Social and Information Networks
Reclaiming the social in social media
Luca Benn, Moritz Merz and David Grüning
Concerns about polarization, antisocial behavior, and mental health have broadly led to two responses to social media: adapting platforms through moderation and prosocial design, and restricting access to these platforms through age limits and comparable measures. Both address consequences of social media while leaving the
attention-driven architecture intact. We argue that the harms commonly attributed to social media arise not from technologies supporting social connection but from their implementation within the
attention economy. In response, we ask what a digital social environment built for genuine human connection would look like. From four principles - Purpose,
Alignment, Transparency, and Access - we can derive four technical properties that fulfill these principles: Operator Blindness, Algorithmic Sovereignty, Operational Parity, and Verifiability. To the best of our knowledge, no deployed alternative satisfies all four. We propose an architecture that does, making the
attention economy not just discouraged but structurally impossible. We explain how the resulting user experience refocuses on each user's individual social environment and differs from that of current platforms.
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Artificial Intelligence
Self-Adapting Group of Experts for Multi-Agent Reasoning
Mohammad Atif Quamar, Nurbek Tastan, Karthik Nandakumar et al. (+1 more)
Multi-
agent systems bring together
language model
agents with different roles to propose, review, and refine solutions. Each
agent's response depends on its model's capabilities, the
reasoning strategy defined by its system
prompt, and the information in its input context. Existing frameworks often adapt communication by changing this context while leaving individual
prompts fixed, even when a problem calls for different skills. We study whether
agents' initial responses can identify a strategy better suited to the current problem and guide its transfer to other
agents. To address this, we introduce SAGE (Self-Adapting Group of Experts), a training-free framework that uses answer agreement, prefix consistency, and reciprocal peer review to select a strategy donor. SAGE transfers the selected donor's
reasoning strategy to the other
agents while preserving their original roles. This transfer uses only the
agents' original system
prompts, without access to the problem or generated solutions. After strategy adaptation,
agents exchange responses through a dynamic, sparse directed acyclic graph that routes information from higher-scoring
agents to lower-scoring
agents. Experiments across multiple
agent backbones and
reasoning benchmarks show that SAGE achieves higher average accuracy than the evaluated baselines. Our code is available at https://github.com/atifquamar07/sage.
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All authors Mohammad Atif Quamar, Nurbek Tastan, Karthik Nandakumar, Junpei Komiyama
Sound
GLAD: Global-Local Adaptive Detector for Robust Speech Deepfake Detection
Zelin Zhao, Guanjie Huang, Danny Hin Kwok Tsang et al. (+1 more)
Recent advances in AI-based
speech synthesis have enabled highly realistic
speech, increasing the importance of
speech deepfake detection (SDD) in preventing misuse. While mainstream Self-Supervised Learning (SSL)-based detectors achieve strong performance, they suffer from poor generalization to unseen domains and often overlook fine-grained signal artifacts due to a bias towards global semantic consistency. In this paper, we conduct the first detailed empirical and visual analysis to validate these limitations explicitly. Our investigation reveals two critical architectural vulnerabilities: (1) a systemic failure to capture localized spoofing traces, and (2) a severe lack of adaptability to domain-driven shifts in SSL layer importance, rendering static aggregation strategies prone to overfitting. To address these vulnerabilities, we propose the Global-Local Adaptive Detector (GLAD). Specifically, to capture localized forgeries, GLAD employs a Hierarchical Global-Local (HGL) backbone that explicitly bridges the granularity gap by fusing global linguistic and acoustic features with fine-grained local signal details. To counter layer importance shifts in out-of-distribution (OOD) scenarios, we introduce a Hierarchical Adaptive Gating (HAG) mechanism that dynamically recalibrates layer-wise focus in a sample-specific manner. Finally, to address shortcut learning induced by environmental biases, we introduce SaniBoost, a composite data augmentation strategy for robust signal standardization and noise sanitization. Extensive experiments demonstrate that GLAD significantly outperforms state-of-the-art methods, particularly on unseen domain cases.The code will be released upon publication.
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All authors Zelin Zhao, Guanjie Huang, Danny Hin Kwok Tsang, Li Liu
Computer Vision
Spectral Super-Resolution using Spatial-Spectral Residual Operator Networks
Seokhyun Chin
Spectral super-resolution of multispectral satellite images can enable high temporal- and spatial-resolution hyperspectral satellite imagery at a modest cost, significantly increasing the applicability of hyperspectral remote sensing. This task is inherently ill-posed, making it well-suited for deep learning-based methods. In this study, the spectral super-resolution task is framed as an operator learning problem, and SSRON is proposed as a Deep Operator Network that effectively learns function-to-function mappings from downsampled spectra to continuous spectra. The model is trained to super-resolve Sentinel-2A-like multispectral imagery to EMIT images. Compared to baseline models, SSRON achieves superior performance across all metrics. The model also demonstrates zero-shot spectral super-resolution capability by predicting bands unseen during training. Furthermore, its continuous-output formulation suggests the potential to estimate spectra at finer wavelength intervals than the native sensor. These results suggest the potential of SSRON and establishes operator learning as a promising direction for spectral super-resolution.
