Computer Vision
RoGSW4RLD: Feed-Forward 4D Gaussian Lifting for Robot World Model Rollouts
Jin Hyun Kim, Min Young Kim, Soohwan Song et al. (+1 more)
Action-conditioned
video world models predict future
robot interactions from multiple cameras, yet their outputs remain disparate
video collections rather than a shared metric scene queryable across viewpoints and time. While existing 4D reconstruction methods offer a path to spatialize these predictions, independently reconstructing and merging each camera stream fails to enforce cross-view consistency. This limitation is particularly detrimental when combining moving
robot-mounted cameras with fixed external views. To address this, we introduce RoGSW4RLD, a feed-forward framework that lifts synchronized multi-camera rollouts into a unified, time-queryable metric 4D Gaussian field. Rather than learning a separate
geometric transition model, RoGSW4RLD directly reconstructs the visual future generated by existing world models. Its core innovation is a two-stage architecture: Stage 1 jointly forms the metric 4D field by fusing cross-view evidence with
robot-specific articulated geometry and kinematics, while Stage 2 refines the field's geometry and appearance while strictly preserving the initial temporal displacements. Evaluated on 256 held-out DROID episodes, RoGSW4RLD significantly outperforms camera-wise reconstruction with calibrated merging, improving novel-view PSNR by 2.15 dB, reducing depth AbsRel by 47%, and lowering
robot displacement error by 61%. These robust gains extend to action-conditioned Cosmos 3 rollouts, demonstrating that predicted
video futures can be successfully translated into consistent, spatially queryable 4D metric representations.
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All authors Jin Hyun Kim, Min Young Kim, Soohwan Song, Daekyum Kim
Networking
Configuration-Induced Delivery Failures in NATS JetStream: Detection and Remediation
Biplab Kumar Das
NATS JetStream's at-least-once delivery guarantee is conditional: five common configuration mistakes silently violate it, causing duplicate message processing, data loss, or redelivery storms with no error logged anywhere. The standard Prometheus NATS exporter exposes only server-level throughput metrics and cannot detect any of these failures. We present nats-lens, a standalone monitor that reads from the JetStream management API and detects all five violation classes without requiring changes to monitored applications or client code. We formally characterize each class with a precise condition, prove that standard Prometheus NATS metrics are structurally incapable of detecting any of them, and implement five targeted detectors. In a controlled evaluation of 30 rounds per scenario on both single-node and 3-node JetStream clusters, nats-lens achieves 100% detection coverage across all five classes—versus 0% for the baseline—with zero false positives over 30 minutes of healthy operation. Detection latency ranges from 2,003 ms to 8,013 ms (within three poll cycles). We confirm
language-agnostic detection empirically using consumers in Rust, Go, and Python. The tool is open source and exposes findings through four output channels: web dashboard, Prometheus metrics, REST API, and NATS health events.
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Computation and Language
Epistemic Policy Divergence in Multi-Turn LLM Contamination: A Protocol-Gradient Investigation
Fahrell Giovanny, Geby Bayuningtyas, Sahrul Mukharom et al. (+1 more)
Large
language models treat conversation history as unverified context, so false premises injected into earlier turns can be adopted as fact later, a failure mode we call session-level contamination. We introduce five contamination protocols along a source-authority gradient, each holding the false premise constant while varying how it is presented, from self-attributed falsehood to explicit instruction override. We evaluated three architecturally diverse models, GPT-5.4 Mini, Gemini-3.1 Flash-Lite, and GLM-4.5-Air, on ten cases spanning five knowledge fields at temperature zero over 22,500 turns, using a dual-track judge checked against a 120-turn human-annotated sample (Cohen's kappa = 1.000 for binary adoption, a ceiling on this sample, and 0.92 linear-weighted for collapse severity). The three models implemented structurally different epistemic policies. GPT-5.4 Mini recorded zero adoptions across all 500 sessions;
token-level probing of the GPT-5.4 base model left the correct
token as the argmax in eight of ten conditions. Gemini-3.1 Flash-Lite showed a steep framing-dependent policy: 0.1 percent for self-attributed falsehoods, 23.5 percent for user-cited sources, 68.2 percent for system-injected authority, and 94.0 percent for instruction override. GLM-4.5-Air showed a shallower gradient (15.8 and 84.2 percent for those two vectors). This 68-percentage-point dissociation is consistent with authority deference and instruction compliance engaging distinct mechanisms within a single architecture. Recovery also differed: GLM recovered in 94.5 percent of affected sessions, whereas 26.1 percent of affected Gemini sessions did not return to factual accuracy within the window (40.0 percent under instruction override). We release the evaluation framework and taxonomy for reproducible testing.
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All authors Fahrell Giovanny, Geby Bayuningtyas, Sahrul Mukharom, Hafiz Budi Firmansyah
Information Retrieval
Signal or Noise? Modality Contribution and Cooperation in Multimodal GraphRAG
Antonios Georgakopoulos, Paul Groth and Lise Stork
Multimodal knowledge graphs (KGs) integrate information from text, figures, tables, and other modalities into a unified structured representation, with the promise that richer evidence enables better inference. In GraphRAG systems built over such graphs, it is commonly assumed that retrieving evidence from more modalities at inference time improves downstream performance. Yet, redundant or overlapping
multimodal evidence may distract
language models in question answering (QA), and whether each modality contributes equally across questions, models, and tasks remains poorly understood. In this work, we study how modality-aware
retrieval affects downstream inference in a
multimodal GraphRAG pipeline, using document visual question answering (DocVQA) as a testbed. We extend an existing KG-based QA framework to be modality-aware, leveraging the graph structure to track which modality supports which facts and to selectively filter evidence at the edge level. This enables us to investigate whether providing all available
multimodal evidence at inference time benefits QA, and to evaluate the contribution and cooperation of modalities across question, task, and model characteristics. Through a controlled analysis within a state-of-the-art
multimodal GraphRAG pipeline, five
multimodal LLMs and two DocVQA
benchmarks, we find that tables and text provide the strongest contributions, and that combining modalities frequently produces redundancy rather than synergy, particularly for pairs involving textual information. Positive cooperation appears mainly between non-text modalities and depends on question intent and task type. Our findings argue for selective, modality-aware
retrieval in the design of more effective GraphRAG systems, where modalities are filtered according to the downstream task rather than retrieved uniformly.
