Artificial Intelligence · Cross-listed from cs.CV
The Importance of Phase in Neural Representations: An Internal Oppenheim-Lim Test of Image Classifiers
Alper Y{\i}ld{\i}r{\i}m
Oppenheim and Lim (1981) showed that natural images stay recognizable when reconstructed from their
Fourier phase alone, while the magnitude carries little of their identity. We ask whether trained image classifiers reproduce this asymmetry inside their hidden layers, and we test it causally: given two images, we transplant the phase of one onto the magnitude of the other at a chosen layer and record which image the prediction follows. In PRISM2D, GFNet, and ViT-B/16 the prediction follows the phase or sign donor, and deleting all image-specific magnitude barely moves accuracy, so identity rides on phase while image-specific magnitude is largely dispensable to the readout. ResNet-50 at first seems to break the pattern, because transplanting sign after its ReLUs does nothing; a fair intervention before the ReLU reveals a strong latent sign code in the late blocks, and a DC-only control shows the readout consumes a channel-wise spatial average. Controls rule out the trivial case in which magnitude simply stops depending on the image. The architectures therefore share a phase/sign identity code but expose it in different bases, set by rectification and readout geometry, which gives a mechanistic account of the texture–shape gap between CNNs and
attention models.
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Artificial Intelligence · Cross-listed from cs.LG
HAMON: Passive Optical Sequence Mixing for Long-Horizon Forecasting
Alper Y{\i}ld{\i}r{\i}m
Simple linear and frequency-domain models remain surprisingly competitive in long-horizon time-series
forecasting, and recent mechanistic evidence suggests that standard
forecasting benchmarks may not require the dense superposed representations that make
transformers powerful in other domains. This raises a substrate-level question: if the core
forecasting operator is often low-complexity and approximately linear, does it need to be implemented as learned digital temporal mixing? We introduce HAMON, a passive diffractive optical
forecasting core in which historical values are encoded onto an optical aperture, future positions are left dark, and cascaded trainable phase masks with free-space diffraction shape the forecast directly in the output field. At inference, prediction is performed by a single passive optical propagation pass with no trainable digital sequence-mixing layer. Across standard
benchmarks, HAMON outperforms the strongest digital baselines considered on ETTm2 at all horizons and on ETTh2 at all but the longest horizon, improving MSE by up to 14% and doing so consistently across horizons rather than at isolated points. It is competitive on Weather and trails the strongest baselines on the remaining ETT settings and on the high-channel-count Traffic and Electricity
datasets. Phase encoding, intensity-compatible readout, and phase-scrambling ablations, together with a TorchOptics cross-simulator check, indicate that the forecasts arise from the data-bearing optical field rather than from a digital
forecasting head. Because the passive core uses standard
Fourier optics, HAMON defines a concrete target for optical hardware and for passive physical sequence mixing.
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Artificial Intelligence · Cross-listed from cs.CV
FusionRS: A Large-Scale RGB-Infrared-Style Remote Sensing Dataset for Cross-Modal Vision-Language Learning
Jiaju Han, Ben Zhang, Xuemeng Sun et al. (+8 more)
Remote sensing
vision-
language models have advanced Earth observation, but available large-scale
vision-
language resources remain RGB-centered, leaving complementary infrared information underexplored. Infrared observations provide distinctive intensity structures, object boundaries, and illumination-invariant cues that complement conventional RGB imagery, yet large-scale RGB-infrared-text resources remain scarce. We introduce FusionRS, the first large-scale RGB-infrared-style-text
dataset for controlled dual-modal remote sensing
vision-
language learning. It contains 600,000 spatially aligned pairs created by translating diverse public RGB remote sensing images into infrared-style counterparts. Each pair retains a conventional scene caption, and a curated subset adds 45,913 IR-aware captions describing observable intensity, contrast, texture, and structure while preserving scene semantics. We train CLIP-style models for RGB-infrared-style-text
alignment and adapt a generative
vision-
language model with mixed task-conditioned caption supervision. Evaluation covers cross-modal
retrieval, scaling and supervision ablations, sensor-captured transfer, and strictly held-out captioning and VQA. FusionRS substantially improves RGB-infrared-style
alignment and infrared-to-text
retrieval over RGB-only and non-IR-aware settings. Ablations show that IR-aware captions improve task-conditioned infrared description, demonstrating the value of modality-specific supervision. FusionRS provides a scalable foundation for controlled RGB-infrared remote sensing
vision-
language learning.
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All authors Jiaju Han, Ben Zhang, Xuemeng Sun, Qike Zhang, Yuxian Dong, Dingyi Lu, Chengyin Hu, Luwei Yang, Fengyu Zhang, Yiwei Wei, Jiujiang Guo
Artificial Intelligence · Cross-listed from cs.CL
TokenPilot: Cache-Efficient Context Management for LLM Agents
Buqiang Xu, Zirui Xue, Dianmou Chen et al. (+12 more)
As LLM
agents are deployed in long-horizon sessions, context accumulation drives up inference costs. Existing approaches utilize text
pruning or dynamic
memory eviction to minimize
token footprints; however, their unconstrained sequence mutations alter layouts, introducing prefix mismatches and cache invalidation. This reveals a critical trade-off between text sparsity and
prompt cache continuity. To address this, we present TokenPilot, a dual-granularity context management framework. Globally, Ingestion-Aware Compaction acts as a framework harness to stabilize
prompt prefixes and eliminate open-world environmental noise at the ingestion gate. Locally, Lifecycle-Aware Eviction monitors the ongoing residual utility of context segments, enforcing a conservative batch-turn schedule to offload content segments only when task relevance expires. Experiments on PinchBench and Claw-Eval under both isolated and continuous modes demonstrate that TokenPilot reduces costs by 61% and 56% in isolated mode, and 61% and 87% in continuous mode, while maintaining competitive performance compared to prior systems. TokenPilot has been integrated into LightRSI at https://github.com/zjunlp/RSI.