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Computation and Language
AwarenessBench: Assessing Cognitive Capabilities of Language Models
Xiaojian Li, Rongwu Xu, Tianyun Zhang et al. (+9 more)
As
language models (LMs) exhibit increasingly consciousness-like behaviors, evaluating their cognitive abilities becomes essential. We introduce AwarenessBench, the first comprehensive
benchmark for assessing the cognitive abilities of LMs in four dimensions: metacognition, self-awareness, social awareness, and situational awareness, covering 15 cognitive functions and 14,381 samples. Evaluating 18 state-of-the-art LMs, we find that all consistently surpass random baselines, with more advanced models performing better. We further compare LMs with human performance across three demographic groups, where the best-performing model surpasses human averages overall, but most still fall markedly short in metacognition and self-awareness. Finally, we show that awareness is a distinct capability: progress in
language modeling or
reasoning does not necessarily translate into improved cognition.
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All authors Xiaojian Li, Rongwu Xu, Tianyun Zhang, Yue Wang, Shuo Chen, Qiner Lyu, Briana Zhang, Peiran Yang, Kyle Xue Chen, Haoyuan Shi, Yu Wang, Wei Xu
Cryptography and Security
"Nothing to See Here”: Unintended Disclosure through Revision Traces of LLM Deliverables
Yage Zhang, Yukun Jiang and Yang Zhang
Large
language model (LLM) assistants increasingly help users draft content for third-party recipients. During private drafting, the user or the model may introduce an item and later remove or replace it. The model may remove the item from the intended content but reveal it again when stating the edit. We call such statements revision traces. For example, after a user removes the password before sharing a configuration file, the model may delete it but leave a comment saying, "Removed the password 'No****4!' as requested." A third-party recipient who sees only the delivered file can therefore recover the withdrawn password from the comment. In an in-the-wild analysis of three public conversation corpora, we identify 26,753 revision requests, of which 2,363 (8.8%) leave revision traces. We study them in greater depth under controlled conditions by introducing RevLeakBench, a
benchmark of 100 tasks across five scenarios with a conversation track and an
agent track. We measure trace occurrence, withdrawn-item recovery, trace position, and required-content retention. Across six models, about half of the deliverables in both tracks state the edit after a revocation, and a reader that sees only the deliverable can recover the withdrawn item from about 13% of them. Telling the model that its entire reply will be forwarded to the recipient still leaves revision traces in 36.4% of the deliverables. We compare
prompt defenses and a delivery boundary, and propose an output-side filter that sharply reduces recovery with little loss of required content. We believe our work can benefit efforts to understand and mitigate unintended disclosure in LLM interactions.
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Computer Vision
BiMoGen: Bidirectional Motion-Text Generation via Unified Masked Discrete Diffusion
Wanjiang Weng, Yongliang Wu, Xiaofeng Tan et al. (+3 more)
Text-to-motion generation and motion-to-text captioning are two fundamental tasks in human motion modeling, both grounded in the same underlying motion-text correspondence. Existing unified approaches mostly rely on autoregressive modeling, which imposes a fixed generation order and is therefore poorly suited to the bidirectional dependencies between
language and motion, allowing early prediction errors to persist as fixed context and degrade both temporal coherence and cross-modal consistency. Masked discrete
diffusion, which models sequences through iterative bidirectional prediction, allows prediction from bidirectional context without a fixed generation order. We therefore propose BiMoGen (Bidirectional Motion-text Generation), a unified masked discrete
diffusion framework for bidirectional motion-text modeling. To stabilize training, we design Decoupled Uni- and Cross-Modal Training, in which masked pretraining first establishes cross-modal correspondence on paired motion-text sequences, after which supervised fine-tuning specializes the model for bidirectional generation. Masked
diffusion nonetheless faces a train-test mismatch, as the model is trained on clean ground-truth context yet encounters self-generated and potentially erroneous context at inference, with errors committed under heavily masked states propagating through subsequent steps. We further introduce Generation-Aware Self-Correction that exposes the model to its own predictions during training and uses correction passes at early sampling steps for further refinement of previously unmasked
tokens. Extensive experiments on HumanML3D and KIT-ML demonstrate competitive performance on both tasks, validating the effectiveness of the proposed two-stage training and self-correction designs. The project page is available at https://wengwanjiang.github.io/BiMoGen-Page.
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All authors Wanjiang Weng, Yongliang Wu, Xiaofeng Tan, Xingyu Zhu, Wenbo Zhu, Hongsong Wang
Computer Vision
Reduce, Then Encode: Multiscale Volumetric Reduction for 2D Foundation Models in Brain MRI
Dexuan Ding, Yuankai Qi, Bogong Wang et al. (+2 more)
Pretrained 2D foundation models offer a practical alternative to dedicated 3D pretraining for
brain structural
magnetic resonance imaging (sMRI), but their use on volumetric data requires bridging the mismatch between a 2D encoder and a 3D volume input. Existing methods typically encode slices independently and integrate their features afterwards. We introduce Multiscale Volumetric Reduction (MVR), a reduce-then-encode approach that compresses each anatomical view from (D) slices into (M << D) complementary 2D components before foundation-model encoding. MVR combines an uncentered-PCA base component derived from the original through-plane intensities with residual detail components constructed from multiscale spatial descriptors. The reduction is estimated from the training volumes without diagnostic labels or gradient-based optimization and remains fixed thereafter. The resulting components are independently processed by a shared frozen 2D foundation model and concatenated for linear probing. Under this frozen-encoder setting, MVR achieves strong overall performance across ADNI, OASIS, and ABIDE relative to the evaluated 2D-to-3D adaptation methods and simple input-reduction baselines, while also generalizing strongly from ADNI to AIBL.