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Computer Vision
PIVOT: Pivot-Aware On Policy Self Distillation for Multi-Turn VLM Agents
Jiazhou Zhou, Hu Zhou, Yucheng Chen et al. (+3 more)
Reinforcement learning with verifiable rewards (RLVR) via Group-Relative Policy Optimization (GRPO) is widely used for multi-turn VLM
agent training, yet it suffers from zero-gradient silence on uniform failures and coarse episode-level credit assignment. While On-Policy
Distillation (OPD) and On-Policy Self-
Distillation (OPSD) mitigate sparse rewards using hindsight information, their underlying mechanisms remain poorly understood. Through controlled counterfactual rollback probes across five multi-turn VLM
agent benchmarks, we reveal that performance gains in OPSD/OPD are largely driven by physical state rollback at the pivot step, defined as the first unrecoverable action without remaining step budget. However, physical state rollbacks are computationally prohibitive and infeasible in real-world environments. To bridge this gap, we present Pivot-Aware Internalized Visual On-Policy Training (PIVOT), an RL framework that internalizes pivot localization and state restoration directly into
token-level parameter updates, eliminating environment rollbacks during RL training and additional skill hints at test time. PIVOT unifies three functional roles within a single architecture: a failure Analyzer non-invasively localizes the pivot step and diagnoses failure modes from visual trajectory collages and action logs; a detached Teacher re-scores failed
tokens under this privileged diagnostic context; and a Student optimizes joint GRPO and confidence-gated OPD objectives. At test time, both Teacher and Analyzer branches are stripped. Evaluated on five multi-turn VLM
agent tasks across cognitive grid puzzles, 3D
embodied control and
navigation, and generative
reasoning, PIVOT achieves 0.90 overall accuracy on Qwen2.5-VL-3B (+8% over SFT+GRPO baseline and +5% over previous SOTA) and scales to 0.92 on Qwen3-VL-2B (+12% over SFT+GRPO baseline).
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All authors Jiazhou Zhou, Hu Zhou, Yucheng Chen, Jinyuan Qu, Ying-Cong Chen, Lei Zhang
Artificial Intelligence
Narrowing the Horizon: Quantifying Topic Saliency Shifts in Generative Monoculture
Oriane Peter, Elena Simperl and Kate Devlin
As Large
Language Models (LLMs) become central to how we access and share information, they play an increasingly powerful role in shaping global knowledge. However, as these models evolve, their outputs risk converging into a generative monoculture, where the diversity of perspectives they represent narrows over time. Studies at the model level often fail to pinpoint which specific topics or viewpoints are being marginalised or amplified in this process. In this paper, we introduce a method to measure shifts in topic saliency across model families, tracking what gains or loses prominence during post-training. Applying this approach to a case study of
climate change discourse, we demonstrate how homogenisation affects the representation of diverse solutions across different models. We also test interventions to counter this trend, showing that specialised models can help preserve a broader range of perspectives. This underscores the importance of monitoring topic saliency to diagnose the risks of monoculture and to ensure AI systems reflect a pluralism of ideas. Data and Code are accessible \href{https://github.com/oriane/topic_saliency_shift}{here}.
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Computational Engineering
Experimental Investigation of Heat-generating Cheese Pallets During an International Export Cold Chain
Dihia Aguenihanai, Jean Moureh, Yasmine Salehy et al. (+2 more)
This study presents an industrial experiment conducted with a dairy partner to investigate the temperature
evolution of heat-generating soft cheeses within pallets during export cold chain. In the production factory, four homogeneous pallets of soft cheese were instrumented with temperature probes placed at three levels (bottom, medium, and top). Temperature was monitored over a 13 days export cold chain from France to Northern Europe. The objective was to evaluate the influence of air ventilation and pallet wrapping film on product temperature. The results clearly highlight the effect of ventilation: pallets placed in a well-ventilated area showed temperatures approximately 3{\textdegree}C lower than those in less ventilated zones. The study also underlines the importance of maintaining good ventilation of pallets before loading them into refrigerated transport vehicles. However, the effect of wrapping film remains unclear and does not allow definitive conclusions. This field study provides practical recommendations for better conditioning of heat-generating products in the cold chain.
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All authors Dihia Aguenihanai, Jean Moureh, Yasmine Salehy, Yvanne Paviet-Salomon, Evelyne Derens-Bertheau
Cryptography and Security
Sustained Participation as a Security Resource: The Bounded Participation Channel
Homayoun Maleki, Nekane Sainz, Jon Legarda et al. (+1 more)
Can sustained, per-identity participation be engineered into a security resource? Most anti-Sybil defenses price identity creation rather than identity survival. Once admitted, an adversary may sustain many identities without paying a recurring cost. We introduce the Bounded Participation Channel (BPC), a formal primitive for repeatedly verifying participation window by window. BPC issues fresh, identity-bound challenges under a strict deadline and enforces four structural properties: identity binding, freshness, real-time response, and bounded per-channel throughput. Together, these yield a provable cost
theorem: sustaining $s$ identities over $T$ windows requires $C(s,T) \geq sT/\tau_h$ participation channel-windows. The guarantee is solver-agnostic: a channel may be operated by a human, an AI system, or a hybrid. We give a hash-based construction with publicly verifiable participation proofs, characterize four admissible challenge families, and evaluate two against GPT-4o, Gemini 2.5 Flash, and Claude Sonnet 4.5 across 600 trials. Despite near-perfect accuracy (97–100%) on the perceptual tasks, the evaluated automated channels remain throughput-bounded under the tested deployment conditions. The results illustrate a key distinction: solvability does not imply unlimited throughput. By requiring participation to be re-earned by every identity in every time window, BPC turns sustained participation into a measurable security resource with a linear structural cost floor, independent of whether the participation is supplied by humans, AI systems, or hybrids.