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All authors Buqiang Xu, Zirui Xue, Dianmou Chen, Chenyang Fu, Chiyu Wu, Caiying Huang, Chen Jiang, Jizhan Fang, Xinle Deng, Yijun Chen, Yunzhi Yao, Xuehai Wang, Jin Shang, Gong Yu, Ningyu Zhang
Artificial Intelligence · Cross-listed from cs.SD
TuneJury: An Open Metric for Improving Music Generation Preference Alignment
Yonghyun Kim, Junwon Lee, Haiwen Xia et al. (+5 more)
We introduce TuneJury, an open, instance-level pairwise reward model for text-to-music that predicts a music preference score from a text
prompt and an audio clip. The released checkpoint is trained on publicly available human-preference labels covering arena-style (A vs. B) votes, metric-
alignment preference pairs, crowdsourced pairwise comparisons, and expert aesthetic ratings. The predicted score margin between two clips is well calibrated on our held-out test split, supporting data filtering via a simple score threshold. TuneJury generalizes to both held-out test pairs and out-of-distribution
benchmarks, remaining competitive with prior baselines on the latter. For generators released after training, we introduce anchor calibration, a post-hoc, per-system Bradley-Terry calibration that recovers agreement at substantially better data efficiency than from-scratch retraining. The same frozen reward drives consistent reward-axis gains across three downstream applications: inference-time best-of-N selection, DITTO-style latent optimization, and expert-iteration post-training. TuneJury is available at https://github.com/yonghyunk1m/TuneJury.
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All authors Yonghyun Kim, Junwon Lee, Haiwen Xia, Yinghao Ma, Junghyun Koo, Koichi Saito, Yuki Mitsufuji, Chris Donahue
Artificial Intelligence
Bayesian Inference and Decision Audits for Public Archives of Frontier AI Evaluations
Yanan Long
Public AI evaluations are often read as terminal leaderboards, yet the underlying evidence is a selective time series shaped by reporting rules,
benchmark revisions, and missingness. Repeated public archives for LiveBench and Open LLM Leaderboard v2 serve as the primary longitudinal record; LMArena provides a preference stress test; and
GAIA and tau-bench contribute limited agentic pilots. Together, these archives instantiate a
Bayesian inference problem: under a fixed reporting convention, one constructed terminal-only example over $1{,}000$ systems is compatible with two pre-terminal histories, yielding times of $23.03$ or $75.13$ to reach within $0.05$ of the ceiling under the same terminal-tail model. In synthetic posterior comparisons, action-facing diagnostics differ across observation regimes. The candidate selection-aware frontier model fails synthetic recovery, objective-archive prediction, preference transfer, and uncertainty calibration; correspondingly, fixed audit gates reject its stronger claims. An archive-and-adjudication protocol reconstructs public evaluation histories, isolates a verified timing boundary, and falsifies unsupported frontier claims.
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Artificial Intelligence · Cross-listed from cs.CV
ActiveSAM: Fast and Accurate Open-Vocabulary Semantic Segmentation with Frozen SAM 3
Tran Dinh Tien and Zhiqiang Shen
Segment Anything Model 3 (SAM 3) provides a strong frozen backbone for concept-prompted
segmentation, but applying it directly to open-vocabulary semantic
segmentation (OVSS) is inefficient: full-resolution decoding is typically run over the entire
dataset vocabulary, whereas each image contains only a small active subset of classes. We introduce ActiveSAM, a training-free inference framework that turns SAM 3 into an active-vocabulary segmenter. ActiveSAM first canonicalizes and expands class
prompts, then uses evidence-proportional grounding to estimate an image-conditioned active set from a low-resolution presence preview. Only retained
prompts receive full-resolution mask prediction, using bucketed
prompt multiplexing with the frozen SAM 3 decoder. The preview stage uses only class-presence evidence and skips unnecessary
segmentation-head computation. To resolve overlapping concept responses, exclusive concept decoding compares each pixel's joint score vector with class signatures estimated once per vocabulary from unlabeled images. ActiveSAM requires no weight updates, no oracle class-presence labels and no per-
dataset hyperparameter tuning. Across eight OVSS
benchmarks, ActiveSAM improves the speed-accuracy tradeoff of training-free open-vocabulary semantic
segmentation, outperforming the current state-of-the-art SegEarth-OV3 by +2.1 mIoU on average while running much faster, with 7.3-12.2x speedups on large-vocabulary
datasets. ActiveSAM also achieves the highest accuracy under image corruptions that simulate real-world distribution shift, making it well-suited for deployment in noisy-input domains such as autonomous driving and
embodied AI. Code is available at https://github.com/VILA-Lab/ActiveSAM
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Artificial Intelligence
When in Doubt, Plan It Out: Committed Small Language Model Deliberation for Reactive Reinforcement Learning
Nathan Gavenski, Juarez Monteiro, Francisco Galuppo et al. (+2 more)
Reinforcement Learning (RL) policies often degrade in unfamiliar environments because they lack explicit deliberation. We propose Plan, Align, Commit, Think (PACT), a hybrid architecture that combines a fast, reactive RL policy with a slow, deliberative Small
Language Model (SLM) planner. PACT invokes the SLM asynchronously to generate and validate candidate action plans. Once a plan is verified through simulation as safe, feasible, and complete, it is executed directly, bypassing the RL policy without retraining or modifying it. Evaluated on three FrozenLake configurations of increasing difficulty, PACT outperforms all baselines while relying on a 2B-parameter SLM backbone, suggesting that deliberative planning and reactive execution are more powerful in concert than either is alone in these settings.