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All authors Dexuan Ding, Yuankai Qi, Bogong Wang, Luping Zhou, Amin Beheshti
Machine Learning
Persistent Partners Raise Prices Among Learning Agents
Paul-Peter Arslan, Yubin Kim and Xiao Xiao
When pricing
agents meet repeatedly on a platform, the platform decides who faces whom. We ask whether that choice moves the prices the
agents learn, and whether a rise comes with learned punishment. In a pre-registered randomised experiment in the Bertrand duopoly of Calvano et al., each
agent's price is set by a tabular Q-learning module, not by the small
language model attached to it, and we randomise whether each
agent keeps its partner, sees its rival's prices and can send messages. Keeping the same partner raises the level of profits, averaged over training, by 0.27 of the gap between competitive and monopoly profit (95% CI 0.20 to 0.35, all twenty paired runs positive), our registered primary result, and the resting price by 0.17 of the Nash-to-monopoly range (post hoc). A plain tabular learner reproduces the effect in all 25 further blocks, and there one permanent partner raises the level more than about three do (+0.23 against +0.05, exploratory). Where rival prices are hidden, the price-setting module cannot see a cut, so cannot punish it, yet the resting price rises as much and the rise lasts to the end of training, while with visible rivals it shrinks with longer training (post hoc). Where the rival is visible, a static best responder accounts for a third to a half of what a forced-deviation probe reads as punishment, on the starts where the rival can see the cut, and net of it the registered test of learned punishment is inconclusive. A test that looks only for punishment would thus miss the rise where the rival is hidden, while a check for profitable deviations flags most of those prices (post hoc). In an exploratory extension, untrained Qwen2.5 7B and 14B models under one
prompt show the effect when the rival's price is left out of the
prompt and inconsistently when it is shown, the 7B result replicating on fresh blocks, while two other model families show none.
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Artificial Intelligence
Structural Alignment for Reliable Industrial AI: Bridging Physical Reality, Data, Models, and Human Intent
Lizhi Xiao, Sihong Wu, Victoria Xiao et al. (+5 more)
Artificial intelligence is increasingly deployed in critical industrial domains, including healthcare, energy grids, subsurface exploration, where failures can have severe consequences for human safety, system stability, and economic outcomes. Yet AI is still evaluated primarily through
benchmark accuracy, a model-centric metric that fails to capture the structural complexity and risks of real-world deployment. We propose a framework that views industrial AI reliability as a problem of structural
alignment across four interacting worlds: physical, representational, machine, and human cognitive. These worlds are connected through two interfaces: digitalization, linking physical reality to computational representations, and goal encoding, translating human cognition to the machine objectives. Together, they define the space of admissible solutions. We characterize the solution space through four attributes: existence, non-uniqueness, robustness, and interpretability and show how mismatches arise at interfaces and propagate across worlds to produce reliability failures. Applications to healthcare, energy grids, and subsurface exploration illustrate that although dominant failure modes differ across domains, for example, interpretability in healthcare, robustness in energy grids, and non-uniqueness in subsurface exploration, all originate from a shared structural mechanism. By shifting the focus from model-centric evaluation to system-level
alignment, this framework offers a principled foundation for assessing and governing reliability in industrial AI systems.
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All authors Lizhi Xiao, Sihong Wu, Victoria Xiao, Yiqiao Song, Chen Gu, Jianwei Ma, Xinming Wu, Aimé Fournier
Computer Vision
Rethinking Visual Token Compression for Video Large Language Models: A Simple Yet Strong Baseline
Xiao Zhang, Wang Zeng, Sheng Jin et al. (+3 more)
Video Large
Language Models (
Video LLMs) have achieved remarkable progress in
video understanding, but their inference efficiency is constrained by the large number of visual
tokens produced by long
videos. Recent
video token compression methods increasingly introduce sophisticated strategies for
token selection,
pruning, and merging. This raises a fundamental question: how much of compression performance can be obtained by simply preserving the structure encoded in the visual representations? We investigate this question with SimpleCluster, a simple and training-free baseline that performs position-aware cross-frame clustering in the visual feature space and represents each cluster using the mean of its original visual features. Extensive experiments across four
video understanding
benchmarks and three representative
Video LLMs show that SimpleCluster achieves competitive or superior performance over recent compression methods across a wide range of
token retention ratios, with particularly strong robustness under extremely low retention rates (e.g., 1%). To understand this behavior, we analyze the feature space preserved by different compression methods in terms of local approximation fidelity and global coverage. The results show that stronger downstream performance is consistently associated with better preservation of the original visual feature distribution, especially its global coverage. These findings highlight feature-space preservation as an important consideration for
video token compression under highly constrained
token budgets. Our code is available at https://github.com/xiaozhang79/SimpleCluster.