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All authors Homayoun Maleki, Nekane Sainz, Jon Legarda, Igor Santos-Grueiro
Artificial Intelligence
Training-Free Clinical Reasoning through Medical Ontologies and Cognitive Mapping: A Symbolic-Probabilistic Knowledge Graph Framework
Surajit Das
Most clinical prediction systems learn patient-variable-outcome associations; we investigate a training-free diagnostic paradigm mapping patient observations to explicit medical knowledge. CKG Reasoner integrates candidate-specific Evidence Feature Nodes, patient-reference matching, a bounded Information Gate, knowledge-weighted evidence accumulation, disease similarity, and decisive clinical rules. Missing-aware normalization and coverage
auditing distinguish absent from unavailable evidence. Candidate ranking is separate from outcome-label-independent K-means clustering, which uses four derived evidence coordinates (evidence strength, relative magnitude, directional similarity, and evidence completeness), not raw predictors or targets, to derive cohort-level assignments. Across six retrospective cohorts - four dengue (N = 1000, 1523, 989, 1018), malaria (N = 2190), and influenza (N = 4569) - a uniform, label-free, cohort-fitted K = 2 protocol yielded positive-class F1 scores of 0.996, 0.634, 0.936, 0.917, 0.695, and 0.842, and all-record accuracies of 0.996, 0.558, 0.914, 0.893, 0.707, and 0.906, respectively, with full partition-decision coverage using the frozen package and disease-specific knowledge representations. Neither scoring nor clustering uses outcome labels. Logistic regression provides a supervised baseline. Influenza incorporates confirmatory molecular PCR and is not independent pre-test prediction. Results characterize knowledge-grounded evidence separation, auditability, and sensitivity, not prospective clinical validity or comparative superiority. FOL/LLM-based clinical explanation remains unevaluated.
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Machine Learning
LionMuon: Alternating Spectral and Sign Descent for Efficient Training
Arman Bolatov, Artem Riabinin, Nikita Kornilov et al. (+4 more)
Pretraining a
language model takes enormous compute, and the right optimizer can save a good part of it.
Muon's spectral step gives a stronger direction than a sign step, but it is expensive. Every step runs Newton-Schulz iterations on the full
matrix and, in distributed training, an extra all-reduce. Sign steps, as in Lion and Signum, are cheap and stay local to each device. We propose LionMuon, which takes one
Muon step every $P$ iterations and Lion steps in between, with a single dual-EMA momentum buffer shared by both.
Muon's compute and communication are paid once per $P$ steps, and the optimizer state is half of AdamW's. A single-EMA variant, SignMuon, already improves on
Muon. We prove complexity bounds under heavy-tailed noise in which the period sets an interpolation between
Muon's and Lion's smoothness and noise constants, and which say when LionMuon is faster than both. On 124M and 355M models trained on FineWeb, LionMuon with $P=2$ and $P=5$ reaches a lower loss than
Muon, AdamW, Lion and Signum at the same number of
tokens. Under 4-GPU data-parallel training it reaches
Muon's final loss with a third less wall-clock on PCIe, and it beats the communication-efficient
Muon variants Dion and MuonBP on loss at no more exposed communication, while keeping the exact gradient. Code: https://github.com/
brain-lab-research/lion-
muon
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All authors Arman Bolatov, Artem Riabinin, Nikita Kornilov, Andrey Veprikov, Samuel Horváth, Martin Takáč, Aleksandr Beznosikov
Artificial Intelligence
AbGaze: Attentive Geometric Representation Learning for End-to-End Antibody Design
Jiashuo Wang, Siqi Fan, Yizhen Luo et al. (+1 more)
Computational antibody design requires representations that capture the
geometric patterns underlying antigen–antibody interactions, yet existing approaches often rely on scalar distances or surface-intrinsic features, leaving cross-molecular geometry largely implicit. We present AbGaze, an end-to-end antibody design framework based on attentive
geometric representation learning, which encodes distance, spatial direction, and surface-normal orientation of antigen surfaces relative to antibody-residue local frames, and adaptively aggregates these
geometric interactions according to their interfacial context. The learned interaction representation is shared across multi-CDR co-design, complex structure prediction, and affinity optimization, with local-frame
geometric supervision further constraining the representation. AbGaze outperforms prior methods across all three tasks: relative to the second-best method, it improves amino-acid recovery by 7.1% and reduces structural error by 14.9% on average over the six CDRs, improves interface docking quality (DockQ) by 6.6%, and raises the affinity improvement rate (IMP) by 32.5%.