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All authors Nathan Gavenski, Juarez Monteiro, Francisco Galuppo, Adriano Veloso, Odinaldo Rodrigues
Artificial Intelligence · Cross-listed from cs.GT
Stable Menus of Public Goods: AI-Enabled Progress
Sara Fish
Using an open problem from the EC 2025 paper "Stable Menus of Public Goods" as a testbed, we conduct experiments to understand the effectiveness of different AI-for-EconCS research workflows. Specifically, we study three questions: Does providing human intuition in the
prompt help? Does automated multi-turn interaction help? And, does an LLM outperform a first-year PhD student? Regarding the first two questions, we provide evidence for the following workflow suggestions: (1) prompting with human intuition can encourage the LLM to have better "taste", (2) multi-turn workflows help when the pipeline encourages "ambitious" steps. Regarding the third question, using an unpublished manuscript written by the paper's senior authors prior to collaborating with the first-year PhD student, we compare the effectiveness of the LLM with that of the first-year PhD student, and find that the LLM is slightly less effective.
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Artificial Intelligence
Consensus-based Agentic Large Language Model Framework for Harmonized Tariff Schedule Code Classification
Truong Thanh Hung Nguyen, Khanh Van Quynh Nguyen, Hoang-Loc Cao et al. (+5 more)
Accurate Harmonized Tariff Schedule (HTS) code classification is essential for customs clearance, duty assessment, trade statistics, and regulatory compliance in maritime logistics. However, exact HTS classification remains challenging because product descriptions are often short, incomplete, or ambiguous, while correct classification depends on hierarchical tariff structures, legal notes, and jurisdiction-specific rules. This paper proposes an agentic large
language model (LLM) framework for Canadian 10-digit HTS code classification in smart-port and maritime logistics environments. The framework integrates multi-
agent information
retrieval, semantic
retrieval over official tariff documents, evidence-grounded
reasoning, consensus-based validation, element-wise voting across hierarchical code components, confidence estimation, and human-in-the-loop escalation. We evaluate the framework on a private
dataset of 3,300 domain-expert-labeled product records collected from logistics and delivery contexts. Experimental results show that exact 10-digit classification remains difficult even for advanced LLMs, with performance decreasing from coarse chapter-level prediction to fine-grained tariff and statistical suffix assignment. These findings demonstrate the need for evidence-grounded, uncertainty-aware, and human-centered classification workflows rather than fully autonomous single-step prediction. The proposed framework supports more interpretable, accountable, and compliance-oriented HTS classification for maritime logistics and smart-port operations. Our code is available at https://github.com/Analytics-Everywhere-Lab/hts.
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All authors Truong Thanh Hung Nguyen, Khanh Van Quynh Nguyen, Hoang-Loc Cao, Tri Duong, Phuc Ho, Van Pham, Loc Nguyen, Hung Cao
Artificial Intelligence
The Shift Toward Open and Reproducible AI Research
Kevin L Coakley, Thijs Snelleman, Holger Hoos et al. (+1 more)
The reproducibility crisis has directed the AI research community toward improving documentation practices. Several studies have identified methodological issues, and in response, the most impactful venues in the field have introduced reproducibility checklists. We seek to understand whether documentation practices have changed over time by assessing all published papers at five leading AI conferences over the past decade. Seven reproducibility variables were identified, quality-assured and used to analyse 56 800 publications. Our analysis reveals that in the period 2014 to 2024, documentation practices have improved; papers sharing both code and data increased nearly sixfold, from 11% to 64% Building on empirical reproducibility rates from a prior study, we estimate - inferred from documentation practices, not direct testing - that reproducibility increased from 28% in 2014 to 64% in 2024. Improvements in documentation practices predate the introduction of reproducibility checklists, suggesting these changes reflect a broader movement toward open science rather than a direct response to formal requirements.
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All authors Kevin L Coakley, Thijs Snelleman, Holger Hoos, Odd Erik Gundersen
Artificial Intelligence · Cross-listed from cs.IR
How Much Do Reviews Really Contribute? A Study on Text-Enriched Matrix Factorization for Recommendations
Eduardo Ferreira da Silva, Mayki dos Santos Oliveira, Joel Machado Pires Denis Dantas Boaventura et al. (+1 more)
Incorporating textual reviews into a Recommender System has become a prominent strategy for enriching collaborative signals with semantic information. However, the actual contribution of review-derived representations remains an open question, particularly when strong collaborative baselines are employed. In this work, we systematically investigate the impact of textual information on
Matrix Factorization by introducing and comparing three enrichment strategies over a common collaborative backbone. First, we propose a learnable gating mechanism that adaptively balances collaborative and textual signals during training. This mechanism is applied to two distinct review representations: (i) aggregated topic profiles extracted from user and item histories, and (ii) full text
embedding representations derived from reviews. Additionally, we explore a cross-
attention mechanism that identifies and emphasizes the most informative dimensions of the textual representation before fusion with collaborative factors. We evaluate six variants: pure, enriched with topic profiles and text via gating; enriched with topics and text via gating; and enhanced with cross-
attention over textual features. Experiments across multiple review-based
datasets reveal that although adaptive fusion mechanisms improve representation flexibility, the marginal contribution of textual signals remains limited compared to the collaborative backbone. These findings suggest that, under typical rating-prediction settings, collaborative information continues to dominate performance, raising important considerations for the effective integration of semantic review signals into recommendation models.