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All authors Xiao Zhang, Wang Zeng, Sheng Jin, Wentao Liu, Chen Qian, Shichao Kan
Machine Learning
The Hidden Ratio in Adam: Stable Structure, Compression, and Sign Dynamics
Yihe Zhou, Tongtian Zhu, Yingxiao Huo et al. (+4 more)
Adam is the default optimizer for training modern deep neural networks, yet its adaptive behavior remains poorly understood due to the complex interaction between its first- and second-moment exponential moving averages (EMAs). We study Adam in the tied-$\beta$ regime, where the two EMA decay rates are equal, and show that its adaptive dynamics can be expressed through a transformed ratio with approximately scale-stable behavior. Empirically, this transformed ratio exhibits a stable, heavy-tailed distribution across tasks, model scales, and training stages, in contrast to the variability of raw moment magnitudes. This empirical stability has both practical and conceptual consequences. First, we derive a recurrence for the transformed ratio, yielding a reparameterization of Adam that replaces the second moment with a compressible state. Leveraging its stable distribution, we show that a fixed 4-bit codebook is sufficient in our experiments to store this state without auxiliary scaling, achieving performance competitive with full-precision Adam. Second, the transformed ratio view clarifies Adam's connection to sign-based methods: Adam reduces to sign-based momentum modulated by the transformed ratio, and replacing it with a constant recovers Signum as a limiting case. This perspective further provides a simple rule for transferring learning rates between the two methods. Together, these results suggest that tied-$\beta$ Adam admits a simple and approximately stable ratio structure underlying its adaptive behavior and demonstrate its utility for both analysis and efficient implementation.
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All authors Yihe Zhou, Tongtian Zhu, Yingxiao Huo, Satya Prakash Dash, Can Wang, Samuel Kaski, Mingfei Sun
Machine Learning
Inductive Feedback for Mixed-Policy Distillation
Amir Moeini, Huaijiang Zhu, Daniel Havir et al. (+1 more)
Verbal feedback can identify errors and prescribe corrections, providing rich supervision for
language-model post-training even when reliable programmatic verifiers are unavailable. Such feedback, often generated by a capable model, can be used to condition the teacher in on-policy
distillation, which trains the student to match the teacher's predictions on student-generated rollouts. However, this approach can transfer teacher preferences that the feedback did not motivate, while leaving much of the feedback's guidance unused. We find that both problems come from the standard on-policy
distillation objective, specifically the divergence it minimizes and the distribution it uses as its target. Our proposed method addresses both limitations. First, to isolate the information conveyed by the feedback from the teacher's inherent preferences, we treat verbal feedback as evidence for or against the hypothesis that a particular
token comes next at a given prefix. We then adopt a probabilistic confirmation framework which uniquely determines an ordering over the vocabulary based on the teacher's predictions before and after it receives feedback. Using a confirmation score consistent with this ordering, we construct a target distribution within a trust region of the student. Second, to learn from guidance that student rollouts can leave unused, we derive a simple shared-rollout
estimator of a symmetric divergence between the student and target distributions over rollouts, reusing student and feedback-conditioned teacher rollouts in both directions through importance weighting. Empirical evaluations show that our method outperforms the common on-policy
distillation recipe and a recent contrastive variant on knowledge-based and agentic
benchmarks.
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All authors Amir Moeini, Huaijiang Zhu, Daniel Havir, Shangtong Zhang
Computation and Language
TRACE: Single-Pass Decoding-Trace Risk Localization for Generation Calibration
Yuebin Xu, Xuemei Peng, Junlan Chen et al. (+2 more)
Reliable confidence estimation is essential for large
language model deployment. However, answer-level calibration remains challenging because generation errors are often localized: a response may be fluent and high-probability overall while still failing at a critical number, entity, or factual claim. Existing
estimators compress
token probabilities, sequence likelihoods,
entropy, or beam statistics into a global score, which can dilute such local risk signals. We propose TRACE, a single-pass, decoded-answer-preserving confidence
estimator that treats decoding-time uncertainty as a trajectory through three steps: (i) recording
token-level surprisal and predictive
entropy during decoding, (ii) applying local risk operators to preserve uncertainty spikes, and (iii) converting localized trace risk into answer-level confidence. TRACE produces a label-free risk score, while TRACE+ calibrates trace-only features into probabilities using a held-out split, without extra generations or external verifiers. We evaluate four tasks against 19 calibration baselines, and TRACE+ reduces Brier from 0.149 to 0.137 and improves AUROC from 0.758 to 0.792 over the strongest likelihood baseline. Across seven LLMs, TRACE+ improves over the best non-TRACE baseline pool from 0.136 to 0.120 Brier and from 0.764 to 0.817 AUROC. Results show that localizing decoding-time risk provides a general approach to calibration.
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All authors Yuebin Xu, Xuemei Peng, Junlan Chen, Zhiyi Chen, Zeyi Wen
Game Theory
Binary $k$-Center under a Hard Threshold with Applications to Delegated Voting
Jakub Dargaj, Aris Filos-Ratsikas and Paul W. Goldberg
We study the problem of covering binary strings of the same length, possibly with missing entries, by a fixed number of center strings, such that each input string is within a given relative distance of the closest center. The problem has applications in binary $k$-center clustering and bioinformatics, but our main motivation comes from computational social choice. In the setting of multi-issue approval voting, we consider an algorithmic question that precedes any election: how should representatives be designed so that as many voters as possible are willing to delegate? We study the problem in two dimensions, namely the number of centers and the agreement threshold, parameters that are application-specific, and provide a complete picture of its computational complexity when entries may be missing. For the special case without missing entries, we extend our hardness results to any number of centers and large values of threshold, leaving a narrow range of thresholds unresolved.