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All authors Jiashuo Wang, Siqi Fan, Yizhen Luo, Zaiqing Nie
Machine Learning · Cross-listed from eess.AS
Simulation-Based Inference for Plate Reverb System Identification
Dylan Sechet, Marc Evrard and Matthieu Kowalski
We address Task A of the 1st DAFx Parameter Estimation Challenge, which aims to retrieve the physical parameters of a plate model from an impulse response. To do so, we use the Simulation-Based Inference (SBI) framework, in which we train a neural network to estimate a density over plate parameters given an impulse response, using a
dataset generated by the simulator. Inference for a new impulse response then requires only a forward pass through the network, without involving the simulator. For each test observation, we fine-tune a specific network: additional simulation rounds are performed by sampling parameters from the current estimated distribution, simulating the corresponding impulse responses, and fine-tuning to produce the specialized network.
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Computer Vision
Beyond Saying Less: Fine-Grained Alignment for Informative and Faithful Vision-Language Models
Xingming Long, Jie Zhang, Yuecong Min et al. (+2 more)
Object
hallucination remains a major challenge for large
vision-
language models. While off-policy preference optimization proves to be an effective solution, on-policy reinforcement learning provides a more promising direction as it directly targets a model's current failure modes. However, we find that without fine-grained reward formulation and allocation, on-policy optimization often falls into an easy shortcut: reducing
hallucinations merely by saying less—making fewer valid claims. To comprehensively resolve this, we propose a fine-grained
alignment framework that couples dense reward signals at the data level with precise credit assignment at the algorithmic level. Specifically, we first construct the Dense Object Presence and Absence (DOPA)
dataset to address sparse annotations that prevent valid object claims from being verified and rewarded. DOPA exhaustively annotates the deterministic presence and absence of every concept across an expanded vocabulary, significantly increasing the density of reliable reward signals during on-policy rollouts. Second, we propose Subsentence-level Credit Assignment for on-Policy Optimization (SCAPO) to prevent response-level shared advantages from allowing local
hallucinations to compromise all other valid outputs within the same response. By assigning credit to each subsentence independently based on its object claims, SCAPO can precisely reinforce faithful generations and penalize
hallucinations. Furthermore, we leverage the resulting faithful image descriptions as auxiliary context to transfer generative gains to discriminative tasks. Experiments demonstrate that our method produces highly informative, faithful descriptions in generative tasks while yielding clear performance gains on discriminative evaluation.
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All authors Xingming Long, Jie Zhang, Yuecong Min, Shiguang Shan, Xilin Chen
Computation and Language
Decide, Don't Generate: Competitive Dimensional ABSA with Jev's Typed Decisions
Yiqun Zhang, Peidong Wang, Zihan Wang et al. (+1 more)
Aspect-based sentiment analysis (ABSA) has largely turned to text generation. We show that competitive dimensional ABSA does not need it. Using Jev, a frozen model that answers typed questions with rubric scores, label probabilities, and yes/no judgments, we decompose all three tasks of SemEval-2026 Task III Track A into such decisions and align them with the annotation scheme through 488 coefficients fitted on CPU, with no text generation and no backbone tuning. On valence-arousal regression over ten corpora in six
languages, the system reaches 1.0645 RMSE, the lowest aggregate error of any participating system. On triplet and quadruplet extraction, it reaches 52.09 and 44.06 continuous F1, above fine-tuned Llama-3.3-70B and GPT-OSS-120B baselines. Analyses and ablations show where the accuracy comes from: supervised calibration roughly halves the raw regression error, exact valence-arousal would add only 4.5 F1 to extraction, and the learned combination of span-boundary evidence, not any single signal, carries the extraction systems.
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All authors Yiqun Zhang, Peidong Wang, Zihan Wang, Shi Feng
Computer Vision
Scaffold Then Internalize: Representation Injection for Diffusion Transformers
Han Fu, Jiacheng Chen, Baoquan Zhao et al. (+4 more)
Recent representation
alignment (REPA) methods accelerate
diffusion transformer training by aligning projections of the
transformer's hidden states with representations from pretrained visual encoders. In this work, we explore a reverse and complementary direction to REPA: rather than projecting
diffusion representations into the encoder's space, we inject encoder representations into the
diffusion transformer, allowing them to actively participate in the denoising process. To this end, we introduce REPresentation Injection (REPI), a training framework based on a scaffold-to-internalization strategy, in which projected encoder representations initially serve as a temporary scaffold and are then progressively internalized by the
diffusion transformer. REPI outperforms REPA across a wide range of backbones and is highly complementary to it: combining the two yields substantial gains over either alone. Notably, with only 160K training steps, REPI + REPA matches vanilla SiT trained for 7M steps, a speedup of over $43.5\times$. Code will be available at https://jeneveuxpas.github.io/REPI
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All authors Han Fu, Jiacheng Chen, Baoquan Zhao, Weidong Chen, Wei Liu, Qing Li, Xudong Mao
Machine Learning
Narrow Multimodal Fine-Tuning Can Induce Emergent Misalignment
Shunchang Liu, Lukas Fluri, Xin Chen et al. (+1 more)
Modern AI models are aligned through post-training to adapt them to downstream tasks. Recent work shows that fine-tuning
language models on narrow tasks can induce emergent misalignment (EM), causing broadly harmful behaviors beyond the training task. However, EM has been studied almost entirely in text-only tasks, leaving its manifestation in
multimodal models unclear. In this paper, we define and analyze EM in the context of
vision-
language models. We first induce EM via fine-tuning on narrow
multimodal tasks targeting vulnerable code, careless household-object use, and conspiratorial interpretations of ordinary scenes. Across fifteen commercial and open-source models with different scales, we find that narrow
multimodal fine-tuning can induce coherent and broadly misaligned behavior that transfers to unrelated tasks, including misaligned opinions, visual factual dishonesty, unsafe image generation, vulnerability to visual jailbreaks, and risky agentic actions. We further find that
multimodal EM does not depend on the apparent harmfulness of training data but is sensitive to training-evaluation modality
alignment. EM can arise under both supervised fine-tuning and preference optimization and can propagate through intermediate
reasoning. Finally, we explore several mitigation strategies, including
prompt inoculation, benign continued training, and activation-level steering, which can partially reduce EM. Overall, our findings suggest that
multimodal EM reflects a behavioral shift rather than a general loss of capability, extending beyond text to the visual modality.