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All authors Eduardo Ferreira da Silva, Mayki dos Santos Oliveira, Joel Machado Pires Denis Dantas Boaventura, Frederico Araújo Durão
Artificial Intelligence · Cross-listed from cs.SD
Probing Low Frame Rate Degradation in Neural Audio Codecs
Alex Gichamba and Moise Busogi
Low frame rates in neural audio codecs are attractive for autoregressive
speech synthesis, where the generation cost scales linearly with the sequence length. Recent work has demonstrated that codecs can operate at 12.5 Hz and below, but the mechanisms underlying low frame rate degradation remain insufficiently understood. We investigate these mechanisms through a controlled frame rate ablation. We reproduce a quality cliff at 6.25 Hz reported in previous works and evaluate candidate explanations: phonemic collisions and codebook saturation, neither of which shows evidence of a fundamental barrier. The cliff is instead caused by suboptimal training configuration: fixed clip duration during training yields too few
tokens at low frame rates, starving the decoder of inter-
token context. Once corrected, WER degrades smoothly with phonemic load down to 3.1 Hz and 1.6 Hz, suggesting the inference-time efficiency gains of low frame rate codecs are more accessible than previously assumed.
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Artificial Intelligence · Cross-listed from cs.LG
Phantoms and Disclosures: A Statistical Framework for Auditing Privacy in Synthetic Data
Kareem Amin, Rudrajit Das, Alessandro Epasto et al. (+4 more)
The rapid adoption of generative AI and Large
Language Models (LLMs) has spurred interest in synthetic data as a
privacy-preserving alternative to sensitive real-world
datasets. However, generating high-utility synthetic data often carries the risk of memorizing and regurgitating private information from the training corpus. In this work, we present a customizable empirical
auditing framework designed to detect and explain such data disclosures. Our framework introduces a mechanism to distinguish between "true disclosures"-where the system directly reproduces a user's information-and "phantom disclosures”-where the system incidentally generates a user's data. By partitioning input data into training and holdout sets and applying rigorous statistical hypothesis testing, we determine if observed disclosures are consistent with strict
privacy baselines, such as zero-learning or specific Differential
Privacy (DP) bounds. Crucially, this approach requires no model access, no canary insertion, and no reference model training -only the synthetic output and a held-out control set. We demonstrate that this framework effectively functions as a membership inference attack, providing empirical lower bounds on
privacy leakage that are tighter than prior data-based
auditing methods. Our approach is model-agnostic, applies to any synthetic data generation mechanism, and requires orders of magnitude fewer computational resources than shadow-model or canary-based alternatives.
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All authors Kareem Amin, Rudrajit Das, Alessandro Epasto, Adel Javanmard, Dennis Kraft, Mónica Ribero, Sergei Vassilvitskii
Artificial Intelligence
A Causal Model of Theory of Mind in Conflict for Artificial Intelligence
Nikolos Gurney
Theory of mind (ToM), the capacity to ascribe mental states to others and use those ascriptions for prediction and inference, is widely assumed to be essential for effective human-machine integration. Existing AI-ToM models address how to mentalize, but leave the question of when largely unaddressed. The central question is: under what situational and
agent-level conditions is ToM engagement causally warranted in conflict? This paper presents a structural causal model formalized as a directed acyclic graph (DAG), treating ToM as a mechanism activated by situational and
agent-level conditions rather than as an always-on capacity. The model specifies four exogenous variables capturing situational and
agent-level conditions, five endogenous mediators, and a mechanistic ToM node producing engagement states through three distinct causal pathways: a tractability pathway, a
reasoning-depth pathway, and an enabling-cause pathway. The primary outcome is epistemic accuracy, which decouples social
reasoning from behavioral policy and generalizes across social phenomena beyond conflict. The framework gives AI systems a principled, resource-rational decision procedure for mentalizing, with implications for efficiency, trust, and the development of robust artificial social intelligence. Simulation validation, empirical human-machine teaming studies, and ethical considerations arising from conflict-optimized mentalizing are discussed.
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Artificial Intelligence · Cross-listed from cs.LG
Scalable Circuit Learning for Interpreting Large Language Models
Naiyu Yin, Dennis Wei, Tian Gao et al. (+3 more)
A prominent research direction in mechanistic interpretability is learning sparse
circuits over LLM components to reveal how they jointly produce model behavior. However, raw
neurons are polysemantic, making learned
circuits hard to interpret. Sparse autoencoder (SAE) features alleviate this, but their high dimensionality makes existing intervention-based
circuit learning methods computationally prohibitive. We propose CircuitLasso, a scalable
circuit learning approach based on sparse linear regression. CircuitLasso recovers
circuits whose structural accuracy matches that of state-of-the-art intervention-based methods on the
benchmark data, at a fraction of the computational cost. For interpretability, CircuitLasso efficiently uncovers relationships among SAE features, showing how human-interpretable semantic features propagate through the model and influence its predictions. Finally, we validate the utility of our learned
circuits by leveraging their insights to achieve comparable performance at substantially lower cost on a domain-generalization task.
