JEV-IDS is an open experimental general network intrusion detection system that uses the Jev System One Model to detect zero‑day intrusions even when labeled data are scarce. The system processes one flow per request and asks the model two questions: a binary attack probability and a finite‑choice traffic category. In tests on a 300‑flow NSL‑KDD pilot split, JEV-IDS achieved an F1‑score of 0.859, precision of 0.941, recall of 0.790, and a novel‑attack recall of 0.838, while being 4.8 times faster and 3.8 times cheaper than GPT‑5.6 Luna and producing 15 times fewer false alarms than a low‑data Random Forest.
By Paulo Severo, Silvio E. Quincozes, Amanda Dias
The paper introduces PACE, a Provenance-Aware Capability Enforcement system designed to secure tool-using large language model agents by mediating every tool call before execution. PACE employs path confinement to limit influence paths and verifies effects against authenticated authority, distinguishing certified execution contracts from evaluated configurations. Experiments on eight agent‑security benchmarks show that the evaluated configuration reduces attack success in most cases while maintaining near‑native utility.
By Fengpeng Li, Qizhou Wang, Yuke Hu, Kemou Li, Jun Liu, Haiwei Wu, Jiantao Zhou, Di Wang
GeoLatent introduces a geometry‑guided latent structuring approach for 3D reasoning from 2D images, separating position, direction, and global geometry under geometric supervision. It combines Common–Residual Geometry Alignment (CR‑GEO) with routed optimization to prevent latent collapse and to direct visual answer learning through the latents while maintaining full attention. The method achieves state‑of‑the‑art performance on SPAR‑Bench and SPBench, improving geometry effective rank and overall accuracy.
By Yakun Zhu, Yi Bin, Yujuan Ding, Zheng Wang, Pengpeng Zeng, Duo Peng, Jingkuan Song, Heng Tao Shen
The paper introduces TESS, a scalable data‑selection framework that replaces per‑sample weights with a selection network to improve transferability across datasets and model sizes. It identifies instability in existing meta‑learning for training‑data selection (MTS) due to weight suppression and overreliance on easy features, and proposes a Pointwise Value Matching objective to address these issues. Experiments on large language model safety and instruction tuning show strong transfer from subsets to full corpora and from smaller to larger models.
By Zilin Du, Bowen Yang, Boyang Albert Li
SPIRAL is a reinforcement‑learning framework that trains language models to employ three inference primitives—sequential reasoning within a trace, parallel sampling of independent traces, and aggregation of those traces—within a single compute pipeline. The model first generates multiple independent chain‑of‑thought traces in parallel, then produces a final aggregation trace conditioned on them, with all components optimized end‑to‑end for the reward of the aggregated response. Experiments on reasoning tasks demonstrate that SPIRAL scales efficiently with inference compute, achieving up to 11× better scaling efficiency and 15% higher performance compared to the GRPO baseline when all three primitives are scaled.
By Jubayer Ibn Hamid, Ifdita Hasan Orney, Michael Y. Li, Omar Shaikh, Yoonho Lee, Dorsa Sadigh, Chelsea Finn, Noah Goodman
ProtoDCS introduces a robust open‑set test‑time adaptation framework for vision‑language models, addressing the challenge of simultaneously handling covariate‑shifted in‑distribution (csID) and out‑of‑distribution (csOOD) data. It replaces brittle thresholding with a double‑check separation using a probabilistic Gaussian Mixture Model and employs an evidence‑driven adaptation strategy that updates prototypes efficiently, reducing overconfidence and computational cost. Experiments on CIFAR‑10/100‑C and Tiny‑ImageNet‑C show state‑of‑the‑art performance, improving both known‑class accuracy and OOD detection metrics.
By Wei Luo, Yangfan Ou, Jin Deng, Zeshuai Deng, Xiquan Yan, Zhiquan Wen, Mingkui Tan
JARQ is a plug‑in refinement method for group‑wise post‑training quantizers that alternates a joint least‑squares fit of all group scales with bounded Babai proposals to move many codes together on the current grid. It solves a bilinear box‑constrained mixed‑integer least‑squares problem without backpropagation, preserves the host model’s bit width, groups, zero points, and inference cost, and improves accuracy across Llama‑2, Llama‑3, and Qwen models when paired with RTN, GPTQ, OmniQuant, and AWQ hosts. In practice, JARQ reduces perplexity in 90 of 96 comparisons, cuts three‑bit RTN perplexity by up to 36%, raises mean multiple‑choice accuracy in 23 of 24 configurations, and enhances QEP, QuaRot, and OJBKQ outputs, all within under a minute per 7B block.