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All authors Shunchang Liu, Lukas Fluri, Xin Chen, Francesco Croce
Artificial Intelligence
EvoIn: Bridging Evolution and Internalization for Agent Fine-Tuning
Shihan Dou, Shaofan Liu, Zhonghang Lu et al. (+8 more)
Recent work has explored improving
agents by jointly evolving their harnesses and models, but often takes a ”potpourri” approach that bundles together new tools, new decision-making procedures, and model adaptation to the evolved harness under a single notion of
agent improvement. In this paper, we instead investigate how
agents can improve their decision-making procedures. In particular, we propose EvoIn, an
agent fine-tuning framework that bridges
evolution and internalization. EvoIn first analyzes
agent execution traces to evolve and validate new decision-making procedures by temporarily instantiating them in the harness. The validated procedures guide the
agent to generate improved
reasoning traces. These traces are then rewritten into self-contained
reasoning traces, removing explicit references to harness instructions while expressing the induced decision logic as the model's own
reasoning. Finally, EvoIn fine-tunes the model on the rewritten traces, internalizing these procedures so that the improved decision-making persists without the evolved harness at inference time. We evaluate EvoIn on diverse
benchmarks and find that it consistently enables
agents to learn stronger decision-making procedures, raising the pass rate by 10.9 points in-domain and by 9.2 points out-of-domain. Results further show that the internalized decision procedures generalize to unseen tasks. Case studies show that
agents can learn to decide how to solve a task before solving it, for example by checking a document's length to choose between reading it in full and searching it. EvoIn is also broadly applicable, showing consistent improvements on another model family.
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All authors Shihan Dou, Shaofan Liu, Zhonghang Lu, Jiahang Lin, Shichun Liu, Binghai Wang, Jiajie Jin, Guanting Dong, Tao Gui, Qi Zhang, Xuanjing Huang
Computer Vision
Domain-adaptive Zero-Shot Image Enhancement via Locality-Constrained Diffusion Guidance
Theresa Neubauer, Dimitrios Lenis, Astrid Berg et al. (+7 more)
Denoising
Diffusion Probabilistic Models have shown remarkable performance in unconditional image generation. In order to generate images with desired semantics, recent works have restricted the solution space by using guidance constraints in the
diffusion sampling process. However, for image enhancement across different domains, these methods struggle to balance two main requirements: looking realistic in the target domain (photorealistic images) and preserving relevant features of the source domain, e.g., low-quality renderings or art paintings. Here, small local changes can alter the fidelity of the image completely, while large changes in other regions might be insignificant. We introduce LocDiff, a locality-constrained guidance method for image enhancement, which serves as a zero-shot extension to pre-trained
diffusion models, ensuring the preservation of critical features during domain adaptation. In this way, we retain important local features, while allowing less critical regions to remain unconstrained and not interfere with the guidance process for relevant regions. We evaluate our method on two different domain-shift tasks: For art-to-photo translation, we apply the method in a fully zero-shot setting, preserving facial identity from paintings while generating photorealistic details. For enhancing low-quality fetal ultrasound renderings, we demonstrate zero-shot inference with auxiliary prior
alignment. Here, the objective is to artificially add high-resolution characteristics and produce photorealistic ultrasound renderings, a target domain for which no ground truth distribution exists. Our experimental results demonstrate that LocDiff achieves favorable realism-faithfulness trade-offs compared to state-of-the-art methods, enabling controllable cross-domain enhancement.
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All authors Theresa Neubauer, Dimitrios Lenis, Astrid Berg, Maria Wimmer, Gaia Romana De Paolis, Philip Matthias Winter, David Major, Johannes Novotny, Ariharasudhan Muthusami, Katja Bühler
Machine Learning
$\lambda$-JEPA Spectral Anti-Collapse Regularization for Self-Supervised Learning
Berker Demirel, Clémentine Dominé, Valentino Maiorca et al. (+3 more)
Joint-
embedding self-supervised learning typically combines an invariance objective across augmented views with additional mechanisms to prevent representational collapse. These objectives are often applied after a projection head, while downstream tasks use the backbone representation before the projector. We find that this mismatch does not necessarily prevent dimensional collapse in the backbone, which can retain low effective rank and potentially limit downstream transfer. To address this, we introduce SACReg, a spectral anti-collapse regularizer motivated by an analysis of $\lambda$-balance, which captures the relative scale of weight
matrices across layers. In a two-layer linear network, we show that (i) $\lambda$-balance prevents collapse, and (ii) our regularizer applied to the backbone induces $\lambda$-balance. In the nonlinear case, this regularizer leads to anti-collapse as well and, in realistic architectures on ImageNet100, it empirically increases the representations' ranks. We apply SACReg to JEPA and propose $\lambda$-JEPA, which improves over LeJEPA and VISReg on ImageNet-1k classification and in average linear-probe transfer performance across eight downstream image
datasets. On
video self-supervised learning, $\lambda$-JEPA improves over LeVJEPA and V-JEPA 2 on the Something-Something-v2 and Kinetics-400
benchmarks. Code is available at https://github.com/berkerdemirel/lambda-jepa.