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All authors Naiyu Yin, Dennis Wei, Tian Gao, Amit Dhurandhar, Karthikeyan Natesan Ramamurthy, Yue Yu
Artificial Intelligence · Cross-listed from cs.RO
CrossMaps: Confidence-Aware Open-Vocabulary Semantic Mapping for Rover Navigation
Jan-Niklas Klein, Sona Ghahremani, Christian Medeiros Adriano et al. (+1 more)
Rovers rely on perception to maintain spatial maps that encode both objects and sensor quality (e.g., range reliability, lighting artifacts, data density), guiding data fusion,
embedding updates, and
navigation under partial observability. To study these coupled perception-
navigation processes, we present CrossMaps, a real-time confidence-aware open-vocabulary semantic mapping pipeline that constructs
language-queryable maps from RGB-D data. Building on VLMaps-style approaches, CrossMaps integrates multi-scale CLIP
embeddings with confidence-aware fusion and a dual-
memory architecture consisting of Short-Term
Memory (STM) and Long-Term
Memory (LTM). The STM aggregates noisy visual observations using
geometric, semantic, and temporal confidence cues, while confident and coherent
cells are promoted to the LTM as persistent semantic landmarks. Designed for deployment with a Jetson Orin-powered UGV alongside SLAM, CrossMaps runs in real time and produces semantic heatmaps that can be queried with natural
language to guide rover
navigation.
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All authors Jan-Niklas Klein, Sona Ghahremani, Christian Medeiros Adriano, Holger Giese
Artificial Intelligence · Cross-listed from cs.LG
A Unified Causal-Origin Taxonomy of Distributional Shifts in Reinforcement Learning
Ardianto Wibowo, Paulo E Santos, Amer Baghdadi et al. (+3 more)
Reinforcement learning (RL) systems often degrade when operating conditions differ from those previously encountered, reflecting distributional shifts in the underlying data-generating process. Such shifts may occur between training and evaluation, as in In-Distribution (ID) and Out-of-Distribution (OOD) generalization, or within non-stationary settings where environment dynamics evolve over time. However, the formal relationship between these views remains unclear, and existing work mainly focuses on mitigation rather than the causal origin of shift within the
agent-environment interaction. This work develops a unified causal-origin taxonomy that characterizes sources of distributional shift in RL and relates ID/OOD generalization to non-stationary settings. We transfer the classical
dataset-shift principle from supervised learning to RL by reformulating distributional shift in terms of the generative interaction process. Using a Partially Observable Markov Decision Process (POMDP), we decompose the interaction into structural components, including the state distribution, observation process, policy, reward, and transition dynamics, together with the shifted-time boundary. The proposed taxonomy distinguishes internal (
agent-driven) and external (environment-driven) distributional shifts. The shifted-time boundary perspective further characterizes explicit, implicit, and hybrid shifts. This formulation unifies ID/OOD generalization and non-stationarity as structured changes in the underlying process. We also introduce an evaluation framework for measuring shift impact and adaptation through performance degradation and recovery metrics. By grounding distributional shift in the causal-origin structure of RL, this work supports systematic analysis of robustness under distributional shift.
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All authors Ardianto Wibowo, Paulo E Santos, Amer Baghdadi, Matthew Stephenson, Karl Sammut, Jean-Philippe Diguet
Artificial Intelligence
RAID: Semantic Graph Diffusion for True Cold-Start and Cross-Lingual Forecasting
Arunkumar V, Manoranjan Gandhudi, Gangadharan G. R. et al. (+2 more)
Time-series foundation models show strong transfer performance when given a non-empty history window. However, true cold-start scenarios, where a new item has no prior observations, violate this assumption. We propose RAID (
Retrieval-Augmented Iterative
Diffusion) a framework, which replaces history-based correlation learning with metadata-driven semantic
retrieval and graph-conditioned
diffusion. RAID maps textual metadata into a shared semantic space using a frozen
multilingual embedding model and constructs an inductive
retrieval graph that extends naturally to unseen items. It first forms a base forecast by aggregating information from semantically related neighbors, then refines this forecast with a gated
diffusion module to model residual uncertainty. Under a strict true cold-start protocol, RAID outperforms strong foundation models and competitive baselines on both
forecasting accuracy and prediction interval coverage, while reducing inference latency by an order of magnitude through non-autoregressive decoding. The shared semantic space also enables zero-shot cross-lingual transfer, allowing a model trained on English descriptions to generalize to items described in other
languages without direct supervision.
