The paper investigates quantum kernel methods for detecting audio deepfakes when training data are scarce and evaluation corpora differ from training corpora. Using only 200 labeled samples and four‑dimensional embeddings, the authors compare a Quantum Support Vector Machine (QSVM), a classical SVM, and a multilayer perceptron (MLP) across several cross‑corpus datasets. While the QSVM outperforms the MLP under severe domain shift from ASVspoof 2019 to ADD 2023, its advantage is inconsistent in other transfer directions, indicating that quantum kernels can be competitive but not universally superior in low‑resource, cross‑corpus settings.
By Lisan Al Amin, Lei Zhang, Vandana P. Janeja
StabilityArc is a method that decodes protein sequence embeddings into generalizable stability landscapes. It uses a shared RoPE transformer to map frozen ESMC-600M residue representations into an Lx20 matrix of substitution effects, with a symmetric, contact-aware residual to predict epistasis. In extensive leave-one-protein-out tests on 134,794 ProteinGym variants, StabilityArc achieves a Spearman correlation of 0.7134, surpassing the best zero‑shot baseline, and further improves Kermut’s performance when used as a prior.
By Aaron L. Feller, Andrew D. Ellington, Claus O. Wilke
The paper introduces a continual‑learning framework for single‑view 6‑DoF grasp synthesis with a parallel‑jaw gripper in cluttered scenes. Instead of fine‑tuning a large parametric model, the method updates grasp scores via memory in a learned embedding space and optionally incorporates user demonstrations to generate new candidate grasps. Experiments in simulation and real‑world trials (over 1500 grasps) show that the approach matches baseline performance before adaptation, improves online on unseen objects, and achieves over 90% success on challenging categories after just 50 online attempts.
By Giulio Schiavi, Andrei Cramariuc, Michael Pantic, Roland Siegwart
JEPA-Bisim introduces a bisimulation encoder to joint-embedding predictive world models, ensuring that states with similar transition dynamics are mapped to nearby latent representations while suppressing irrelevant slow features such as background changes and distractors. The approach improves robustness on navigation (PointMaze) and manipulation (PushT) tasks under varied test-time visual conditions, achieving up to tenfold smaller latent spaces than DINO-WM. It remains effective across different pre-trained visual encoders, including DINOv2, SimDINOv2, and iBOT.
By Leonardo F. Toso, Davit Shadunts, Yunyang Lu, Gloria Geng, Nihal Sharma, Donglin Zhan, Nam H. Nguyen, James Anderson
JEPA‑TTT is a method that continuously adapts the latent dynamics predictor of a pretrained Joint‑Embedding Predictive Architecture (JEPA) world model during test time. It performs self‑supervised updates across episodes while keeping the visual encoder and reward head fixed, using dense replay to sample prediction windows from a growing buffer. In experiments on eight dynamics shifts across four continuous‑control environments, JEPA‑TTT reduces latent prediction error by 83% and improves planning performance by 153% compared to the frozen model.
By Zheyuan Zhang, Suyu Ye, Nakul Agarwal, Hossein Nourkhiz Mahjoub, Ehsan Moradi Pari, Daniel Khashabi, Tianmin Shu, Vaishnav Tadiparthi
The paper shows that the geometric patterns seen in language model embeddings—such as circles for months and saddle-shaped manifolds—arise from group-invariant statistics in word co‑occurrence data. By extending previous work on translation symmetry to arbitrary finite, compact, and homogeneous groups, the authors prove that embeddings correspond to matrix elements of the irreducible representations of the symmetry group. They validate this theory experimentally with the cyclic group <Z_{12}> for months, a dihedral group for musical chords, and a spherical‑harmonic embedding for celestial objects.
By Liam Storan, Andreas Tolias, Nina Miolane
Variational Streaming Flow (VSF) extends the efficient Streaming Flow (SF) framework by learning a latent distribution conditioned on system dynamics, enabling probabilistic forecasting in physical time. Unlike SF’s deterministic velocity field, VSF produces multiple plausible future trajectories, improving predictive accuracy and distributional fidelity across deterministic and stochastic dynamical systems. The method supports long‑horizon rollouts over 1,000 steps, handles bifurcating dynamics, and can be integrated as a plug‑and‑play predictor into Joint‑Embedding Predictive Architecture (JEPA) world models to enhance navigation, motion planning, and manipulation tasks.
By Hans Hao-Hsun Hsu, Minseon Gwak, Soon Hoe Lim, Pan Li, N. Benjamin Erichson
AF‑Muon is an AdamW‑free extension of the Muon optimizer that retains Muon’s matrix update for hidden weights while applying a support‑aware finite‑cap linear minimization oracle to tied vocabulary tables and an RMS‑normalized update for one‑dimensional auxiliary parameters. This design eliminates second‑moment state, reducing optimizer‑state memory by about 20% compared to Hybrid Muon. Across nine tied‑token settings—including decoder‑only language models, T5‑style encoder‑decoders, and ImageGPT‑style variants—AF‑Muon consistently improves mean validation loss and perplexity over both Hybrid Muon and a SCION‑style Sign endpoint, with robust gains confirmed by long‑horizon runs and hyperparameter studies.