By Xinyu Wang, Sicheng Lyu, Xiao-Wen Chang
JET (Justification Evaluation in Transformer) leverages pretrained language and vision‑language models to choose among a limited set of answers without extra training. It directly evaluates candidate likelihoods, reuses computation across candidates, and runs experiments on desktop CPUs and consumer GPUs to measure decision accuracy and execution cost. Results show high accuracy on the MMLU test set, significant speedups from prefix reuse and cache management, and a 30.8% reduction in process time through input preparation optimizations, all while maintaining unchanged outputs.
By Shenghao Ding
The paper introduces Concept-Driven Domain Adaptation (CDDA), a three-stage framework that adapts vision‑language models for concept‑to‑example video retrieval in educational settings. CDDA first structures textual embeddings using textbook and teacher‑handbook concept pairs, then transfers this geometry to documentary visuals with a frozen visual encoder, and finally jointly fine‑tunes both encoders with sparse visual concept supervision. On a middle‑school physics benchmark, CDDA outperforms several multimodal baselines in retrieving concept‑driven moments while preserving concrete image‑text alignment.
By Haiming Zhao, Tai Wang, Kun Zhang, Xicheng Peng, Zhiyang Li
arXiv:2610. 01578v1 Announce Type: new Abstract: Why can masked prediction learn useful representations that unmasked reconstruction misses?
By Jorge Medina Moreira, Lorenzo Bardone, Lenka Zdeborov\'a
The paper introduces OMAF, an Online MARL framework that uses a one-step Transformer-based flow policy to generate coordinated actions efficiently. By replacing costly iterative sampling with a single-step action generation and a joint optimization scheme that couples softmax Q-value estimation with flow policy objectives, OMAF maintains expressive multimodal behavior while improving training speed. Experiments on 10 tasks from MPE and MAMuJoCo demonstrate up to 3.4× higher returns and 10.5× better sample efficiency compared to baseline methods.
By Zhuoran Li, Yunzhan Li, Xun Wang, Yihan Du, Longbo Huang
Mem++ is a non‑destructive memory framework for large language model agents that records every document in full, along with its date and author, instead of compressing it at write time. At read time, it retrieves only documents up to the requested time and fuses lexical and semantic rankings, leaving the choice of which version to use to the answering model. Evaluations on OrgMemBench show Mem++ outperforms the strongest baseline by 8.0 to 13.1 points and achieves the best overall score with gpt‑4.1‑mini, while also ranking highly on other benchmarks such as LoCoMo and LongMemEval‑S.
By Ahmad Yehia, Aly O. Abdelkareem, Islam Ahmed, Hesham Omran, Khaled Alashmouny, Christian Claudel, Abduallah Mohamed
The paper introduces Graph of Concept Predictors (GCP), a reasoning-aware active distillation framework that captures a large language model’s intermediate reasoning as a directed acyclic graph of concepts and mirrors this structure in a smaller student model. GCP improves sample efficiency by using a graph-aware acquisition strategy that weighs concept uncertainty, gradient diversity, and node centrality, and enhances training stability through targeted sub‑module retraining that updates only the most influential concept predictors. Experiments on eight NLP classification benchmarks show that GCP achieves better performance under limited annotation budgets while providing more interpretable and controllable training dynamics.
By Ziyang Yu, Liang Zhao
The study examines whether asking large language models (LLMs) to predict whether readers would share misinformation adds useful information beyond predicting credibility. Eight LLM versions were audited on 290 synthetic misinformation articles, yielding 1,256 paired survey responses. While credibility scores explained a substantial portion of sharing variation, adding sharing predictions to credibility did not meaningfully reduce error, indicating that sharing answers contain structured variation but lack established incremental validity for human sharing.