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All authors Arunkumar V, Manoranjan Gandhudi, Gangadharan G. R., Arun Prakash, S. Senthilkumar
Artificial Intelligence
MA-SBI: Misspecification-Aware Simulation-Based Inference via Side-Channel Guidance
Arunkumar V, Manoranjan Gandhudi, Gangadharan G. R. et al. (+2 more)
Simulation-based inference (SBI) of latent parameters is often hindered by simulator misspecification, the mismatch between simulated and real-world observations caused by inherent modeling simplifications. RoPE, the recent state-of-the-art for robust SBI, addresses this through optimal transport between learned representations of real and simulated observations, but requires ground-truth parameter calibration pairs that are typically unavailable in the very settings where SBI is needed. What practitioners do have is unstructured side-information such as regime labels, instruction text, and policy bulletins. We propose Misspecification-Aware Simulation-Based Inference (MA-SBI), a calibration-free framework that turns this side-channel into a posterior correction. A learned corrector maps side-channel text to an observation-space shift applied before any pre-trained amortized posterior, requiring no retraining and no parameter ground-truth. Our main
theorem bounds achievable bias reduction by the mutual information between misspecification and side-channel, with a non-vacuous constant that extends to all sub-Gaussian noise via Donsker-Varadhan. On hide-the-calibration
benchmarks, MA-SBI with text alone matches the oracle posterior across 10 seeds and two backbones (TOST equivalence), while RoPE given more data does not. The two approaches are complementary: where misspecification is structural and recoverable from parameter pairs, RoPE dominates, as the theory predicts. A
stochastic variant improves posterior-predictive log-likelihood on real COVID and OxCGRT epidemiological data, and correctly leaves the posterior unchanged on a well-specified cognitive-science corpus.
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All authors Arunkumar V, Manoranjan Gandhudi, Gangadharan G. R., Arun Prakash, S. Senthilkumar
Artificial Intelligence · Cross-listed from cs.LG
Demystifying Variance in Circuit Discovery of LLMs
Frank Zhengqing Wu, Francesco Tonin and Volkan Cevher
Circuit discovery is a key technique in mechanistic interpretability to pinpoint the model components that are crucial for performing a given task. Although the current state-of-the-art method (EAP-IG) performs well on the metric of (un)faithfulness, it suffers from substantial variability. This includes resampling variance, where the
circuit changes when we probe with a new batch of data from the same distribution; rephrasing variance, where the discovered
circuit shifts when the
prompts are rephrased; and sample-wise variance, where a
circuit with low population unfaithfulness exhibits large fluctuations in unfaithfulness across individual samples. This paper studies the roots of these variances. We demonstrate that CEAP, our new
circuit discovery method that improves upon EAP-IG with a theoretical guarantee, can substantially lessen resampling variance. We further show that rephrasing variance arises because
prompts with different templates tend to activate different
circuits in the model. This leads us to argue that it may be challenging to find a comprehensive
circuit that explains and controls the model's behavior on a task, which can be expressed in countless templates, suggesting that LLMs may be inherently hard to steer. We show that sparsity, which has been claimed to form more compact and interpretable task
circuits, fails to solve this problem. Regarding sample-wise variance, we argue that it is largely benign: extremely poor unfaithfulness scores often stem from how unfaithfulness is defined, rather than from defects in the measured
circuits. We show that the magnitude of unfaithfulness is affected by selective contribution scaling, a neural mechanism that accounts for the extremely poor scores sometimes observed.
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Artificial Intelligence
Greed Is Learned: Visible Incentives as Reward-Hacking Triggers
Tong Che and Rui Wu
Safety evaluations test a policy on
prompts that omit the incentive information deployment supplies: a commission, a performance score, a dashboard naming which action pays best. We measure what that omission hides. In MoneyWorld, a synthetic workplace environment, we train five instruction-tuned models from three families with RL on non-safety tasks in which a visible payoff signal identifies a rewarded shortcut that sacrifices task quality. We then freeze each policy, present held-out safety conflicts, and change only the displayed signal. Each menu contains one compliant action and three violations. We report three findings, with rates for Qwen2.5-14B-Instruct. (i) Payoff signals control frozen safety choices: unsafe choice is 100% when the signal names an unsafe option and 0% when it is hidden or names the safe one. Hidden- and random-signal training controls stay at or below 0.3%, and the switch reproduces on all five bases. Numerical payouts reproduce it under sampled-action rewards, reaching 98.6% unsafe choice at a $1 advantage. (ii) Payoff identification and unsafe choice separate under a training-menu intervention: training on task-completing actions at the same payouts retains 99.8% identification while reducing unsafe choice to 7.9% at matched update budgets. Payoff-reading competence alone does not explain transfer. (iii) The switch does not reproduce in executed retail customer-service tasks using the same frozen adapters. In MoneyWorld, omitting incentive information conceals unsafe choices that appear when the same policy sees which action pays best.
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Artificial Intelligence · Cross-listed from cs.CL
IMPACTeen: Intentions, Manipulation, Persuasion, Annotations, and Consequences in Teen Communication Dataset
Aleksander Szczęsny, Wiktoria Mieleszczenko-Kowszewicz, Maciej Markiewicz et al. (+5 more)
IMPACTeen is a
dataset of textual social influence scenarios spanning interpersonal, media-based, and digital settings in an adolescent context. It contains 1,021 texts, 5,100 individual annotation records, and gold labels for social influence techniques, with each text annotated from five distinct perspectives: teenagers, parents, psychologists, communication experts, and teachers. The resource was constructed through constrained LLM generation, followed by a two-step human editing and validation phase aimed at ensuring youth-context realism. A multi-dimensional annotation covered influence presence, techniques, intentions, consequences, resistance, reactions, and annotation confidence. The
dataset supports research on social influence detection, annotator disagreement, cross-lingual modeling, and the training and evaluation of
language models. The
dataset was created in Polish and is accompanied by a corresponding English version.