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All authors Berker Demirel, Clémentine Dominé, Valentino Maiorca, Marco Fumero, Marco Mondelli, Francesco Locatello
Artificial Intelligence
The Argument and the Letterhead: Source-Position Coherence in AI Evaluation
Michele Loi
An argument can be surprising coming from a particular speaker without being a bad argument. Do AI evaluators keep these judgments apart? Two preregistered descriptive studies and a later Jev supplement collected 2,976 usable evaluations of six fixed texts about US AI policy, Germany's debt brake and Swiss
nuclear energy. Each text was presented under several source attributions. The key comparison asks whether the gap between two sources changes when the argument changes. On Sol, for example, a national-security argument received mean ratings of 0.359 under CODEPINK and 0.639 under College Republicans; a civil-rights argument received 0.742 and 0.721. A constant preference for one source cannot explain that pattern. Related interactions appeared across topics and recent model configurations, including those with
reasoning enabled, while several comparisons yielded small effects. The later European Jev supplement yielded five interactions below the adopted absolute reference of 0.05; its distinct rubric and interrupted collection limit comparison with the chat systems. Some written evaluations explicitly invoked a mismatch between a source and its attributed position. Taken together, the numerical and verbal evidence supports source-position coherence as a plausible explanation, alongside competing accounts involving credibility, authenticity and interpretation of the task. The paper develops this inference through controlled comparisons, reports conditional post hoc p-values in an appendix, and documents the human decisions and delegated checks behind an AI-conducted study.
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Artificial Intelligence
Textual User Taste: Natural-Language User Context for Foundation-Model Recommender System at Scale
Ghazal Fazelnia, Paul Gigioli, Eliza Klyce et al. (+16 more)
Foundation model recommender systems require user context that can be consumed by large
language models, reasoned over, and refined through natural-
language interaction. Traditional behavioral
embedding vectors remain highly effective for
retrieval and ranking, but they are opaque to users and not natively expressed for
language model workflows. We present Textual User Taste, a system that generates structured natural-
language taste profiles from listening behavior, interaction signals, content metadata, and optional user feedback, and deploys them to millions of Spotify users. We describe the end-to-end production lifecycle required to generate, evaluate, optimize, and maintain these representations at industrial scale, including
prompt development and compression, user steering, and integration with downstream personalization systems. Because no unique ground-truth taste profile exists, we introduce a multi-faceted evaluation framework to evaluate taste profiles as a production representation: they carry user-specific predictive signal independently, and when integrated with behavioral
embeddings, improve MRR by 0.6% for future-track prediction and NDCG@7 by 2.2% for search ranking. Our evaluation also reveals that taste profiles support positive natural-
language steering, while exposing important limitations, including challenges with negation and short-term temporal adaptation. These findings position taste profiles not as replacements for behavioral
embeddings, but as an interpretable and steerable interface between evolving user context and foundation-model recommender systems.
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All authors Ghazal Fazelnia, Paul Gigioli, Eliza Klyce, Sharon Zheng, Katie Zelvin, Ye Myat Thein, Anurag Deshpande, Seda Davtyan, Kate Remeika, Maya Hristakeva, Erik Franco, Karen Banzon, Peng Ge, Jacqueline Wood, Nandini Singh, David Murgatroyd, Mounia Lalmas, Yves Raimond, Andreas Damianou
Software Engineering
Is there a future for models in the LLM era?
Marco Calamo, Massimo Mecella and Monique Snoeck
In an era where software development is deeply tied with Large
Language Models, does Model Driven Engineering (MDE) still make sense? This raises the question of the extent to which MDE can be successfully combined with an LLM approach to address the downsides of each approach separately. In this paper, we try to answer that question by investigating a central Research Question: Can
Agents powered by Large
Language Models improve code generated from UML diagrams and text specifications by Model Driven Engineering (MDE) tools? To investigate this problem, we developed a novel LLM-powered Multi-Agentic approach called ARTHUR (Architecture Refactoring Through Hybrid UML
Reasoning), a framework that aims at combining the reliability of MDE and the ease of use of LLMs. ARTHUR is designed to refactor legacy Java code produced by traditional, rule-based, MDE code generators from UML diagrams. To asses the answer to our question, we have refactored the legacy code of several projects from our custom
dataset crafted for this purpose. \name{} which made it possible to add support for modern frameworks like Spring Boot, while ensuring compliance with Model-Based Testing techniques to verify that the code still corresponds to the initial model's specifications. We then measured the results obtained in terms of time and cost, passing test rate, and compile@k, pass@k and \texttt{pass$^k$} metrics. We also observed the effect of generating code directly from the conceptual model without the refactoring. Our preliminary test results show that MDE could not be more far from retirement, after all.
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Game Theory
Truthful-in-Expectation MMS Allocations for Chores
Zehan Lin, Biaoshuai Tao, Xiaowei Wu et al. (+1 more)
We study truthful-in-expectation (TIE) mechanisms for allocating indivisible chores alongside ex-post maximin share (MMS) guarantees. For goods, Bu and Tao (FOCS 2025) established a (1/n)-approximation for TIE mechanisms, and this was substantially improved by Babaioff, Feige, and Manaker Morag (FOCS 2026), who established an \Omega(1/\log n) approximation, where n is the number of
agents. The corresponding problem for chores has received less
attention. The best-known result is due to Aziz, Li, and Wu (MAPR 2024), who gave a TIE mechanism with an O(\sqrt{\log n}) MMS approximation guarantee that holds only in expectation. They also established a 6/5 lower bound for TIE mechanisms for two
agents. In this paper, we present the first TIE mechanism for chores that achieves a constant ex-post MMS approximation guarantee. Specifically, our mechanism guarantees an ex-post ratio of 1.97 for any number of
agents n, which improves to 4/3 for n=2 and 3/2 for n=3. On the hardness side, we tighten the two-
agent lower bound to 4/3, showing that our mechanism is optimal for n=2. More generally, we establish a lower bound of 13/12 on the ex-post MMS approximation ratio achievable by TIE mechanisms for every n\ge 3.