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Social and Information Networks
Attentional DoS: Repeat Reposting, Collective Attention, and Information Diffusion on X
Genta Toya, Bruno T. Sugano, Kei Ichikawa et al. (+6 more)
Collective
attention is a finite shared resource, and social media posts compete for limited opportunities to be seen. On X, users can undo a repost and repost it again. By repeating this cycle, a user can put the same post back into followers' timelines any number of times without making new content. We call this procedure repeat reposting, and we read it as placing repeated demand on this shared resource (Attentional DoS). We formalize this idea and explore repeat reposting in a large-scale
dataset of cascades with at least 1,000 reactions, originating from posts classified as Japanese on X, covering April 2025 to March 2026. Repeat reposts are rare, appearing in a small share of all (user, post) pairs. Even so, close to a million posts have at least one repeater, and most repeats come from a small group of habitual accounts. The central result is that subsequent audience growth is associated less with the number of repeats than with the estimated reach of the repeating accounts. Through the lens of seriality, how habitually the same groups of accounts repeat reposts across many posts, a distinct distributed form emerges: several serial amplifiers converge on the same post (Attentional DDoS). Its synchrony, how closely their actions are timed together, forms a continuum from bursts on the scale of minutes to a daily clock. A check of the content of 99.7% of amplified posts shows that most ADDoS repeat events are directed at Chinese-script content, which our vocabulary matching and sample inspection indicate is predominantly commercial spam. Repeat reposting thus lets a post re-enter the competition for visibility. The observed pattern is better characterized as repeated temporal coverage of an existing audience and the self-reinforcement of posts that are already growing, rather than as evidence that repeat reposting takes reach from other content.
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All authors Genta Toya, Bruno T. Sugano, Kei Ichikawa, Qianyun Wu, Shuhei Saigusa, Yasuhiro Hashimoto, Masashi Toyoda, Naoki Yoshinaga, Kazutoshi Sasahara
Game Theory
Online Resource Allocation with Replenishable Budgets
Eleonora Fidelia Chiefari, Francesco Emanuele Stradi and Alberto Marchesi
Online Resource Allocation (ORA) is a fundamental framework for sequential decision-making problems under budget constraints. Classical ORA models typically assume that resources are monotonic, meaning that selecting actions can only decrease the available budget. In this work, we study a more general setting with replenishable budgets, in which actions may either consume or replenish resources over time. This extension is necessary to capture scenarios such as inventory systems or energy
markets in which capacity can be actively recovered. We develop a dual-based
algorithm that recovers best-of-both-worlds guarantees for standard ORA when the replenishment factor $\beta = 0$, and improves them when $\beta > 0$. In particular, our
algorithm attains $\widetilde{\mathcal O}(\sqrt{T})$ regret in the
stochastic setting and $\widetilde{\mathcal O}(\sqrt{T})$ $\alpha$-regret in the
adversarial setting, where $\alpha$ depends on the per-round budget and on the replenishment factor of the void action. Moreover, the
algorithm ensures strict satisfaction of the budget constraints.
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Software Engineering
MCP Error Messages Written for Developers Hurt the Most Capable Agents Most
Xiaonan Xu and Wenjing Wu
Many Model Context Protocol (MCP) servers wrap web APIs built for human developers, and their error messages tell the reader to run a command, edit a configuration, open a web page or wait. Many
agents that read them can only call the server's tools. In 150 widely used MCP servers, 949 of 3,001 error messages tell the caller what to do next, and half of these steps depend on something the server cannot see about the caller. On credential errors, 62 of 67 steps ask for a terminal command, a configuration change or a web page; on rate limits, 20 of 30 say to wait and retry without naming the call to repeat. We tested five OpenAI models that act only through the tools of Berkeley Function Calling Leaderboard tasks, and the
agents did what the step said. On expired credentials, a terminal command in the step left 45% of tasks recovered, and the loss it caused grew from 18 points for GPT-5.5 to 69 for GPT-6 Astra. On a rate limit, GitHub's "Wait before retrying." left 6%. We tested two remedies. For MCP developers, naming a server tool in the step raised recovery on expired credentials to 84%, with the login tool in place of the command, and on a rate limit to 88%, with the call to repeat in place of the bare wait. For
agent developers, deleting the step with a one-sentence
prompt before the model reads it raised recovery on expired credentials to 82%.
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Machine Learning
Identifying Neural Source Dynamics from Unknown Local Interventions
Ayana Mussabayeva, Jiaqi Sun, Anuar Aimoldin et al. (+2 more)
Electroencephalography (
EEG) records mixtures of
brain-source activity. Even with a known anatomical forward model, experiments that excite only part of the source-state space leave the dynamics unidentified, and repetition cannot resolve the ambiguity. We show that unknown local mechanism changes can supply the missing information. We consider linear dynamics among fixed anatomical sources with known source-state initialization patterns. Changing one source's update rule for one transition leaves a rank-one, source-specific signature in subsequent
EEG: subtracting matched baseline responses isolates it, and the forward model identifies the source and calibrates its response history. Combining these histories with initialization responses recovers source interactions without baseline reachability and without first identifying the intervention coefficients. We establish sufficient recovery conditions, a direct
estimator, and a noise-sensitivity bound conditional on correct source labels. Simulated
EEG on anatomy derived from
magnetic resonance imaging confirms the information gain: with baseline excitation confined to four of twelve source coordinates, eight unknown changes recover all dynamics in 32/32 systems, whereas baseline realization, baseline regression through an invertible forward model, and changes that leave the tested states unexposed all fail, and explicitly constructed alternative dynamics reproduce every baseline mean. Where baseline information suffices, direct reconstruction is also more reliable than a matched-information spectral
estimator. Nonlocal changes and forward-model error limit accuracy even when source labels are correct.