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All authors Aleksander Szczęsny, Wiktoria Mieleszczenko-Kowszewicz, Maciej Markiewicz, Beata Bajcar, Tomasz Adamczyk, Jolanta Babiak, Grzegorz Chodak, Przemysław Kazienko
Artificial Intelligence · Cross-listed from cs.RO
Binary Tracking for Spatial QA and Navigation with Open Vision-Language Models
Dongbin Na, Chanwoo Kim, Soonbin Rho et al. (+3 more)
This work addresses spatial question answering for service
robots traversing long
egocentric routes. Given a query such as "where can I find a dry cleaner on the way back home?", the system returns a metric coordinate that downstream
navigation components can act on. Prior Spatial Question Answering approaches leverage
retrieval-augmented
agents built on closed-source models such as GPT-4o for path exploration. However,
robots operating in the real world often cannot reliably depend on online closed-source models due to network instability, communication latency, and deployment cost. It creates a need for open-source based Spatial Question Answering approaches that can run onboard the
robot, yet prior research in this direction remains limited. This work proposes BinTrack, a simple yet effective, fully open-source spatial-localization
agent that leverages the temporal ordering of a
robot's trajectory. BinTrack performs a binary search over the trajectory segments between two anchor landmarks identified from a query. It improves overall accuracy by up to 22.8% over other open-source implementations and even matches the reported closed-source model result on the global category of the SpaceLocQA
benchmark, the most challenging setting that has so far required strong
reasoning agents such as GPT-4o. Furthermore, its optimized inference strategy consistently yields more than a 1.5x inference speedup over previous approaches. Finally, this work releases GangnamLoop, a novel and practical multi-trip outdoor
benchmark collected by deploying a real quadruped
robot on public streets with the anonymization policy. It revisits the same locations under different outdoor conditions and pairs the
robot's low viewpoint with the human owner's. The source codes and
datasets are publicly available at https://github.com/ndb796/BinaryTracking
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All authors Dongbin Na, Chanwoo Kim, Soonbin Rho, Giyun Choi, Gangbok Lee, Dooyoung Hong
Artificial Intelligence · Cross-listed from cs.CV
Semantic Flip: Synthetic OOD Generation for Robust Refusal in Embodied Question Answering and Spatial Localization
Dongbin Na, Chanwoo Kim, Giyun Choi et al. (+1 more)
Detecting unanswerable user queries remains essential for the reliable deployment of real-world
embodied agents. However, modern
vision-
language models (VLMs) often generate overly confident answers even when the available visual
memory cannot support the query. Such overconfidence poses various task-dependent risks. The
agent may provide misleading information to the user in
Embodied Question Answering and select an arbitrary coordinate and physically guide the user there in spatial
reasoning for
navigation. Despite these high stakes, only a few prior studies directly address when and how an
embodied VLM should respond with "I do not know." This work proposes Semantic Flip, a simple yet effective framework that synthesizes auxiliary out-of-distribution (OOD) samples for
embodied refusal without requiring external OOD annotations. The key idea is to independently transform the query and
video memory to construct auxiliary OOD pairs that lack sufficient visual grounding. These synthesized pairs enable training a lightweight rejection module on top of a frozen pretrained VLM. The module attaches to any existing VLM-based pipeline without retraining the underlying model. Across two complementary
benchmarks, Semantic Flip consistently outperforms strong prompting baselines. This work also introduces SpaceReject, a new refusal
benchmark for spatial localization with deliberately unanswerable queries over long
video memory, where Semantic Flip achieves an $F_1$ score of 0.9559. The source codes and
datasets are publicly available at https://github.com/ndb796/SemanticFlip.
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All authors Dongbin Na, Chanwoo Kim, Giyun Choi, Dooyoung Hong
Artificial Intelligence
Symbolic Informalization: Fluent, Productive, Multilingual
Aarne Ranta
Symbolic informalization enables a reliable conversion of formal mathematics to natural
language. It has the potential to make machine-checked content human-readable without loss of precision. In a traditional proof system usage, symbolic informalization generalizes the limited mechanisms of syntactic sugar into the ordinary
language of mathematics. In a setting where proofs are constructed by artificial intelligence and autoformalization, symbolic informalization can explain what precisely has been constructed. This paper outlines the project Informath, which aims to show how symbolic informalization can produce fluent text with a reasonable development effort and address multiple formal and natural
languages. Informath is based on an interlingual architecture, where Dedukti works as a hub between different proof systems (Agda, Lean, Rocq) and Grammatical Framework (GF) takes care of linguistic correctness and variation in different natural
languages.
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Artificial Intelligence · Cross-listed from cs.LG
Beyond Weights and Gradients: A Taxonomy of Federated Learning Messages
Alvaro Javier Vargas Guerrero, Xinguang Wang, Quang Manh Doan et al. (+1 more)
Federated Learning is rapidly evolving beyond the exchange of traditional model weights and gradients, yet existing definitions fail to capture the full scope of modern payloads like synthetic data and
federated analytics. This paper addresses the gap by proposing a formal mathematical definition of a
federated message that accounts for both utility and
privacy. We introduce a taxonomy that organizes these exchanges into three categories: model structures, statistical summaries, and data-conditioned representations. By evaluating these groups based on computational demands, communication costs, and
privacy risks, we provide a clearer understanding of the trade-offs involved in decentralized training. Our review of 202 recent publications highlights a significant shift since 2021 toward diverse messaging paradigms, signaling a move away from standard deep learning updates toward more specialized information sharing. This framework provides a structured path for future research to optimize
federated systems for varying hardware and security requirements.