By Arash Lagzian, Paniz Halvachi, Junming Zhang, Zhouhan Lin, Dianbo Liu
The paper investigates whether response safety can be measured by the cosine similarity between a response embedding and the mean embedding of known‑safe responses. Using four frozen encoders and prompt‑controlled datasets, the authors find that a simple prototype (mean safe embedding) performs poorly (ROC‑AUC 0.457‑0.545) while an explicit safe‑minus‑unsafe reference achieves higher scores (0.588‑0.738). The study shows that a class mean is merely a location, not a safety direction, and that a reference with sufficient unsafe mass is needed to orient safety judgments.
By Sahil Kadadekar
The paper introduces an action‑on‑item schema that pairs interaction roles with content embeddings, enabling a shared update mechanism across different user history types such as movies, news, and dialogue. It demonstrates theoretical properties like invariance to relabeling and bounded state changes, and presents the Multi‑Timescale State Hypothesis (MTSH) implemented in PerTIDE. Experiments on PENS, MovieLens, and MIND datasets show that a frozen source‑trained core outperforms random baselines and that PerTIDE achieves significant MRR gains over comparable models.
By Parthiv Chatterjee, Kashish Kanjaria, Vashisth Purani, Sourish Dasgupta, Tanmoy Chakraborty
GC--EI--MS is an important technique for analyzing volatile and semivolatile compounds in complex samples. However, conventional methods rely heavily on reference spectral library matching, limiting t...
arXiv:2610.00929v1 Announce Type: cross
Abstract: Models specialized for the same task converge to similar behavior, yet the parameter updates that produce it share no common coordinate system, so we...
By Junghwan Park, Woojin Cho
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
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 memetic trojans, a new class of attacks that embed adversarial payloads within socially contagious content shared by large language model agents. Unlike traditional agent worms that rely on malicious retransmission, memetic trojans exploit agents’ natural tendency to share content that aligns with their internal motivations, leading to high virality and significant amplification of exposure. Experiments on the Moltbook platform show that such contagions can be retransmitted in about 50% of subsequent posts and upvoted 2.5 times more than average, with simulations indicating up to a 3.19x increase in expected exposure across the network.
whyItMatters":"The study reveals that endogenous social transmission is a distinct security vulnerability in multi‑agent systems, implying that defenses must address network-level dynamics rather than just prompt‑injection detection or agent compromise prevention."
By Birk Torpmann-Hagen, Finn Schwall, Leon Moonen
The paper presents an automated framework for building ontology networks by discovering typed semantic links between concepts across domains. It uses domain‑adapted DistilBERT embeddings, clustering to prune candidates, and GPT‑4o for generating interpretable relationships, reducing 800k raw pairs to 95k high‑quality candidates. Human validation on 429 links shows an 80.19% precision and 0.890 F1, outperforming five similarity‑based baselines and demonstrating the need for LLM reasoning in ontology link construction.
By Nouha Hayouni, Sheeba Samuel, Alsayed Algergawy
arXiv:2610. 01871v1 Announce Type: cross Abstract: Multimodal Retrieval-Augmented Generation (MRAG) has emerged as a reliable and cost-effective technique of grounding the generative capabilities of Multimodal Large Language Models (MLLMs) into relevant, up-to-date, external knowledge.
By Maria Carmen Jica, Ali Satvaty, Suzan Verberne, Fatih Turkmen
The paper investigates automated reward systems for training language models in conversational humor, examining how reward exploits can undermine intended behavior. It evaluates two reward approaches—an embedding-based surprise reward and an audience-model laughter prediction—showing that each can be tricked by word shuffling or laughter cues, respectively. Countermeasures such as fluency filtering and cue normalization mitigate some attacks but also risk rejecting genuine witty responses, highlighting the difficulty of designing robust rewards.
By Sam Larson
The study investigates whether the organization of mathematical training data for large language models (LLMs) should be based on topic or on reasoning approach. Using two counterbalanced experimental designs, the authors compare fine‑tuned models trained on problems that share the same solution method (same‑approach, SA) versus those that share the same topic (same‑topic, ST). Across five base models and multiple training seeds, SA consistently outperforms ST, with mean gains of 10.8–14.3 percentage points, even though ST sources are more similar to targets in embedding and lexical metrics.
By Sajad Goudarzi, Samaneh Zamanifard, Seyed Amin Seyed Haeri, Moloud Nasiri, Hamed Rahimian
The paper introduces Decision Titan, a variant of the Decision Transformer that incorporates Test‑Time Training (TTT) layers to store episodic memories in network parameters. It evaluates this architecture on the X‑Maze environment, showing that Decision Titan can learn long‑term dependencies up to 20 times longer than its context window and generalise to sequences 1.7 times longer than the training data. The study also finds that temporal generalisation depends on the choice of time embeddings and that the ability to learn long‑term dependencies hinges on how relevant information is encoded.
By Jude Waide, Robert Lieck