and other‑judgment.
By Nykko Vitali, Mahzarin R. Banaji
The paper introduces CRASM, a deterministic, constraint‑ and role‑aware semantic mapping framework for aligning engineering concepts across incompatible industrial standards, and its extension CRASM‑Gate, which optionally employs a large language model through a confidence gate while preserving deterministic validation. The framework decomposes the mapping process into standard‑specific canonicalization, bounded retrieval, deterministic rules, role interpretation, ranking, ambiguity refusal, and target validation. Experiments on 14,400 sample decisions across six standard pairs show CRASM‑Gate achieving a mean F1 of 0.9938 and perfect structural validity, outperforming a model‑only baseline and reducing latency significantly compared to generative‑model‑only approaches.
By Kabeh Mohsenzadegan, Vahid Tavakkoli, Kyandoghere Kyamakya
arXiv:2609.37853v1 Announce Type: new
Abstract: Large language models (LLMs) are increasingly deployed as social agents, yet credible human-like interaction requires more than fluent responses or per...
By Wentao Liu, Xi Chen, Siyu Song, Biao Yuan, Yu Zhang, Zhou Zhuotong, Jingying Zhou, Guohao Feng, Shasha Hu, Tianfu Wang, Shangshang Yang, Haoyang Liu, Youjia Li, Xiaokun Wang, Min Ji, Ji Wang
arXiv:2609.36440v1 Announce Type: new
Abstract: Large vision-language models (LVLMs) have recently achieved remarkable progress across multimodal tasks, yet object hallucination remains a persistent...
By Jae-Ho Lee, Jeong-Eun Lee, Gyeong-Moon Park
arXiv:2609.36832v1 Announce Type: new
Abstract: Text-to-video (T2V) diffusion models can generate realistic depictions of actions such as kicking, stabbing, and shooting, raising safety concerns that...
By Ping Liu, Chi Zhang
arXiv:2609.36064v1 Announce Type: cross
Abstract: Foundation models are powerful generators, but many engineering domains require structured representations that general-purpose systems handle poorly...
By Sahand Rezaei-Shoshtari, Patryk Wozniczka, Shu Ishida, Gregg Streuber, Farnoosh Javadi, Jeffrey Landes, Angela Ju, Muhammad Azam, Bryan Lim, Johan Luttun, Indrajeet Haldar, Jonathan Shaw, Beatriz Guerra, Ivan Sosnovik, James Stoddart, Robert Giaquinto, Adam Gaier
arXiv:2609.36187v1 Announce Type: new
Abstract: Symbolic Regression (SR) is a data-driven method for scientific discovery which searches for interpretable analytical relationships within data. Recent...
By Cristina Rossetti, Anna V. Kononova, Thomas B\"ack, Fei Liu, Niki Van Stein
arXiv:2609.36662v1 Announce Type: cross
Abstract: The pre-training of Large Language Models (LLMs) is increasingly conducted across multiple data centers. As training scales to a larger number of acc...
By Pengyu He, Yan Zhang, Ruien Li, Guangwen Yang
arXiv:2609.36663v1 Announce Type: new
Abstract: We present GenLimitLib, a source-aligned Lean 4 library for language generation in the limit. Introduced by Kleinberg and Mullainathan at NeurIPS 2024,...
By Shuangping Li, Peng Zhang