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All authors Alvaro Javier Vargas Guerrero, Xinguang Wang, Quang Manh Doan, Guy Nagels
Artificial Intelligence · Cross-listed from cs.CL
Compositional Reasoning Depth Predicts Clinical AI Failure: Empirical Evidence Consistent with Transformer Compositionality Limits in Electronic Health Record Question Answering
Sanjay Basu
Aggregate accuracy
benchmarks conceal a systematic structure in how large
language models fail at electronic health record (EHR) question answering: questions requiring more inferential steps produce disproportionately more errors. Motivated by theoretical results on
transformer compositionality limits, we introduce a pre-specified hop-count taxonomy – the number of distinct
reasoning steps required to answer a clinical question from an EHR – as a principled predictor of model failure. We annotate 313 clinician-generated MedAlign EHR question-answer pairs across four hop levels and evaluate 301 questions in a within-model ablation (claude-sonnet-4-6, zero-shot vs. extended thinking) and cross-architecture replications (gpt-4o and gpt-5.4-2026-03-05, zero-shot). All three models, spanning two providers and two OpenAI generations (GPT-4 and GPT-5), show monotone accuracy decline with hop count: Claude Sonnet zero-shot falls from 30.6% (hop=1) to 17.6% (hop=4) (Cochran-Armitage z=-2.30, p=0.011; OR per hop 0.72, 95% CI [0.56,0.92], p=0.008); GPT-4o replicates this (37.8% to 14.7%; OR 0.58 [0.45,0.75], p<0.001); and gpt-5.4-2026-03-05 confirms it (37.8% to 23.5%; OR 0.80 [0.66,0.98], p=0.027). A pre-specified context-sufficiency audit shows higher-hop questions are not differentially disadvantaged by EHR truncation (answerability 93-95% at hops 2-4 vs. 79% at hop=1), so the decline reflects compositional
reasoning difficulty. Extended thinking did not significantly flatten the accuracy-depth curve across three
reasoning conditions, and thinking-
token usage scaled with hop count (r=0.31, p<0.0001), consistent with the predicted O(k) computational requirement. Hop count is thus a theory-motivated, cross-architecture predictor of large-
language-model error on EHR question answering, with direct implications for deployment risk stratification of clinical AI.
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Artificial Intelligence · Cross-listed from cs.LG
Upper Bounds on the Generalization Error of Deep Learning Models via Local Robustness and Stability
Abdul-Rauf Nuhu, Parham M. Kebria, Vahid Hemmati et al. (+3 more)
Generalization is a critical property of data-driven models, particularly deep learning models deployed in safety-critical applications. Robustness-based generalization bounds have gained
attention as a principled way to link robustness properties to generalization performance, often in a data-dependent manner. However, most existing bounds suffer from vacuousness in practical settings, yielding loose upper bounds that greatly exceed the actual error rates and limiting their usefulness for real-world evaluation. While this issue is often attributed to the uncertainty term, a substantial part of the problem originates from the robustness term itself, particularly for the 0-1 loss. Existing approaches typically treat the robustness term as a global measure, ignoring its variation across different sub-regions of the input space. In this work, we propose a generalization bound that addresses this limitation by scaling the robustness term according to the number of stable and unstable samples within each sub-region. Our bounds incorporate both data- and model-dependent factors while maintaining practical relevance (yielding tighter upper bounds on true error). Experiments on models trained on the ImageNet
dataset show that our bounds remain consistently non-vacuous and achieve the tightest estimates among existing methods, closely aligning with empirical performance across a range of robust deep neural networks.
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All authors Abdul-Rauf Nuhu, Parham M. Kebria, Vahid Hemmati, Mahmoud N. Mahmoud, Edward Tunstel, Abdollah Homaifar
Artificial Intelligence · Cross-listed from cs.CV
Federated Medical Image Segmentation under Real-World Label Noise: A Benchmark Suite for Noisy Label Learning Method Selection
Markus Bujotzek, Dimitrios Bounias, Stefan Denner et al. (+4 more)
While
federated learning (FL) enables collaborative medical image
segmentation without centralizing sensitive data, real-world deployment is frequently complicated by cross-site label imperfections such as contour disagreement, missing or additional structures, and confused labels.
Federated noisy label learning (FNLL) aims to mitigate these effects, yet remains underused in practice as existing evidence is largely based on synthetic noise, simplified settings, and limited real-world noisy evaluation. We address this gap by introducing a
benchmark suite that combines diverse real-world noisy
datasets, deployment-relevant client-noise scenarios, and label-noise-targeted evaluation to support systematic FNLL assessment and informed method selection. The suite combines curated real-world noisy medical image
segmentation datasets from diverse sources with a comprehensive
federated segmentation framework including various client-noise scenarios and noise-targeted evaluation. The presented suite provides a realistic and discriminative basis for FNLL evaluation in medical image
segmentation and establishes a reusable foundation for fair benchmarking,
dataset-specific label-noise characterization, and future method development under realistic
federated settings. Code is available at https://github.com/MIC-DKFZ/FedSegNoiseBench.
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All authors Markus Bujotzek, Dimitrios Bounias, Stefan Denner, Ralf Floca, Maximilian Fischer, Peter Neher, Klaus Maier-Hein