By Zonghuan Xu, Xiang Zheng, Yutao Wu, Xingjun Ma
ESCROW is a post‑deployment maintenance framework for large‑language‑model agents that operate under policy‑bound enterprise workflows. It ensures that any skill revisions proposed by the LLM are only deployed after empirical evaluation, using distributed diagnosis, consensus, per‑category non‑regression guards, cross‑cycle anti‑regression checks, and an accuracy–cost Pareto search to produce a versioned, auditable diff for each change. In a real production financial document‑auditing system, ESCROW achieved the best accuracy–cost trade‑off compared with baseline methods and demonstrated transferability on a public $ au$‑bench.
By Ruoqi Shu, Chen Dan, Xuhui Wang, Tianhua Xu, Mengxi Luo, Yanming Mai, Bo Wan
The paper introduces PrecepTron, a 32‑billion‑parameter language model fine‑tuned with low‑rank adaptation to evaluate clinical reasoning in large language models (LLMs) at a physician level. It also releases GRAND‑ROUNDS, a benchmark of 9,217 scored responses from 160 clinicians across seven studies. Using PrecepTron, the authors replicate key findings from major medical AI studies and explore new questions about LLM diagnostic accuracy, demonstrating that fine‑tuned models can provide consistent, scalable physician‑level scoring.
By Thomas A. Buckley, Zahir Kanjee, Peter G. Brodeur, Byron Crowe, Anthony M. Pettinato, Aashna P. Shah, Adrian D. Haimovich, Liam G. McCoy, Daniel Restrepo, Jason A. Freed, Ethan Goh, Jonathan H. Chen, Laura Zwaan, Katherine E. Goodman, Daniel J. Morgan, Raja-Elie E. Abdulnour, Adam Rodman, Arjun K. Manrai
The paper investigates the gap between intent and behavior in jailbreak attacks on LLM-based robots, showing that many intent jailbreaks fail to produce harmful physical actions due to robot-specific constraints. It introduces POEF, an automated red‑teaming framework that optimizes jailbreak prompts using hidden‑layer gradients and evaluates policy feasibility with a multi‑agent system, achieving an 80% success rate on commercial robots. The authors also propose defense strategies and emphasize the need for stronger countermeasures before widespread deployment of LLM-based robots.
By Xuancun Lu, Zhengxian Huang, Xinfeng Li, Chi Zhang, Xiaoyu Ji, Wenyuan Xu
The paper introduces MolPAIR, a framework that enhances tabular foundation models (TFMs) for predicting molecular properties under structural shifts by incorporating molecule-level and molecular-pair contexts without task‑specific parameter updates. MolPAIR refines initial predictions from a global TFM by using a second frozen TFM to predict differences in prediction errors between a query molecule and labeled reference molecules. Experiments on 58 MoleculeACE and Polaris tasks show that MolPAIR improves performance on 46 tasks across multiple molecular representations and TFM backbones, demonstrating that explicit molecular comparisons strengthen tabular in‑context learning for structural generalization.
By Jinmo Lee, Dooho Lee, Minho Jeong, Jaemin Yoo
The paper introduces Importance-Aware Class-Balanced Sparsification (ICS) for wireless split learning, a lightweight method that ranks feature channels on the server using Grad‑CAM scores from the true‑class logit. By aggregating per‑class scores into a class‑balanced importance vector, clients can retain the top‑N feature channels without extra forward or backward passes, reducing communication overhead while mitigating head‑class bias under label skew. The authors provide a non‑asymptotic convergence bound, analyze overhead against baselines, and extend the approach to parallel split learning and transformer models, demonstrating consistent performance gains, especially under severe non‑i.i.d. data partitions.
By Bumjun Kim, Yoon Huh, Wan Choi
The study investigates whether a second review by a different large language model (LLM) improves error detection in code, documentation, and analyses. Using 30 artifacts with 150 planted errors and 900 review sessions across three reviewer models, the authors find that a top-tier cross-model reviewer performs similarly to same-model reviews in fresh sessions, identifies partly different errors, and that a combination of a fresh same-model review and a cross-model review detects more errors than two fresh same-model reviews. However, the results do not conclusively separate the effects of model difference from reviewer capability, and a lightweight cross-model reviewer does not outperform same-model review.
whyItMatters":"The findings suggest that while cross-model reviews can enhance error detection, the benefit depends on reviewer skill and the specific review strategy, informing how teams might structure LLM-based code review processes."
By Tae-Eun Song