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All authors Ayana Mussabayeva, Jiaqi Sun, Anuar Aimoldin, Olivier Oullier, Kun Zhang
Computation and Language
Multilinguality in Hybrid Attention LLMs
Lucas Bandarkar, Junlin Hu, Chenyuan Yang et al. (+2 more)
In response to the growing demand for long sequences in agentic and
reasoning use cases, many state-of-the-art LLMs combine multiple variants of
attention to mitigate the quadratic complexity of traditional softmax
attention. These hybrid
attention LLMs aim to balance the strengths and limitations of full
attention and alternatives based on recurrence. This work presents a first study of how hybrid
attention impacts the multilinguality of LLMs. Beyond the impact on long sequences in poorly tokenized
languages, our study is motivated by the possibility that the inductive biases of the recurrent state alter linguistic processing. Our interpretability analysis confirms this, showing that cross-lingual representations in hybrid models develop in patterns tied to the ordering of recurrent and full-
attention layers. Across diverse models, we notably observe a pronounced spike in cross-lingual
alignment around the first full-
attention layer. These findings lead us to question the conventional ordering of
attention layers. In
distillation experiments on
multilingual data, all alternative layer orderings outperform the standard throughout training, learning up to 2.5X faster. These stark, replicable results
prompt our theory that
multilingual models would benefit from starting with a full-
attention layer rather than recurrent layers.
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All authors Lucas Bandarkar, Junlin Hu, Chenyuan Yang, Mohsen Fayyaz, Nanyun Peng
Machine Learning
First Learn, Then Memorize: The Spectral Bias of Diffusion Models
Raphaël Urfin, Tony Bonnaire, Giulio Biroli et al. (+1 more)
Diffusion models trained on a finite
dataset first learn to generate novel, high-quality samples and only much later collapse onto their training set. We identify the mechanism behind this separation of timescales and the object that probes it. The training dynamics of the score function are governed—exactly, and at any width—by the Gram
matrix of the Neural Tangent Kernel (NTK) evaluated on the noisy training data, so the timescales of generalization and of memorization must be encoded in its spectrum. We show that they are, and that the structure responsible has no analogue in standard kernel settings. The use of multiple noise realizations per sample ($m$ noised copies at a fixed noise level) in the score-matching loss is what restructures the Gram
matrix spectrum into two distinct parts. The first, of large
eigenvalues, carries the global features of the target distribution and is present already for $m=1$. The second, which the repeated noising creates, consists of the smallest
eigenvalues and is supported on eigenvectors aligned with the sample-specific noise directions; it sets a memorization timescale parametrically larger in the training set size $n$. We establish this picture on two fronts. Analytically, we solve the spectrum in the lazy high-dimensional limit for both linear ($n \asymp d$) and
polynomial ($n \asymp d^k$) sample complexities, and prove through a bias–variance decomposition that the first bulk minimizes the approximation error while the second drives the error associated with memorization. Empirically, we show the same two-bulk structure in Convolutional NTKs on CelebA and in finite-width U-Nets trained well beyond the lazy regime, and we make the link causal: truncating the Gram
matrix at rank $r$ tunes the generalization–memorization transition, and an $L_2$ penalty targeting the second bulk suppresses memorization in feature-learning U-Nets.
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All authors Raphaël Urfin, Tony Bonnaire, Giulio Biroli, Marc Mézard
Computation and Language
How Well Can LLMs Simulate Real Learner Evaluations of Educational Feedback?
Momoka Furuhashi, Kouta Nakayama, Takashi Kodama et al. (+2 more)
While recent studies have explored human behavior and preference simulation using large
language models (LLMs), it remains unclear how well LLMs can simulate subjective evaluations from real learners in educational settings. We investigate this question using real learner evaluation data on feedback for high-school biology questions at both the group and individual levels. We compare performance with and without learner-specific information, such as personality traits and evaluation examples, across six models. Our results show that LLMs still have a limited ability to simulate learner evaluations. Providing learner profiles and examples improves score calibration and individual-level simulation, but more often fails to improve group-level consistency. These findings highlight the need to investigate which learner information and adaptation strategies are effective for learner preference simulation.
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All authors Momoka Furuhashi, Kouta Nakayama, Takashi Kodama, Saku Sugawara, Kyosuke Takami
Robotics
From Pixel to Poses: Object-centric Tool Manipulation Learning from Human Demonstrations
Bangjun Wang, Longyan Wu, Yukun Wei et al. (+8 more)
Scaling up robotic manipulation is primarily bottlenecked by the scarcity of real-world
robot data. While recent approaches leverage human
video demonstrations to mitigate this shortage, they remain computationally expensive and still rely on paired human-
robot data for domain
alignment. Although current state-of-the-arts excel at long-horizon tasks, they struggle with the delicate and precise control required for complex tool manipulation. To overcome these limitations, we introduce P2P-T, from Pixel to Poses for Tool Manipulation, a data-efficient, object-centric framework that learns tool use directly from human demonstrations. P2P-T bridges the cognitive and physical execution gap through a two-stage approach. First, pretraining an object-centric world model to extract stable pose priors; second, integrating these priors into an efficient, pose-aware low-level policy. By utilizing a robust automated data processing pipeline powered by modern foundation models, P2P-T completely bypasses the need for human-
robot aligned data. This reduces overall training overhead drastically. With minimal per-task fine-tuning, our framework achieves a 73% improvement over the previous state of the art in execution performance on complex, real-world tool manipulation tasks that currently remain out of reach for standard large-scale pretrained models.