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All authors Zehan Lin, Biaoshuai Tao, Xiaowei Wu, Yuhao Zhang
Computation and Language
Measuring Collapse and Correction in Homogeneous-Panel LLM Debate
Xin Li, Mengbing Liu and Chau Yuen
Multi-
agent large
language model (LLM) debate is often evaluated by whether final answers improve, but movement is not necessarily improvement: the same discussion can rescue an initially wrong majority or destroy an initially correct one. Standard final-accuracy evaluations conflate these opposing mechanisms. We introduce an auditable protocol for homogeneous debate on multiple-choice questions (MCQs) that records each run as a transition ledger over collapse, correction, onset, and signed intervention utility. On 6,925 MMLU-Pro debates, the protocol identifies 253 collapses and a parallel correction ledger that changes how interventions should be judged. Replay experiments reveal the central tradeoff: a leave-one-model-out probe-gated freeze prevents 29 collapses but loses 108 corrections under equal weights, so collapse prevention alone can recommend the wrong policy. A compact pre-debate 8-probe screen is a triage signal: its unadjusted family-level association with conditional-collapse risk is high (G=7, Spearman rho=0.893, exact two-sided p=0.0123), but initial-majority accuracy is a close comparator (rho=0.821; family partial rho=0.767, p=0.0877), so we do not treat it as calibrated or capability-adjusted prediction. Round-level traces localize many collapses to the first debate round, where early disagreement can precede both harmful cascades and useful recovery. We release replayable schemas, coders, audits, cost cards, and zero-API rebuild scripts so future model-scaffold rows can be compared under the same denominators and signed utility ledger.
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Computer Vision
Evaluating Hierarchy-Aware Deep Learning for the Recognition of Tironian Notes
Yule Kang, Thomas Gorges, Janne van der Loop et al. (+4 more)
Tironian notes are generally regarded as the first Latin shorthand system and are notable for their large, fine-grained symbol inventory. Their high visual similarity and large class set make manual reading time-consuming, leaving manuscripts that contain Tironian notes inaccessible to many researchers. Automatic recognition is also challenging because models must distinguish subtle differences in stroke shape and sign structure while realistic training data remain scarce. However, standard flat classifiers do not explicitly use visual or structural relations between related signs. This paper investigates whether structural relationships between Tironian notes can support automatic recognition. We use the Supertextus Notarum Tironianarum (SNT) by Martin Hellmann, which provides idealized sign forms and a hierarchical organization of Tironian notes. We compare flat ResNet18, ConvNeXt, Shifted Window
Transformer (Swin), and
Vision Transformer (ViT) classifiers with Hierarchical Deep Convolutional Neural Network (HD-CNN)-style coarse-to-fine models and hierarchy-aware routing models based on visual class cleaning and similarity-based re-clustering. The models are evaluated on handwritten samples and manuscript-domain samples from Vergilius Turonensis, both with and without limited few-shot adaptation to the manuscript domain. The results show that the relative performance of flat and hierarchical models depends on adaptation. On Vergilius Turonensis, HD-CNN achieves the best non-adapted Top-1 result with 45.43%, while flat classification reaches the best Top-1 result after few-shot adaptation with 82.09%. Overall, the results indicate that hierarchical structure can support Tironian note recognition, especially under non-adapted conditions.
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All authors Yule Kang, Thomas Gorges, Janne van der Loop, Franziska Marske, Nikolaus Weichselbaumer, Tino Licht, Vincent Christlein
Distributed Computing
Weaver: A System for AI-RAN Compute Sharing with Foundation Model Training
Leyang Xue, Tianxin Wang, Xin Zhe Khooi et al. (+6 more)
The emergence of AI-RAN infrastructure, which equips
cell sites with GPU-accelerated hardware, creates an opportunity to colocate non-RAN workloads with primary RAN processing. We explore using this spare capacity for decentralized training of foundation models (FMs), one of the most compute-intensive AI workloads. We present the first characterization of spare GPU capacity in AI-RAN systems at both micro-scale–across transmission slots within a
cell site–and macro-scale–across sites. Our analysis finds that 40-85% of GPU capacity is unused; although this capacity is temporally bursty at individual sites, it is spatially complementary across sites. To safely and efficiently harness these resources, we present Weaver, a system that opportunistically trains FMs alongside latency-critical RAN workloads without degrading RAN performance. Weaver adopts a RAN-first design: a spare-compute controller integrated into the MAC scheduler uses compute-aware scheduling to smooth RAN GPU demand and exposes more usable spare GPU capacity. A two-level elastic training framework then adapts to dynamic, heterogeneous spare capacity within and across sites. Experiments on an O-RAN-aligned system prototype show that Weaver creates up to 4.9x more usable spare compute and utilizes up to 83% of the available spare capacity. On a multi-site testbed, Weaver improves training throughput by 2.1-3.7x over baseline approaches.