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All authors Bangjun Wang, Longyan Wu, Yukun Wei, Shenghe Shao, Chaoyi Huang, Wenze Cui, Zetong Xu, Hanlin Wu, Long Chen, Yi Ma, Hongyang Li
Computation and Language
Deep Learning Methods in Neuroscience: From Modeling Molecular Mechanisms to Classifying States of Consciousness
Elena Benderskaya, Anastasiia Alifanova, Svetlana Batalova et al. (+2 more)
A critical analysis of contemporary approaches to the study of conscious states. The review focuses on methods of classification, clustering, modeling of
brain states under anesthesia and identification of measurable neurobiological characteristics of
brain function. A comparative analysis was conducted in the following three major areas: automatic detection of states of consciousness using neural networks based on
EEG and fMRI data; modeling of the structural-functional dynamics of the
brain under the effects of anesthetics; and detection of neurophysiological indicators which correlate with the level of consciousness. The obtained conclusions demonstrate the growing effectiveness of deep neural models in the classification and prediction of
brain states and the analysis of dynamic structural-functional connectivity. Nonetheless, significant limitations were also identified, including the limited interpretability of the models, the lack of standardized metrics, and the problem of the specificity of consciousness markers. Our findings support the need for developing hybrid, generalizible, physiologically grounded architectures. Furthermore, such approaches may improve the translational potential of computational models in clinical neuroscience. Diverse methods of machine and computational modeling have demonstrated their effectiveness in tasks of automatic clustering and classification of
brain states, the development of multilevel models and the identification of connectivity patterns correlated with levels of consciousness. A larger-scale analysis and a larger
dataset, as well as the implementation of model interpretability approaches are required for the practical application of the analyzed models. The models based on
EEG and LFP are the most promising for clinical application due to their availability and the possibility of real-time monitoring.
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All authors Elena Benderskaya, Anastasiia Alifanova, Svetlana Batalova, Vasilisa Zhuk, Anna Kovalenko
Artificial Intelligence
A decision-support system applied to Law: Reasoning and explainability of the decision
Jeremy Bouche-Pillon, Pascale Zaraté, Yannick Chevalier et al. (+1 more)
The emergence of the digital transition brought an increasing need to control the processing of digital information, including in Law Enforcement Agencies (LEAs). At the EU level, in recent years, many regulations have emerged to control data processing and exchange. Texts other than the GDPR, such as the ”Law Enforcement Directive (LED)”, appeared to regulate specifically how Law Enforcement Agencies (LEAs) could process data. A formal representation of these regulations can be part of decision systems that support LEAs in processing data in compliance with the regulations. Although many new formalisms have emerged to represent legal norms and rules, few are provided with a
reasoning mechanism. Furthermore, systems used in decision-making processes in critical contexts such as medical diagnoses or legal decisions cannot be fully automated, and the explainability of their results is essential to ensure user confidence in decisions. This explainability aspect, while crucial, is lacking in most modern approaches that rely on machine learning. This paper describes a framework to operate formal rules from regulations, by focusing on explainability of the decision. After describing the general architecture of the proposed decision support framework, the paper showcases how symbolic AI and the SPARQL query
language can support legal
reasoning. It then describes an
algorithm to generate a justification for the
reasoning results, and outlines the procedure to be followed when the
reasoning does not lead to a satisfactory conclusion. We notably focus on a method based on decision trees to determine what additional information to request from the user.
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All authors Jeremy Bouche-Pillon, Pascale Zaraté, Yannick Chevalier, Nathalie Aussenac-Gilles
Data Structures and Algorithms · Cross-listed from math.CO
A sharp higher-order Cheeger inequality
Yunpeng Li
Let $\lambda_k(G)$ be the $k$th
eigenvalue of the normalized
Laplacian of a finite undirected weighted graph $G$ with positive degrees, where $k$ is an integer satisfying $1\le k\le |V(G)|$. Let $\varphi_k(G)$ be the minimum possible maximum conductance of $k$ disjoint nonempty vertex sets. We prove $\varphi_k(G)\le C\sqrt{\lambda_k(G)\log(k+1)}$ for an absolute constant $C$. The number of sets and the spectral index are both $k$, and conductance is measured in the original graph. The logarithmic dependence is optimal up to an absolute constant. The proof combines
geometric partitioning of the spectral
embedding with minimum-cut improvement and adaptive projections in coefficient space. A single conductance threshold is used throughout the construction. The resulting maps have disjoint supports, and the sum of their Gram
matrices is bounded below by an absolute positive multiple of the identity. A dyadic maximal estimate bounds the sum of their internal energies uniformly over unit coefficient vectors. A dimension argument using local
eigenvalues then yields exactly $k$ disjoint sparse cuts.
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Computer Vision
Mohammad Mahdi, Luc Van Gool and Danda Pani Paudel
Exo-to-
egocentric video generation aims to synthesize what a person sees from their own viewpoint given third-person footage and a target head trajectory. The task requires transferring appearance and semantics across large viewpoint changes while hallucinating content never observed by the exocentric camera. Existing approaches either impose additional input requirements, such as a ground-truth initial
egocentric frame or multiple synchronized exocentric views, or remain limited to category-specific settings. EgoX is the first to address cross-activity and in-the-wild generalization, but requires a human-provided caption of the non-existent
egocentric view at inference and introduces a computationally expensive geometry-guided
attention bias that can propagate reconstruction errors and suppress textual and visual context. We therefore propose Ego-Forge, a caption-free and bias-free framework for exo-to-
egocentric generation. It introduces Dynamic Captioning, which derives conditioning
tokens directly from the model's hidden states and adapts them to the
diffusion timestep and network depth, replacing external text conditioning. By scaling training by an order of magnitude and using all available exocentric viewpoints, Ego-Forge learns cross-view correspondence implicitly and eliminates the need for geometry-guided
attention, requiring only a lightweight depth prior. Ego-Forge achieves state-of-the-art performance on Ego-Exo4D, runs faster end-to-end, requires no external annotation at inference, and generalizes to in-the-wild scenes, including cases where over-reliance on geometry blocks appearance inference. Our model and source code will be made publicly available.
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Computation and Language
From Input to Output: A Flexible Agent for Dual-End Interpretation of Sparse Autoencoder Features
Dewen Liu, Zixuan Li, Jonathan Pan et al. (+4 more)
Sparse autoencoders (SAEs) are an important tool for mechanistic interpretability, but interpreting their many features remains challenging. Existing methods characterize input-side activation patterns and output-side intervention effects, yet often leave their functional connection implicit, while input-side evidence collection typically relies on costly large-corpus scans. We introduce functional interpretation, which characterizes an SAE feature as a mapping from its activating input semantics to its output effects under intervention, and present Dual-End Agentic Feature Interpretation (DAFI), an
agent that actively gathers evidence and refines input-side, output-side, and functional interpretations through component-specific feedback. Its short-context
token probing enables on-demand activation evidence collection without a full corpus scan. On GemmaScope, DAFI improves Input score by 13.1 percentage points over SAGE and Output score by 38.9 points over
Token Change, while being substantially more
token-efficient than a general-purpose coding
agent. Skills distilled from successful refinements raise the held-out joint pass rate from 58.0% to 92.0% and improve both interpretation quality and efficiency when transferred to a new model-SAE setting. Across features with reliable endpoint interpretations, 70.7% exhibit non-equivalent input and output semantics. On AxBench, DAFI also improves steering-feature selection over output-score filtering. Code is available at https://github.com/THUAIS-Lab/DAFI.
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All authors Dewen Liu, Zixuan Li, Jonathan Pan, Zhao Wu, Zijun Yao, Juanzi Li, Xiaozhi Wang
Operating Systems
Planarian: Managing Agent State with Statepoints
Jinnan Guo, Hao Mark Chen, Kapil Vaswani et al. (+2 more)
LLM
agents solve complex tasks by iteratively changing files, invoking local tools, and interacting with remote services, which modifies state across their local environment and remote services. Today,
agents and users must manage these changes explicitly, whether reverting exploratory actions or recovering from erroneous ones. Doing so safely requires coordinated actions, yet current
agent harnesses lack unified abstractions and mechanisms for managing local and remote state consistently and efficiently. We describe Planarian, an
agent runtime with state management that enables
agents and users to recover from erroneous actions and explore alternative executions over consistent local and remote environment state. Planarian introduces the abstraction of
agent statepoints, which are consistent, restorable point-in-time versions of the environment state. Planarian exposes three state-management primitives to
agents and users: (i) snapshot creates a new statepoint spanning local and remote state without requiring external services to support checkpoints: it relies on efficient incremental process and file system snapshotting to capture local sandboxed state, and transparently records compensating actions to undo remote state changes; (ii) rollback restores the environment to a previous statepoint by reverting to a prior local checkpoint and replaying compensating actions for remote state changes; and (iii) fork creates multiple isolated branches from a statepoint, enabling the
agent to explore alternatives in parallel. We show that Planarian enables
agents to undo mistakes and explore alternatives in parallel, improving task quality by up to 15x, and allows users to recover from erroneous actions with only 3% overhead.
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All authors Jinnan Guo, Hao Mark Chen, Kapil Vaswani, Andrew Paverd, Peter Pietzuch
Machine Learning
Do Temporal Link Predictors Need Learned Memory? A Smoothed-Count Baseline with a Handful of Parameters
Lisi Qarkaxhija and Ingo Scholtes
Many temporal link predictors summarize past interactions through learned node representations. We examine whether simple counts of recurring interaction patterns can provide competitive predictions without learning these representations. We propose a temporal link predictor based on statistical
language modelling. It pools transition and co-occurrence counts across sources to predict links that a source has never formed. We smooth sparse estimates using destination frequencies or Kneser-Ney continuation counts. A shared log-linear rule combines these estimates with popularity, source history, and recency, without node
embeddings. In our main evaluation, the model achieves the highest MRR among the compared methods on 7 out of 16
datasets from TGB and TGB-Seq. It also outperforms EdgeBank and Base3 on all 16
datasets and the heuristic family on 14. These gains extend to
datasets designed to limit repeated edges. With only 9–13 learned parameters, our model provides a simple and competitive baseline for evaluating future neural temporal link predictors.
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