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All authors Leyang Xue, Tianxin Wang, Xin Zhe Khooi, Jiaxun Yang, Dheeraj Mahendiran, Yufeng Xia, Mun Choon Chan, Myungjin Lee, Mahesh K. Marina
Machine Learning
Multi-Attractor GNNs: Set-Valued Expressivity Beyond Unique Equilibria
Jialin Liu
Recurrent and
equilibrium graph neural networks (GNNs) often enforce a unique fixed point or use one training target per graph. Yet many combinatorial and scientific problems admit multiple valid solutions, with no preferred one. A designated target can then impose an arbitrary selection rule. For tasks invariant to node relabeling, a symmetric graph may have a symmetric solution set but no symmetric solution. We show that multiple
equilibria enable one weight-tied message-passing GNN to represent set-valued equivariant maps: different initializations approach different valid solutions. Under stated regularity assumptions, we first construct globally Lipschitz, permutation-equivariant dynamics that converge almost surely to valid solutions and reach every solution branch with positive probability. We then establish approximate realization by recurrent message passing with continuous component maps, with arbitrarily small update and limiting errors and arbitrarily high probability. This goes beyond standard universality arguments: although message passing alone cannot distinguish symmetric nodes, the evolving state keeps nodes distinguishable at every finite step without auxiliary node identifiers. Such dynamics can be learned without solution labels using problem-specific energies. On Ising ground states, structural module detection in
protein graphs, and chemical reaction steady states, the learned updates produce multiple high-quality predictions with high numerical convergence rates. They achieve better average solution quality than the tested unique-
equilibrium, single-target, and feedforward baselines, while remaining competitive with much larger
diffusion-based solvers.
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Computation and Language
When Words Speak Louder than Images: Towards Understanding Language Bias in Vision-Language Models
Yizhou Fang, Siyue Chen, Zimo Qi et al. (+3 more)
Despite substantial progress across downstream applications,
vision-
language models (VLMs) remain susceptible to
language bias, often prioritizing linguistic cues over visual evidence and consequently producing incorrect predictions. Prior studies have proposed various approaches to understanding and mitigating
language bias in VLMs, yet their findings often conflict due to the difficulty of tracing how
language bias propagates within black-box VLMs. Building on the word completion task, we trace how
language bias propagates through VLM inference by (1) proposing a diagnostic framework that decomposes the inference process into four distinct yet interdependent stages to trace the propagation of
language bias; and (2) examining how two key factors underlying
language bias, i.e., linguistic priors and cross-modal coverage, evolve across these stages and ultimately give rise to incorrect predictions. The linguistic prior captures the strength of statistical bias induced by the
language model component of a VLM and represents the origin of
language bias, whereas cross-modal coverage measures the extent to which linguistic cues cover the visual content. By decomposing inference into four stages and characterizing the interplay between linguistic priors and cross-modal coverage across these stages, we propose a systematic framework for tracing the propagation of
language bias throughout the inference process; and uncover the underlying mechanism of
language bias by revealing the interplay between linguistic priors and cross-modal coverage.
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All authors Yizhou Fang, Siyue Chen, Zimo Qi, Zhiyu Xue, Xi Chen, Guangliang Liu
Machine Learning
eval-unlearn: Benchmarking unlearning in Text-to-Image Diffusion Models
Mansi, Nikhil Raghavan, Zixia Huang et al. (+4 more)
The rising number of concept unlearning techniques for text-to-image (T2I)
diffusion models has produced a fragmented evaluation landscape. Methods are assessed under heterogeneous experimental conditions making principled cross-method comparison difficult. We present eval-unlearn, an open-source Python library providing a unified, reproducible benchmarking framework for concept unlearning in T2I
Diffusion models. eval-unlearn integrates twelve published unlearning techniques spanning fine-tuning, closed-form model editing, and inference-time intervention, alongside nine complementary evaluation metrics covering erasure efficacy,
adversarial robustness, generative quality, and concept retention. Its plugin architecture lets third-party techniques and metrics self-register without modifying the core framework, and its streaming, batched pipeline supports efficient evaluation of both standard NSFW concepts and arbitrary general concepts. As a further contribution, we release a public leaderboard on HuggingFace along with an interactive tool for real-time evaluation of unlearning techniques. The leaderboard compares nudity concept erasure case study across all twelve techniques, exposing significant accuracy-quality trade-offs that are obscured by heterogeneous evaluation. eval-unlearn is released under the MIT license; the package, code, leaderboard, and documentation are all available at https://eval-unlearn.readthedocs.io.
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All authors Mansi, Nikhil Raghavan, Zixia Huang, Kai Sheng Ong, Ji Shen Lim, Brandon Siao Xiang Ling, Francesco Leofante
Machine Learning
SpikeCredit: Temporal Credit Carrier for Reinforcement Learning with Sparse Rewards
Yingchao Yu, Pengfei Sun, Wenxuan Pan et al. (+4 more)
Reinforcement learning (RL) with sparse rewards is challenging because delayed outcomes provide little guidance about which intermediate computations caused success or failure. We argue that reliable credit assignment requires policy dynamics that preserve and expose credit-relevant information over time, a role we formalize as Temporal Credit Carriers (TCCs) and that spiking neural networks (SNNs) naturally fulfill through graded membrane traces and event-driven spikes. Based on this hypothesis, we propose SpikeCredit, an SNN-based framework for RL with sparse rewards that first performs task-adaptive TCC selection and then closes the loop between a fast TCC-reading pathway, where self-motion feedback constraint uses local behavior-grounded cues to constrain transition-level credit recovery, and a slow TCC-writing pathway, where credit-targeted trace
alignment feeds recovered credit back into the actor to make future TCC dynamics more credit-readable. Across sparse-reward MuJoCo tasks, SpikeCredit improves Last10 return over sparse SNN baselines by +1169% on Ant, +953% on Hopper, +723% on Swimmer, and +1781% on Walker2d, and exceeds the dense-reward baseline on Swimmer by +113%. Mechanistic analyses further show substantially stronger
alignment with dense rewards than the sparse SNN baseline. These results position spiking dynamics as credit-preserving substrates for sparse-reward RL.
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All authors Yingchao Yu, Pengfei Sun, Wenxuan Pan, Wei Chen, Yitian Hong, Kuangrong Hao, Yaochu Jin